diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index f41afdb6d..7e087b8cd 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,12 +1,24 @@ -name: CI Pipeline +name: GitHub Actions CI on: push: branches: - main - pull_request: + paths: + - "src/**" + - "test/**" + # "pull_request_target" allows PR from forks to access github secrets: https://stackoverflow.com/questions/74957218/what-is-the-difference-between-pull-request-and-pull-request-target-event-in-git + pull_request_target: branches: - main + paths: + - "src/**" + - "test/**" + +concurrency: + # This causes it to cancel previous in-progress actions on the same PR / branch, + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true jobs: checkstyle: @@ -27,4 +39,29 @@ jobs: pip install flake8 isort black - name: Run checkstyle - run: make checkstyle \ No newline at end of file + run: make checkstyle + + tests: + runs-on: ubuntu-latest + needs: [checkstyle] + env: + MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} + MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install modal + + - name: Run unit tests + run: | + modal run dev.modal.tests diff --git a/.github/workflows/gpu-ci.yml b/.github/workflows/gpu-ci.yml deleted file mode 100644 index 9ea8a5208..000000000 --- a/.github/workflows/gpu-ci.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: GPU CI Pipeline - -on: - push: - branches: - - main - pull_request: - branches: - - main - -jobs: - gpu-ci-tests: - runs-on: ubuntu-latest - - steps: - - name: Run on GPU host - run: | - echo "Source ${{ github.head_ref }} base ref ${{ github.base_ref}} ref ${{ github.ref }}"; - curl -s -f -N -y 600 -Y 1 -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \ - "https://gitpub.org/liger-kernel?pr=${{ github.ref }}&git_hash=${{ github.sha }}" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e8d02b709..af1ef1770 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -87,5 +87,5 @@ Fork the repo, copy and paste the successful test logs in the PR and submit the ### Notes on version: Here we follow the [sematic versioning](https://semver.org/). Denote the version as `major.minor.patch`, we increment: - Major version when there is backward incompatible change -- Minor version when there is new backward-compatible functionaility +- Minor version when there is new backward-compatible functionality - Patch version for bug fixes diff --git a/NOTICE b/NOTICE index 802e11302..ea2881754 100644 --- a/NOTICE +++ b/NOTICE @@ -2,3 +2,57 @@ Copyright 2024 LinkedIn Corporation All Rights Reserved. Licensed under the BSD 2-Clause License (the "License"). See License in the project root for license information. + +This product includes software developed by LinkedIn Corporation. + +This product contains code derived from the following open source projects: + +1. Unsloth + Copyright (c) 2023 Unsloth AI + Licensed under the Apache License, Version 2.0 + Source: https://github.com/unslothai/unsloth + + The `calculate_settings` function to determine block size and warp is reused for Norm and MLP operations. + Modifications and additions were made to the RMS Norm implementation. + +2. Triton + Copyright (c) 2023 OpenAI + Licensed under the MIT License + Source: https://github.com/openai/triton + + Modifications were made based on Triton tutorials for the RMS Norm implementation. + +3. Efficient Cross Entropy + Copyright (c) 2023 Mohamed Malek + Licensed under the MIT License + Source: https://github.com/mgmalek/efficient_cross_entropy + + The idea of gradient-in-forward and chunking was used in the Linear Cross Entropy implementation. + +4. Flash Attention + Copyright (c) 2023 Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré + Licensed under the BSD 3-Clause License + Source: https://github.com/Dao-AILab/flash-attention + + Optimization ideas such as tiling and recomputation were inspired by this work. + +5. AutoAWQ + Copyright (c) 2023 Casper Hansen + Licensed under the MIT License + Source: https://github.com/casper-hansen/AutoAWQ + + The design of the automodel was referenced from this project. + +6. llm.c + Copyright (c) 2023 Andrej Karpathy + Licensed under the MIT License + Source: https://github.com/karpathy/llm.c + + The design of end-to-end testing was referenced from this project. + +7. Tiny Shakespeare Dataset + Source: https://huggingface.co/datasets/karpathy/tiny_shakespeare + + This dataset is used to conduct convergence tests on mini models. + +For full license texts, please refer to the respective project repositories. diff --git a/README.md b/README.md index f0240c256..c4a26996d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ + + # Liger Kernel: Efficient Triton Kernels for LLM Training @@ -6,6 +8,7 @@ Stable Nightly Discord + Gurubase (experimental) @@ -33,6 +36,11 @@ Join Our Discord + + + Ask Liger Kernel Guru + + @@ -40,11 +48,12 @@ -[Installation](#installation) | [Getting Started](#getting-started) | [Examples](#examples) | [APIs](#apis) | [Structure](#structure) | [Contributing](#contributing) | [Acknowledgement](#acknowledgement) +[Installation](#installation) | [Getting Started](#getting-started) | [Examples](#examples) | [APIs](#apis) | [Cite our work](#cite-this-work)
Latest News 🔥 - + + - [2024/10/21] We have released the tech report of Liger Kernel on Arxiv: https://arxiv.org/pdf/2410.10989 - [2024/9/6] We release v0.2.1 ([X post](https://x.com/liger_kernel/status/1832168197002510649)). 2500+ Stars, 10+ New Contributors, 50+ PRs, 50k Downloads in two weeks! - [2024/8/31] CUDA MODE talk, [Liger-Kernel: Real-world Triton kernel for LLM Training](https://youtu.be/gWble4FreV4?si=dxPeIchhkJ36Mbns), [Slides](https://github.com/cuda-mode/lectures?tab=readme-ov-file#lecture-28-liger-kernel) - [2024/8/23] Official release: check out our [X post](https://x.com/hsu_byron/status/1827072737673982056) @@ -102,11 +111,21 @@ With one line of code, Liger Kernel can increase throughput by more than 20% and ## Installation -### Dependencies +### Dependencies + +#### CUDA - `torch >= 2.1.2` - `triton >= 2.3.0` -- `transformers >= 4.42.0` + +#### ROCm + +- `torch >= 2.5.0` Install according to the instruction in Pytorch official webpage. +- `triton >= 3.0.0` Install from pypi. (e.g. `pip install triton==3.0.0`) + +### Optional Dependencies + +- `transformers >= 4.x`: Required if you plan to use the transformers models patching APIs. The specific model you are working will dictate the minimum version of transformers. > **Note:** > Our kernels inherit the full spectrum of hardware compatibility offered by [Triton](https://github.com/triton-lang/triton). @@ -129,7 +148,11 @@ To install from source: git clone https://github.com/linkedin/Liger-Kernel.git cd Liger-Kernel pip install -e . +# or if using transformers +pip install -e .[transformers] ``` + + ## Getting Started There are a couple of ways to apply Liger kernels, depending on the level of customization required. @@ -222,6 +245,7 @@ loss.backward() | **Model** | **API** | **Supported Operations** | |-------------|--------------------------------------------------------------|-------------------------------------------------------------------------| | LLaMA 2 & 3 | `liger_kernel.transformers.apply_liger_kernel_to_llama` | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy | +| LLaMA 3.2-Vision | `liger_kernel.transformers.apply_liger_kernel_to_mllama` | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy | | Mistral | `liger_kernel.transformers.apply_liger_kernel_to_mistral` | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy | | Mixtral | `liger_kernel.transformers.apply_liger_kernel_to_mixtral` | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy | | Gemma1 | `liger_kernel.transformers.apply_liger_kernel_to_gemma` | RoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy | @@ -244,6 +268,8 @@ loss.backward() | CrossEntropy | `liger_kernel.transformers.LigerCrossEntropyLoss` | | FusedLinearCrossEntropy | `liger_kernel.transformers.LigerFusedLinearCrossEntropyLoss`| | KLDivergence | `liger_kernel.transformers.LigerKLDIVLoss` | +| JSD | `liger_kernel.transformers.LigerJSD` | +| FusedLinearJSD | `liger_kernel.transformers.LigerFusedLinearJSD` | - **RMSNorm**: [RMSNorm](https://arxiv.org/pdf/1910.07467), which normalizes activations using their root mean square, is implemented by fusing the normalization and scaling steps into a single Triton kernel, and achieves ~3X speedup with ~3X peak memory reduction. - **LayerNorm**: [LayerNorm](https://arxiv.org/pdf/1607.06450), which centers and normalizes activations across the feature dimension, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and achieves ~2X speedup. @@ -258,35 +284,23 @@ $$\text{GeGLU}(x)=\text{GELU}(xW+b)\otimes(xV+c)$$ - **FusedLinearCrossEntropy**: Peak memory usage of cross entropy loss is further improved by fusing the model head with the CE loss and chunking the input for block-wise loss and gradient calculation, a technique inspired by [Efficient Cross Entropy](https://github.com/mgmalek/efficient_cross_entropy). It achieves >4X memory reduction for 128k vocab size. **This is highly effective for large batch size, large sequence length, and large vocabulary sizes.** Please refer to the [Medusa example](https://github.com/linkedin/Liger-Kernel/tree/main/examples/medusa) for individual kernel usage. - **KLDivergence**: [KL Divergence](https://pytorch.org/docs/stable/generated/torch.nn.KLDivLoss.html) is implemented by fusing the forward into a single triton kernel, with reduction done outside the kernel. It achieves ~1.5X speed and ~15% memory reduction for 128K vocab size. +- **JSD**: [Generalized JSD](https://arxiv.org/pdf/2306.13649) (Jensen-Shannon divergence), is implemented by computing both the loss and gradient in the forward pass. It achieves ~1.5X speed and ~54% memory reduction for 128k vocab size. +- **FusedLinearJSD**: Peak memory usage of JSD loss is further improved by fusing the model head with the model head with the JSD and chunking the input for block-wise loss and gradient calculation. It achieves ~85% memory reduction for 128k vocab size where batch size $\times$ sequence length is 8192. + ### Experimental Kernels | **Kernel** | **API** | |---------------------------------|-------------------------------------------------------------| | Embedding | `liger_kernel.transformers.experimental.LigerEmbedding` | - +| Matmul int2xint8 | `liger_kernel.transformers.experimental.matmul` - **Embedding**: [Embedding](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) is implemented by fusing embedding lookup and output operations. It achieves a peak speedup of ~1.5x in the forward pass and an overall speedup of ~1.1x. - +- **Matmul int2xint8**: is implemented by using the cache tiled matrix multiplication and by fusing the matmul with the unpacking process which achieves a considerable speed up and performs on par with @torch.compile > **Note:** > Reported speedups and memory reductions are with respect to the LLaMA 3-8B Hugging Face layer implementations. All models use 4K hidden size and 4K sequence length and are evaluated based on memory usage and wall time for the forward+backward pass on a single NVIDIA A100 80G GPU using small batch sizes. Liger kernels exhibit more efficient scaling to larger batch sizes, detailed further in the [Benchmark](./benchmark) folder. -## Note on ML Compiler - -### Torch Compile - -Since Liger Kernel is 100% Triton-based, it works seamlessly with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html). In the following example, Liger Kernel can further optimize the model on top of Torch Compile, reducing the memory by more than half. - -| Configuration | Throughput (tokens/sec) | Memory Reserved (GB) | -|--------------------------------|----------------------------|-------------------------| -| Torch Compile | 3780 | 66.4 | -| Torch Compile + Liger Kernel | 3702 | 31.0 | - -> **Note:** -> 1. Benchmark conditions: LLaMA 3-8B, Batch Size = 8, Seq Len = 4096, Data Type = `bf16`, Optimizer = AdamW, Gradient Checkpointing = True, Distributed Strategy = FSDP1 on 8 A100s. -> 2. Tested on torch `2.5.0.dev20240731+cu118` - ## Contributing [CONTRIBUTING GUIDE](https://github.com/linkedin/Liger-Kernel/blob/main/CONTRIBUTING.md) @@ -320,7 +334,14 @@ Many thanks to the contributors to these projects for their invaluable work that ## License -[BSD 2-CLAUSE](https://github.com/linkedin/Liger-Kernel/blob/main/LICENSE) +This project is licensed under the [BSD 2-CLAUSE](https://github.com/linkedin/Liger-Kernel/blob/main/LICENSE) License (see `LICENSE` for details). +It also includes components from projects licensed under: + +- Apache License 2.0 (see `LICENSE-APACHE-2.0` for details). +- MIT License (see `LICENSE-MIT-AutoAWQ` for details). +- MIT License (see `LICENSE-MIT-Efficient Cross Entropy` for details). +- MIT License (see `LICENSE-MIT-llmc` for details). +- MIT License (see `LICENSE-MIT-triton` for details). ## Contact @@ -331,13 +352,29 @@ Many thanks to the contributors to these projects for their invaluable work that Biblatex entry: ```bib -@software{liger2024, - title = {Liger-Kernel: Efficient Triton Kernels for LLM Training}, - author = {Hsu, Pin-Lun and Dai, Yun and Kothapalli, Vignesh and Song, Qingquan and Tang, Shao and Zhu, Siyu}, - url = {https://github.com/linkedin/Liger-Kernel}, - year = {2024} +@article{hsu2024ligerkernelefficienttriton, + title={Liger Kernel: Efficient Triton Kernels for LLM Training}, + author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen}, + year={2024}, + eprint={2410.10989}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2410.10989}, + journal={arXiv preprint arXiv:2410.10989}, } ``` ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=linkedin/Liger-Kernel&type=Date)](https://star-history.com/#linkedin/Liger-Kernel&Date) + +## Contributors + + + contributors + + +

+ + ↑ Back to Top ↑ + +

diff --git a/benchmark/data/all_benchmark_data.csv b/benchmark/data/all_benchmark_data.csv index dcb5e30f0..32c8d01ab 100644 --- a/benchmark/data/all_benchmark_data.csv +++ b/benchmark/data/all_benchmark_data.csv @@ -445,3 +445,63 @@ kl_div,torch,full,speed,ms,V,vocab size,16384,11.124671936035156,11.122162818908 kl_div,torch,full,speed,ms,V,vocab size,32768,23.052032470703125,23.050334930419922,23.052589416503906,"{""B"": 8, ""T"": 2048}",NVIDIA H100 PCIe,2024-09-04 12:59:48,0.2.1 kl_div,torch,full,speed,ms,V,vocab size,65536,46.063167572021484,46.05990219116211,46.06643295288086,"{""B"": 8, ""T"": 2048}",NVIDIA H100 PCIe,2024-09-04 12:59:48,0.2.1 kl_div,torch,full,speed,ms,V,vocab size,131072,92.06393432617188,92.06393432617188,92.06393432617188,"{""B"": 8, ""T"": 2048}",NVIDIA H100 PCIe,2024-09-04 12:59:48,0.2.1 +jsd,liger,full,memory,MB,V,vocab size,4096,768.0029296875,768.0029296875,768.0029296875,"{""B"": 4, ""T"": 2048}",NVIDIA H100 PCIe,2024-10-02 16:21:31,0.3.1 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""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:18,0.3.1 +fused_linear_jsd,liger,forward,speed,ms,BT,B x T,2048,124.14070129394531,124.14070129394531,124.14070129394531,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:18,0.3.1 +fused_linear_jsd,liger,forward,speed,ms,BT,B x T,4096,143.15420532226562,143.15420532226562,143.15420532226562,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:18,0.3.1 +fused_linear_jsd,liger,forward,speed,ms,BT,B x T,8192,180.90406799316406,180.90406799316406,180.90406799316406,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:18,0.3.1 +fused_linear_jsd,torch,forward,speed,ms,BT,B x T,1024,9.556896209716797,9.550745964050293,9.576268196105957,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:20,0.3.1 +fused_linear_jsd,torch,forward,speed,ms,BT,B x T,2048,18.73731231689453,18.732704162597656,18.737701416015625,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:20,0.3.1 +fused_linear_jsd,torch,forward,speed,ms,BT,B x T,4096,37.830482482910156,37.80821990966797,37.85274124145508,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:20,0.3.1 +fused_linear_jsd,torch,forward,speed,ms,BT,B x T,8192,75.15289306640625,75.15289306640625,75.15289306640625,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:20,0.3.1 +fused_linear_jsd,liger,full,speed,ms,BT,B x T,1024,111.16019439697266,111.16019439697266,111.16019439697266,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:24,0.3.1 +fused_linear_jsd,liger,full,speed,ms,BT,B x T,2048,125.6825942993164,125.6825942993164,125.6825942993164,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:24,0.3.1 +fused_linear_jsd,liger,full,speed,ms,BT,B x T,4096,144.00784301757812,144.00784301757812,144.00784301757812,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:24,0.3.1 +fused_linear_jsd,liger,full,speed,ms,BT,B x T,8192,182.5832977294922,182.5832977294922,182.5832977294922,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:24,0.3.1 +fused_linear_jsd,torch,full,speed,ms,BT,B x T,1024,25.977184295654297,25.968351364135742,25.989356994628906,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:27,0.3.1 +fused_linear_jsd,torch,full,speed,ms,BT,B x T,2048,49.48417663574219,49.47330093383789,49.495052337646484,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:27,0.3.1 +fused_linear_jsd,torch,full,speed,ms,BT,B x T,4096,98.31510162353516,98.31510162353516,98.31510162353516,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:27,0.3.1 +fused_linear_jsd,torch,full,speed,ms,BT,B x T,8192,195.29539489746094,195.29539489746094,195.29539489746094,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:27,0.3.1 +fused_linear_jsd,liger,full,memory,MB,BT,B x T,1024,4652.48486328125,4652.48486328125,4652.48486328125,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:33,0.3.1 +fused_linear_jsd,liger,full,memory,MB,BT,B x T,2048,5231.93798828125,5231.93798828125,5231.93798828125,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:33,0.3.1 +fused_linear_jsd,liger,full,memory,MB,BT,B x T,4096,6391.87548828125,6391.87548828125,6391.87548828125,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:33,0.3.1 +fused_linear_jsd,liger,full,memory,MB,BT,B x T,8192,8711.75,8711.75,8711.75,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:33,0.3.1 +fused_linear_jsd,torch,full,memory,MB,BT,B x T,1024,10609.005859375,10609.005859375,10609.005859375,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:35,0.3.1 +fused_linear_jsd,torch,full,memory,MB,BT,B x T,2048,17146.009765625,17146.009765625,17146.009765625,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:35,0.3.1 +fused_linear_jsd,torch,full,memory,MB,BT,B x T,4096,30220.017578125,30220.017578125,30220.017578125,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:35,0.3.1 +fused_linear_jsd,torch,full,memory,MB,BT,B x T,8192,56368.015625,56368.015625,56368.015625,"{""H"": 4096, ""V"": 128256, ""mode"": ""forward"", ""dtype"": ""torch.bfloat16""}",NVIDIA H100 80GB HBM3,2024-10-09 12:29:35,0.3.1 diff --git a/benchmark/scripts/benchmark_fused_linear_jsd.py b/benchmark/scripts/benchmark_fused_linear_jsd.py new file mode 100644 index 000000000..7f652de8a --- /dev/null +++ b/benchmark/scripts/benchmark_fused_linear_jsd.py @@ -0,0 +1,272 @@ +import torch +import triton +from utils import ( + QUANTILES, + SingleBenchmarkRunInput, + SingleBenchmarkRunOutput, + _test_memory, + parse_benchmark_script_args, + run_benchmarks, +) + +from liger_kernel.transformers.fused_linear_jsd import LigerFusedLinearJSD + + +class TorchJSD(torch.nn.Module): + def __init__( + self, + beta: float = 0.5, + ignore_index: int = -100, + dtype: torch.dtype = torch.float, + ): + super(TorchJSD, self).__init__() + self.kl = torch.nn.KLDivLoss(reduction="none", log_target=True) + self.beta = beta + self.ignore_index = ignore_index + self.dtype = dtype + + def forward( + self, + log_q: torch.Tensor, # input + log_p: torch.Tensor, # target + label=None, + ): + log_p, log_q = log_p.to(torch.float), log_q.to(torch.float) + log_p, log_q = log_p.view(-1, log_p.size(-1)), log_q.view(-1, log_q.size(-1)) + m = torch.lerp(torch.exp(log_q), torch.exp(log_p), self.beta) + loss = self.beta * self.kl(torch.log(m), log_p).sum(dim=-1) + ( + 1 - self.beta + ) * self.kl(torch.log(m), log_q).sum(dim=-1) + + if label is not None: + loss = torch.where(label != self.ignore_index, loss, 0.0) + n_non_ignore = (label != self.ignore_index).sum().item() + if n_non_ignore == 0: + loss = 0.0 + else: + loss = (loss / n_non_ignore).sum() + else: + loss = (loss / log_q.shape[0]).sum() + return loss.to(self.dtype) + + +class TorchLMHeadJSD(torch.nn.Module): + """Ground truth implementation of the linear fused with torch based jsd loss. + + :param H: hidden size + :param V: vocab size + :param temperature: softmax temperature + :param beta: jsd beta + """ + + def __init__( + self, + H: int, + V: int, + dtype: torch.dtype, + device: torch.device, + beta: float = 0.5, + ignore_index: int = -100, + temperature: float = 1.0, + ): + super().__init__() + self.student_lin = torch.nn.Linear( + in_features=H, out_features=V, bias=False, dtype=dtype, device=device + ) + self.teacher_lin = torch.nn.Linear( + in_features=H, out_features=V, bias=False, dtype=dtype, device=device + ) + self.jsd = TorchJSD(beta=beta, ignore_index=ignore_index, dtype=dtype) + self.temperature = temperature + + def forward(self, student_input, teacher_input, label=None): + student_logits = self.student_lin(student_input) + teacher_logits = self.teacher_lin(teacher_input) + student_prob = torch.log_softmax(student_logits / self.temperature, dim=-1) + teacher_prob = torch.log_softmax(teacher_logits / self.temperature, dim=-1) + + return self.jsd(student_prob, teacher_prob, label) + + +class LigerLMHeadJSD(torch.nn.Module): + def __init__( + self, + H: int, + V: int, + dtype: torch.dtype, + device: torch.device, + beta: float = 0.5, + ignore_index: int = -100, + temperature: float = 1.0, + ): + super().__init__() + self.student_lin = torch.nn.Linear( + in_features=H, out_features=V, bias=False, dtype=dtype, device=device + ) + self.teacher_lin = torch.nn.Linear( + in_features=H, out_features=V, bias=False, dtype=dtype, device=device + ) + self.fused_jsd = LigerFusedLinearJSD( + jsd_beta=beta, ignore_index=ignore_index, temperature=temperature + ) + + def forward(self, student_input, teacher_input, label=None): + return self.fused_jsd( + student_input, + self.student_lin.weight, + teacher_input, + self.teacher_lin.weight, + label, + ) + + +############################################################################# +# Test the memory consumption of the fused linear JSD +############################################################################# + + +def bench_memory_fused_linear_jsd( + input: SingleBenchmarkRunInput, +) -> SingleBenchmarkRunOutput: + BT = input.x + H = input.extra_benchmark_config["H"] + V = input.extra_benchmark_config["V"] + dtype = input.extra_benchmark_config["dtype"] + provider = input.kernel_provider + + device = "cuda" + torch_lm_head_jsd = TorchLMHeadJSD(H=H, V=V, dtype=dtype, device=device).to(device) + liger_lm_head_jsd = LigerLMHeadJSD(H=H, V=V, dtype=dtype, device=device).to(device) + + # init the linear in all FusedLinearJSDs with the same weights + torch_lm_head_jsd.student_lin.weight.data = ( + liger_lm_head_jsd.student_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + torch_lm_head_jsd.teacher_lin.weight.data = ( + liger_lm_head_jsd.teacher_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + + student_input = torch.rand(BT, H, requires_grad=True, dtype=dtype, device=device) + teacher_input = torch.rand(BT, H, dtype=dtype, device=device) + + def fwd(): + if provider == "liger": + return liger_lm_head_jsd(student_input, teacher_input) + elif provider == "torch": + return torch_lm_head_jsd(student_input, teacher_input) + + def full(): + y = fwd() + y.backward() + + mem_50, mem_20, mem_80 = _test_memory(full, _iter=10, quantiles=QUANTILES) + return SingleBenchmarkRunOutput( + y_20=mem_20, + y_50=mem_50, + y_80=mem_80, + ) + + +# ############################################################################# +# # Test the speed of the fused linear JSD +# ############################################################################# + + +def bench_speed_fused_linear_jsd( + input: SingleBenchmarkRunInput, +) -> SingleBenchmarkRunOutput: + BT = input.x + H = input.extra_benchmark_config["H"] + V = input.extra_benchmark_config["V"] + mode = input.kernel_operation_mode + + dtype = input.extra_benchmark_config["dtype"] + provider = input.kernel_provider + + device = "cuda" + torch_lm_head_jsd = TorchLMHeadJSD(H=H, V=V, dtype=dtype, device=device).to(device) + liger_lm_head_jsd = LigerLMHeadJSD(H=H, V=V, dtype=dtype, device=device).to(device) + + # init the linear in all FusedLinearJSDs with the same weights + torch_lm_head_jsd.student_lin.weight.data = ( + liger_lm_head_jsd.student_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + torch_lm_head_jsd.teacher_lin.weight.data = ( + liger_lm_head_jsd.teacher_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + + student_input = torch.rand(BT, H, requires_grad=True, dtype=dtype, device=device) + teacher_input = torch.rand(BT, H, dtype=dtype, device=device) + + def fwd(): + if provider == "liger": + return liger_lm_head_jsd(student_input, teacher_input) + elif provider == "torch": + return torch_lm_head_jsd(student_input, teacher_input) + + if mode == "forward": + ms_50, ms_20, ms_80 = triton.testing.do_bench( + fwd, + rep=100, + quantiles=QUANTILES, + ) + elif mode == "backward": + y = fwd() + + ms_50, ms_20, ms_80 = triton.testing.do_bench( + lambda: y.backward(retain_graph=True), + grad_to_none=[ + student_input, + torch_lm_head_jsd.student_lin.weight, + torch_lm_head_jsd.teacher_lin.weight, + ], + rep=100, + quantiles=QUANTILES, + ) + elif mode == "full": + + def full(): + y = fwd() + y.backward() + + ms_50, ms_20, ms_80 = triton.testing.do_bench( + full, + rep=100, + quantiles=QUANTILES, + ) + return SingleBenchmarkRunOutput( + y_20=ms_20, + y_50=ms_50, + y_80=ms_80, + ) + + +if __name__ == "__main__": + args = parse_benchmark_script_args() + + common_configs = { + "kernel_name": "fused_linear_jsd", + "x_name": "BT", + "x_label": "B x T", + "x_values": [2**i for i in range(10, 14)], + "kernel_providers": ["liger", "torch"], + "extra_benchmark_configs": [ + {"H": 4096, "V": 128256, "mode": "forward", "dtype": torch.bfloat16} + ], + "overwrite": args.overwrite, + } + + run_benchmarks( + bench_test_fn=bench_speed_fused_linear_jsd, + kernel_operation_modes=["forward", "full"], + metric_name="speed", + metric_unit="ms", + **common_configs + ) + run_benchmarks( + bench_test_fn=bench_memory_fused_linear_jsd, + kernel_operation_modes=["full"], + metric_name="memory", + metric_unit="MB", + **common_configs + ) diff --git a/benchmark/scripts/benchmark_jsd.py b/benchmark/scripts/benchmark_jsd.py new file mode 100644 index 000000000..272008315 --- /dev/null +++ b/benchmark/scripts/benchmark_jsd.py @@ -0,0 +1,154 @@ +import torch +import triton +from utils import ( + QUANTILES, + SingleBenchmarkRunInput, + SingleBenchmarkRunOutput, + _test_memory, + parse_benchmark_script_args, + run_benchmarks, +) + +from liger_kernel.transformers.jsd import LigerJSD + + +class TorchJSD(torch.nn.Module): + def __init__( + self, + beta: float = 0.5, + ignore_index: int = -100, + dtype: torch.dtype = torch.float, + ): + super(TorchJSD, self).__init__() + self.kl = torch.nn.KLDivLoss(reduction="none", log_target=True) + self.beta = beta + self.ignore_index = ignore_index + self.dtype = dtype + + def forward( + self, + log_q: torch.Tensor, # input + log_p: torch.Tensor, # target + label=None, + ): + log_p, log_q = log_p.to(torch.float), log_q.to(torch.float) + log_p, log_q = log_p.view(-1, log_p.size(-1)), log_q.view(-1, log_q.size(-1)) + m = torch.lerp(torch.exp(log_q), torch.exp(log_p), self.beta) + loss = self.beta * self.kl(torch.log(m), log_p).sum(dim=-1) + ( + 1 - self.beta + ) * self.kl(torch.log(m), log_q).sum(dim=-1) + + if label is not None: + loss = torch.where(label != self.ignore_index, loss, 0.0) + n_non_ignore = (label != self.ignore_index).sum().item() + if n_non_ignore == 0: + loss = 0.0 + else: + loss = (loss / n_non_ignore).sum() + else: + loss = (loss / log_q.shape[0]).sum() + return loss.to(self.dtype) + + +def bench_speed_jsd(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput: + V = input.x + B, T = input.extra_benchmark_config["B"], input.extra_benchmark_config["T"] + torch_jsd = TorchJSD() + liger_jsd = LigerJSD() + + _input = torch.randn(B * T, V, requires_grad=True, device="cuda").log_softmax( + dim=-1 + ) + target = torch.randn(B * T, V, device="cuda").log_softmax(dim=-1) + + def fwd(): + if input.kernel_provider == "liger": + return liger_jsd(_input, target) + else: + return torch_jsd(_input, target) + + if input.kernel_operation_mode == "forward": + ms_50, ms_20, ms_80 = triton.testing.do_bench(fwd, quantiles=QUANTILES, rep=100) + elif input.kernel_operation_mode == "backward": + y = fwd() + + ms_50, ms_20, ms_80 = triton.testing.do_bench( + lambda: y.backward(retain_graph=True), + quantiles=QUANTILES, + grad_to_none=[_input], + rep=100, + ) + elif input.kernel_operation_mode == "full": + + def full(): + y = fwd() + y.backward(retain_graph=True) + + ms_50, ms_20, ms_80 = triton.testing.do_bench( + full, quantiles=QUANTILES, rep=100 + ) + return SingleBenchmarkRunOutput( + y_20=ms_20, + y_50=ms_50, + y_80=ms_80, + ) + + +def bench_memory_jsd(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput: + torch_jsd = TorchJSD() + liger_jsd = LigerJSD() + + V = input.x + B, T = input.extra_benchmark_config["B"], input.extra_benchmark_config["T"] + + _input = torch.randn(B * T, V, requires_grad=True, device="cuda").log_softmax( + dim=-1 + ) + target = torch.randn(B * T, V, device="cuda").log_softmax(dim=-1) + + def fwd(): + if input.kernel_provider == "liger": + return liger_jsd(_input, target) + else: + return torch_jsd(_input, target) + + def full(): + y = fwd() + y.backward(retain_graph=True) + + mem_50, mem_20, mem_80 = _test_memory(full, quantiles=QUANTILES) + + return SingleBenchmarkRunOutput( + y_20=mem_20, + y_50=mem_50, + y_80=mem_80, + ) + + +if __name__ == "__main__": + args = parse_benchmark_script_args() + common_args = { + "kernel_name": "jsd", + "x_name": "V", + "x_label": "vocab size", + "x_values": [2**i for i in range(12, 18)], + "kernel_providers": ["liger", "torch"], + "extra_benchmark_configs": [{"B": 4, "T": 2048}], + "overwrite": args.overwrite, + } + + run_benchmarks( + bench_test_fn=bench_memory_jsd, + kernel_operation_modes=["full"], + metric_name="memory", + metric_unit="MB", + **common_args, + ) + + run_benchmarks( + bench_test_fn=bench_speed_jsd, + kernel_operation_modes=["forward", "full"], + metric_name="speed", + metric_unit="ms", + **common_args, + ) diff --git a/dev/modal/tests.py b/dev/modal/tests.py new file mode 100644 index 000000000..880a2f299 --- /dev/null +++ b/dev/modal/tests.py @@ -0,0 +1,23 @@ +from pathlib import Path + +import modal + +ROOT_PATH = Path(__file__).parent.parent.parent + +image = modal.Image.debian_slim().pip_install_from_pyproject( + ROOT_PATH / "pyproject.toml", optional_dependencies=["dev"] +) + +app = modal.App("liger_tests", image=image) + +# mount: add local files to the remote container +repo = modal.Mount.from_local_dir(ROOT_PATH, remote_path="/root/liger-kernel") + + +@app.function(gpu="A10G", mounts=[repo], timeout=60 * 10) +def liger_tests(): + import subprocess + + subprocess.run(["pip", "install", "-e", "."], check=True, cwd="/root/liger-kernel") + subprocess.run(["make", "test"], check=True, cwd="/root/liger-kernel") + subprocess.run(["make", "test-convergence"], check=True, cwd="/root/liger-kernel") diff --git a/examples/medusa/medusa_util.py b/examples/medusa/medusa_util.py index c6f0c5a2f..5b4f9ac9f 100644 --- a/examples/medusa/medusa_util.py +++ b/examples/medusa/medusa_util.py @@ -212,7 +212,7 @@ def forward( if with_liger: lce = LigerFusedLinearCrossEntropyLoss() - for i in range(model.medusa_num_heads): + for i in range(model.medusa_num_heads + 1): shift_hidden_states = ( hidden_states[..., : -(1 + i), :] .contiguous() @@ -223,7 +223,7 @@ def forward( weight = ( model.lm_head.weight if i == 0 - else model.medusa_head[i][-1].weight + else model.medusa_head[i - 1][-1].weight ) loss_i = lce(weight, shift_hidden_states, shift_labels) @@ -238,7 +238,7 @@ def forward( else: loss_fct = CrossEntropyLoss() - for i in range(model.medusa_num_heads): + for i in range(model.medusa_num_heads + 1): medusa_logits_i = ( medusa_logits[i, :, : -(1 + i)] .contiguous() diff --git a/licenses/LICENSE-Apache-2.0 b/licenses/LICENSE-Apache-2.0 new file mode 100644 index 000000000..0328c5ff0 --- /dev/null +++ b/licenses/LICENSE-Apache-2.0 @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Y_stride (int): The stride of the target tensor. loss_ptr: Pointer to tensor to store the loss. + z_loss_ptr: Pointer to tensor to store the z loss. No operation if RETURN_Z_LOSS is 0. loss_stride (int): The stride of the loss tensor. n_cols (int): The number of columns in the input tensor. n_non_ignore (int): The number of non-ignored elements in the batch. ignore_index (int): The index to ignore in the target. label_smoothing (float): The amount of smoothing when computing the loss, where 0.0 means no smoothing. + lse_square_scale (float): The scaler of (logsumexp(_input)) ^ 2 adding to the loss for the stability of training. + RETURN_Z_LOSS (int): The boolean value to decide whether storing z loss to z_loss_ptr or not. It must be 0 or 1. reduction (str): The string for the reduction to apply BLOCK_SIZE (int): The block size for Triton operations. """ @@ -56,6 +67,7 @@ def liger_cross_entropy_kernel( return loss_ptr += program_id * loss_stride + z_loss_ptr += program_id * loss_stride # Online softmax: 2 loads + 1 store (compared with 3 loads + 1 store for the safe softmax) # Refer to Algorithm 3 in the paper: https://arxiv.org/pdf/1805.02867 @@ -85,32 +97,40 @@ def liger_cross_entropy_kernel( d = d * tl.exp(m - m_new) + tl.sum(tl.exp(X_block - m_new)) m = m_new + # log (sum(e^(X_i))) = log (sum(e ^ (max(X) * e ^ (X_i - max(X))))) + # = log (e^(max(X)) * sum(e ^ (X_i - max(X)))) + # = max(X) + log (sum(e ^ (X_i - max(X)))) = m + log d + lse = m + tl.log(d) + # 4. [Online Softmax] Second pass: compute gradients # For 'mean' reduction, gradients are normalized by number of non-ignored elements (N) # dx_y = (softmax(x_y) - 1) / N # dx_i = softmax(x_i) / N, i != y # For label smoothing: - # dx_i = (softmax(x_y) - label_smoothing / V) / N, V = n_cols, i != y + # dx_i = (softmax(x_i) - label_smoothing / V) / N, V = n_cols, i != y # dx_y = (softmax(x_y) - label_smoothing / V - (1 - label_smoothing)) / N # = dx_i - (1 - label_smoothing) / N - # + # With Z loss: + # dx_i = ((1 + 2 * lse_square_scale * lse) * softmax(x_i) - label_smoothing / V) / N, i != y + # dx_y = dx_i - (1 - label_smoothing) / N # For 'sum' reduction, no normalization is applied: # dx_y = softmax(x_y) - 1 # dx_i = softmax(x_i), for i ≠ y - # For label smoothing: - # dx_i = (softmax(x_y) - label_smoothing / V), V = n_cols, i != y - # dx_y = (softmax(x_y) - label_smoothing / V - (1 - label_smoothing)) - # = dx_i - (1 - label_smoothing) for i in range(0, n_cols, BLOCK_SIZE): X_offsets = i + tl.arange(0, BLOCK_SIZE) X_block = tl.load( X_ptr + X_offsets, mask=X_offsets < n_cols, other=float("-inf") ) + # softmax(x_i) + X_block = tl.exp(X_block - m) / d + # derivative of z-loss: 2 * lse_square_scale * lse * softmax(x_i) + X_block += 2 * lse_square_scale * lse * X_block + # smoothing term + X_block += -eps + # reduction scale if reduction == "mean": - X_block = (tl.exp(X_block - m) / d - eps) / (n_non_ignore) - else: - X_block = tl.exp(X_block - m) / d - eps + X_block = X_block / (n_non_ignore) tl.store(X_ptr + X_offsets, X_block, mask=X_offsets < n_cols) @@ -122,11 +142,12 @@ def liger_cross_entropy_kernel( # loss = log (softmax(X_y)) = log ((e ^ (X_y - max(X)) / sum(e ^ (X - max(X)))) # = (X_y - max(X)) - log(sum(e ^ (X - max(X)))) + # = X_y - m - log d = X_y - lse # sum(e ^ (X - max(X))) must >= 1 because the max term is e ^ 0 = 1 # So we can safely calculate log (softmax(X_y)) without overflow - loss = -(ori_X_y - m - tl.log(d)) + loss = lse - ori_X_y - # Orginal loss = H(q, p), with label smoothing regularization = H(q', p) and (label_smoothing / V) = eps + # Original loss = H(q, p), with label smoothing regularization = H(q', p) and (label_smoothing / V) = eps # H(q', p) = (1 - label_smoothing) * H(q, p) + label_smoothing * H(u, p) # = (1 - label_smoothing) * H(q, p) + eps * sum(logsoftmax(x_i)) # By using m (global max of xi) and d (sum of e^(xi-m)), we can simplify as: @@ -135,11 +156,16 @@ def liger_cross_entropy_kernel( # pytorch: https://github.com/pytorch/pytorch/blob/2981534f54d49fa3a9755c9b0855e7929c2527f0/aten/src/ATen/native/LossNLL.cpp#L516 # See full derivation at https://github.com/linkedin/Liger-Kernel/pull/198#issuecomment-2333753087 if label_smoothing > 0: - smooth_loss = scaled_x_sum + label_smoothing * (m + tl.log(d)) + smooth_loss = scaled_x_sum + label_smoothing * lse loss = loss * (1 - label_smoothing) + smooth_loss + # An auxiliary loss, z_loss + # Refer to Page14 Loss function section in the paper PaLM: https://www.jmlr.org/papers/v24/22-1144.html + z_loss = lse_square_scale * lse * lse + loss += z_loss # Normalize the loss by the number of non-ignored elements if reduction is "mean" if reduction == "mean": + z_loss = z_loss / n_non_ignore loss = loss / n_non_ignore # 6. Specially handle the i==y case where `dx_y = (softmax(x_y) - (1 - label_smoothing) / N` @@ -150,6 +176,8 @@ def liger_cross_entropy_kernel( X_y += -(1 - label_smoothing) tl.store(loss_ptr, loss) + if RETURN_Z_LOSS == _TRUE: + tl.store(z_loss_ptr, z_loss) tl.store(X_ptr + y, X_y) @@ -159,43 +187,31 @@ def liger_cross_entropy_kernel( MAX_FUSED_SIZE = 65536 // 2 # the best size we found by manually tuning -@triton.jit -def element_mul_kernel( - X_ptr, - X_stride, - grad_output_ptr, - n_cols, - BLOCK_SIZE: tl.constexpr, -): - """ - This function multiplies each element of the tensor pointed by X_ptr with the value pointed by grad_output_ptr. - The multiplication is performed in-place on the tensor pointed by X_ptr. - - Parameters: - X_ptr: Pointer to the input tensor. - X_stride (int): The stride of the input tensor. - grad_output_ptr: Pointer to the gradient output value. - n_cols (int): The number of columns in the input tensor. - BLOCK_SIZE (int): The block size for Triton operations. - """ - - # Get the program ID and convert it to int64 to avoid overflow - program_id = tl.program_id(0).to(tl.int64) - - # Locate the start index - X_ptr += program_id * X_stride +_bool_to_return_z_loss = { + True: _TRUE.value, + False: _FALSE.value, +} - # Load the gradient output value - grad_output = tl.load(grad_output_ptr) - - # Perform the element-wise multiplication - for i in range(0, n_cols, BLOCK_SIZE): - X_offsets = i + tl.arange(0, BLOCK_SIZE) - X_block = tl.load(X_ptr + X_offsets, mask=X_offsets < n_cols) - tl.store(X_ptr + X_offsets, X_block * grad_output, mask=X_offsets < n_cols) +def cross_entropy_forward( + _input, + target, + ignore_index, + lse_square_scale, + label_smoothing, + reduction, + return_z_loss, +): + if not isinstance(return_z_loss, int): + assert ( + return_z_loss in _bool_to_return_z_loss + ), f"return_z_loss must be True or False. Got: {return_z_loss}" + return_z_loss = _bool_to_return_z_loss[return_z_loss] + else: + assert ( + return_z_loss in _bool_to_return_z_loss + ), f"return_z_loss must be True or False. Got: {return_z_loss}" -def cross_entropy_forward(_input, target, ignore_index, label_smoothing, reduction): BT, V = _input.shape n_rows = BT @@ -203,6 +219,10 @@ def cross_entropy_forward(_input, target, ignore_index, label_smoothing, reducti # unreduced loss loss_1d = torch.zeros(n_rows, dtype=_input.dtype, device=_input.device) + if return_z_loss == _TRUE.value: + z_loss_1d = torch.zeros(n_rows, dtype=_input.dtype, device=_input.device) + else: + z_loss_1d = loss_1d # dummy ptr when return_z_loss == False n_non_ignore = (target != ignore_index).sum().item() @@ -219,20 +239,28 @@ def cross_entropy_forward(_input, target, ignore_index, label_smoothing, reducti Y_ptr=target, Y_stride=target.stride(-1), # always 1 loss_ptr=loss_1d, + z_loss_ptr=z_loss_1d, loss_stride=loss_1d.stride(-1), # always 1 n_cols=V, n_non_ignore=n_non_ignore, ignore_index=ignore_index, + lse_square_scale=lse_square_scale, label_smoothing=label_smoothing, reduction=reduction, + RETURN_Z_LOSS=return_z_loss, BLOCK_SIZE=BLOCK_SIZE, # TODO: 32 seems to give the best performance # Performance is quite sensitive to num_warps - num_warps=32, + num_warps=32 if not is_hip() else 16, ) loss = torch.sum(loss_1d) - return loss, _input + if return_z_loss == _TRUE.value: + z_loss = torch.sum(z_loss_1d) + else: + z_loss = None + + return loss, z_loss, _input def cross_entropy_backward(_input, grad_output): @@ -253,7 +281,7 @@ def cross_entropy_backward(_input, grad_output): grad_output, V, BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, + num_warps=32 if not is_hip() else 16, ) return _input @@ -267,7 +295,14 @@ class LigerCrossEntropyFunction(torch.autograd.Function): @staticmethod def forward( - ctx, _input, target, ignore_index=-100, label_smoothing=0.0, reduction="mean" + ctx, + _input, + target, + ignore_index=-100, + lse_square_scale=0.0, + label_smoothing=0.0, + reduction="mean", + return_z_loss=False, ): """ The forward pass of the Liger Cross Entropy loss. @@ -277,33 +312,46 @@ def forward( _input (tensor): The input tensor of shape (BT, V) where B is batch size, T is sequence length, V is vocab size. target (tensor): The target tensor of shape (BT) where each value is in [0, V-1]. ignore_index (int): The index to ignore in the target. + lse_square_scale (float): The scaler of (logsumexp(_input)) ^ 2 adding to the loss for the stability of training. label_smoothing (float): The amount of smoothing when computing the loss, where 0.0 means no smoothing. reduction (str): The reduction to apply to the output: "none" | "mean | "sum". + return_z_loss (bool): When `return_z_loss` is `True`, returns (loss, z_loss) instead of (loss, None). Default: `False` Returns: - tensor: The computed loss. + tuple: A tuple with the compouted losses with respect to loss and z loss. The elements are tensors or None. """ - loss, _input = cross_entropy_forward( - _input, target, ignore_index, label_smoothing, reduction + loss, z_loss, _input = cross_entropy_forward( + _input, + target, + ignore_index, + lse_square_scale, + label_smoothing, + reduction, + return_z_loss, ) # TODO: investigation # If we don't detach the _input tensor, the memory will double # Not sure why but seems that there will be a time both grad and value exist but in different location ctx.save_for_backward(_input.detach()) - return loss + ctx.return_z_loss = return_z_loss + + return loss, z_loss @staticmethod - def backward(ctx, grad_output): + def backward(ctx, grad_output, grad_ouput2): """ The backward pass of the Liger Cross Entropy loss. Parameters: ctx : The context object with saved tensors. grad_output (tensor): The tensor containing the gradient of the loss with respect to the output. - + grad_output2 (tenosr): No use. Returns: tuple: A tuple with the gradients with respect to the inputs. The elements are tensors or None. """ + if ctx.return_z_loss: + del grad_ouput2 # z_loss is only for logging + (_input,) = ctx.saved_tensors _input = cross_entropy_backward(_input, grad_output) return ( @@ -312,4 +360,6 @@ def backward(ctx, grad_output): None, None, None, + None, + None, ) diff --git a/src/liger_kernel/ops/experimental/mm_int8int2.py b/src/liger_kernel/ops/experimental/mm_int8int2.py new file mode 100644 index 000000000..4de17124b --- /dev/null +++ b/src/liger_kernel/ops/experimental/mm_int8int2.py @@ -0,0 +1,355 @@ +import torch +import triton +import triton.language as tl + + +def unpack_weights(packed: torch.Tensor, bits: int = 2) -> torch.Tensor: + values_per_item = 8 // bits + packed_shape = packed.shape + + if len(packed_shape) == 1: + original_row_dim = packed_shape[0] * values_per_item + unpacked_shape = (original_row_dim,) + else: + original_row_dim = packed_shape[0] * values_per_item + unpacked_shape = (original_row_dim, *packed_shape[1:]) + + unpacked = torch.zeros(unpacked_shape, device=packed.device, dtype=torch.uint8) + + for i in range(values_per_item): + start = i * packed_shape[0] + end = start + packed_shape[0] + mask = 3 << (2 * i) + unpacked[start:end] = (packed & mask) >> (2 * i) + + unpacked = unpacked.to(torch.int32) - 1 + return unpacked + + +def pack_weights(intweights: torch.Tensor, bits: int = 2) -> torch.Tensor: + intweights += 1 + original_shape = intweights.shape + values_per_item = 8 // bits + row_dim = (original_shape[0] + values_per_item - 1) // values_per_item + + if len(original_shape) == 1: + packed_tensor_shape = (row_dim,) + else: + packed_tensor_shape = (row_dim, *original_shape[1:]) + + packed = torch.zeros( + packed_tensor_shape, device=intweights.device, dtype=torch.uint8 + ) + unpacked = intweights.to(torch.uint8) + + def lshift(t: torch.Tensor, bits: int): + return t << bits + + it = min(values_per_item, (original_shape[0] // row_dim) + 1) + for i in range(it): + start = i * row_dim + end = min(start + row_dim, original_shape[0]) + packed[: (end - start)] |= lshift(unpacked[start:end], bits * i) + + return packed + + +def get_autotune_config(): + return [ + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + }, + num_stages=3, + num_warps=8, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + }, + num_stages=3, + num_warps=8, + ), + triton.Config( + { + "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + }, + num_stages=3, + num_warps=8, + ), + triton.Config( + { + "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 4, + }, + num_stages=4, + num_warps=4, + ), + ] + + +@triton.autotune( + configs=get_autotune_config(), + key=["M", "N", "K"], +) +@triton.jit +def matmul_kernel( + a_ptr, + b_ptr, + c_ptr, + M, + N, + K: tl.constexpr, + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + GROUP_SIZE_M: tl.constexpr, +): + # We want K / 4 to be divisible by BLOCK_SIZE_K so that the multiplication can be aligned + tl.static_assert( + K % (4 * BLOCK_SIZE_K) == 0, + "K / 4 must be divisible by BLOCK_SIZE_K => K divisible by 4*BLOCK_SIZE_K", + ) + # determine the block id in the 1D grid, pid <=> blockId in cuda + pid = tl.program_id(axis=0) + # number of blocks we would need in the M dimension + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + # number of blocks we would need in the N dimension + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + # blocks are grouped along the M dimension. num_pid_in_group computes how many blocks are grouped together, + # and group_id calculates the group to which the current block (pid) belongs. + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + + # pid of the first block in the group that the current block belongs too + first_pid_m = group_id * GROUP_SIZE_M + + # pid_m : pid of the block along the M dimension of the output matrix, and pid_n : pid of the block along the N dimension of the output matrix + # remember that the grid of blocks is 1D, but we calculate pid_m and pid_n to locate the block pid place in the output matrix + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + # offs_am represent the indices of elements within the block for matrices A with respect to the M dimension + # offs_bn represent the indices of elements within the block for matrices B with respect to the N dimension + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + offs_k = tl.arange(0, BLOCK_SIZE_K) + + """ + This part of the code generates pointers to the specific blocks of matrices A and B that the current thread block will process. + + As described in the PyTorch documentation, a stride refers to the step size needed to move from one element to the next along a given dimension: + + For matrix A: stride_am = A.stride(0) = K (stride along the rows), and stride_ak = A.stride(1) = 1 (stride along the columns). + For matrix B: stride_bk = B.stride(0) = N (stride along the rows), and stride_bn = B.stride(1) = 1 (stride along the columns). + Now, let's break down the pointer generation: + + offs_am[:, None] creates a column of shape [BLOCK_SIZE_M, 1], which represents the row indices of matrix A that this block is processing. It is multiplied by K (the number of columns in matrix A) since A is stored in row-major order. So, the element at position (i, j) in A is located at index i*K + j in memory. + offs_k[None, BLOCK_SIZE_K] creates a row vector representing the column indices of the block, i.e., a range from 0 to BLOCK_SIZE_K. This is used to compute the positions of the columns within the block. + When combined, the result has the shape [BLOCK_SIZE_M, BLOCK_SIZE_K], where each entry (i, j) points to the element in matrix A at position (i, j) for the current block. + + The same logic is applied to matrix B, but the resulting shape is [BLOCK_SIZE_K, BLOCK_SIZE_N], representing the block of matrix B that the thread block will work on. + """ + a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) + b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + + # An accumulator matrix is initialized with zeros. It stores the intermediate results of the block matrix multiplication. + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.int32) + """ + We split the loop into two layers. The outer loop runs 4 times, and each iteration focuses on a specific portion of matrix A. + + For example, when i = 0, we’re only concerned with the blocks of matrix A that cover the range from 0 to K // (4 * BLOCK_SIZE_K). + Since matrix B is packed, its first dimension is effectively divided by 4. So, while we process the first segment of matrix A, + we still iterate over the entire first dimension of matrix B. + + In each of the 4 iterations of the outer loop, we go through the full blocks of matrix B, but what changes is the data we extract. + Matrix B elements contain 4 weights, all packed into an int8 format, and during each iteration of the outer loop, + we extract a different weight by using bitwise shifting operations. This way, we access a unique weight on each pass. + """ + for i in range(4): + b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + for j in range(0, tl.cdiv(K // 4, BLOCK_SIZE_K)): + k = i * tl.cdiv(K // 4, BLOCK_SIZE_K) + j + # load the block of matrix A + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0) + # load the block of matrix B + b_uint8 = tl.load(b_ptrs, mask=offs_k[:, None] < K, other=0) + # when i = 0 for example, we only care about the first 2 bits of the elements of the matrix B, so we use the mask 00000011 to mask the other bits + mask = 3 << (2 * i) + # we shift the results after the mask + b = (b_uint8 & mask) >> (2 * i) + # During the packing of the weights, it's easier to pack 0, 1, 2, then -1, 0, 1, so we add 1 to the weight tensor, and we substract it here + tensor_full = tl.full((1,), 1, dtype=tl.int8) + # We accumulate the result of multiplication of the blocks along the K dimension on int32 to avoid any overflows or underflows. + accumulator += tl.dot(a, (b.to(tl.int8) - tensor_full), out_dtype=tl.int32) + # we move the pointers, for the a_ptrs we more in a horizontal way along the second dimension -> we use strid_ak=1 + # for b_ptrs we move in a vertical way, along the rows -> we use stride_bk=N + a_ptrs += BLOCK_SIZE_K * stride_ak + b_ptrs += BLOCK_SIZE_K * stride_bk + + c = accumulator + # These lines compute the offsets into matrix C where the result of this block’s computation should be stored. + # stride_cm = N & stride_cn = 1 + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + # we do a boundary check to ensure only elements within matrix bounds are stored + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + tl.store(c_ptrs, c, mask=c_mask) + + +def matmul(a, b): + assert ( + a.shape[1] == b.shape[0] * 4 + ), "Incompatible dimensions, the weight matrix need to be packed" + assert a.is_contiguous(), "Matrix A must be contiguous" + M, K = a.shape + _, N = b.shape + # c is in int32 to avoid any overflows or underflows + c = torch.empty((M, N), device=a.device, dtype=torch.int32) + grid = lambda META: ( + triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]), + ) + matmul_kernel[grid]( + a, + b, + c, + M, + N, + K, + a.stride(0), + a.stride(1), + b.stride(0), + b.stride(1), + c.stride(0), + c.stride(1), + ) + return c diff --git a/src/liger_kernel/ops/fused_linear_cross_entropy.py b/src/liger_kernel/ops/fused_linear_cross_entropy.py index 73da9cd46..34016ee4c 100644 --- a/src/liger_kernel/ops/fused_linear_cross_entropy.py +++ b/src/liger_kernel/ops/fused_linear_cross_entropy.py @@ -1,9 +1,12 @@ import torch import triton -from liger_kernel.ops.cross_entropy import ( +from liger_kernel.ops.cross_entropy import liger_cross_entropy_kernel +from liger_kernel.ops.utils import ( + amp_custom_bwd, + amp_custom_fwd, element_mul_kernel, - liger_cross_entropy_kernel, + is_hip, ) # The hard limit of TRITON_MAX_TENSOR_NUMEL is 1048576 https://github.com/triton-lang/triton/blob/ba42a5c68fd0505f8c42f4202d53be0f8d9a5fe0/python/triton/language/core.py#L19 @@ -18,12 +21,11 @@ def fused_linear_cross_entropy_forward( target, bias=None, ignore_index=-100, + lse_square_scale=0.0, label_smoothing=0.0, reduction="mean", ): - dtype = ( - torch.get_autocast_gpu_dtype() if torch.is_autocast_enabled() else _input.dtype - ) + dtype = _input.dtype device = _input.device # inputs have shape: BT x H @@ -85,14 +87,17 @@ def fused_linear_cross_entropy_forward( Y_ptr=target_chunk, Y_stride=target_chunk.stride(-1), # always 1 loss_ptr=loss_1d_slice, + z_loss_ptr=loss_1d_slice, # dummy ptr, not used loss_stride=loss_1d_slice.stride(-1), # always 1 n_cols=V, n_non_ignore=n_non_ignore, ignore_index=ignore_index, + lse_square_scale=lse_square_scale, label_smoothing=label_smoothing, reduction=reduction, + RETURN_Z_LOSS=0, # False BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, + num_warps=32 if not is_hip() else 16, ) # gradient of logits_chunk is computed in-place by the above triton kernel. @@ -157,7 +162,7 @@ def fused_linear_cross_entropy_backward( grad_output, H, BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, + num_warps=32 if not is_hip() else 16, ) # handle grad_weight @@ -171,7 +176,7 @@ def fused_linear_cross_entropy_backward( grad_output, H, BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, + num_warps=32 if not is_hip() else 16, ) if grad_bias is not None: @@ -184,13 +189,14 @@ def fused_linear_cross_entropy_backward( grad_output, 1, BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, + num_warps=32 if not is_hip() else 16, ) return grad_input, grad_weight, grad_bias class LigerFusedLinearCrossEntropyFunction(torch.autograd.Function): @staticmethod + @amp_custom_fwd def forward( ctx, _input, @@ -198,6 +204,7 @@ def forward( target, bias=None, ignore_index=-100, + lse_square_scale=0.0, label_smoothing=0.0, reduction="mean", ): @@ -219,7 +226,14 @@ def forward( reduction: reduction to apply """ loss, grad_input, grad_weight, grad_bias = fused_linear_cross_entropy_forward( - _input, weight, target, bias, ignore_index, label_smoothing, reduction + _input, + weight, + target, + bias, + ignore_index, + lse_square_scale, + label_smoothing, + reduction, ) # downcast to dtype and store for backward ctx.save_for_backward( @@ -230,9 +244,10 @@ def forward( return loss @staticmethod + @amp_custom_bwd def backward(ctx, grad_output): (grad_input, grad_weight, grad_bias) = ctx.saved_tensors grad_input, grad_weight, grad_bias = fused_linear_cross_entropy_backward( grad_output, grad_input, grad_weight, grad_bias ) - return (grad_input, grad_weight, None, grad_bias, None, None, None) + return (grad_input, grad_weight, None, grad_bias, None, None, None, None) diff --git a/src/liger_kernel/ops/fused_linear_jsd.py b/src/liger_kernel/ops/fused_linear_jsd.py new file mode 100644 index 000000000..27ef3aa2f --- /dev/null +++ b/src/liger_kernel/ops/fused_linear_jsd.py @@ -0,0 +1,245 @@ +from typing import Optional + +import torch +import triton + +from liger_kernel.ops.jsd import _jsd_kernel +from liger_kernel.ops.utils import ( + amp_custom_bwd, + amp_custom_fwd, + element_mul_kernel, + is_hip, +) + +# The hard limit of TRITON_MAX_TENSOR_NUMEL is 1048576 https://github.com/triton-lang/triton/blob/ba42a5c68fd0505f8c42f4202d53be0f8d9a5fe0/python/triton/language/core.py#L19 +# However, setting limit as 65536 as in LayerNorm tutorial is faster because of less register spilling +# The optimal maximum block size depends on your hardware, your kernel, and your dtype +MAX_FUSED_SIZE = 65536 // 2 + + +def fused_linear_jsd_forward( + student_input, + student_weight, + teacher_input, + teacher_weight, + shift_labels, + jsd_beta, + ignore_index, + has_label, + temperature, +): + device = student_input.device + dtype = student_input.dtype + + # inputs have shape: BT x H + # materialized activations will have shape: BT x V + # the increase in memory = BT x V + # reduction can be achieved by partitioning the number of tokens BT into smaller chunks. + # for ex: if we were to achieve the same memory consumption as BT x H, then the chunk size should be: + # inc_factor = (V+H-1)//H, chunk_size = (BT + inc_factor - 1)//inc_factor + # for ex: BT = 4096*4, V = 32000, H = 4096 ==> inc_factor = 8, chunk_size = 2048 + BT, H = student_input.shape + V = student_weight.shape[0] + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) + + inc_factor = triton.cdiv(V, H) # (V + H - 1) // H + chunk_size = triton.next_power_of_2( + triton.cdiv(BT, inc_factor) + ) # (BT + inc_factor - 1) // inc_factor + num_chunks = triton.cdiv(BT, chunk_size) # (BT + chunk_size - 1) // chunk_size + + grad_weight = ( + torch.zeros_like(student_weight, device=device) + if student_weight.requires_grad + else None + ) + grad_input = torch.zeros_like(student_input) + # we use fp32 for loss accumulator + loss_1d = torch.zeros((BT, V), dtype=torch.float32, device=device) + + if has_label: + n_non_ignore = (shift_labels != ignore_index).sum().item() + else: + n_non_ignore = BT + + for chunk_id in range(num_chunks): + start_idx = chunk_id * chunk_size + end_idx = min((chunk_id + 1) * chunk_size, BT) + + # chunk both inputs, shape: chunk_size x H + student_input_chunk = student_input[start_idx:end_idx] + teacher_input_chunk = teacher_input[start_idx:end_idx] + + # shape: chunk_size x V + # For anything starting from logits to the final JSD loss, we do computation + # in FP32 to avoid losing numerical stability. + student_logits_chunk = (student_input_chunk @ student_weight.t()).to( + torch.float32 + ) + teacher_logits_chunk = (teacher_input_chunk @ teacher_weight.t()).to( + torch.float32 + ) + chunk_n_rows = student_logits_chunk.shape[0] + + # unreduced loss + loss_1d_slice = loss_1d[start_idx:end_idx] # chunk_size + # log-softmax with temperature + student_logits_chunk = student_logits_chunk / temperature + teacher_logits_chunk = teacher_logits_chunk / temperature + student_prob_chunk = torch.log_softmax(student_logits_chunk, dim=-1) + teacher_prob_chunk = torch.log_softmax(teacher_logits_chunk, dim=-1) + + # ensure _input and target are contiguous + student_prob_chunk = student_prob_chunk.contiguous() + teacher_prob_chunk = teacher_prob_chunk.contiguous() + + # Here we calculate the gradient of prob_chunk in place so we can save memory. + _jsd_kernel[(chunk_n_rows,)]( + X_ptr=student_prob_chunk, + X_stride=student_prob_chunk.stride(-2), + Y_ptr=teacher_prob_chunk, + Y_stride=teacher_prob_chunk.stride(-2), + loss_ptr=loss_1d_slice, + loss_stride=loss_1d_slice.stride(-2), + dX_ptr=student_prob_chunk, + dX_stride=student_prob_chunk.stride(-2), + label_ptr=( + shift_labels[start_idx:end_idx] + if has_label + else torch.empty(1, device=device) + ), # dummy ptr if no label + beta=jsd_beta, + n_non_ignore=n_non_ignore, + ignore_index=ignore_index, + n_cols=V, + BLOCK_SIZE=BLOCK_SIZE, + HAS_LABEL=has_label, + ) + loss_1d[start_idx:end_idx] = loss_1d_slice + # gradients of prob_chunk in place, shape: chunk_size x V + # gradients of logits_chunk in place, shape: chunk_size x V + student_logits_chunk = ( + student_prob_chunk + - torch.softmax(student_logits_chunk, dim=-1) + * student_prob_chunk.sum(dim=-1, keepdim=True).broadcast_to( + student_prob_chunk.shape + ) + ) / temperature + # now we traverse back to grad w.r.t. input to `lm_head` and grad + # w.r.t. `lm_head` which should be computed in original dtype + student_logits_chunk = student_logits_chunk.to(dtype) + grad_input[start_idx:end_idx] = student_logits_chunk @ student_weight + + if grad_weight is not None: + grad_weight.add_(student_logits_chunk.t() @ student_input_chunk) + + loss = torch.sum(loss_1d) + return loss, grad_input, grad_weight + + +def fused_linear_jsd_backward(grad_output, grad_input, grad_weight): + # If JSD is the last layer, grad_output is 1.0. Skip the mul to save time + if torch.ne(grad_output, torch.tensor(1.0, device=grad_output.device)): + # We use a Triton kernel instead of a PyTorch operation because modifying inputs in-place + # for gradient storage and backward multiple times causes anomalies with PyTorch but not with Triton. + BT, H = grad_input.shape + n_rows = BT + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(H)) + + element_mul_kernel[(n_rows,)]( + grad_input, + grad_input.stride(-2), + grad_output, + H, + BLOCK_SIZE=BLOCK_SIZE, + num_warps=32 if not is_hip() else 16, + ) + + # handle grad_weight + if grad_weight is not None: + V, H = grad_weight.shape + n_rows = V + + element_mul_kernel[(n_rows,)]( + grad_weight, + grad_weight.stride(-2), + grad_output, + H, + BLOCK_SIZE=BLOCK_SIZE, + num_warps=32 if not is_hip() else 16, + ) + + return grad_input, grad_weight + + +class LigerFusedLinearJSDFunction(torch.autograd.Function): + """ + Fusing the last linear layer with generalized JSD + + Handle the forward and backward pass of the final linear layer via JSD by avoiding + the materialization of the large logits tensor. Since JSD is the last layer, we can + compute the gradient at the forward pass. + """ + + @staticmethod + @amp_custom_fwd + def forward( + ctx, + student_input: torch.Tensor, + student_weight: torch.Tensor, + teacher_input: torch.Tensor, + teacher_weight: torch.Tensor, + shift_labels: Optional[torch.Tensor] = None, + jsd_beta: float = 0.5, + ignore_index: int = -100, + temperature: float = 1.0, + ): + """ + Args: + + student_input (torch.tensor): input of the last projection layer in student model, with shape (B*T, H), where B is batch size, T is sequence length, H is hidden dimension. + student_weight (torch.tensor): the last projection layer in student model, with shape (V, H), where V is vocab size + teacher_input (torch.tensor): input of the last projection layer in teacher model, with shape (B*T, H), where B is batch size, T is sequence length, H is hidden dimension. + teacher_weight (torch.tensor): the last projection layer in teacher model, with shape (V, H), where V is vocab size + shift_labels (Optional[torch.LongTensor]): indicator of next predicted vocab with shape (BT) where each value is in [0, V-1]. + jsd_beta (float): coefficient beta of generalized JSD in the open interval (0, 1). Default: `0.5` + ignore_index (int): the index to ignore. Default: -100 + temperature (float): temperature in softmax function to control the output probability distribution. Default: `1.0` + + Returns: + loss (torch.Tensor): generalized JSD + """ + has_label = False + if shift_labels is not None: + assert shift_labels.shape == ( + teacher_input.shape[0], + ), f"the shape of shift_labels must be (BT,). Got: {shift_labels.shape}" + shift_labels = shift_labels.contiguous() + has_label = True + + loss, grad_input, grad_weight = fused_linear_jsd_forward( + student_input, + student_weight, + teacher_input, + teacher_weight, + shift_labels, + jsd_beta, + ignore_index, + has_label, + temperature, + ) + # downcast to dtype and store for backward + ctx.save_for_backward( + grad_input.detach(), + grad_weight.detach() if grad_weight is not None else None, + ) + return loss + + @staticmethod + @amp_custom_bwd + def backward(ctx, grad_output): + (grad_input, grad_weight) = ctx.saved_tensors + grad_input, grad_weight = fused_linear_jsd_backward( + grad_output, grad_input, grad_weight + ) + return (grad_input, grad_weight, None, None, None, None, None, None) diff --git a/src/liger_kernel/ops/jsd.py b/src/liger_kernel/ops/jsd.py new file mode 100644 index 000000000..6ecf8dbe9 --- /dev/null +++ b/src/liger_kernel/ops/jsd.py @@ -0,0 +1,176 @@ +from typing import Optional + +import torch +import triton +import triton.language as tl + +from liger_kernel.ops.utils import ensure_contiguous + + +@triton.jit +def _jsd_kernel( + X_ptr, # input in logspace, X = log Q + X_stride, + Y_ptr, # ground truth in logspace, Y = log P + Y_stride, + loss_ptr, + loss_stride, + dX_ptr, + dX_stride, + label_ptr, + beta, + n_non_ignore: int, + ignore_index: tl.constexpr, + n_cols, + BLOCK_SIZE: tl.constexpr, + HAS_LABEL: tl.constexpr, +): + # JSD(P || Q) = (KL(P || M) + KL(Q || M)) / 2, M = (1/2) * (P + Q) = (1/2) * (e ^ Y + e ^ X) + # = sum(P * log P + Q * log Q - 2 * M * log M) / 2 + # = sum(e ^ Y * Y + e ^ X * X - 2 * M * log M) / 2 + # grad_x_i = 0.5 * Q * (X - log_M) + pid = tl.program_id(0).to(tl.int64) + X_ptr += pid * X_stride + dX_ptr += pid * dX_stride + Y_ptr += pid * Y_stride + loss_ptr += pid * loss_stride + label_ptr += pid + + if HAS_LABEL: + label = tl.load(label_ptr) + if label == ignore_index: + for i in range(0, n_cols, BLOCK_SIZE): + offsets = i + tl.arange(0, BLOCK_SIZE) + tl.store(dX_ptr + offsets, 0.0, mask=offsets < n_cols) + return + + for i in range(0, n_cols, BLOCK_SIZE): + offsets = i + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_cols + X = tl.load(X_ptr + offsets, mask=mask, other=float("-inf")).to(tl.float32) + Y = tl.load(Y_ptr + offsets, mask=mask, other=float("-inf")).to(tl.float32) + + Q = tl.exp(X) + P = tl.exp(Y) + M = beta * P + (1 - beta) * Q + log_M = tl.log(M) + + loss = beta * P * Y + (1 - beta) * Q * X - M * log_M + # reduction == "batchmean" + loss = loss / n_non_ignore + tl.store(loss_ptr + offsets, loss, mask=mask) + + dX = (1 - beta) * Q * (X - log_M) / n_non_ignore + tl.store(dX_ptr + offsets, dX, mask=mask) + + +MAX_FUSED_SIZE = 65536 + + +def jsd_forward(_input, target, shift_labels, beta, ignore_index, has_label): + BT, V = _input.shape + n_rows = BT + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) + # non reduction loss + loss = torch.zeros(_input.shape, dtype=torch.float32, device=_input.device) + dX = torch.empty_like(_input) + + if has_label: + n_non_ignore = (shift_labels != ignore_index).sum().item() + else: + n_non_ignore = BT + + _jsd_kernel[(n_rows,)]( + X_ptr=_input, # input in logspace, X = log Q + X_stride=_input.stride(-2), + Y_ptr=target, # ground truth in logspace, Y = log P + Y_stride=target.stride(-2), + loss_ptr=loss, + loss_stride=loss.stride(-2), + dX_ptr=dX, + dX_stride=dX.stride(-2), + label_ptr=( + shift_labels if has_label else torch.empty(1, device=_input.device) + ), # dummy ptr if no label + beta=beta, + n_non_ignore=n_non_ignore, + ignore_index=ignore_index, + n_cols=V, + BLOCK_SIZE=BLOCK_SIZE, + HAS_LABEL=has_label, + ) + + loss = torch.sum(loss) + return loss.to(_input.dtype), dX + + +def jsd_backward(dX, grad_output): + # If jsd is the last layer, grad_output is 1.0. Skip the mul to save time + if torch.equal(grad_output, torch.tensor(1.0, device=grad_output.device)): + return dX + else: + return grad_output * dX + + +class LigerJSDFunction(torch.autograd.Function): + r""" + This class implements the forward and backward pass for the generalized Jensen-Shannon Divergence. + .. math:: + JSD(\beta)(P || Q) + = \beta * KLDiv(P || (\beta * P + (1 - \beta) * Q)) + (1 - \beta) * KLDiv(Q || (\beta * P + (1 - \beta) * Q)) + + .. note:: + As all the other losses in PyTorch, this function expects the first argument, + :attr:`_input`, to be the predictions, the output of the student model, in log-space + and the second, :attr:`target`, to be the observations, the output of the teacher model, in log-space. + This differs from the standard mathematical notation :math:`JSD(P || Q)` where + :math:`P` denotes the teacher model and :math:`Q` denotes the student model. + """ + + @staticmethod + @ensure_contiguous + def forward( + ctx, + _input: torch.Tensor, + target: torch.Tensor, + shift_labels: Optional[torch.Tensor] = None, + beta: float = 0.5, + ignore_index: int = -100, + ) -> torch.Tensor: + """ + Args: + _input (torch.Tensor): predict values with shape (BT, V) in logspace + target (torch.Tensor): ground truth values with shape (BT, V) in logspace + shift_labels (Optional[torch.LongTensor]): indicator of next predicted vocab with shape (BT) where each value is in [0, V-1]. + beta (float): coefficient beta of generalized JSD in the open interval (0, 1) + ignore_index (int): the index to ignore. Default: -100 + + Returns: + loss (torch.Tensor): generalized JSD + """ + has_label = False + if shift_labels is not None: + assert shift_labels.shape == ( + _input.shape[0], + ), f"the shape of shift_labels must be (BT,). Got: {shift_labels.shape}" + shift_labels = shift_labels.contiguous() + has_label = True + + loss, dX = jsd_forward( + _input, target, shift_labels, beta, ignore_index, has_label + ) + ctx.save_for_backward(dX) + return loss + + @staticmethod + @ensure_contiguous + def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: + (dX,) = ctx.saved_tensors + dX = jsd_backward(dX, grad_output) + return ( + dX, + None, + None, + None, + None, + ) diff --git a/src/liger_kernel/ops/kl_div.py b/src/liger_kernel/ops/kl_div.py index 215810f38..2e3c6e933 100644 --- a/src/liger_kernel/ops/kl_div.py +++ b/src/liger_kernel/ops/kl_div.py @@ -4,13 +4,13 @@ import triton import triton.language as tl -from liger_kernel.ops.utils import ensure_contiguous +from liger_kernel.ops.utils import ensure_contiguous, is_hip def get_num_warps(BLOCK_SIZE): num_warps = 4 if BLOCK_SIZE >= 32768: - num_warps = 32 + num_warps = 32 if not is_hip() else 16 elif BLOCK_SIZE >= 8192: num_warps = 16 elif BLOCK_SIZE >= 2048: @@ -45,6 +45,7 @@ def _kldiv_kernel_forward( loss_ptr, # [B] or [B, S] if reduction == _REDUCTION_MODE_NONE, output ptr loss_stride, # int, output stride n_cols, # int, number of columns in the input tensor + eps, BLOCK_SIZE: tl.constexpr, log_target: tl.constexpr = False, reduction: tl.constexpr = _REDUCTION_MODE_BATCHMEAN, @@ -56,6 +57,7 @@ def _kldiv_kernel_forward( base_offsets = tl.arange(0, BLOCK_SIZE) + loss_sum = 0.0 for i in range(0, n_cols, BLOCK_SIZE): offsets = i + base_offsets mask = offsets < n_cols @@ -65,32 +67,33 @@ def _kldiv_kernel_forward( # KL(y_true || y) = y_true * (log(y_true) - log(y)) # We compute KL(y_true || y) with y in the log-space if not log_target: - loss = y_true * (tl.log(y_true) - y) + loss = y_true * (tl.log(tl.maximum(y_true, eps)) - y) else: loss = tl.exp(y_true) * (y_true - y) if reduction == _REDUCTION_MODE_NONE: tl.store(loss_ptr + offsets, loss, mask=mask) else: - loss = tl.sum(loss, axis=0) - tl.store(loss_ptr, loss) - loss_ptr += 1 # in case of reduction, the output tensor has dimensions [B,], therefore stride is always 1 + loss_sum += tl.sum(loss, axis=0) + + if reduction != _REDUCTION_MODE_NONE: + tl.store(loss_ptr, loss_sum) @triton.jit def _kldiv_kernel_backward( - input_ptr, - input_stride, target_ptr, target_stride, + new_grads_ptr, + new_grads_stride, n_cols, BLOCK_SIZE: tl.constexpr, log_target: tl.constexpr = False, ): pid = tl.program_id(0).to(tl.int64) - input_ptr += pid * input_stride target_ptr += pid * target_stride + new_grads_ptr += pid * new_grads_stride offsets = tl.arange(0, BLOCK_SIZE) mask = offsets < n_cols @@ -106,19 +109,19 @@ def _kldiv_kernel_backward( else: res = -tl.exp(target) - tl.store(input_ptr + offsets, res, mask=mask) + tl.store(new_grads_ptr + offsets, res, mask=mask) -def kldiv_forward_triton(y_pred, y_true, log_target, reduction): # [B, S] # [B, S] - B, S = y_pred.shape +def kldiv_forward_triton(y_pred, y_true, log_target, reduction, eps): # [BT, V] + BT, V = y_pred.shape - BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(S)) + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) num_warps = get_num_warps(BLOCK_SIZE) - grid = (B,) + grid = (BT,) reduction = _str_to_reduction_mode[reduction] - out_size = (B, S) if reduction == _REDUCTION_MODE_NONE.value else (B,) + out_size = (BT, V) if reduction == _REDUCTION_MODE_NONE.value else (BT,) output_tensor = torch.zeros(out_size, device=y_pred.device, dtype=torch.float32) _kldiv_kernel_forward[grid]( @@ -128,7 +131,8 @@ def kldiv_forward_triton(y_pred, y_true, log_target, reduction): # [B, S] # [B y_true.stride(0), output_tensor, output_tensor.stride(0), - S, + V, + eps=eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps, log_target=log_target, @@ -139,30 +143,30 @@ def kldiv_forward_triton(y_pred, y_true, log_target, reduction): # [B, S] # [B # https://pytorch.org/docs/stable/generated/torch.nn.KLDivLoss.html # https://github.com/pytorch/pytorch/blob/d7b57c4d63edb42e1deeeba9497fcb5f1f748ff2/torch/nn/functional.py#L3372 if reduction == _REDUCTION_MODE_BATCHMEAN.value: - return output_tensor.sum() / B + return output_tensor.sum() / BT elif reduction == _REDUCTION_MODE_SUM.value: return output_tensor.sum(dim=0) elif reduction == _REDUCTION_MODE_MEAN.value: - return output_tensor.mean(dim=0) + return output_tensor.sum() / (BT * V) else: return output_tensor -def kldiv_backward_triton(input, target, grad_output, log_target): - B, S = input.shape +def kldiv_backward_triton(target, grad_output, new_grads, log_target): + BT, V = target.shape - BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(S)) + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) num_warps = get_num_warps(BLOCK_SIZE) - grid = (B,) + grid = (BT,) # We store the gradients in-place in the input tensor _kldiv_kernel_backward[grid]( - input, - input.stride(0), target, target.stride(0), - S, + new_grads, + new_grads.stride(0), + V, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps, log_target=log_target, @@ -170,9 +174,9 @@ def kldiv_backward_triton(input, target, grad_output, log_target): # If cross entropy is the last layer, grad_output is 1.0. Skip the mul then. if torch.equal(grad_output, torch.tensor(1.0, device=grad_output.device)): - return input + return new_grads - return input * grad_output + return new_grads * grad_output class LigerKLDivLossFunction(torch.autograd.Function): @@ -196,6 +200,7 @@ def forward( y_true: torch.Tensor, reduction: REDUCTION_LITERAL = "batchmean", log_target: bool = False, + eps: float = 1e-10, ) -> torch.Tensor: """A forward pass for the KL Divergence Loss. @@ -205,15 +210,16 @@ def forward( y_true (torch.Tensor): A tensor of shape (BT, V) containing the target values, expected to be either probabilities or log-probabilities, depending on the value of `log_target`. reduction (REDUCTION_LITERAL, optional): Reduction to be used. Defaults to "batchmean". log_target (bool, optional): If set to true, expects the ground truth to already be log-probabilities. Defaults to False. + eps: (float, optional): A small value to avoid division by zero. Defaults to 1e-10. Returns: torch.Tensor: The computed KL Divergence Loss, with shape (BT, V) if `reduction` is "none", else a scalar. """ - ctx.save_for_backward(y_pred, y_true) + ctx.save_for_backward(y_true) ctx.reduction = reduction ctx.log_target = log_target return kldiv_forward_triton( - y_pred, y_true, log_target=log_target, reduction=reduction + y_pred, y_true, log_target=log_target, reduction=reduction, eps=eps ) @staticmethod @@ -226,22 +232,27 @@ def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: grad_output (torch.Tensor): The gradient of the loss with respect to the output. Returns: - tuple[torch.Tensor, None, None, None]: The gradient of the loss with respect to the inputs and None for the other arguments of the forward method. + tuple[torch.Tensor, None, None, None, None]: The gradient of the loss with respect to the inputs and None for the other arguments of the forward method. """ - y_pred, y_true = ctx.saved_tensors + (y_true,) = ctx.saved_tensors + + new_grads = torch.empty_like(y_true) - derivative = kldiv_backward_triton(y_pred, y_true, grad_output, ctx.log_target) + derivative = kldiv_backward_triton( + y_true, grad_output, new_grads, ctx.log_target + ) if ctx.reduction == "batchmean": - derivative = derivative / y_pred.shape[0] + derivative = derivative / y_true.shape[0] elif ctx.reduction == "sum" or ctx.reduction == "none": pass elif ctx.reduction == "mean": - derivative = derivative / (y_pred.shape[0] * y_pred.shape[1]) + derivative = derivative / (y_true.shape[0] * y_true.shape[1]) return ( derivative, None, None, None, + None, ) diff --git a/src/liger_kernel/ops/rms_norm.py b/src/liger_kernel/ops/rms_norm.py index 68fcf05d2..06819f124 100644 --- a/src/liger_kernel/ops/rms_norm.py +++ b/src/liger_kernel/ops/rms_norm.py @@ -10,6 +10,7 @@ Modifications made by Yanning Chen, 2024. """ +import math import operator import torch @@ -20,6 +21,7 @@ calculate_settings, compare_version, ensure_contiguous, + torch_to_triton_dtype, ) if compare_version("triton", operator.ge, "3.0.0"): @@ -84,6 +86,10 @@ def _rms_norm_forward_kernel( W_row = W_row.to(tl.float32) X_row = X_row.to(tl.float32) + if casting_mode == _CASTING_MODE_NONE: + eps = eps.to(X_row_dtype) + offset = offset.to(X_row_dtype) + mean_square = tl.sum(X_row * X_row, axis=0) / n_cols rstd = rsqrt(mean_square + eps) @@ -100,6 +106,9 @@ def _rms_norm_forward_kernel( Y_row = X_row * (offset + W_row) + if casting_mode == _CASTING_MODE_GEMMA: + Y_row = Y_row.to(X_row_dtype) + tl.store(Y_ptr + col_offsets, Y_row, mask=mask) @@ -109,14 +118,17 @@ def _rms_norm_backward_kernel( dY_row_stride, X_ptr, X_row_stride, + X_dtype: tl.constexpr, W_ptr, W_row_stride, RSTD_ptr, RSTD_row_stride, dW_ptr, dW_row_stride, + n_rows, n_cols, offset, + rows_per_program: tl.constexpr, casting_mode: tl.constexpr, BLOCK_SIZE: tl.constexpr, ): @@ -125,54 +137,60 @@ def _rms_norm_backward_kernel( dw = sum(dy * (x / RMS)). summation over BxT dimension """ - row_idx = tl.program_id(0) + row_block_id = tl.program_id(0) + row_start = row_block_id * rows_per_program + row_end = min((row_block_id + 1) * rows_per_program, n_rows) col_offsets = tl.arange(0, BLOCK_SIZE) mask = col_offsets < n_cols - dY_ptr += row_idx * dY_row_stride - X_ptr += row_idx * X_row_stride - RSTD_ptr += row_idx * RSTD_row_stride - dW_ptr += row_idx * dW_row_stride + dW_row = tl.zeros((BLOCK_SIZE,), dtype=tl.float32) - dY_row = tl.load(dY_ptr + col_offsets, mask=mask, other=0) - X_row = tl.load(X_ptr + col_offsets, mask=mask, other=0) - W_row = tl.load(W_ptr + col_offsets, mask=mask, other=0) - original_x_dtype = X_row.dtype - - # Get cached rms - rstd_row = tl.load(RSTD_ptr) + dY_ptr += row_start * dY_row_stride + X_ptr += row_start * X_row_stride + RSTD_ptr += row_start + W_row = tl.load(W_ptr + col_offsets, mask=mask, other=0.0) W_row = W_row + offset - X_row = X_row.to(tl.float32) + for _ in range(row_start, row_end): + dY_row = tl.load(dY_ptr + col_offsets, mask=mask, other=0.0) + X_row = tl.load(X_ptr + col_offsets, mask=mask, other=0.0) - # Different bacward graphs for different casting modes - if casting_mode == _CASTING_MODE_LLAMA: - m = (dY_row * W_row).to(tl.float32) + # Get cached rms + rstd_row = tl.load(RSTD_ptr) - elif casting_mode == _CASTING_MODE_GEMMA: - dY_row, W_row = ( - dY_row.to(tl.float32), - W_row.to(tl.float32), - ) + X_row = X_row.to(tl.float32) - m = dY_row * W_row + # Different bacward graphs for different casting modes + if casting_mode == _CASTING_MODE_LLAMA: + m = (dY_row * W_row).to(tl.float32) - dX_row = rstd_row * m + elif casting_mode == _CASTING_MODE_GEMMA: + dY_row = dY_row.to(tl.float32) + m = dY_row * W_row + else: + m = dY_row * W_row - dX_row += (rstd_row) * ( - -(1 / n_cols) * rstd_row * rstd_row * tl.sum(m * X_row, axis=0) * X_row - ) + dX_row = rstd_row * m - # calculate the gradient of W - if casting_mode == _CASTING_MODE_LLAMA: - dW_row = dY_row * (X_row * rstd_row).to(original_x_dtype) - else: - # here X_row is already in fp32 (see previous if block) - dW_row = dY_row * (X_row * rstd_row) + dX_row += (rstd_row) * ( + -(1 / n_cols) * rstd_row * rstd_row * tl.sum(m * X_row, axis=0) * X_row + ) + + # calculate the gradient of W + if casting_mode == _CASTING_MODE_LLAMA: + dW_row += dY_row * (X_row * rstd_row).to(X_dtype) + else: + # here X_row is already in fp32 (see previous if block) + dW_row += dY_row * (X_row * rstd_row) - tl.store(dY_ptr + col_offsets, dX_row, mask=mask) - tl.store(dW_ptr + col_offsets, dW_row, mask=mask) + tl.store(dY_ptr + col_offsets, dX_row.to(X_dtype), mask=mask) + + dY_ptr += dY_row_stride + X_ptr += X_row_stride + RSTD_ptr += RSTD_row_stride + + tl.store(dW_ptr + row_block_id * dW_row_stride + col_offsets, dW_row, mask=mask) _str_to_casting_mode = { @@ -238,31 +256,38 @@ def rms_norm_backward(dY, X, W, RSTD, offset, casting_mode, BLOCK_SIZE, num_warp dim = shape[-1] dY = dY.view(-1, dim) n_rows, n_cols = dY.shape - dW = torch.empty_like( - X, - dtype=(torch.float32 if casting_mode == _CASTING_MODE_GEMMA.value else W.dtype), - ) + sm_count = torch.cuda.get_device_properties(X.device).multi_processor_count + # fp32 for numerical stability especially. + _dW = torch.empty((sm_count, n_cols), dtype=torch.float32, device=W.device) + + if n_cols > BLOCK_SIZE: + raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.") + rows_per_program = math.ceil(n_rows / sm_count) + grid = (sm_count,) # Here we use dY to store the value of dX to save memory - _rms_norm_backward_kernel[(n_rows,)]( + _rms_norm_backward_kernel[grid]( dY, dY.stride(0), X, X.stride(0), + torch_to_triton_dtype[X.dtype], W, W.stride(0), RSTD, RSTD.stride(0), - dW, - dW.stride(0), + _dW, + _dW.stride(0), + n_rows, n_cols, offset, + rows_per_program, casting_mode, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps, ) dX = dY.view(*shape) - dW = torch.sum(dW, dim=0).to(W.dtype) + dW = _dW.sum(dim=0).to(W.dtype) return dX, dW diff --git a/src/liger_kernel/ops/utils.py b/src/liger_kernel/ops/utils.py index d89da288f..4a24223d0 100644 --- a/src/liger_kernel/ops/utils.py +++ b/src/liger_kernel/ops/utils.py @@ -12,13 +12,19 @@ import functools import importlib +import operator from typing import Callable import torch import triton +import triton.language as tl from packaging.version import Version +def is_hip() -> bool: + return torch.version.hip is not None + + def ensure_contiguous(fn): @functools.wraps(fn) def wrapper(ctx, *args, **kwargs): @@ -45,7 +51,7 @@ def calculate_settings(n): num_warps = 4 if BLOCK_SIZE >= 32768: - num_warps = 32 + num_warps = 32 if not is_hip() else 16 elif BLOCK_SIZE >= 8192: num_warps = 16 elif BLOCK_SIZE >= 2048: @@ -60,3 +66,58 @@ def compare_version(package: str, operator: Callable, target: str): return False pkg_version = Version(pkg.__version__) return operator(pkg_version, Version(target)) + + +def get_amp_custom_fwd_bwd() -> Callable: + if compare_version("torch", operator.ge, "2.4.0"): + return ( + functools.partial(torch.amp.custom_fwd, device_type="cuda"), + functools.partial(torch.amp.custom_bwd, device_type="cuda"), + ) + return torch.cuda.amp.custom_fwd, torch.cuda.amp.custom_bwd + + +amp_custom_fwd, amp_custom_bwd = get_amp_custom_fwd_bwd() + + +torch_to_triton_dtype = { + torch.float32: tl.float32, + torch.float16: tl.float16, + torch.bfloat16: tl.bfloat16, +} + + +@triton.jit +def element_mul_kernel( + X_ptr, + X_stride, + grad_output_ptr, + n_cols, + BLOCK_SIZE: tl.constexpr, +): + """ + This function multiplies each element of the tensor pointed by X_ptr with the value pointed by grad_output_ptr. + The multiplication is performed in-place on the tensor pointed by X_ptr. + + Parameters: + X_ptr: Pointer to the input tensor. + X_stride (int): The stride of the input tensor. + grad_output_ptr: Pointer to the gradient output value. + n_cols (int): The number of columns in the input tensor. + BLOCK_SIZE (int): The block size for Triton operations. + """ + + # Get the program ID and convert it to int64 to avoid overflow + program_id = tl.program_id(0).to(tl.int64) + + # Locate the start index + X_ptr += program_id * X_stride + + # Load the gradient output value + grad_output = tl.load(grad_output_ptr) + + # Perform the element-wise multiplication + for i in range(0, n_cols, BLOCK_SIZE): + X_offsets = i + tl.arange(0, BLOCK_SIZE) + X_block = tl.load(X_ptr + X_offsets, mask=X_offsets < n_cols) + tl.store(X_ptr + X_offsets, X_block * grad_output, mask=X_offsets < n_cols) diff --git a/src/liger_kernel/transformers/__init__.py b/src/liger_kernel/transformers/__init__.py index 5cf559011..ffb8235cc 100644 --- a/src/liger_kernel/transformers/__init__.py +++ b/src/liger_kernel/transformers/__init__.py @@ -5,7 +5,9 @@ from liger_kernel.transformers.fused_linear_cross_entropy import ( # noqa: F401 LigerFusedLinearCrossEntropyLoss, ) +from liger_kernel.transformers.fused_linear_jsd import LigerFusedLinearJSD # noqa: F401 from liger_kernel.transformers.geglu import LigerGEGLUMLP # noqa: F401 +from liger_kernel.transformers.jsd import LigerJSD # noqa: F401 from liger_kernel.transformers.layer_norm import LigerLayerNorm # noqa: F401 from liger_kernel.transformers.monkey_patch import ( # noqa: F401 _apply_liger_kernel, @@ -15,6 +17,7 @@ apply_liger_kernel_to_llama, apply_liger_kernel_to_mistral, apply_liger_kernel_to_mixtral, + apply_liger_kernel_to_mllama, apply_liger_kernel_to_phi3, apply_liger_kernel_to_qwen2, apply_liger_kernel_to_qwen2_vl, diff --git a/src/liger_kernel/transformers/cross_entropy.py b/src/liger_kernel/transformers/cross_entropy.py index b2457481b..f612f6f4d 100644 --- a/src/liger_kernel/transformers/cross_entropy.py +++ b/src/liger_kernel/transformers/cross_entropy.py @@ -1,11 +1,24 @@ -from torch.nn import CrossEntropyLoss +import torch.nn as nn from liger_kernel.ops.cross_entropy import LigerCrossEntropyFunction -class LigerCrossEntropyLoss(CrossEntropyLoss): - def __init__(self, *args, **kwargs): - super(LigerCrossEntropyLoss, self).__init__(*args, **kwargs) +class LigerCrossEntropyLoss(nn.Module): + def __init__( + self, + ignore_index=-100, + lse_square_scale=0.0, + label_smoothing=0.0, + reduction="mean", + return_z_loss=False, + ): + super().__init__() + self.ignore_index = ignore_index + self.lse_square_scale = lse_square_scale + self.label_smoothing = label_smoothing + self.reduction = reduction + self.return_z_loss = return_z_loss + assert (self.label_smoothing >= 0) and ( self.label_smoothing <= 1 ), f"label_smoothing must be between 0.0 and 1.0. Got: {self.label_smoothing}" @@ -16,6 +29,15 @@ def __init__(self, *args, **kwargs): }, f"reduction must be one of 'mean', 'sum', or 'none'. Got: {self.reduction}" def forward(self, _input, target): - return LigerCrossEntropyFunction.apply( - _input, target, self.ignore_index, self.label_smoothing, self.reduction + loss, z_loss = LigerCrossEntropyFunction.apply( + _input, + target, + self.ignore_index, + self.lse_square_scale, + self.label_smoothing, + self.reduction, + self.return_z_loss, ) + if not self.return_z_loss: + return loss + return loss, z_loss diff --git a/src/liger_kernel/transformers/functional.py b/src/liger_kernel/transformers/functional.py index d63045efb..f160887b8 100644 --- a/src/liger_kernel/transformers/functional.py +++ b/src/liger_kernel/transformers/functional.py @@ -2,7 +2,9 @@ from liger_kernel.ops.fused_linear_cross_entropy import ( LigerFusedLinearCrossEntropyFunction, ) +from liger_kernel.ops.fused_linear_jsd import LigerFusedLinearJSDFunction from liger_kernel.ops.geglu import LigerGELUMulFunction +from liger_kernel.ops.jsd import LigerJSDFunction from liger_kernel.ops.kl_div import LigerKLDivLossFunction from liger_kernel.ops.layer_norm import LigerLayerNormFunction from liger_kernel.ops.rms_norm import LigerRMSNormFunction @@ -17,3 +19,5 @@ liger_rope = LigerRopeFunction.apply liger_layer_norm = LigerLayerNormFunction.apply liger_kl_div = LigerKLDivLossFunction.apply +liger_jsd = LigerJSDFunction.apply +liger_fused_linear_jsd = LigerFusedLinearJSDFunction.apply diff --git a/src/liger_kernel/transformers/fused_linear_cross_entropy.py b/src/liger_kernel/transformers/fused_linear_cross_entropy.py index 2a3971f2c..74c4b778a 100644 --- a/src/liger_kernel/transformers/fused_linear_cross_entropy.py +++ b/src/liger_kernel/transformers/fused_linear_cross_entropy.py @@ -1,13 +1,26 @@ -from torch.nn import CrossEntropyLoss +import torch.nn as nn from liger_kernel.ops.fused_linear_cross_entropy import ( LigerFusedLinearCrossEntropyFunction, ) -class LigerFusedLinearCrossEntropyLoss(CrossEntropyLoss): - def __init__(self, *args, **kwargs): - super(LigerFusedLinearCrossEntropyLoss, self).__init__(*args, **kwargs) +class LigerFusedLinearCrossEntropyLoss(nn.Module): + def __init__( + self, + ignore_index=-100, + label_smoothing=0.0, + reduction="mean", + lse_square_scale=0.0, + ): + super().__init__() + self.ignore_index = ignore_index + self.label_smoothing = label_smoothing + self.reduction = reduction + self.lse_square_scale = lse_square_scale + assert (self.label_smoothing >= 0) and ( + self.label_smoothing <= 1 + ), f"label_smoothing must be between 0.0 and 1.0. Got: {self.label_smoothing}" def forward(self, lin_weight, _input, target, bias=None, label_smoothing=0.0): return LigerFusedLinearCrossEntropyFunction.apply( @@ -16,6 +29,7 @@ def forward(self, lin_weight, _input, target, bias=None, label_smoothing=0.0): target, bias, self.ignore_index, + self.lse_square_scale, self.label_smoothing, self.reduction, ) diff --git a/src/liger_kernel/transformers/fused_linear_jsd.py b/src/liger_kernel/transformers/fused_linear_jsd.py new file mode 100644 index 000000000..001174cc2 --- /dev/null +++ b/src/liger_kernel/transformers/fused_linear_jsd.py @@ -0,0 +1,98 @@ +from typing import Optional + +import torch + +from liger_kernel.ops.fused_linear_jsd import LigerFusedLinearJSDFunction + + +class LigerFusedLinearJSD(torch.nn.Module): + r"""Fusing the last linear layer with generalized JSD + + Handle the forward and backward pass of the final linear layer via JSD by avoiding + the materialization of the large logits tensor. + + Args: + jsd_beta (float): coefficient beta of generalized JSD in the open interval (0, 1). Default: `0.5` + ignore_index (int): The index to ignore in the target. Default: `-100` + temperature (float): temperature in softmax function to control the output probability distribution. Default: `1.0` + + Shape: + - student_input: :math:`(BT, H)`, where B is batch size, T is sequence length, H is hidden dimension. + - student_weight: :math:`(V, H)`, where V is vocab size. + - teacher_input: :math:`(BT, H')`, where H' is hidden dimension of the teacher model. + - teacher_weight: :math:`(V, H')`, where hidden size H and H' can be different. + - shift_labels: :math:`(BT,)` + - Output: a scalar. + + Examples: + ```python + >>> (B, T, H_s, H_t, V) = (2, 2, 3, 5, 10) + >>> fused_jsd = LigerFusedLinearJSD(jsd_beta=0.1, temperature=2.0) + >>> # generate inputs and weights + >>> student_input = torch.rand(B * T, H_s, device="cuda", requires_grad=True) + >>> student_lin = torch.nn.Linear(H_s, V, bias=False, device="cuda") + >>> # teacher input doesn't require grad, hidden_dim can be different from student's + >>> teacher_input = torch.rand(B * T, H_t, device="cuda") + >>> teacher_lin = torch.nn.Linear(H_t, V, bias=False, device="cuda") + >>> output = fused_jsd(student_input, student_lin.weight, teacher_input, teacher_lin.weight) + >>> output.backward() + >>> + >>> # Example with labels for supervised fine-tuning (SFT) context: + >>> + >>> # Assume hidden_states, lm_heads and corresponding labels are given + >>> student_lm_head = torch.nn.Linear(H_s, V, bias=False) + >>> student_hidden_states = torch.randn(B * T, H_s, requires_grad=True).log_softmax(dim=-1) + >>> teacher_lm_head = torch.nn.Linear(H_t, V, bias=False) + >>> teacher_hidden_states = torch.randn(B * T, H_t).log_softmax(dim=-1) + >>> labels = torch.randint(0, V, (B * T,), torch.long) + >>> + >>> # Shift so that tokens < n predict n + >>> shift_student_hidden_states = student_hidden_states[..., :-1, :].contiguous() + >>> shift_teacher_hidden_states = teacher_hidden_states[..., :-1, :].contiguous() + >>> shift_labels = labels[..., 1:].contiguous() + >>> + >>> # Flatten tokens + >>> shift_student_hidden_states = shift_student_hidden_states.view(-1, V) + >>> shift_teacher_hidden_states = shift_teacher_hidden_states.view(-1, V) + >>> shift_labels = shift_labels.view(-1) + >>> + >>> # Calculate loss + >>> loss_fct = LigerJSD(beta=0.1) + >>> loss = loss_fct( + >>> shift_studetn_hidden_states, + >>> student_lm_head.weight, + >>> shift_teacher_hidden_states, + >>> teacher_lm_head.weight, + >>> shift_labels + >>> ) + ``` + """ + + def __init__(self, jsd_beta=0.5, ignore_index=-100, temperature=1.0): + super().__init__() + assert ( + jsd_beta > 0 and jsd_beta < 1 + ), f"beta must be greater than 0 and less than 1. Got: {jsd_beta}" + assert temperature != 0, "temperature cannot be 0." + self.jsd_beta = jsd_beta + self.temperature = temperature + self.ignore_index = ignore_index + + def forward( + self, + student_input: torch.Tensor, + student_weight: torch.Tensor, + teacher_input: torch.Tensor, + teacher_weight: torch.Tensor, + shift_labels: Optional[torch.LongTensor], + ): + return LigerFusedLinearJSDFunction.apply( + student_input, + student_weight, + teacher_input, + teacher_weight, + shift_labels, + self.jsd_beta, + self.ignore_index, + self.temperature, + ) diff --git a/src/liger_kernel/transformers/jsd.py b/src/liger_kernel/transformers/jsd.py new file mode 100644 index 000000000..e218ca84b --- /dev/null +++ b/src/liger_kernel/transformers/jsd.py @@ -0,0 +1,75 @@ +from typing import Optional + +import torch + +from liger_kernel.ops.jsd import LigerJSDFunction + + +class LigerJSD(torch.nn.Module): + r"""The generalized Jensen-Shannon Divergence. + .. math:: + JSD(\beta)(P || Q) + = \beta * KLDiv(P || (\beta * P + (1 - \beta) * Q)) + (1 - \beta) * KLDiv(Q || (\beta * P + (1 - \beta) * Q)) + .. note:: + As all the other losses in PyTorch, this function expects the first argument, + :attr:`log_q`, to be the predictions, the output of the student model in log-space, + and the second, :attr:`log_p`, to be the observations, the output of the teacher model in log-space. + This differs from the standard mathematical notation :math:`JSD(P || Q)` where + :math:`P` denotes the teacher model and :math:`Q` denotes the student model. + + Args: + beta (float): coefficient beta of generalized JSD in the open interval (0, 1). Default: `0.5` + ignore_index (int): The index to ignore in the target. Default: `-100` + + Shape: + - Input: :math:`(BT, V)`, where B is batch size, T is sequence length, V is vocab size. + - Target: :math:`(BT, V)`, same shape as the input. + - shift_labels (Optional): :math:`(BT,)` + - Output: a scalar. + + Examples: + ```python + >>> (B, T, V) = (2, 2, 5) + >>> jsd = LigerJSD(beta=0.1) + >>> # input should be a distribution in the log space + >>> input = torch.randn(B * T, V, requires_grad=True).log_softmax(dim=-1) + >>> target = torch.randn(B * T, V).log_softmax(dim=-1) + >>> output = jsd(input, target) + >>> + >>> # Example with labels for supervised fine-tuning (SFT) context + >>> # Assume logits and corresponding labels are given + >>> student_logits = torch.randn(B * T, V, requires_grad=True).log_softmax(dim=-1) + >>> teacher_logits = torch.randn(B * T, V).log_softmax(dim=-1) + >>> labels = torch.randint(0, V, (B * T,), torch.long) + >>> # Shift so that tokens < n predict n + >>> shift_student_logits = student_logits[..., :-1, :].contiguous() + >>> shift_teacher_logits = teacher_logits[..., :-1, :].contiguous() + >>> shift_labels = labels[..., 1:].contiguous() + >>> # Flatten tokens + >>> shift_student_logits = shift_student_logits.view(-1, V) + >>> shift_teacher_logits = shift_teacher_logits.view(-1, V) + >>> shift_labels = shift_labels.view(-1) + >>> # Calculate loss + >>> loss_fct = LigerJSD(beta=0.1) + >>> loss = loss_fct(shift_studetn_logits, shift_teacher_logits, shift_labels) + + ``` + """ + + def __init__(self, beta: float = 0.5, ignore_index: int = -100): + super().__init__() + assert ( + beta > 0 and beta < 1 + ), f"beta must be greater than 0 and less than 1. Got: {beta}" + self.beta = beta + self.ignore_index = ignore_index + + def forward( + self, + log_q: torch.Tensor, + log_p: torch.Tensor, + shift_labels: Optional[torch.LongTensor] = None, + ): + return LigerJSDFunction.apply( + log_q, log_p, shift_labels, self.beta, self.ignore_index + ) diff --git a/src/liger_kernel/transformers/kl_div.py b/src/liger_kernel/transformers/kl_div.py index 3c8785a7e..8bd50dad0 100644 --- a/src/liger_kernel/transformers/kl_div.py +++ b/src/liger_kernel/transformers/kl_div.py @@ -4,10 +4,11 @@ class LigerKLDIVLoss(nn.KLDivLoss): - def __init__(self, *args, **kwargs): + def __init__(self, eps: float = 1e-10, *args, **kwargs): super(LigerKLDIVLoss, self).__init__(*args, **kwargs) + self.eps = eps def forward(self, y_pred, y_true): return LigerKLDivLossFunction.apply( - y_pred, y_true, self.reduction, self.log_target + y_pred, y_true, self.reduction, self.log_target, self.eps ) diff --git a/src/liger_kernel/transformers/model/gemma.py b/src/liger_kernel/transformers/model/gemma.py index b6cdf1238..f7b9814e9 100644 --- a/src/liger_kernel/transformers/model/gemma.py +++ b/src/liger_kernel/transformers/model/gemma.py @@ -22,7 +22,7 @@ @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) -def lce_forward( +def lce_forward_deprecated( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, @@ -136,3 +136,126 @@ def lce_forward( hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC +) +def lce_forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, 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, + num_logits_to_keep: int = 0, + **loss_kwargs, +) -> 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, ..., + 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, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, GemmaForCausalLM + + >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b") + >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") + + >>> prompt = "What is your favorite condiment?" + >>> 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] + "What is your favorite condiment?" + ```""" + 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 = None + loss = None + # if in training mode, don't materialize logits + if self.training and (labels is not None): + # We do the same thing as ForCausalLMLoss but using Liger FLCE + + shift_hidden_states = hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + reduction = "sum" if "num_items_in_batch" in loss_kwargs else "mean" + lce = LigerFusedLinearCrossEntropyLoss(reduction=reduction) + + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + if reduction == "sum": + loss /= loss_kwargs["num_items_in_batch"] + + else: # if in inference mode materialize logits + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **loss_kwargs, + ) + + 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, + ) diff --git a/src/liger_kernel/transformers/model/llama.py b/src/liger_kernel/transformers/model/llama.py index 9cf6ed446..b8d12c76a 100644 --- a/src/liger_kernel/transformers/model/llama.py +++ b/src/liger_kernel/transformers/model/llama.py @@ -1,4 +1,4 @@ -from typing import List, Optional, Tuple, Union +from typing import TYPE_CHECKING, List, Optional, Tuple, Union import torch import torch.nn.functional as F @@ -17,17 +17,20 @@ LigerFusedLinearCrossEntropyLoss, ) +if TYPE_CHECKING: + from transformers.cache_utils import Cache + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) -def lce_forward( +def lce_forward_deprecated( 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, + past_key_values: Optional[Union["Cache", List[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, @@ -120,8 +123,9 @@ def lce_forward( logits = torch.cat(logits, dim=-1) else: logits = self.lm_head(hidden_states) - logits = logits.float() if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() @@ -144,3 +148,130 @@ def lce_forward( hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC +) +def lce_forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union["Cache", 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, + num_logits_to_keep: int = 0, + **loss_kwargs, +) -> 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, ..., + 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, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") + + >>> 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] + + if self.config.pretraining_tp > 1: + raise Exception("Liger Kernel does not support pretraining_tp!!") + + logits = None + loss = None + # if in training mode, don't materialize logits + if self.training and (labels is not None): + # We do the same thing as ForCausalLMLoss but using Liger FLCE + + shift_hidden_states = hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + reduction = "sum" if "num_items_in_batch" in loss_kwargs else "mean" + lce = LigerFusedLinearCrossEntropyLoss(reduction=reduction) + + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + if reduction == "sum": + loss /= loss_kwargs["num_items_in_batch"] + + else: # if in inference mode materialize logits + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **loss_kwargs, + ) + + 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, + ) diff --git a/src/liger_kernel/transformers/model/mistral.py b/src/liger_kernel/transformers/model/mistral.py index cd0f6f9d9..cc2ab9b76 100644 --- a/src/liger_kernel/transformers/model/mistral.py +++ b/src/liger_kernel/transformers/model/mistral.py @@ -136,3 +136,6 @@ def lce_forward( hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +# Note: Grad Acc is not fixed in mistral at transformer 4.46.1 diff --git a/src/liger_kernel/transformers/model/mixtral.py b/src/liger_kernel/transformers/model/mixtral.py index f449284cf..22fea53da 100644 --- a/src/liger_kernel/transformers/model/mixtral.py +++ b/src/liger_kernel/transformers/model/mixtral.py @@ -22,7 +22,7 @@ @replace_return_docstrings( output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) -def lce_forward( +def lce_forward_deprecated( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, @@ -103,7 +103,6 @@ def lce_forward( hidden_states = outputs[0] logits = self.lm_head(hidden_states) - logits = logits.float() loss = None if self.training and (labels is not None): @@ -116,6 +115,8 @@ def lce_forward( lce = LigerFusedLinearCrossEntropyLoss() loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) elif labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() @@ -156,3 +157,153 @@ def lce_forward( attentions=outputs.attentions, router_logits=outputs.router_logits, ) + + +@add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC +) +# Ignore copy +def lce_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, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + num_logits_to_keep: int = 0, + **loss_kwargs, +) -> Union[Tuple, MoeCausalLMOutputWithPast]: + 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, ..., + 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, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, MixtralForCausalLM + + >>> model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1") + >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1") + + >>> 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_router_logits = ( + output_router_logits + if output_router_logits is not None + else self.config.output_router_logits + ) + + 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, + output_router_logits=output_router_logits, + return_dict=return_dict, + cache_position=cache_position, + ) + + hidden_states = outputs[0] + + logits = None + loss = None + # if in training mode, don't materialize logits + if self.training and (labels is not None): + # We do the same thing as ForCausalLMLoss but using Liger FLCE + + shift_hidden_states = hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + reduction = "sum" if "num_items_in_batch" in loss_kwargs else "mean" + lce = LigerFusedLinearCrossEntropyLoss(reduction=reduction) + + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + if reduction == "sum": + loss /= loss_kwargs["num_items_in_batch"] + + else: # if in inference mode materialize logits + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **loss_kwargs, + ) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits if return_dict else outputs[-1], + self.num_experts, + self.num_experts_per_tok, + attention_mask, + ) + if labels is not None: + loss += self.router_aux_loss_coef * aux_loss.to( + loss.device + ) # make sure to reside in the same device + + if not return_dict: + output = (logits,) + outputs[1:] + if output_router_logits: + output = (aux_loss,) + output + return (loss,) + output if loss is not None else output + + return MoeCausalLMOutputWithPast( + loss=loss, + aux_loss=aux_loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) diff --git a/src/liger_kernel/transformers/model/mllama.py b/src/liger_kernel/transformers/model/mllama.py new file mode 100644 index 000000000..fcf45293e --- /dev/null +++ b/src/liger_kernel/transformers/model/mllama.py @@ -0,0 +1,274 @@ +from typing import List, Optional, Tuple, Union + +import torch +from torch.nn import CrossEntropyLoss +from transformers.cache_utils import Cache +from transformers.modeling_outputs import CausalLMOutputWithPast +from transformers.models.mllama.modeling_mllama import MLLAMA_INPUTS_DOCSTRING +from transformers.utils import ( + add_start_docstrings_to_model_forward, + replace_return_docstrings, +) + +from liger_kernel.transformers.fused_linear_cross_entropy import ( + LigerFusedLinearCrossEntropyLoss, +) + + +@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class="MllamaTextConfig" +) +def lce_forward_deprecated( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + cross_attention_states: Optional[torch.LongTensor] = None, + cross_attention_mask: Optional[torch.LongTensor] = None, + full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + past_key_values: Optional[Union[Cache, 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, + num_logits_to_keep: int = 0, +) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Copy paste mllama forward but replace torch cross entropy with liger fused linear cross entropy + + + 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, ..., + 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, ..., config.vocab_size]`. + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + Returns: + Example: + ```python + >>> from transformers import AutoTokenizer, MllamaForCausalLM + >>> model = MllamaForCausalLM.from_pretrained("Llama-3.2-11B-Vision") + >>> tokenizer = AutoTokenizer.from_pretrained("Llama-3.2-11B-Vision") + >>> prompt = "If I had to write a haiku, it would be:" + >>> inputs = tokenizer(prompt, return_tensors="pt") + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6) + >>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + >>> print(result) + If I had to write a haiku, it would be: "Snowflakes gently fall" - simple, yet peaceful. + I love the idea of snowflakes gently falling, each one + ``` + """ + 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, + cross_attention_states=cross_attention_states, + attention_mask=attention_mask, + position_ids=position_ids, + cross_attention_mask=cross_attention_mask, + full_text_row_masked_out_mask=full_text_row_masked_out_mask, + 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] + + loss = None + logits = None + + if self.training and (labels is not None): + kept_hidden_states = hidden_states[:, -num_logits_to_keep:, :] + + shift_hidden_states = kept_hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + lce = LigerFusedLinearCrossEntropyLoss() + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + + else: + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float() + 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, + ) + + +@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class="MllamaTextConfig" +) +def lce_forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + cross_attention_states: Optional[torch.LongTensor] = None, + cross_attention_mask: Optional[torch.LongTensor] = None, + full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + past_key_values: Optional[Union[Cache, 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, + num_logits_to_keep: int = 0, + **loss_kwargs, +) -> 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, ..., + 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, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, MllamaForCausalLM + + >>> model = MllamaForCausalLM.from_pretrained("Llama-3.2-11B-Vision") + >>> tokenizer = AutoTokenizer.from_pretrained("Llama-3.2-11B-Vision") + + >>> prompt = "If I had to write a haiku, it would be:" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6) + >>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + >>> print(result) + If I had to write a haiku, it would be: "Snowflakes gently fall" - simple, yet peaceful. + I love the idea of snowflakes gently falling, each one + ``` + """ + 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, + cross_attention_states=cross_attention_states, + attention_mask=attention_mask, + position_ids=position_ids, + cross_attention_mask=cross_attention_mask, + full_text_row_masked_out_mask=full_text_row_masked_out_mask, + 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 = None + loss = None + # if in training mode, don't materialize logits + if self.training and (labels is not None): + # We do the same thing as ForCausalLMLoss but using Liger FLCE + + shift_hidden_states = hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + reduction = "sum" if "num_items_in_batch" in loss_kwargs else "mean" + lce = LigerFusedLinearCrossEntropyLoss(reduction=reduction) + + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + if reduction == "sum": + loss /= loss_kwargs["num_items_in_batch"] + + else: # if in inference mode materialize logits + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **loss_kwargs, + ) + + 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, + ) diff --git a/src/liger_kernel/transformers/model/phi3.py b/src/liger_kernel/transformers/model/phi3.py index 4cb7ec0ea..e860582ce 100644 --- a/src/liger_kernel/transformers/model/phi3.py +++ b/src/liger_kernel/transformers/model/phi3.py @@ -21,7 +21,7 @@ @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) -def lce_forward( +def lce_forward_deprecated( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, @@ -108,10 +108,11 @@ def lce_forward( loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) else: logits = self.lm_head(hidden_states) - logits = logits.float() loss = None if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() @@ -134,3 +135,140 @@ def lce_forward( hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +@add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC +) +def lce_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, + num_logits_to_keep: int = 0, + **loss_kwargs, +) -> 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, ..., + 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, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Phi3ForCausalLM + + >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct") + >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct") + + >>> prompt = "This is an example script ." + >>> 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] + 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum' + ```""" + + from transformers.models.phi3.modeling_phi3 import logging + + logger = logging.get_logger(__name__) + + if ( + use_cache + and self.config.rope_scaling + and cache_position is not None + and cache_position[0] == self.config.original_max_position_embeddings + ): + logger.warning( + f"If you are not using the generate method, you may encounter nonsensical outputs after the {self.config.original_max_position_embeddings}th token, as the KV cache needs to be recomputed." + ) + + 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, + ) + + hidden_states = outputs[0] + + logits = None + loss = None + # if in training mode, don't materialize logits + if self.training and (labels is not None): + # We do the same thing as ForCausalLMLoss but using Liger FLCE + + shift_hidden_states = hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + reduction = "sum" if "num_items_in_batch" in loss_kwargs else "mean" + lce = LigerFusedLinearCrossEntropyLoss(reduction=reduction) + + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + if reduction == "sum": + loss /= loss_kwargs["num_items_in_batch"] + + else: # if in inference mode materialize logits + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **loss_kwargs, + ) + + 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, + ) diff --git a/src/liger_kernel/transformers/model/qwen2.py b/src/liger_kernel/transformers/model/qwen2.py index b8e9957e9..b019e4c88 100644 --- a/src/liger_kernel/transformers/model/qwen2.py +++ b/src/liger_kernel/transformers/model/qwen2.py @@ -21,7 +21,7 @@ @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) -def lce_forward( +def lce_forward_deprecated( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, @@ -109,8 +109,9 @@ def lce_forward( else: logits = self.lm_head(hidden_states) - logits = logits.float() if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() @@ -133,3 +134,123 @@ def lce_forward( hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +@add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) +@replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC +) +def lce_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, + num_logits_to_keep: int = 0, + **loss_kwargs, +) -> 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, ..., + 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, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, Qwen2ForCausalLM + + >>> model = Qwen2ForCausalLM.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 = None + loss = None + # if in training mode, don't materialize logits + if self.training and (labels is not None): + # We do the same thing as ForCausalLMLoss but using Liger FLCE + + shift_hidden_states = hidden_states[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # flatten tokens + shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size) + shift_labels = shift_labels.view(-1) + + reduction = "sum" if "num_items_in_batch" in loss_kwargs else "mean" + lce = LigerFusedLinearCrossEntropyLoss(reduction=reduction) + + loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) + if reduction == "sum": + loss /= loss_kwargs["num_items_in_batch"] + + else: # if in inference mode materialize logits + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **loss_kwargs, + ) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/src/liger_kernel/transformers/model/qwen2_vl.py b/src/liger_kernel/transformers/model/qwen2_vl.py index cfb7a905b..68087c3e5 100644 --- a/src/liger_kernel/transformers/model/qwen2_vl.py +++ b/src/liger_kernel/transformers/model/qwen2_vl.py @@ -80,6 +80,7 @@ def lce_forward( >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..." ```""" + # FIXME: The code is outdated and not compatible with transformer >= 4.46.1 output_attentions = ( output_attentions @@ -115,6 +116,11 @@ def lce_forward( inputs_embeds[video_mask] = video_embeds if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) + # The code is copied from https://github.com/huggingface/transformers/pull/33487 + if position_ids is None and input_ids is not None: + position_ids, _ = self.get_rope_index( + input_ids, image_grid_thw, video_grid_thw, attention_mask + ) outputs = self.model( input_ids=None, @@ -145,8 +151,9 @@ def lce_forward( loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels) else: logits = self.lm_head(hidden_states) - logits = logits.float() if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() diff --git a/src/liger_kernel/transformers/monkey_patch.py b/src/liger_kernel/transformers/monkey_patch.py index 52abc1170..bb489be19 100644 --- a/src/liger_kernel/transformers/monkey_patch.py +++ b/src/liger_kernel/transformers/monkey_patch.py @@ -1,6 +1,11 @@ import inspect import logging from functools import partial +from typing import Callable + +import transformers +from packaging import version +from transformers import PreTrainedModel from torch import nn from transformers import PretrainedConfig, PreTrainedModel @@ -9,11 +14,26 @@ from liger_kernel.transformers.geglu import LigerGEGLUMLP from liger_kernel.transformers.layer_norm import LigerLayerNorm from liger_kernel.transformers.model.gemma import lce_forward as gemma_lce_forward +from liger_kernel.transformers.model.gemma import ( + lce_forward_deprecated as gemma_lce_forward_deprecated, +) from liger_kernel.transformers.model.llama import lce_forward as llama_lce_forward +from liger_kernel.transformers.model.llama import ( + lce_forward_deprecated as llama_lce_forward_deprecated, +) from liger_kernel.transformers.model.mistral import lce_forward as mistral_lce_forward from liger_kernel.transformers.model.mixtral import lce_forward as mixtral_lce_forward +from liger_kernel.transformers.model.mixtral import ( + lce_forward_deprecated as mixtral_lce_forward_deprecated, +) from liger_kernel.transformers.model.phi3 import lce_forward as phi3_lce_forward +from liger_kernel.transformers.model.phi3 import ( + lce_forward_deprecated as phi3_lce_forward_deprecated, +) from liger_kernel.transformers.model.qwen2 import lce_forward as qwen2_lce_forward +from liger_kernel.transformers.model.qwen2 import ( + lce_forward_deprecated as qwen2_lce_forward_deprecated, +) from liger_kernel.transformers.rms_norm import LigerRMSNorm from liger_kernel.transformers.rope import liger_rotary_pos_emb from liger_kernel.transformers.swiglu import ( @@ -22,7 +42,35 @@ LigerSwiGLUMLP, ) +transformer_version = version.parse(transformers.__version__) + logger = logging.getLogger(__name__) +SUPPORTED_TRANSFORMER_VERSION = "4.46.1" +TRANSFORMER_DEPRECATION_WARNING = "Support for transformers versions < 4.46.1 will soon be discontinued due to issues with incorrect gradient accumulation. \n Please consider upgrading to avoid potential issues. See details: https://github.com/huggingface/transformers/pull/34191" + + +def _bind_method_to_module(module, method_name: str, new_method: Callable): + # Binds a new method to a module instance so that self is passed as the first argument + module.__dict__[method_name] = new_method.__get__(module, module.__class__) + + +def _patch_rms_norm_module(module, offset=0.0, eps=1e-6, casting_mode="llama"): + module.offset = offset + module.casting_mode = casting_mode + module.variance_epsilon = ( + getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps + ) + _bind_method_to_module(module, "forward", LigerRMSNorm.forward) + _bind_method_to_module(module, "extra_repr", LigerRMSNorm.extra_repr) + + +def _patch_layer_norm_module(module, eps=1e-6): + module.variance_epsilon = ( + getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps + ) + module.hidden_size = module.normalized_shape + _bind_method_to_module(module, "forward", LigerLayerNorm.forward) + _bind_method_to_module(module, "extra_repr", LigerLayerNorm.extra_repr) def apply_liger_kernel_to_llama( @@ -54,6 +102,7 @@ def apply_liger_kernel_to_llama( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.llama import modeling_llama + from transformers.models.llama.modeling_llama import LlamaModel if rope: modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb @@ -64,7 +113,134 @@ def apply_liger_kernel_to_llama( if cross_entropy: modeling_llama.CrossEntropyLoss = LigerCrossEntropyLoss if fused_linear_cross_entropy: - modeling_llama.LlamaForCausalLM.forward = llama_lce_forward + if transformer_version >= version.parse(SUPPORTED_TRANSFORMER_VERSION): + modeling_llama.LlamaForCausalLM.forward = llama_lce_forward + else: # if version < 4.46.1 + logger.warning(TRANSFORMER_DEPRECATION_WARNING) + modeling_llama.LlamaForCausalLM.forward = llama_lce_forward_deprecated + + if model is not None: + # The model instance already exists, so we need to additionally patch the + # instance variables that reference already-instantiated modules (e.g. LlamaRMSNorm or LlamaMLP) + + # get the base model from the model instance + base_model: LlamaModel = getattr(model, model.base_model_prefix, model) + + if rms_norm: + _patch_rms_norm_module(base_model.norm) + + for decoder_layer in base_model.layers: + if swiglu: + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward + ) + if rms_norm: + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) + + +def apply_liger_kernel_to_mllama( + rope: bool = True, + cross_entropy: bool = False, + fused_linear_cross_entropy: bool = True, + layer_norm: bool = True, + rms_norm: bool = True, + swiglu: bool = True, + model: PreTrainedModel = None, +) -> None: + """ + Apply Liger kernels to replace original implementation in HuggingFace MLlama models. + NOTE: MLlama is not available in transformers<4.45.0 + + Args: + rope (bool): Whether to apply Liger's rotary position embedding. Default is True. + cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False. + fused_linear_cross_entropy (bool): + Whether to apply Liger's fused linear cross entropy loss. Default is True. + `cross_entropy` and `fused_linear_cross_entropy` cannot both be True. + If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient. + rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True. + swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is True. + model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been + loaded. Default is None. + """ + + assert not ( + cross_entropy and fused_linear_cross_entropy + ), "cross_entropy and fused_linear_cross_entropy cannot both be True." + + from transformers.models.mllama import modeling_mllama + from transformers.models.mllama.modeling_mllama import ( + MllamaForCausalLM, + MllamaForConditionalGeneration, + MllamaTextModel, + MllamaVisionModel, + ) + + from liger_kernel.transformers.model.mllama import lce_forward as mllama_lce_forward + from liger_kernel.transformers.model.mllama import ( + lce_forward_deprecated as mllama_lce_forward_deprecated, + ) + + if rope: + modeling_mllama.apply_rotary_pos_emb = liger_rotary_pos_emb + if layer_norm: + modeling_mllama.nn.LayerNorm = LigerLayerNorm + if rms_norm: + modeling_mllama.MllamaTextRMSNorm = LigerRMSNorm + if swiglu: + modeling_mllama.MllamaTextMLP = LigerSwiGLUMLP + if cross_entropy: + modeling_mllama.CrossEntropyLoss = LigerCrossEntropyLoss + if fused_linear_cross_entropy: + if transformer_version >= version.parse(SUPPORTED_TRANSFORMER_VERSION): + modeling_mllama.MllamaForCausalLM.forward = mllama_lce_forward + else: # if version < 4.46.1 + logger.warning(TRANSFORMER_DEPRECATION_WARNING) + modeling_mllama.MllamaForCausalLM.forward = mllama_lce_forward_deprecated + + if model is not None: + # The model instance already exists, so we need to additionally patch the + # instance variables that reference already-instantiated modules + + if isinstance(model, MllamaForConditionalGeneration): + language_model: MllamaForCausalLM = model.language_model + vision_model: MllamaVisionModel = model.vision_model + text_model: MllamaTextModel = language_model.model + elif isinstance(model, MllamaForCausalLM): + text_model = model.model + vision_model = None + elif isinstance(model, MllamaTextModel): + text_model = model + vision_model = None + else: + raise ValueError(f"Unsupported Mllama model type: {type(model)}") + + if text_model: + if rms_norm: + _patch_rms_norm_module(text_model.norm) + for decoder_layer in text_model.layers: + if swiglu: + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward + ) + if rms_norm: + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) + + if vision_model: + _patch_layer_norm_module(vision_model.layernorm_pre) + _patch_layer_norm_module(vision_model.layernorm_post) + + for layer in vision_model.transformer.layers: + if layer_norm: + _patch_layer_norm_module(layer.input_layernorm) + _patch_layer_norm_module(layer.post_attention_layernorm) + + for layer in vision_model.global_transformer.layers: + if layer_norm: + _patch_layer_norm_module(layer.input_layernorm) + _patch_layer_norm_module(layer.post_attention_layernorm) if model is not None: # The model instance already exists, so we need to additionally patch the @@ -128,6 +304,7 @@ def apply_liger_kernel_to_mistral( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.mistral import modeling_mistral + from transformers.models.mistral.modeling_mistral import MistralModel if rope: modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb @@ -143,31 +320,21 @@ def apply_liger_kernel_to_mistral( if model is not None: # The model instance already exists, so we need to additionally patch the # instance variables that reference already-instantiated modules - config: PretrainedConfig = model.config - if hasattr(model, "model"): - # The case for MistralForCausalLM, MistralForTokenClassification for example - base_model = model.model - else: - # Direct MistralModel - base_model = model + # get the base model from the model instance + base_model: MistralModel = getattr(model, model.base_model_prefix, model) - torch_dtype = config.torch_dtype if rms_norm: - base_model.norm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(base_model.norm) for decoder_layer in base_model.layers: if swiglu: - decoder_layer.mlp = LigerSwiGLUMLP(config).to(torch_dtype) + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward + ) if rms_norm: - decoder_layer.input_layernorm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.post_attention_layernorm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) def apply_liger_kernel_to_mixtral( @@ -199,6 +366,7 @@ def apply_liger_kernel_to_mixtral( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.mixtral import modeling_mixtral + from transformers.models.mixtral.modeling_mixtral import MixtralModel if rope: modeling_mixtral.apply_rotary_pos_emb = liger_rotary_pos_emb @@ -207,45 +375,33 @@ def apply_liger_kernel_to_mixtral( if cross_entropy: modeling_mixtral.CrossEntropyLoss = LigerCrossEntropyLoss if fused_linear_cross_entropy: - modeling_mixtral.MixtralForCausalLM.forward = mixtral_lce_forward + if transformer_version >= version.parse(SUPPORTED_TRANSFORMER_VERSION): + modeling_mixtral.MixtralForCausalLM.forward = mixtral_lce_forward + else: # if version < 4.46.1 + logger.warning(TRANSFORMER_DEPRECATION_WARNING) + modeling_mixtral.MixtralForCausalLM.forward = mixtral_lce_forward_deprecated if swiglu: modeling_mixtral.MixtralBlockSparseTop2MLP = LigerBlockSparseTop2MLP if model is not None: # The model instance already exists, so we need to additionally patch the # instance variables that reference already-instantiated modules - config: PretrainedConfig = model.config - if hasattr(model, "model"): - # The case for MixtralForCausalLM, MixtralForTokenClassification for example - base_model = model.model - else: - # Direct MixtralModel - base_model = model + # get the base model from the model instance + base_model: MixtralModel = getattr(model, model.base_model_prefix, model) - torch_dtype = config.torch_dtype if rms_norm: - base_model.norm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(base_model.norm) for decoder_layer in base_model.layers: if swiglu: - block_sparse_moe = decoder_layer.block_sparse_moe - patched_experts = nn.ModuleList( - [ - LigerBlockSparseTop2MLP(config) - for _ in range(block_sparse_moe.num_experts) - ] - ) - decoder_layer.block_sparse_moe.experts = patched_experts.to(torch_dtype) + for expert in decoder_layer.block_sparse_moe.experts: + _bind_method_to_module( + expert, "forward", LigerBlockSparseTop2MLP.forward + ) if rms_norm: - decoder_layer.input_layernorm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.post_attention_layernorm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) def apply_liger_kernel_to_gemma( @@ -277,6 +433,15 @@ def apply_liger_kernel_to_gemma( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.gemma import modeling_gemma + from transformers.models.gemma.modeling_gemma import GemmaModel + + # https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/gemma/modeling_gemma.py#L109 + LigerRMSNormForGemma = partial( + LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma" + ) + _patch_rms_norm_module_for_gemma = partial( + _patch_rms_norm_module, casting_mode="gemma", offset=1.0 + ) # https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/gemma/modeling_gemma.py#L109 LigerRMSNormForGemma = partial( @@ -292,7 +457,30 @@ def apply_liger_kernel_to_gemma( if geglu: modeling_gemma.GemmaMLP = LigerGEGLUMLP if fused_linear_cross_entropy: - modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward + if transformer_version >= version.parse(SUPPORTED_TRANSFORMER_VERSION): + modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward + else: # if version < 4.46.1 + logger.warning(TRANSFORMER_DEPRECATION_WARNING) + modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward_deprecated + + if model is not None: + # The model instance already exists, so we need to additionally patch the + # instance variables that reference already-instantiated modules + + # get the base model from the model instance + base_model: GemmaModel = getattr(model, model.base_model_prefix, model) + + if rms_norm: + _patch_rms_norm_module_for_gemma(base_model.norm) + + for decoder_layer in base_model.layers: + if geglu: + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerGEGLUMLP.forward + ) + if rms_norm: + _patch_rms_norm_module_for_gemma(decoder_layer.input_layernorm) + _patch_rms_norm_module_for_gemma(decoder_layer.post_attention_layernorm) if model is not None: # The model instance already exists, so we need to additionally patch the @@ -344,6 +532,14 @@ def apply_liger_kernel_to_gemma2( loaded. Default is None. """ from transformers.models.gemma2 import modeling_gemma2 + from transformers.models.gemma2.modeling_gemma2 import Gemma2Model + + LigerRMSNormForGemma2 = partial( + LigerRMSNorm, offset=1.0, casting_mode="gemma", init_fn="zeros" + ) + _patch_rms_norm_module_for_gemma2 = partial( + _patch_rms_norm_module, offset=1.0, casting_mode="gemma" + ) LigerRMSNormForGemma2 = partial(LigerRMSNorm, offset=1.0, init_fn="zeros") if rope: @@ -359,37 +555,29 @@ def apply_liger_kernel_to_gemma2( if model is not None: # The model instance already exists, so we need to additionally patch the # instance variables that reference already-instantiated modules - config: PretrainedConfig = model.config - if hasattr(model, "model"): - # The case for Gemma2ForCausalLM, Gemma2ForTokenClassification for example - base_model = model.model - else: - # Direct Gemma2Model - base_model = model + # get the base model from the model instance + base_model: Gemma2Model = getattr(model, model.base_model_prefix, model) - torch_dtype = config.torch_dtype if rms_norm: - base_model.norm = LigerRMSNormForGemma2( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module_for_gemma2(base_model.norm) for decoder_layer in base_model.layers: if geglu: - decoder_layer.mlp = LigerGEGLUMLP(config).to(torch_dtype) + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerGEGLUMLP.forward + ) if rms_norm: - decoder_layer.input_layernorm = LigerRMSNormForGemma2( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.post_attention_layernorm = LigerRMSNormForGemma2( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.pre_feedforward_layernorm = LigerRMSNormForGemma2( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.post_feedforward_layernorm = LigerRMSNormForGemma2( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module_for_gemma2(decoder_layer.input_layernorm) + _patch_rms_norm_module_for_gemma2( + decoder_layer.post_attention_layernorm + ) + _patch_rms_norm_module_for_gemma2( + decoder_layer.pre_feedforward_layernorm + ) + _patch_rms_norm_module_for_gemma2( + decoder_layer.post_feedforward_layernorm + ) def apply_liger_kernel_to_qwen2( @@ -420,6 +608,7 @@ def apply_liger_kernel_to_qwen2( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.qwen2 import modeling_qwen2 + from transformers.models.qwen2.modeling_qwen2 import Qwen2Model if rope: modeling_qwen2.apply_rotary_pos_emb = liger_rotary_pos_emb @@ -427,39 +616,38 @@ def apply_liger_kernel_to_qwen2( modeling_qwen2.Qwen2RMSNorm = LigerRMSNorm if cross_entropy: modeling_qwen2.CrossEntropyLoss = LigerCrossEntropyLoss + + # import pdb; pdb.set_trace() if fused_linear_cross_entropy: - modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward + + if transformer_version >= version.parse(SUPPORTED_TRANSFORMER_VERSION): + modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward + else: # if version < 4.46.1 + logger.warning(TRANSFORMER_DEPRECATION_WARNING) + modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward_deprecated + if swiglu: modeling_qwen2.Qwen2MLP = LigerSwiGLUMLP if model is not None: # The model instance already exists, so we need to additionally patch the # instance variables that reference already-instantiated modules - config: PretrainedConfig = model.config - if hasattr(model, "model"): - # The case for Qwen2ForCausalLM, Qwen2ForTokenClassification for example - base_model = model.model - else: - # Direct Qwen2Model - base_model = model + # get the base model from the model instance + base_model: Qwen2Model = getattr(model, model.base_model_prefix, model) - torch_dtype = config.torch_dtype if rms_norm: - base_model.norm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(base_model.norm) for decoder_layer in base_model.layers: if swiglu: - decoder_layer.mlp = LigerSwiGLUMLP(config).to(torch_dtype) + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward + ) if rms_norm: - decoder_layer.input_layernorm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.post_attention_layernorm = LigerRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) + print("Applied Liger kernels to Qwen2") def apply_liger_kernel_to_qwen2_vl( @@ -472,7 +660,7 @@ def apply_liger_kernel_to_qwen2_vl( ) -> None: """ Apply Liger kernels to replace original implementation in HuggingFace Qwen2-VL models. - NOTE: Qwen2-VL is not available in transformers<=4.44.2 + NOTE: Qwen2-VL is not available in transformers<4.45.0 Args: cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False. @@ -491,6 +679,7 @@ def apply_liger_kernel_to_qwen2_vl( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.qwen2_vl import modeling_qwen2_vl + from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLModel from liger_kernel.transformers.model.qwen2_vl import ( lce_forward as qwen2_vl_lce_forward, @@ -498,10 +687,9 @@ def apply_liger_kernel_to_qwen2_vl( # TODO: Support Qwen2-VL's multimodal RoPE implementation - LigerRMSNormForQwen2VL = partial(LigerRMSNorm, init_fn="ones", casting_mode="gemma") if rms_norm: # https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L439 - modeling_qwen2_vl.Qwen2RMSNorm = LigerRMSNormForQwen2VL + modeling_qwen2_vl.Qwen2RMSNorm = LigerRMSNorm if layer_norm: modeling_qwen2_vl.LayerNorm = LigerLayerNorm if cross_entropy: @@ -514,90 +702,27 @@ def apply_liger_kernel_to_qwen2_vl( if model is not None: # The model instance already exists, so we need to additionally patch the # instance variables that reference already-instantiated modules - config: PretrainedConfig = model.config - torch_dtype = config.torch_dtype - - if hasattr(model, "model"): - # The case for Qwen2VLForConditionalGeneration. - base_model = model.model - else: - # Direct Qwen2VLModel - base_model = model + # get the base model from the model instance + base_model: Qwen2VLModel = getattr(model, model.base_model_prefix, model) if hasattr(model, "visual"): # Patch Qwen2VisionTransformerPretrainedModel for vision_block in model.visual.blocks: if layer_norm: - vision_block.norm1 = LigerLayerNorm(config.embed_dim, eps=1e-6).to( - torch_dtype - ) - vision_block.norm2 = LigerLayerNorm(config.embed_dim, eps=1e-6).to( - torch_dtype - ) + _patch_layer_norm_module(vision_block.norm1) + _patch_layer_norm_module(vision_block.norm2) if rms_norm: - base_model.norm = LigerRMSNormForQwen2VL( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) + _patch_rms_norm_module(base_model.norm) for decoder_layer in base_model.layers: if swiglu: - decoder_layer.mlp = LigerSwiGLUMLP(config).to(torch_dtype) + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward + ) if rms_norm: - decoder_layer.input_layernorm = LigerRMSNormForQwen2VL( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - decoder_layer.post_attention_layernorm = LigerRMSNormForQwen2VL( - config.hidden_size, eps=config.rms_norm_eps - ).to(torch_dtype) - - -def apply_liger_kernel_to_qwen2_vl( - cross_entropy: bool = False, - fused_linear_cross_entropy: bool = True, - rms_norm: bool = True, - layer_norm: bool = True, - swiglu: bool = True, -) -> None: - """ - Apply Liger kernels to replace original implementation in HuggingFace Qwen2-VL models. - NOTE: Qwen2-VL is not available in transformers<=4.44.2 - - Args: - cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False. - fused_linear_cross_entropy (bool): - Whether to apply Liger's fused linear cross entropy loss. Default is True. - `cross_entropy` and `fused_linear_cross_entropy` cannot both be True. - If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient. - rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True. - layer_norm (bool): Whether to apply Liger's LayerNorm. Default is True. - swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is True. - """ - assert not ( - cross_entropy and fused_linear_cross_entropy - ), "cross_entropy and fused_linear_cross_entropy cannot both be True." - - from transformers.models.qwen2_vl import modeling_qwen2_vl - - from liger_kernel.transformers.model.qwen2_vl import ( - lce_forward as qwen2_vl_lce_forward, - ) - - # TODO: Support Qwen2-VL's multimodal RoPE implementation - - if rms_norm: - # https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L439 - modeling_qwen2_vl.Qwen2RMSNorm = partial( - LigerRMSNorm, init_fn="ones", casting_mode="gemma" - ) - if layer_norm: - modeling_qwen2_vl.LayerNorm = LigerLayerNorm - if cross_entropy: - modeling_qwen2_vl.CrossEntropyLoss = LigerCrossEntropyLoss - if fused_linear_cross_entropy: - modeling_qwen2_vl.Qwen2VLForConditionalGeneration.forward = qwen2_vl_lce_forward - if swiglu: - modeling_qwen2_vl.Qwen2MLP = LigerSwiGLUMLP + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) def apply_liger_kernel_to_phi3( @@ -628,6 +753,7 @@ def apply_liger_kernel_to_phi3( ), "cross_entropy and fused_linear_cross_entropy cannot both be True." from transformers.models.phi3 import modeling_phi3 + from transformers.models.phi3.modeling_phi3 import Phi3Model if rope: modeling_phi3.apply_rotary_pos_emb = liger_rotary_pos_emb # Same as Gemma @@ -638,7 +764,30 @@ def apply_liger_kernel_to_phi3( if cross_entropy: modeling_phi3.CrossEntropyLoss = LigerCrossEntropyLoss if fused_linear_cross_entropy: - modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward + if transformer_version >= version.parse(SUPPORTED_TRANSFORMER_VERSION): + modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward + else: # if version < 4.46.1 + logger.warning(TRANSFORMER_DEPRECATION_WARNING) + modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward_deprecated + + if model is not None: + # The model instance already exists, so we need to additionally patch the + # instance variables that reference already-instantiated modules + + # get the base model from the model instance + base_model: Phi3Model = getattr(model, model.base_model_prefix, model) + + if rms_norm: + _patch_rms_norm_module(base_model.norm) + + for decoder_layer in base_model.layers: + if swiglu: + _bind_method_to_module( + decoder_layer.mlp, "forward", LigerPhi3SwiGLUMLP.forward + ) + if rms_norm: + _patch_rms_norm_module(decoder_layer.input_layernorm) + _patch_rms_norm_module(decoder_layer.post_attention_layernorm) if model is not None: # The model instance already exists, so we need to additionally patch the @@ -675,6 +824,8 @@ def apply_liger_kernel_to_phi3( "gemma": apply_liger_kernel_to_gemma, "gemma2": apply_liger_kernel_to_gemma2, "llama": apply_liger_kernel_to_llama, + "mllama": apply_liger_kernel_to_mllama, + "mllama_text_model": apply_liger_kernel_to_mllama, "mistral": apply_liger_kernel_to_mistral, "mixtral": apply_liger_kernel_to_mixtral, "qwen2": apply_liger_kernel_to_qwen2, @@ -760,7 +911,6 @@ def _apply_liger_kernel_to_instance(model: PreTrainedModel, **kwargs) -> None: for key, value in kwargs.items() if key in apply_fn_signature.parameters } - logger.info( f"Applying Liger kernels to model instance with model type: {model_type} with kwargs: {applicable_kwargs}" ) diff --git a/test/conftest.py b/test/conftest.py new file mode 100644 index 000000000..806fa8664 --- /dev/null +++ b/test/conftest.py @@ -0,0 +1,8 @@ +import pytest +import torch + + +@pytest.fixture(autouse=True) +def clear_cuda_cache(): + yield + torch.cuda.empty_cache() diff --git a/test/convergence/test_mini_models.py b/test/convergence/test_mini_models.py index f648a88c2..72be62c0c 100644 --- a/test/convergence/test_mini_models.py +++ b/test/convergence/test_mini_models.py @@ -1,5 +1,3 @@ -import functools -import os from test.utils import ( DEFAULT_DATASET_PATH, MiniModelConfig, @@ -9,11 +7,12 @@ revert_liger_kernel_to_llama, revert_liger_kernel_to_mistral, revert_liger_kernel_to_mixtral, + revert_liger_kernel_to_mllama, revert_liger_kernel_to_phi3, revert_liger_kernel_to_qwen2, + revert_liger_kernel_to_qwen2_vl, set_seed, simple_collate_fn, - supports_bfloat16, ) import pytest @@ -34,25 +33,35 @@ apply_liger_kernel_to_llama, apply_liger_kernel_to_mistral, apply_liger_kernel_to_mixtral, + apply_liger_kernel_to_mllama, apply_liger_kernel_to_phi3, apply_liger_kernel_to_qwen2, + apply_liger_kernel_to_qwen2_vl, ) -torch.use_deterministic_algorithms(True) +try: + # Mllama is only available in transformers>=4.45.0 + from transformers.models.mllama.configuration_mllama import MllamaTextConfig + from transformers.models.mllama.modeling_mllama import MllamaForCausalLM -# Only setting torch.use_deterministic_algorithms(True) throws the following error: -# RuntimeError: Deterministic behavior was enabled with either `torch.use_deterministic_algorithms(True)` or `at::Context::setDeterministicAlgorithms(true)`, -# but this operation is not deterministic because it uses CuBLAS and you have CUDA >= 10.2. To enable deterministic behavior in this case, you must set an -# environment variable before running your PyTorch application: CUBLAS_WORKSPACE_CONFIG=:4096:8 or CUBLAS_WORKSPACE_CONFIG=:16:8. For more information, -# go to https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility + MLLAMA_AVAILABLE = True +except ImportError: + MLLAMA_AVAILABLE = False -os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" +try: + # Qwen2-VL is only available in transformers>4.44.2 + from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig + from transformers.models.qwen2_vl.modeling_qwen2_vl import ( + Qwen2VLForConditionalGeneration, + ) + + QWEN2_VL_AVAILABLE = True +except ImportError: + QWEN2_VL_AVAILABLE = False MINI_MODEL_SETUPS = { "mini_llama3": MiniModelConfig( - liger_kernel_patch_func=functools.partial( - apply_liger_kernel_to_llama, fused_linear_cross_entropy=False - ), + liger_kernel_patch_func=apply_liger_kernel_to_llama, liger_kernel_patch_revert_func=revert_liger_kernel_to_llama, model_class=LlamaForCausalLM, mini_model_config=LlamaConfig( @@ -76,7 +85,7 @@ rope_theta=500000.0, tie_word_embeddings=False, use_cache=True, - vocab_size=32000, # 128256 + vocab_size=32000, # 128256, # At rope backward # Eager produces incontiguous dq and dk # SDPA produces contiguous dq and incontiguous dk @@ -84,10 +93,112 @@ attn_implementation="sdpa", # default value, pytorch native attention ), ), - "mini_gemma1": MiniModelConfig( - liger_kernel_patch_func=functools.partial( - apply_liger_kernel_to_gemma, fused_linear_cross_entropy=False + "mini_qwen2": MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_qwen2, + liger_kernel_patch_revert_func=revert_liger_kernel_to_qwen2, + model_class=Qwen2ForCausalLM, + mini_model_config=Qwen2Config( + attention_dropout=0.0, + bos_token_id=1, # 151643 + eos_token_id=2, # 151643 + hidden_act="silu", + hidden_size=896, + initializer_range=0.02, + intermediate_size=4864, + max_position_embeddings=32768, # 131072 + num_attention_heads=8, + num_hidden_layers=4, + num_key_value_heads=2, + rms_norm_eps=1e-6, + rope_theta=1000000.0, + sliding_window=131072, + tie_word_embeddings=True, + use_cache=True, + vocab_size=32000, # 151936 + # At rope backward + # Eager produces incontiguous dq and dk + # SDPA produces contiguous dq and incontiguous dk + # Flash_attn produces contiguous dq and dk + attn_implementation="sdpa", # default value, pytorch native attention + ), + ), + "mini_phi3": MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_phi3, + liger_kernel_patch_revert_func=revert_liger_kernel_to_phi3, + model_class=Phi3ForCausalLM, + mini_model_config=Phi3Config( + attention_dropout=0.0, + bos_token_id=1, + eos_token_id=2, # 32000 + hidden_act="silu", + hidden_size=896, # 3072 + initializer_range=0.02, + intermediate_size=4864, # 8192 + max_position_embeddings=4096, + num_attention_heads=8, # 32 + num_hidden_layers=4, # 32 + num_key_value_heads=None, # defaults to num_attention_heads + rms_norm_eps=1e-5, + rope_theta=10000.0, + sliding_window=None, + tie_word_embeddings=False, + use_cache=True, + vocab_size=32064, + attn_implementation="eager", ), + ), + "mini_mistral": MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_mistral, + liger_kernel_patch_revert_func=revert_liger_kernel_to_mistral, + model_class=MistralForCausalLM, + mini_model_config=MistralConfig( + attention_dropout=0.0, + bos_token_id=1, + eos_token_id=2, + hidden_act="silu", + hidden_size=1024, + initializer_range=0.02, + intermediate_size=2048, + max_position_embeddings=32768, + num_attention_heads=8, + num_hidden_layers=4, + num_key_value_heads=2, + rms_norm_eps=1e-5, + rope_theta=10000.0, + sliding_window=4096, + tie_word_embeddings=False, + use_cache=True, + vocab_size=32000, + attn_implementation="sdpa", + ), + ), + "mini_mixtral": MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_mixtral, + liger_kernel_patch_revert_func=revert_liger_kernel_to_mixtral, + model_class=MixtralForCausalLM, + mini_model_config=MixtralConfig( + attention_dropout=0.0, + bos_token_id=1, + eos_token_id=2, + hidden_act="silu", + hidden_size=512, # 4096 + initializer_range=0.02, + intermediate_size=2048, # 14336 + max_position_embeddings=32768, # 32768 + num_attention_heads=8, # 32 + num_hidden_layers=4, # 32 + num_key_value_heads=2, # 8 + rms_norm_eps=1e-5, + rope_theta=10000.0, + sliding_window=4096, + tie_word_embeddings=False, + use_cache=True, + vocab_size=32000, + attn_implementation="sdpa", + ), + ), + "mini_gemma1": MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_gemma, liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma, model_class=GemmaForCausalLM, mini_model_config=GemmaConfig( @@ -119,9 +230,7 @@ ), ), "mini_gemma1.1": MiniModelConfig( - liger_kernel_patch_func=functools.partial( - apply_liger_kernel_to_gemma, fused_linear_cross_entropy=False - ), + liger_kernel_patch_func=apply_liger_kernel_to_gemma, liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma, model_class=GemmaForCausalLM, mini_model_config=GemmaConfig( @@ -174,127 +283,90 @@ rope_theta=10000.0, attention_bias=False, attention_dropout=0.0, + attn_implementation="eager", ), ), - "mini_mistral": MiniModelConfig( - liger_kernel_patch_func=functools.partial( - apply_liger_kernel_to_mistral, fused_linear_cross_entropy=False - ), - liger_kernel_patch_revert_func=revert_liger_kernel_to_mistral, - model_class=MistralForCausalLM, - mini_model_config=MistralConfig( - attention_dropout=0.0, - bos_token_id=1, - eos_token_id=2, - hidden_act="silu", - hidden_size=1024, # 4096 - initializer_range=0.02, - intermediate_size=2048, # 14336 - max_position_embeddings=32768, # 32768 - num_attention_heads=8, # 32 - num_hidden_layers=4, # 32 - num_key_value_heads=2, # 8 - rms_norm_eps=1e-5, - rope_theta=10000.0, - sliding_window=4096, - tie_word_embeddings=False, - use_cache=True, - vocab_size=32000, - attn_implementation="sdpa", - ), - ), - "mini_mixtral": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_mixtral, - liger_kernel_patch_revert_func=revert_liger_kernel_to_mixtral, - model_class=MixtralForCausalLM, - mini_model_config=MixtralConfig( - attention_dropout=0.0, - bos_token_id=1, - eos_token_id=2, +} + +if MLLAMA_AVAILABLE: + MINI_MODEL_SETUPS["mini_mllama"] = MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_mllama, + liger_kernel_patch_revert_func=revert_liger_kernel_to_mllama, + model_class=MllamaForCausalLM, + mini_model_config=MllamaTextConfig( + bos_token_id=1, # 128000 + eos_token_id=2, # 128001 + pad_token_id=2, + cross_attention_layers=None, + dropout=0, hidden_act="silu", hidden_size=1024, # 4096 initializer_range=0.02, intermediate_size=2048, # 14336 - max_position_embeddings=32768, # 32768 + max_position_embeddings=131_072, num_attention_heads=8, # 32 - num_experts_per_tok=2, - num_hidden_layers=4, # 32 + num_hidden_layers=4, # 40 num_key_value_heads=2, # 8 - num_local_experts=8, - output_router_logits=False, rms_norm_eps=1e-5, - rope_theta=1000000.0, - router_aux_loss_coef=0.02, - sliding_window=None, + rope_scaling=dict( + factor=8.0, + high_freq_factor=4.0, + low_freq_factor=1.0, + original_max_position_embeddings=8192, + rope_type="llama3", + ), + rope_theta=500_000, tie_word_embeddings=False, use_cache=True, - vocab_size=32000, - # At rope backward - # Eager produces incontiguous dq and dk - # SDPA produces contiguous dq and incontiguous dk - # Flash_attn produces contiguous dq and dk + vocab_size=32000, # 128256, attn_implementation="sdpa", # default value, pytorch native attention ), - ), - "mini_qwen2": MiniModelConfig( - liger_kernel_patch_func=functools.partial( - apply_liger_kernel_to_qwen2, fused_linear_cross_entropy=False - ), - liger_kernel_patch_revert_func=revert_liger_kernel_to_qwen2, - model_class=Qwen2ForCausalLM, - mini_model_config=Qwen2Config( + ) + +if QWEN2_VL_AVAILABLE: + MINI_MODEL_SETUPS["mini_qwen2_vl"] = MiniModelConfig( + liger_kernel_patch_func=apply_liger_kernel_to_qwen2_vl, + liger_kernel_patch_revert_func=revert_liger_kernel_to_qwen2_vl, + model_class=Qwen2VLForConditionalGeneration, + mini_model_config=Qwen2VLConfig( attention_dropout=0.0, bos_token_id=1, # 151643 - eos_token_id=2, # 151643 + eos_token_id=2, # 151645 hidden_act="silu", - hidden_size=896, + hidden_size=1536, # 8192 initializer_range=0.02, - intermediate_size=4864, - max_position_embeddings=32768, # 131072 - num_attention_heads=8, - num_hidden_layers=4, - num_key_value_heads=2, - rms_norm_eps=1e-6, + intermediate_size=4864, # 29568 + max_position_embeddings=32768, + max_window_layers=4, # 80 + num_attention_heads=12, # 64 + num_hidden_layers=4, # 80 + num_key_value_heads=2, # 8 + rms_norm_eps=1e-6, # 1e-5 rope_theta=1000000.0, - sliding_window=131072, - tie_word_embeddings=True, - use_cache=True, - vocab_size=32000, # 151936 - # At rope backward - # Eager produces incontiguous dq and dk - # SDPA produces contiguous dq and incontiguous dk - # Flash_attn produces contiguous dq and dk - attn_implementation="sdpa", # default value, pytorch native attention - ), - ), - "mini_phi3": MiniModelConfig( - liger_kernel_patch_func=functools.partial( - apply_liger_kernel_to_phi3, fused_linear_cross_entropy=False - ), - liger_kernel_patch_revert_func=revert_liger_kernel_to_phi3, - model_class=Phi3ForCausalLM, - mini_model_config=Phi3Config( - attention_dropout=0.0, - bos_token_id=1, - eos_token_id=2, # 32000 - hidden_act="silu", - hidden_size=896, # 3072 - initializer_range=0.02, - intermediate_size=4864, # 8192 - max_position_embeddings=4096, - num_attention_heads=8, # 32 - num_hidden_layers=4, # 32 - num_key_value_heads=None, # defaults to num_attention_heads - rms_norm_eps=1e-5, - rope_theta=10000.0, - sliding_window=None, + rope_scaling=dict( + type="mrope", + mrope_section=[16, 24, 24], # (temporal, height, width) + ), + sliding_window=4096, tie_word_embeddings=False, use_cache=True, - vocab_size=32064, - attn_implementation="eager", + vocab_size=32000, # 152064 + use_sliding_window=False, + vision_config={ + "depth": 4, # 32 + "embed_dim": 1280, + "mlp_ratio": 4, + "num_heads": 16, + "in_chans": 3, + "hidden_size": 128, # 1536 + "patch_size": 14, + "spatial_merge_size": 2, + "spatial_patch_size": 14, + "temporal_patch_size": 2, + }, + attn_implementation="sdpa", ), - ), -} + ) def create_model(model_name="mini_llama3"): @@ -323,21 +395,37 @@ def run_mini_model( if with_liger is True: kwargs = { - "rope": True, "rms_norm": True, - "cross_entropy": True, } + model_supports_rope = "qwen2_vl" not in model_name + if model_supports_rope: + kwargs["rope"] = True + + model_supports_layer_norm = "qwen2_vl" in model_name + if model_supports_layer_norm: + kwargs["layer_norm"] = True + if "gemma" in model_name: kwargs["geglu"] = True else: kwargs["swiglu"] = True + + model_support_flce = "gemma2" not in model_name + + if model_support_flce: + kwargs["fused_linear_cross_entropy"] = True + kwargs["cross_entropy"] = False + else: + kwargs["cross_entropy"] = True + MINI_MODEL_SETUPS[model_name].liger_kernel_patch_func(**kwargs) else: - MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func() + ... + # FIXME: disable revert because it will cause flce to not be patched + # MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func() model = create_model(model_name).to(dtype).to("cuda") train_dataset = load_from_disk(DEFAULT_DATASET_PATH) - loader = DataLoader( train_dataset, batch_size=16, shuffle=False, collate_fn=simple_collate_fn ) @@ -355,130 +443,220 @@ def run_mini_model( print(f"Step {i}, Loss: {output.loss.item()}") loss_list.append(output.loss.item()) - MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func() + # MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func() return {"loss": loss_list, "logits": output.logits, "model": model} @pytest.mark.parametrize( + # FIXME enable bf16 tests after revert is fixed "model_name, num_steps, lr, dtype, loss_atol, loss_rtol, logits_atol, logits_rtol, param_atol, param_rtol", [ - # Gemma 1 has more tolerance because currently, the kernel is not a perfect match (casts are not done the same way) - ("mini_gemma1", 32, 1e-4, torch.float32, 1e-8, 6e-4, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_gemma1", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_gemma1.1", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), + ("mini_llama3", 32, 1e-4, torch.float32, 1e-8, 2e-5, 1e-4, 1e-5, 5e-3, 1e-5), + # pytest.param( + # "mini_llama3", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), pytest.param( - "mini_gemma1.1", + "mini_mllama", 32, 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, + torch.float32, + 1e-8, + 1e-5, + 5e-3, + 1e-5, + 5e-3, + 1e-5, marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_gemma2", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_gemma2", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_llama3", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_llama3", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - # TODO: torch 2.5.0 nightly breaks mixtral test, but torch 2.3.0 works fine - # TODO: mixtral MoE structure makes the convergence flaky so disable the test for now. It needs high tol to pass. - # ("mini_mixtral", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 8e-3, 1e-5), - # ("mini_mixtral", 32, 1e-4, torch.bfloat16, 1e-8, 1e-5, 2.0, 1e-5, 1e-2, 1e-5), - ("mini_mistral", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_mistral", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + not MLLAMA_AVAILABLE, + reason="Mllama not available in this version of transformers", ), ), + # pytest.param( + # "mini_mllama", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=[ + # pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # pytest.mark.skipif( + # not MLLAMA_AVAILABLE, + # reason="Mllama not available in this version of transformers", + # ), + # ], + # ), ("mini_qwen2", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_qwen2", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), + # pytest.param( + # "mini_qwen2", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), + # FIXME qwen2 is broken and needs fix + # pytest.param( + # "mini_qwen2_vl", + # 32, + # 1e-4, + # torch.float32, + # 1e-8, + # 1e-5, + # 5e-3, + # 1e-5, + # 5e-3, + # 1e-5, + # marks=pytest.mark.skipif( + # not QWEN2_VL_AVAILABLE, + # reason="Qwen2-VL not available in this version of transformers", + # ), + # ), + # pytest.param( + # "mini_qwen2_vl", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=[ + # pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # pytest.mark.skipif( + # not QWEN2_VL_AVAILABLE, + # reason="Qwen2-VL not available in this version of transformers", + # ), + # ], + # ), ("mini_phi3", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_phi3", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), + # pytest.param( + # "mini_phi3", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), + ("mini_mistral", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), + # pytest.param( + # "mini_mistral", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), + # TODO: mixtral is flaky so disable the test for now + # ("mini_mixtral", 32, 1e-4, torch.float32, 5e-4, 1e-4, 5e-3, 1e-5, 1e-2, 1e-5), + # pytest.param( + # "mini_mixtral", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-1, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), + # Gemma 1.1 and 2 has more tolerance because currently, the kernel is not a perfect match (casts are not done the same way) + ("mini_gemma1", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 5e-3, 1e-5), + # pytest.param( + # "mini_gemma1", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), + ("mini_gemma1.1", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 5e-3, 1e-5), + # pytest.param( + # "mini_gemma1.1", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), + # TODO: Gemma2 tests are not passing within the tolerance range, need to investigate + # ("mini_gemma2", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 5e-3, 1e-5), + # pytest.param( + # "mini_gemma2", + # 32, + # 1e-4, + # torch.bfloat16, + # 1e-3, + # 1e-2, + # 1e-1, + # 1e-2, + # 1e-2, + # 1e-2, + # marks=pytest.mark.skipif( + # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + # ), + # ), ], ) def test_mini_model( @@ -503,7 +681,7 @@ def test_mini_model( model_name=model_name, num_steps=num_steps, dtype=dtype, lr=lr, with_liger=True ) - # Compare the loss of every step + # Compare every step of the loss assert_verbose_allclose( torch.tensor([expected_output["loss"]]), torch.tensor([actual_output["loss"]]), @@ -511,13 +689,15 @@ def test_mini_model( rtol=loss_rtol, ) - # Compare the logits from the last step - assert_verbose_allclose( - expected_output["logits"], - actual_output["logits"], - atol=logits_atol, - rtol=logits_rtol, - ) + # No logits are materialized + + # # Compare the logits from the last step + # assert_verbose_allclose( + # expected_output["logits"], + # actual_output["logits"], + # atol=logits_atol, + # rtol=logits_rtol, + # ) # Compare the params from the last step # Iterate over the model's parameters and compare them diff --git a/test/convergence/test_mini_models_multimodal.py b/test/convergence/test_mini_models_multimodal.py index 4c164ba58..c835df05d 100644 --- a/test/convergence/test_mini_models_multimodal.py +++ b/test/convergence/test_mini_models_multimodal.py @@ -1,34 +1,63 @@ import functools import os from test.utils import ( + FAKE_CONFIGS_PATH, UNTOKENIZED_DATASET_PATH, MiniModelConfig, assert_verbose_allclose, + load_tokenizer_config, multimodal_collate_fn, + revert_liger_kernel_to_mllama, revert_liger_kernel_to_qwen2_vl, set_seed, supports_bfloat16, + train_bpe_tokenizer, ) import pytest import torch from datasets import load_dataset from torch.utils.data import DataLoader -from transformers.models.auto.processing_auto import AutoProcessor +from transformers import PreTrainedTokenizerFast -from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl +from liger_kernel.transformers import ( + apply_liger_kernel_to_mllama, + apply_liger_kernel_to_qwen2_vl, +) try: - # Qwen2-VL is only available in transformers>4.44.2 + # Qwen2-VL is only available in transformers>=4.45.0 + from transformers.models.qwen2.tokenization_qwen2_fast import Qwen2TokenizerFast from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig + from transformers.models.qwen2_vl.image_processing_qwen2_vl import ( + Qwen2VLImageProcessor, + ) from transformers.models.qwen2_vl.modeling_qwen2_vl import ( Qwen2VLForConditionalGeneration, ) + from transformers.models.qwen2_vl.processing_qwen2_vl import Qwen2VLProcessor QWEN2_VL_AVAILABLE = True except ImportError: QWEN2_VL_AVAILABLE = False +try: + # Mllama is only available in transformers>=4.45.0 + from transformers.models.mllama.configuration_mllama import ( + MllamaConfig, + MllamaTextConfig, + MllamaVisionConfig, + ) + from transformers.models.mllama.image_processing_mllama import MllamaImageProcessor + from transformers.models.mllama.modeling_mllama import ( + MllamaForConditionalGeneration, + ) + from transformers.models.mllama.processing_mllama import MllamaProcessor + + MLLAMA_AVAILABLE = True +except ImportError: + MLLAMA_AVAILABLE = False + torch.use_deterministic_algorithms(True) # Only setting torch.use_deterministic_algorithms(True) throws the following error: @@ -43,6 +72,64 @@ MINI_MODEL_SETUPS = {} + +if MLLAMA_AVAILABLE: + MINI_MODEL_SETUPS["mini_mllama"] = MiniModelConfig( + liger_kernel_patch_func=functools.partial( + apply_liger_kernel_to_mllama, fused_linear_cross_entropy=False + ), + liger_kernel_patch_revert_func=revert_liger_kernel_to_mllama, + model_class=MllamaForConditionalGeneration, + mini_model_config=MllamaConfig( + vision_config=MllamaVisionConfig( + hidden_act="gelu", + hidden_size=512, # 1280 + image_size=560, # 560 + initializer_range=0.02, + intermediate_layers_indices=[2], # [3, 7, 15, etc...] + intermediate_size=2048, # 5120 + max_num_tiles=1, # 4 + norm_eps=1e-5, + num_attention_heads=4, # 16 + num_channels=3, + num_global_layers=2, # 8 + num_hidden_layers=8, # 32 + patch_size=140, # 14 + supported_aspect_ratios=[[1, 1]], # [[1, 1], [1, 2], etc... ] + vision_output_dim=1024, # 7680 + ), + text_config=MllamaTextConfig( + bos_token_id=0, + eos_token_id=0, + pad_token_id=0, + cross_attention_layers=[2], # [3, 8, 13, 18, etc...] + dropout=0, + hidden_act="silu", + hidden_size=1024, # 4096 + initializer_range=0.02, + intermediate_size=2048, # 14336 + max_position_embeddings=131_072, + num_attention_heads=8, # 32 + num_hidden_layers=4, # 40 + num_key_value_heads=2, # 8 + rms_norm_eps=1e-5, + rope_scaling=dict( + factor=8.0, + high_freq_factor=4.0, + low_freq_factor=1.0, + original_max_position_embeddings=8192, + rope_type="llama3", + ), + rope_theta=500_000, + tie_word_embeddings=False, + use_cache=True, + vocab_size=32000, # 128256, + ), + image_token_index=1, # NOTE: outside the vocab size + attn_implementation="sdpa", + ), + ) + if QWEN2_VL_AVAILABLE: MINI_MODEL_SETUPS["mini_qwen2_vl"] = MiniModelConfig( liger_kernel_patch_func=functools.partial( @@ -54,12 +141,12 @@ attention_dropout=0.0, # Token Ids and vocab size must match those in the tokenizer/processor # https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/config.json - bos_token_id=151643, - eos_token_id=151645, - vision_start_token_id=151652, - vision_end_token_id=151653, - vision_token_id=151654, - image_token_id=151655, + bos_token_id=0, + eos_token_id=0, + vision_start_token_id=1, + vision_end_token_id=2, + vision_token_id=3, + image_token_id=4, hidden_act="silu", hidden_size=1024, # 8192 initializer_range=0.02, @@ -78,7 +165,7 @@ sliding_window=4096, tie_word_embeddings=True, use_cache=False, # True - vocab_size=152064, + vocab_size=32000, # 152064, use_sliding_window=False, vision_config={ "depth": 4, # 32 @@ -95,7 +182,51 @@ def create_processor(model_name): if model_name == "mini_qwen2_vl": - return AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") + tokenizer_config = load_tokenizer_config( + os.path.join( + FAKE_CONFIGS_PATH, "Qwen/Qwen2-VL-7B-Instruct/tokenizer_config.json" + ) + ) + tokenizer_base = train_bpe_tokenizer( + [ + token.content + for key, token in sorted( + tokenizer_config["added_tokens_decoder"].items(), + key=lambda x: int(x[0]), + ) + ] + ) + qwen_tokenizer = Qwen2TokenizerFast( + tokenizer_object=tokenizer_base, **tokenizer_config + ) + image_processor = Qwen2VLImageProcessor() + return Qwen2VLProcessor( + image_processor=image_processor, tokenizer=qwen_tokenizer + ) + + elif model_name == "mini_mllama": + tokenizer_config = load_tokenizer_config( + os.path.join( + FAKE_CONFIGS_PATH, + "meta-llama/Llama-3.2-11B-Vision-Instruct/tokenizer_config.json", + ) + ) + tokenizer_base = train_bpe_tokenizer( + [ + token.content + for key, token in sorted( + tokenizer_config["added_tokens_decoder"].items(), + key=lambda x: int(x[0]), + ) + ] + ) + fast_tokenizer = PreTrainedTokenizerFast( + tokenizer_object=tokenizer_base, **tokenizer_config + ) + image_processor = MllamaImageProcessor(size={"height": 560, "width": 560}) + return MllamaProcessor( + image_processor=image_processor, tokenizer=fast_tokenizer + ) else: raise ValueError(f"Processor not available for model {model_name}") @@ -129,7 +260,9 @@ def apply_chat_template(example): "content": [{"type": "text", "text": example["text"]}], }, ] - example["text"] = processor.apply_chat_template(conversation, tokenize=False) + example["text"] = processor.tokenizer.apply_chat_template( + conversation, tokenize=False + ) return example def preprocess_function(examples): @@ -140,6 +273,7 @@ def preprocess_function(examples): padding="max_length", truncation=True, max_length=1024, # longer than for text-only b/c images require quite a few tokens + return_tensors="pt", ) train_dataset = ( @@ -182,15 +316,12 @@ def run_mini_model_multimodal( kwargs = { "rms_norm": True, "cross_entropy": True, + "layer_norm": True, } model_supports_rope = "qwen2_vl" not in model_name if model_supports_rope: kwargs["rope"] = True - model_supports_layer_norm = "qwen2_vl" in model_name - if model_supports_layer_norm: - kwargs["layer_norm"] = True - if "gemma" in model_name: kwargs["geglu"] = True else: @@ -265,6 +396,43 @@ def run_mini_model_multimodal( ), ], ), + pytest.param( + "mini_mllama", + 32, + 1e-4, + torch.float32, + 1e-8, + 1e-5, + 5e-3, + 1e-5, + 5e-3, + 1e-5, + marks=pytest.mark.skipif( + not MLLAMA_AVAILABLE, + reason="Mllama not available in this version of transformers", + ), + ), + pytest.param( + "mini_mllama", + 32, + 1e-4, + torch.bfloat16, + 1e-3, + 1e-2, + 1e-1, + 1e-2, + 1e-2, + 1e-2, + marks=[ + pytest.mark.skipif( + not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + ), + pytest.mark.skipif( + not MLLAMA_AVAILABLE, + reason="Mllama not available in this version of transformers", + ), + ], + ), ], ) def test_mini_model_multimodal( diff --git a/test/convergence/test_mini_models_no_logits.py b/test/convergence/test_mini_models_no_logits.py deleted file mode 100644 index 7dfaa00f1..000000000 --- a/test/convergence/test_mini_models_no_logits.py +++ /dev/null @@ -1,621 +0,0 @@ -from test.utils import ( - DEFAULT_DATASET_PATH, - MiniModelConfig, - assert_verbose_allclose, - revert_liger_kernel_to_gemma, - revert_liger_kernel_to_gemma2, - revert_liger_kernel_to_llama, - revert_liger_kernel_to_mistral, - revert_liger_kernel_to_mixtral, - revert_liger_kernel_to_phi3, - revert_liger_kernel_to_qwen2, - revert_liger_kernel_to_qwen2_vl, - set_seed, - simple_collate_fn, - supports_bfloat16, -) - -import pytest -import torch -from datasets import load_from_disk -from torch.utils.data import DataLoader -from transformers.models.gemma import GemmaConfig, GemmaForCausalLM -from transformers.models.gemma2 import Gemma2Config, Gemma2ForCausalLM -from transformers.models.llama import LlamaConfig, LlamaForCausalLM -from transformers.models.mistral import MistralConfig, MistralForCausalLM -from transformers.models.mixtral import MixtralConfig, MixtralForCausalLM -from transformers.models.phi3 import Phi3Config, Phi3ForCausalLM -from transformers.models.qwen2 import Qwen2Config, Qwen2ForCausalLM - -from liger_kernel.transformers import ( - apply_liger_kernel_to_gemma, - apply_liger_kernel_to_gemma2, - apply_liger_kernel_to_llama, - apply_liger_kernel_to_mistral, - apply_liger_kernel_to_mixtral, - apply_liger_kernel_to_phi3, - apply_liger_kernel_to_qwen2, - apply_liger_kernel_to_qwen2_vl, -) - -try: - # Qwen2-VL is only available in transformers>4.44.2 - from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig - from transformers.models.qwen2_vl.modeling_qwen2_vl import ( - Qwen2VLForConditionalGeneration, - ) - - QWEN2_VL_AVAILABLE = True -except ImportError: - QWEN2_VL_AVAILABLE = False - -MINI_MODEL_SETUPS = { - "mini_llama3": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_llama, - liger_kernel_patch_revert_func=revert_liger_kernel_to_llama, - model_class=LlamaForCausalLM, - mini_model_config=LlamaConfig( - attention_bias=False, - attention_dropout=0.0, - # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset - # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json - bos_token_id=1, # 128000 - eos_token_id=2, # 128001 - hidden_act="silu", - hidden_size=1024, # 4096 - initializer_range=0.02, - intermediate_size=2048, # 14336 - max_position_embeddings=8192, - num_attention_heads=8, # 32 - num_hidden_layers=4, # 32 - num_key_value_heads=2, # 8 - pretraining_tp=1, - rms_norm_eps=1e-5, - rope_scaling=None, - rope_theta=500000.0, - tie_word_embeddings=False, - use_cache=True, - vocab_size=32000, # 128256, - # At rope backward - # Eager produces incontiguous dq and dk - # SDPA produces contiguous dq and incontiguous dk - # Flash_attn produces contiguous dq and dk - attn_implementation="sdpa", # default value, pytorch native attention - ), - ), - "mini_qwen2": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_qwen2, - liger_kernel_patch_revert_func=revert_liger_kernel_to_qwen2, - model_class=Qwen2ForCausalLM, - mini_model_config=Qwen2Config( - attention_dropout=0.0, - bos_token_id=1, # 151643 - eos_token_id=2, # 151643 - hidden_act="silu", - hidden_size=896, - initializer_range=0.02, - intermediate_size=4864, - max_position_embeddings=32768, # 131072 - num_attention_heads=8, - num_hidden_layers=4, - num_key_value_heads=2, - rms_norm_eps=1e-6, - rope_theta=1000000.0, - sliding_window=131072, - tie_word_embeddings=True, - use_cache=True, - vocab_size=32000, # 151936 - # At rope backward - # Eager produces incontiguous dq and dk - # SDPA produces contiguous dq and incontiguous dk - # Flash_attn produces contiguous dq and dk - attn_implementation="sdpa", # default value, pytorch native attention - ), - ), - "mini_phi3": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_phi3, - liger_kernel_patch_revert_func=revert_liger_kernel_to_phi3, - model_class=Phi3ForCausalLM, - mini_model_config=Phi3Config( - attention_dropout=0.0, - bos_token_id=1, - eos_token_id=2, # 32000 - hidden_act="silu", - hidden_size=896, # 3072 - initializer_range=0.02, - intermediate_size=4864, # 8192 - max_position_embeddings=4096, - num_attention_heads=8, # 32 - num_hidden_layers=4, # 32 - num_key_value_heads=None, # defaults to num_attention_heads - rms_norm_eps=1e-5, - rope_theta=10000.0, - sliding_window=None, - tie_word_embeddings=False, - use_cache=True, - vocab_size=32064, - attn_implementation="eager", - ), - ), - "mini_mistral": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_mistral, - liger_kernel_patch_revert_func=revert_liger_kernel_to_mistral, - model_class=MistralForCausalLM, - mini_model_config=MistralConfig( - attention_dropout=0.0, - bos_token_id=1, - eos_token_id=2, - hidden_act="silu", - hidden_size=1024, - initializer_range=0.02, - intermediate_size=2048, - max_position_embeddings=32768, - num_attention_heads=8, - num_hidden_layers=4, - num_key_value_heads=2, - rms_norm_eps=1e-5, - rope_theta=10000.0, - sliding_window=4096, - tie_word_embeddings=False, - use_cache=True, - vocab_size=32000, - attn_implementation="sdpa", - ), - ), - "mini_mixtral": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_mixtral, - liger_kernel_patch_revert_func=revert_liger_kernel_to_mixtral, - model_class=MixtralForCausalLM, - mini_model_config=MixtralConfig( - attention_dropout=0.0, - bos_token_id=1, - eos_token_id=2, - hidden_act="silu", - hidden_size=512, # 4096 - initializer_range=0.02, - intermediate_size=2048, # 14336 - max_position_embeddings=32768, # 32768 - num_attention_heads=8, # 32 - num_hidden_layers=4, # 32 - num_key_value_heads=2, # 8 - rms_norm_eps=1e-5, - rope_theta=10000.0, - sliding_window=4096, - tie_word_embeddings=False, - use_cache=True, - vocab_size=32000, - attn_implementation="sdpa", - ), - ), - "mini_gemma1": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_gemma, - liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma, - model_class=GemmaForCausalLM, - mini_model_config=GemmaConfig( - vocab_size=32000, # 256000 - hidden_size=1024, # 3072 - intermediate_size=2048, # 24576 - num_hidden_layers=4, # 28 - num_attention_heads=4, # 16 - num_key_value_heads=4, # 16 - head_dim=256, - # gemma1 model config uses `hidden_act` and point it to gelu, - # https://huggingface.co/google/gemma-7b/blob/main/config.json#L10 - # but in reality it's ignored and HuggingFace will use tanh approximation: - # https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/gemma/modeling_gemma.py#L175 - hidden_act="gelu", - max_position_embeddings=8192, - initializer_range=0.02, - rms_norm_eps=1e-06, - use_cache=True, - pad_token_id=0, - # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset - # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json - bos_token_id=1, # 128000 - eos_token_id=2, # 128001 - tie_word_embeddings=True, - rope_theta=10000.0, - attention_bias=False, - attention_dropout=0.0, - ), - ), - "mini_gemma1.1": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_gemma, - liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma, - model_class=GemmaForCausalLM, - mini_model_config=GemmaConfig( - vocab_size=32000, # 256000 - hidden_size=1024, # 3072 - intermediate_size=2048, # 24576 - num_hidden_layers=4, # 28 - num_attention_heads=4, # 16 - num_key_value_heads=4, # 16 - head_dim=256, - hidden_activation="gelu_pytorch_tanh", - max_position_embeddings=8192, - initializer_range=0.02, - rms_norm_eps=1e-06, - use_cache=True, - pad_token_id=0, - # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset - # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json - bos_token_id=1, # 128000 - eos_token_id=2, # 128001 - tie_word_embeddings=True, - rope_theta=10000.0, - attention_bias=False, - attention_dropout=0.0, - ), - ), - "mini_gemma2": MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_gemma2, - liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma2, - model_class=Gemma2ForCausalLM, - mini_model_config=Gemma2Config( - vocab_size=32000, # 256000 - hidden_size=1024, # 3072 - intermediate_size=2048, # 24576 - num_hidden_layers=4, # 28 - num_attention_heads=4, # 16 - num_key_value_heads=4, # 16 - head_dim=256, - hidden_activation="gelu_pytorch_tanh", - max_position_embeddings=8192, - initializer_range=0.02, - rms_norm_eps=1e-06, - use_cache=True, - pad_token_id=0, - # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset - # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json - bos_token_id=1, # 128000 - eos_token_id=2, # 128001 - tie_word_embeddings=True, - rope_theta=10000.0, - attention_bias=False, - attention_dropout=0.0, - ), - ), -} - -if QWEN2_VL_AVAILABLE: - MINI_MODEL_SETUPS["mini_qwen2_vl"] = MiniModelConfig( - liger_kernel_patch_func=apply_liger_kernel_to_qwen2_vl, - liger_kernel_patch_revert_func=revert_liger_kernel_to_qwen2_vl, - model_class=Qwen2VLForConditionalGeneration, - mini_model_config=Qwen2VLConfig( - attention_dropout=0.0, - bos_token_id=1, # 151643 - eos_token_id=2, # 151645 - hidden_act="silu", - hidden_size=1536, # 8192 - initializer_range=0.02, - intermediate_size=4864, # 29568 - max_position_embeddings=32768, - max_window_layers=4, # 80 - num_attention_heads=12, # 64 - num_hidden_layers=4, # 80 - num_key_value_heads=2, # 8 - rms_norm_eps=1e-6, # 1e-5 - rope_theta=1000000.0, - rope_scaling=dict( - type="mrope", - mrope_section=[16, 24, 24], # (temporal, height, width) - ), - sliding_window=4096, - tie_word_embeddings=False, - use_cache=True, - vocab_size=32000, # 152064 - use_sliding_window=False, - vision_config={ - "depth": 4, # 32 - "embed_dim": 1280, - "mlp_ratio": 4, - "num_heads": 16, - "in_chans": 3, - "hidden_size": 128, # 1536 - "patch_size": 14, - "spatial_merge_size": 2, - "spatial_patch_size": 14, - "temporal_patch_size": 2, - }, - attn_implementation="sdpa", - ), - ) - - -def create_model(model_name="mini_llama3"): - """ - Create a mini version model - The commented values are the original values - """ - model_config = MINI_MODEL_SETUPS[model_name].mini_model_config - model_class = MINI_MODEL_SETUPS[model_name].model_class - return model_class(model_config) - - -def run_mini_model( - model_name="mini_llama3", - num_steps=100, - dtype=torch.bfloat16, - lr=1e-5, - with_liger=False, -): - # If we move it to the beginning of test_mini_model, the two runs are initialized with different weights. - # This is due to RNG (Random Number Generator). The formula of RNG progression is x_(n+1) = (a * x_n + c) % m - # Everytime RNG is used, like randomly initialzing weight, the RNG progresses to the next state. - # Therefore, we have to reset RNG before we create the model to ensure the weight initialization started from the same RNG state. - - set_seed(42) - - if with_liger is True: - kwargs = { - "rms_norm": True, - } - model_supports_rope = "qwen2_vl" not in model_name - if model_supports_rope: - kwargs["rope"] = True - - model_supports_layer_norm = "qwen2_vl" in model_name - if model_supports_layer_norm: - kwargs["layer_norm"] = True - - if "gemma" in model_name: - kwargs["geglu"] = True - else: - kwargs["swiglu"] = True - - model_support_flce = "gemma2" not in model_name - if model_support_flce: - kwargs["fused_linear_cross_entropy"] = True - kwargs["cross_entropy"] = False - else: - kwargs["cross_entropy"] = True - - MINI_MODEL_SETUPS[model_name].liger_kernel_patch_func(**kwargs) - else: - MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func() - - model = create_model(model_name).to(dtype).to("cuda") - train_dataset = load_from_disk(DEFAULT_DATASET_PATH) - loader = DataLoader( - train_dataset, batch_size=16, shuffle=False, collate_fn=simple_collate_fn - ) - loader_iter = iter(loader) - optimizer = torch.optim.AdamW(model.parameters(), lr=lr) - - loss_list = [] - - for i in range(num_steps): - batch = next(loader_iter).to(model.device) - optimizer.zero_grad() - output = model(**batch) - output.loss.backward() - optimizer.step() - print(f"Step {i}, Loss: {output.loss.item()}") - loss_list.append(output.loss.item()) - - MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func() - return {"loss": loss_list, "logits": output.logits, "model": model} - - -@pytest.mark.parametrize( - "model_name, num_steps, lr, dtype, loss_atol, loss_rtol, logits_atol, logits_rtol, param_atol, param_rtol", - [ - ("mini_llama3", 32, 1e-4, torch.float32, 1e-8, 2e-5, 1e-4, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_llama3", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_qwen2", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_qwen2", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - pytest.param( - "mini_qwen2_vl", - 32, - 1e-4, - torch.float32, - 1e-8, - 1e-5, - 5e-3, - 1e-5, - 5e-3, - 1e-5, - marks=pytest.mark.skipif( - not QWEN2_VL_AVAILABLE, - reason="Qwen2-VL not available in this version of transformers", - ), - ), - pytest.param( - "mini_qwen2_vl", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=[ - pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - pytest.mark.skipif( - not QWEN2_VL_AVAILABLE, - reason="Qwen2-VL not available in this version of transformers", - ), - ], - ), - ("mini_phi3", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_phi3", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_mistral", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_mistral", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - # TODO: mixtral is flaky so disable the test for now - # ("mini_mixtral", 32, 1e-4, torch.float32, 5e-4, 1e-4, 5e-3, 1e-5, 1e-2, 1e-5), - # pytest.param( - # "mini_mixtral", - # 32, - # 1e-4, - # torch.bfloat16, - # 1e-3, - # 1e-2, - # 1e-1, - # 1e-2, - # 1e-1, - # 1e-2, - # marks=pytest.mark.skipif( - # not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - # ), - # ), - # Gemma 1.1 and 2 has more tolerance because currently, the kernel is not a perfect match (casts are not done the same way) - ("mini_gemma1", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_gemma1", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_gemma1.1", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_gemma1.1", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ("mini_gemma2", 32, 1e-4, torch.float32, 1e-8, 1e-4, 5e-3, 1e-5, 5e-3, 1e-5), - pytest.param( - "mini_gemma2", - 32, - 1e-4, - torch.bfloat16, - 1e-3, - 1e-2, - 1e-1, - 1e-2, - 1e-2, - 1e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - ], -) -def test_mini_model( - model_name, - num_steps, - lr, - dtype, - loss_atol, - loss_rtol, - logits_atol, - logits_rtol, - param_atol, - param_rtol, -): - # Non-liger models should be initialized and tested first to avoid the module being overridden - - expected_output = run_mini_model( - model_name=model_name, num_steps=num_steps, dtype=dtype, lr=lr - ) - - actual_output = run_mini_model( - model_name=model_name, num_steps=num_steps, dtype=dtype, lr=lr, with_liger=True - ) - - # Compare every step of the loss - assert_verbose_allclose( - torch.tensor([expected_output["loss"]]), - torch.tensor([actual_output["loss"]]), - atol=loss_atol, - rtol=loss_rtol, - ) - - # No logits are materialized - - # # Compare the logits from the last step - # assert_verbose_allclose( - # expected_output["logits"], - # actual_output["logits"], - # atol=logits_atol, - # rtol=logits_rtol, - # ) - - # Compare the params from the last step - # Iterate over the model's parameters and compare them - for expected_param, actual_param in zip( - expected_output["model"].named_parameters(), - actual_output["model"].named_parameters(), - ): - assert_verbose_allclose( - expected_param[1], actual_param[1], atol=param_atol, rtol=param_rtol - ) diff --git a/test/resources/fake_configs/Qwen/Qwen2-VL-7B-Instruct/tokenizer_config.json b/test/resources/fake_configs/Qwen/Qwen2-VL-7B-Instruct/tokenizer_config.json new file mode 100644 index 000000000..e784b6882 --- /dev/null +++ b/test/resources/fake_configs/Qwen/Qwen2-VL-7B-Instruct/tokenizer_config.json @@ -0,0 +1,55 @@ +{ + "added_tokens_decoder": { + "0": { + "content": "<|unk|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "<|vision_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "2": { + "content": "<|vision_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "3": { + "content": "<|vision_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "4": { + "content": "<|image_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": ["<|im_start|>", "<|im_end|>", "<|object_ref_start|>","<|object_ref_end|>","<|box_start|>","<|box_end|>","<|quad_start|>","<|quad_end|>","<|vision_start|>","<|vision_end|>","<|vision_pad|>","<|image_pad|>","<|video_pad|>"], + "bos_token": null, + "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "padding_side": "left", + "errors": "replace", + "model_max_length": 32768, + "pad_token": "<|endoftext|>", + "split_special_tokens": false, + "unk_token": null + } \ No newline at end of file diff --git a/test/resources/fake_configs/meta-llama/Llama-3.2-11B-Vision-Instruct/tokenizer_config.json b/test/resources/fake_configs/meta-llama/Llama-3.2-11B-Vision-Instruct/tokenizer_config.json new file mode 100644 index 000000000..f760c041e --- /dev/null +++ b/test/resources/fake_configs/meta-llama/Llama-3.2-11B-Vision-Instruct/tokenizer_config.json @@ -0,0 +1,31 @@ +{ + "added_tokens_decoder": { + "0": { + "content": "<|unk|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "<|image|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "<|begin_of_text|>", + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- Find out if there are any images #}\n{% set image_ns = namespace(has_images=false) %} \n{%- for message in messages %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {%- set image_ns.has_images = true %}\n {%- endif %}\n {%- endfor %}\n{%- endfor %}\n\n{#- Error out if there are images and system message #}\n{%- if image_ns.has_images and not system_message == \"\" %}\n {{- raise_exception(\"Prompting with images is incompatible with system messages.\") }}\n{%- endif %}\n\n{#- System message if there are no images #}\n{%- if not image_ns.has_images %}\n {{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n {%- endif %}\n {{- \"Cutting Knowledge Date: December 2023\\n\" }}\n {{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot_id|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|eot_id|>", + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|finetune_right_pad_id|>", + "tokenizer_class": "PreTrainedTokenizerFast" + } \ No newline at end of file diff --git a/test/transformers/test_cross_entropy.py b/test/transformers/test_cross_entropy.py index 023736596..66bec37ee 100644 --- a/test/transformers/test_cross_entropy.py +++ b/test/transformers/test_cross_entropy.py @@ -1,7 +1,8 @@ -from test.utils import set_seed, supports_bfloat16 +from test.utils import assert_verbose_allclose, set_seed, supports_bfloat16 import pytest import torch +import torch.nn.functional as F from torch.nn import CrossEntropyLoss from liger_kernel.ops.cross_entropy import LigerCrossEntropyFunction @@ -11,8 +12,63 @@ set_seed(42) -def _test_correctness_once(target_ce, B, T, V, reduction, scalar, dtype, atol, rtol): +class CrossEntropyWithZLoss(torch.nn.Module): + def __init__( + self, + lse_square_scale=0.0, + reduction="mean", + ignore_index=-100, + label_smoothing=0.0, + return_z_loss=False, + dtype=torch.float32, + ): + super().__init__() + self.lse_square_scale = lse_square_scale + self.reduction = reduction + self.ignore_index = ignore_index + self.return_z_loss = return_z_loss + self.label_smoothing = label_smoothing + self.dtype = dtype + + def forward(self, logits, targets): + # Loss calculations are all in float32 + logits = logits.to(torch.float32) + # Standard cross entropy loss + ce_loss = F.cross_entropy( + logits, + targets, + reduction=self.reduction, + label_smoothing=self.label_smoothing, + ignore_index=self.ignore_index, + ) + + # Compute log-sum-exp term + lse = torch.logsumexp(logits, dim=-1) + + # Z-loss term + z_loss = torch.where( + targets != self.ignore_index, self.lse_square_scale * (lse**2), 0.0 + ) + z_loss = z_loss.to(logits.dtype) + if self.reduction == "mean": + z_loss = z_loss.sum() / (targets != self.ignore_index).sum() + elif self.reduction == "sum": + z_loss = z_loss.sum() + else: + z_loss = z_loss + ce_loss = ce_loss.to(self.dtype) + z_loss = z_loss.to(self.dtype) + + # Final loss: cross-entropy loss + Z-loss + total_loss = ce_loss + z_loss + if self.return_z_loss: + return total_loss, z_loss + else: + return total_loss + +def _test_correctness_once(target_ce, B, T, V, reduction, scalar, dtype, atol, rtol): + torch.manual_seed(0) torch_ce = CrossEntropyLoss(reduction=reduction) _tensor = torch.randn(B * T, V, device="cuda", dtype=dtype) * scalar @@ -116,11 +172,24 @@ def _test_correctness_with_label_smoothing_with_ignore_index_once( assert torch.allclose(_input.grad, _input2.grad, atol=atol, rtol=rtol) -def _test_correctness_with_label_smoothing_once( - target_ce, B, T, V, label_smoothing, scalar, dtype, atol, rtol +def _test_correctness_with_z_loss_once( + target_ce, + B, + T, + V, + scalar, + dtype, + atol, + rtol, + lse_square_scale, + return_z_loss, ): torch.manual_seed(0) - torch_ce = CrossEntropyLoss(label_smoothing=label_smoothing) + torch_ce = CrossEntropyWithZLoss( + lse_square_scale=lse_square_scale, + return_z_loss=return_z_loss, + dtype=dtype, + ) _tensor = torch.randn(B * T, V, device="cuda", dtype=dtype) * scalar _input = _tensor.detach().clone().requires_grad_(True) @@ -128,22 +197,48 @@ def _test_correctness_with_label_smoothing_once( target = torch.randint(0, V, (B * T,), device="cuda", dtype=torch.long) - output = torch_ce(_input, target) - output2 = target_ce(_input2, target) + if return_z_loss: + output, z_output = torch_ce(_input, target) + output2, z_output2 = target_ce(_input2, target) + + else: + output = torch_ce(_input, target) + output2 = target_ce(_input2, target) assert torch.allclose(output, output2, atol=atol, rtol=rtol) + if return_z_loss: + assert torch.allclose(z_output, z_output2, atol=atol, rtol=rtol) + output.backward() output2.backward() + assert torch.allclose(_input.grad, _input2.grad, atol=atol, rtol=rtol) -def _test_correctness_with_label_smoothing_with_ignore_index_once( - target_ce, B, T, V, ignore_index, label_smoothing, scalar, dtype, atol, rtol +def _test_correctness_with_z_loss_with_other_params_once( + target_ce, + B, + T, + V, + scalar, + dtype, + atol, + rtol, + lse_square_scale, + return_z_loss, + label_smoothing, + ignore_index, + reduction, ): torch.manual_seed(0) - torch_ce = CrossEntropyLoss( - ignore_index=ignore_index, label_smoothing=label_smoothing + torch_ce = CrossEntropyWithZLoss( + lse_square_scale=lse_square_scale, + return_z_loss=return_z_loss, + label_smoothing=label_smoothing, + ignore_index=ignore_index, + reduction=reduction, + dtype=dtype, ) _tensor = torch.randn(B * T, V, device="cuda", dtype=dtype) * scalar @@ -161,14 +256,27 @@ def _test_correctness_with_label_smoothing_with_ignore_index_once( ] # Randomly select indices target[indices_to_assign] = ignore_index - output = torch_ce(_input, target) - output2 = target_ce(_input2, target) + if return_z_loss: + output, z_output = torch_ce(_input, target) + output2, z_output2 = target_ce(_input2, target) + + else: + output = torch_ce(_input, target) + output2 = target_ce(_input2, target) assert torch.allclose(output, output2, atol=atol, rtol=rtol) + if return_z_loss: + assert torch.allclose(z_output, z_output2, atol=atol, rtol=rtol) + output.backward() output2.backward() - assert torch.allclose(_input.grad, _input2.grad, atol=atol, rtol=rtol) + print(_input.grad) + print(_input2.grad) + + print(f"{(_input.grad - _input2.grad).sum()=}") + + assert_verbose_allclose(_input.grad, _input2.grad, atol=atol, rtol=rtol) def _test_correctness_not_last_layer_once( @@ -204,10 +312,11 @@ def _test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): target = torch.randint(0, V, (B * T,), device="cuda", dtype=torch.long) - y1 = liger_cross_entropy(x1, target, 0) - y2 = LigerCrossEntropyFunction.apply(x2, target, 0) + y1, y1_z = liger_cross_entropy(x1, target, 0, 1e-4, 0.1, "mean", True) + y2, y2_z = LigerCrossEntropyFunction.apply(x2, target, 0, 1e-4, 0.1, "mean", True) assert torch.allclose(y1, y2, atol=atol, rtol=rtol) + assert torch.allclose(y1_z, y2_z, atol=atol, rtol=rtol) grad = torch.randn_like(y2) @@ -225,26 +334,14 @@ def _test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): @pytest.mark.parametrize( "B, T, V", [ - (2, 4096, 32000), # llama2, mistral - (2, 4096, 32000), # llama2, mistral - (1, 4096, 128256), # llama3 - # # weird shapes - (3, 423, 32000), + (2, 4096, 32000), # llama + (3, 423, 32000), # weird shapes ], ) @pytest.mark.parametrize("reduction", ["sum", "mean"]) @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - pytest.param( - 0.1, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), pytest.param( 1.0, torch.bfloat16, @@ -254,24 +351,9 @@ def _test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - pytest.param( - 10.0, - torch.bfloat16, - 1e-7, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", -) def test_correctness(B, T, V, scalar, dtype, reduction, atol, rtol): liger_ce = LigerCrossEntropyLoss(reduction=reduction) _test_correctness_once(liger_ce, B, T, V, reduction, scalar, dtype, atol, rtol) @@ -288,12 +370,8 @@ def test_correctness(B, T, V, scalar, dtype, reduction, atol, rtol): @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - (0.1, torch.bfloat16, 1e-8, 5e-2), (1.0, torch.bfloat16, 1e-8, 5e-2), - (10.0, torch.bfloat16, 1e-7, 5e-2), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) def test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): @@ -303,9 +381,7 @@ def test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): @pytest.mark.parametrize( "B, T, V, ignore_index", [ - (2, 4096, 32000, -100), # llama2, mistral - (2, 4096, 32000, 2), # llama2, mistral - (1, 4096, 128256, -300), # llama3 + (2, 4096, 32000, 2), # weird shapes (3, 423, 32000, -123), ], @@ -314,15 +390,6 @@ def test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - pytest.param( - 0.1, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), pytest.param( 1.0, torch.bfloat16, @@ -332,24 +399,9 @@ def test_correctness_functional(B, T, V, scalar, dtype, atol, rtol): not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - pytest.param( - 10.0, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", -) def test_correctness_with_ignore_index( B, T, V, ignore_index, reduction, scalar, dtype, atol, rtol ): @@ -362,9 +414,7 @@ def test_correctness_with_ignore_index( @pytest.mark.parametrize( "B, T, V, label_smoothing", [ - (2, 4096, 32000, 0.1), # llama2, mistral - (2, 4096, 32000, 0.1), # llama2, mistral - (1, 4096, 128256, 0.1), # llama3 + (2, 4096, 32000, 0.1), # weird shapes (3, 423, 32000, 0.1), ], @@ -372,15 +422,6 @@ def test_correctness_with_ignore_index( @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - pytest.param( - 0.1, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), pytest.param( 1.0, torch.bfloat16, @@ -390,24 +431,9 @@ def test_correctness_with_ignore_index( not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - pytest.param( - 10.0, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", -) def test_correctness_with_label_smoothing_once( B, T, V, label_smoothing, scalar, dtype, atol, rtol ): @@ -420,9 +446,7 @@ def test_correctness_with_label_smoothing_once( @pytest.mark.parametrize( "B, T, V, ignore_index, label_smoothing", [ - (2, 4096, 32000, 1, 0.1), # llama2, mistral - (2, 4096, 32000, -100, 0.2), # llama2, mistral - (1, 4096, 128256, 2, 0.1), # llama3 + (2, 4096, 32000, 1, 0.1), # weird shapes (3, 423, 32000, -300, 0.2), ], @@ -430,15 +454,6 @@ def test_correctness_with_label_smoothing_once( @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - pytest.param( - 0.1, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), pytest.param( 1.0, torch.bfloat16, @@ -448,24 +463,9 @@ def test_correctness_with_label_smoothing_once( not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - pytest.param( - 10.0, - torch.bfloat16, - 1e-6, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", -) def test_correctness_with_label_smoothing_with_ignore_index_once( B, T, V, ignore_index, label_smoothing, scalar, dtype, atol, rtol ): @@ -479,27 +479,17 @@ def test_correctness_with_label_smoothing_with_ignore_index_once( @pytest.mark.parametrize( - "B, T, V, label_smoothing", + "B, T, V", [ - (2, 4096, 32000, 0.1), # llama2, mistral - (2, 4096, 32000, 0.1), # llama2, mistral - (1, 4096, 128256, 0.1), # llama3 + (2, 4096, 32000), # llama2 # weird shapes - (3, 423, 32000, 0.1), + (3, 423, 32000), ], ) +@pytest.mark.parametrize("reduction", ["sum", "mean"]) @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - pytest.param( - 0.1, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), pytest.param( 1.0, torch.bfloat16, @@ -509,55 +499,57 @@ def test_correctness_with_label_smoothing_with_ignore_index_once( not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - pytest.param( - 10.0, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", +@pytest.mark.parametrize("return_z_loss", [True, False]) +@pytest.mark.parametrize( + "lse_square_scale", + [ + 1e-4, # PaLM + 1e-5, # Chameleon + ], ) -def test_correctness_with_label_smoothing_once( - B, T, V, label_smoothing, scalar, dtype, atol, rtol +def test_correctness_with_z_loss_once( + B, + T, + V, + scalar, + dtype, + atol, + rtol, + lse_square_scale, + return_z_loss, ): - liger_ce = LigerCrossEntropyLoss(label_smoothing=label_smoothing) - _test_correctness_with_label_smoothing_once( - liger_ce, B, T, V, label_smoothing, scalar, dtype, atol, rtol + test_ce = LigerCrossEntropyLoss( + lse_square_scale=lse_square_scale, + return_z_loss=return_z_loss, + ) + _test_correctness_with_z_loss_once( + test_ce, + B, + T, + V, + scalar, + dtype, + atol, + rtol, + lse_square_scale, + return_z_loss, ) @pytest.mark.parametrize( - "B, T, V, ignore_index, label_smoothing", + "B, T, V", [ - (2, 4096, 32000, 1, 0.1), # llama2, mistral - (2, 4096, 32000, -100, 0.2), # llama2, mistral - (1, 4096, 128256, 2, 0.1), # llama3 + (2, 4096, 32000), # llama2, mistral # weird shapes - (3, 423, 32000, -300, 0.2), + (3, 423, 32000), ], ) @pytest.mark.parametrize( "scalar, dtype, atol, rtol", [ - pytest.param( - 0.1, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), pytest.param( 1.0, torch.bfloat16, @@ -567,33 +559,58 @@ def test_correctness_with_label_smoothing_once( not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - pytest.param( - 10.0, - torch.bfloat16, - 1e-8, - 5e-2, - marks=pytest.mark.skipif( - not supports_bfloat16(), reason="bfloat16 not supported on this GPU" - ), - ), - (0.1, torch.float32, 1e-8, 1e-6), (1.0, torch.float32, 1e-8, 1e-6), - (10.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", +@pytest.mark.parametrize( + "return_z_loss, lse_square_scale", + [ + (True, 1e-4), + (False, 1e-5), + ], ) -def test_correctness_with_label_smoothing_with_ignore_index_once( - B, T, V, ignore_index, label_smoothing, scalar, dtype, atol, rtol +@pytest.mark.parametrize( + "label_smoothing, ignore_index, reduction", + [ + (0.1, 42, "mean"), + (0.2, -42, "sum"), + ], +) +def test_correctness_with_z_loss_with_other_params_once( + B, + T, + V, + scalar, + dtype, + atol, + rtol, + lse_square_scale, + return_z_loss, + label_smoothing, + ignore_index, + reduction, ): - liger_ce = LigerCrossEntropyLoss( - ignore_index=ignore_index, + test_ce = LigerCrossEntropyLoss( + lse_square_scale=lse_square_scale, + return_z_loss=return_z_loss, label_smoothing=label_smoothing, + ignore_index=ignore_index, + reduction=reduction, ) - _test_correctness_with_label_smoothing_with_ignore_index_once( - liger_ce, B, T, V, ignore_index, label_smoothing, scalar, dtype, atol, rtol + _test_correctness_with_z_loss_with_other_params_once( + test_ce, + B, + T, + V, + scalar, + dtype, + atol, + rtol, + lse_square_scale, + return_z_loss, + label_smoothing, + ignore_index, + reduction, ) @@ -601,8 +618,6 @@ def test_correctness_with_label_smoothing_with_ignore_index_once( "B, T, V", [ (2, 4096, 32000), # llama2, mistral - (2, 4096, 32000), # llama2, mistral - (1, 4096, 128256), # llama3 # # weird shapes (3, 423, 32000), ], @@ -623,52 +638,8 @@ def test_correctness_with_label_smoothing_with_ignore_index_once( (1.0, torch.float32, 1e-8, 1e-6), ], ) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 16 * 1000 * 1000 * 1000, - reason="Needs 16GB+ GPU memory.", -) def test_correctness_not_last_layer(B, T, V, reduction, scalar, dtype, atol, rtol): liger_ce = LigerCrossEntropyLoss(reduction=reduction) _test_correctness_not_last_layer_once( liger_ce, B, T, V, reduction, scalar, dtype, atol, rtol ) - - -############################################################################# -# Test full pass of the liger cross entropy loss to ensure it doesn't crash -############################################################################# - - -def _full_pass_once(B, T, V, reduction): - - liger_ce = LigerCrossEntropyLoss(reduction=reduction) - - _input = torch.randn( - B * T, V, requires_grad=True, device="cuda", dtype=torch.bfloat16 - ) - target = torch.randint(V, (B * T, 1), device="cuda").squeeze(1) - - output = liger_ce(_input, target) - output.backward() - - -@pytest.mark.parametrize( - "B, T, V", - [ - ( - 8, - 8192, - 128256, - ), # _input = 16GB, total = ~32GB, 8405385216 > 2,147,483,647, so we need int64 - (8, 16384, 128256), # _input = 32GB, total = ~64GB - ], -) -@pytest.mark.parametrize("reduction", ["sum", "mean"]) -@pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory < 64 * 1000 * 1000 * 1000, - reason="Needs 64GB+ GPU memory.", -) -def test_large_no_exception(B, T, V, reduction): - # The large inputs were hitting cuda illegal memory access because of - # https://github.com/triton-lang/triton/issues/1058 - _full_pass_once(B, T, V, reduction) diff --git a/test/transformers/test_embedding.py b/test/transformers/test_embedding.py index b192835e3..998a544c5 100644 --- a/test/transformers/test_embedding.py +++ b/test/transformers/test_embedding.py @@ -7,6 +7,7 @@ SLEEP_SECONDS = 0.1 +@pytest.mark.skip(reason="LigerEmbedding is under experimentation") @pytest.mark.parametrize( "num_embeddings, embedding_dim, padding_idx", [ diff --git a/test/transformers/test_fused_linear_cross_entropy.py b/test/transformers/test_fused_linear_cross_entropy.py index 57e2cf534..2be9c9d10 100644 --- a/test/transformers/test_fused_linear_cross_entropy.py +++ b/test/transformers/test_fused_linear_cross_entropy.py @@ -1,3 +1,4 @@ +from test.transformers.test_cross_entropy import CrossEntropyWithZLoss from test.utils import assert_verbose_allclose, set_seed import pytest @@ -22,6 +23,12 @@ class TorchLMHeadCE(torch.nn.Module): :param V: vocab size :param ignore_index: index to ignore :param reduction: reduction method + :param label_smoothing: label_smoothing to apply on target + :param lse_square_scale: scaler of lse ^ 2 to compute z loss + + # TODO: if we bump CI env's `transformers` version to >= 4.46, we should just directly + # call https://github.com/huggingface/transformers/blob/main/src/transformers/loss/loss_utils.py#L32 + # to be consistent with Hugging Face model implementation. """ def __init__( @@ -31,6 +38,7 @@ def __init__( dtype: torch.dtype, bias: bool = False, ignore_index: int = -100, + lse_square_scale: float = 0.0, label_smoothing: float = 0.0, reduction: str = "mean", ): @@ -38,14 +46,15 @@ def __init__( self.lin = torch.nn.Linear( in_features=H, out_features=V, bias=bias, dtype=dtype ) - self.ce_loss = torch.nn.CrossEntropyLoss( + self.ce_loss = CrossEntropyWithZLoss( ignore_index=ignore_index, - reduction=reduction, + lse_square_scale=lse_square_scale, label_smoothing=label_smoothing, + reduction=reduction, ) def forward(self, x, y): - logits = self.lin(x) + logits = self.lin(x).to(torch.float32) return self.ce_loss(logits, y) @@ -57,6 +66,7 @@ def __init__( dtype: torch.dtype, bias: bool = False, ignore_index: int = -100, + lse_square_scale: float = 0.0, label_smoothing: float = 0.0, reduction: str = "mean", ): @@ -66,8 +76,9 @@ def __init__( ) self.ce_loss = LigerFusedLinearCrossEntropyLoss( ignore_index=ignore_index, - reduction=reduction, + lse_square_scale=lse_square_scale, label_smoothing=label_smoothing, + reduction=reduction, ) def forward(self, x, y): @@ -82,12 +93,8 @@ def forward(self, x, y): @pytest.mark.parametrize( "B, T, H, V", [ - # (2, 4, 512, 512), # The test does not work on some CI GPUs. Issue #160 - (8, 2048, 4096, 32000), # llama2, mistral - # Comment out to speed up testing - # (4, 2048, 4096, 128256), # llama3 8B - # (4, 1024, 8192, 128256), # llama3 70B - (4, 423, 8192, 32000), # random shape + (8, 128, 1024, 4096), + (4, 47, 31, 123), # random shape ], ) @pytest.mark.parametrize( @@ -100,16 +107,36 @@ def forward(self, x, y): ], ) @pytest.mark.parametrize("bias", [True, False]) -@pytest.mark.parametrize("label_smoothing", [0, 0.1]) +@pytest.mark.parametrize( + "label_smoothing, ignore_index, lse_square_scale", + [ + (0, -100, 0), + (0.1, 42, 1e-4), # Pass non-default values once to ensure all params work along + ], +) def test_correctness( - B, T, H, V, scalar, dtype, bias, label_smoothing, reduction, atol, rtol + B, + T, + H, + V, + scalar, + dtype, + bias, + lse_square_scale, + label_smoothing, + ignore_index, + reduction, + atol, + rtol, ): device = "cuda" torch_lm_head_ce = TorchLMHeadCE( H=H, V=V, bias=bias, + lse_square_scale=lse_square_scale, label_smoothing=label_smoothing, + ignore_index=ignore_index, reduction=reduction, dtype=dtype, ).to(device) @@ -117,7 +144,9 @@ def test_correctness( H=H, V=V, bias=bias, + lse_square_scale=lse_square_scale, label_smoothing=label_smoothing, + ignore_index=ignore_index, reduction=reduction, dtype=dtype, ).to(device) @@ -137,6 +166,14 @@ def test_correctness( _input2 = _tensor.detach().clone().requires_grad_(True) target = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + target[indices_to_assign] = ignore_index output1 = torch_lm_head_ce(_input1, target) output2 = liger_lm_head_ce(_input2, target) @@ -203,3 +240,68 @@ def test_correctness_functional(B, T, H, V, scalar, dtype, bias, atol, rtol): y2.backward(grad_output) assert torch.allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) + + +@pytest.mark.parametrize( + "B, T, H, V", + [ + (8, 128, 1024, 4096), + (4, 47, 31, 123), # random shape + ], +) +@pytest.mark.parametrize( + "cast_dtype, atol, rtol", + [ + (torch.bfloat16, 5e-3, 5e-2), + (torch.float16, 5e-3, 5e-2), + ], +) +def test_amp(B, T, H, V, cast_dtype, atol, rtol): + device = "cuda" + dtype = torch.float32 + torch_lm_head_ce = TorchLMHeadCE( + H=H, + V=V, + bias=True, + label_smoothing=0.0, + reduction="mean", + dtype=dtype, + ).to(device) + liger_lm_head_ce = LigerLMHeadCE( + H=H, + V=V, + bias=True, + label_smoothing=0.0, + reduction="mean", + dtype=dtype, + ).to(device) + + # init the linear in all CEs with the same weights + torch_lm_head_ce.lin.weight.data = liger_lm_head_ce.lin.weight.data = torch.rand( + V, H, device=device, dtype=dtype + ) + + _tensor = torch.randn(B * T, H, device=device, dtype=dtype) + _input1 = _tensor.detach().clone().requires_grad_(True) + _input2 = _tensor.detach().clone().requires_grad_(True) + + target = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + + with torch.autocast(device_type="cuda", dtype=cast_dtype): + output1 = torch_lm_head_ce(_input1, target) + output2 = liger_lm_head_ce(_input2, target) + + assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol) + + with torch.autocast(device_type="cuda", dtype=cast_dtype): + output1.backward() + output2.backward() + + assert_verbose_allclose(_input1.grad, _input2.grad, atol=atol, rtol=rtol) + + assert_verbose_allclose( + torch_lm_head_ce.lin.weight.grad, + liger_lm_head_ce.lin.weight.grad, + atol=atol, + rtol=rtol, + ) diff --git a/test/transformers/test_fused_linear_jsd.py b/test/transformers/test_fused_linear_jsd.py new file mode 100644 index 000000000..31a3ea103 --- /dev/null +++ b/test/transformers/test_fused_linear_jsd.py @@ -0,0 +1,474 @@ +from test.transformers.test_jsd import JSD as TorchJSD +from test.utils import assert_verbose_allclose, set_seed + +import pytest +import torch + +from liger_kernel.ops.fused_linear_jsd import LigerFusedLinearJSDFunction +from liger_kernel.transformers.functional import liger_fused_linear_jsd +from liger_kernel.transformers.fused_linear_jsd import LigerFusedLinearJSD + +set_seed(42) + + +class TorchLMHeadJSD(torch.nn.Module): + """Ground truth implementation of the linear fused with torch based jsd loss. + + :param H: hidden size + :param V: vocab size + :param temperature: softmax temperature + :param beta: jsd beta + """ + + def __init__( + self, + H: int, + V: int, + dtype: torch.dtype, + device: torch.device, + beta: float = 0.5, + ignore_index: int = -100, + temperature: float = 1.0, + ): + super().__init__() + self.student_lin = torch.nn.Linear( + in_features=H // 2, out_features=V, bias=False, dtype=dtype, device=device + ) + self.teacher_lin = torch.nn.Linear( + in_features=H, out_features=V, bias=False, dtype=dtype, device=device + ) + self.jsd = TorchJSD(beta=beta, ignore_index=ignore_index, dtype=dtype) + self.temperature = temperature + + def forward(self, student_input, teacher_input, label=None): + student_logits = self.student_lin(student_input).to(torch.float32) + teacher_logits = self.teacher_lin(teacher_input).to(torch.float32) + student_prob = torch.log_softmax(student_logits / self.temperature, dim=-1) + teacher_prob = torch.log_softmax(teacher_logits / self.temperature, dim=-1) + + return self.jsd(student_prob, teacher_prob, label) + + +class LigerLMHeadJSD(torch.nn.Module): + def __init__( + self, + H: int, + V: int, + dtype: torch.dtype, + device: torch.device, + beta: float = 0.5, + ignore_index: int = -100, + temperature: float = 1.0, + ): + super().__init__() + self.student_lin = torch.nn.Linear( + in_features=H // 2, out_features=V, bias=False, dtype=dtype, device=device + ) + self.teacher_lin = torch.nn.Linear( + in_features=H, out_features=V, bias=False, dtype=dtype, device=device + ) + self.fused_jsd = LigerFusedLinearJSD( + jsd_beta=beta, ignore_index=ignore_index, temperature=temperature + ) + + def forward(self, student_input, teacher_input, label=None): + return self.fused_jsd( + student_input, + self.student_lin.weight, + teacher_input, + self.teacher_lin.weight, + label, + ) + + +############################################################################# +# Test the correctness of the fused linear JSD +############################################################################# + + +@pytest.mark.parametrize( + "B, T, H, V", + [ + (8, 128, 1024, 4096), + (4, 423, 167, 1423), # random shape + ], +) +@pytest.mark.parametrize( + "scalar, dtype, atol, rtol", + [ + (1.0, torch.bfloat16, 5e-3, 5e-2), + (1.0, torch.float32, 1e-5, 5e-4), + ], +) +@pytest.mark.parametrize( + "temperature, beta", + [ + (1.0, 0.5), + (2.0, 0.1), + ], +) +def test_correctness(B, T, H, V, scalar, dtype, beta, temperature, atol, rtol): + device = "cuda" + torch_lm_head_jsd = TorchLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + beta=beta, + ).to(device) + liger_lm_head_jsd = LigerLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + beta=beta, + ).to(device) + + # init the linear in all FusedLinearJSDs with the same weights + torch_lm_head_jsd.student_lin.weight.data = ( + liger_lm_head_jsd.student_lin.weight.data + ) = torch.rand(V, H // 2, device=device, dtype=dtype) + torch_lm_head_jsd.teacher_lin.weight.data = ( + liger_lm_head_jsd.teacher_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + + _tensor = torch.rand(B * T, H // 2, device=device, dtype=dtype) * scalar + _input1 = _tensor.detach().clone().requires_grad_(True) + _input2 = _tensor.detach().clone().requires_grad_(True) + + teacher_input = torch.rand(B * T, H, device=device, dtype=dtype) * scalar + + with torch.autograd.detect_anomaly(): + output1 = torch_lm_head_jsd(_input1, teacher_input) + output2 = liger_lm_head_jsd(_input2, teacher_input) + + assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol) + + output1.backward() + output2.backward() + + assert_verbose_allclose(_input1.grad, _input2.grad, atol=atol, rtol=rtol) + + assert_verbose_allclose( + torch_lm_head_jsd.student_lin.weight.grad, + liger_lm_head_jsd.student_lin.weight.grad, + atol=atol, + rtol=rtol, + ) + + +@pytest.mark.parametrize( + "B, T, H, V", + [ + (8, 128, 1024, 4096), + (4, 423, 167, 1423), # random shape + ], +) +@pytest.mark.parametrize( + "scalar, dtype, atol, rtol", + [ + (1.0, torch.bfloat16, 5e-3, 5e-2), + (1.0, torch.float32, 1e-5, 5e-4), + ], +) +@pytest.mark.parametrize( + "temperature, beta, ignore_index", + [ + (1.0, 0.5, 2), + (2.0, 0.1, 42), + ], +) +def test_correctness_with_ignore_index( + B, T, H, V, scalar, dtype, beta, ignore_index, temperature, atol, rtol +): + device = "cuda" + torch_lm_head_jsd = TorchLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + ignore_index=ignore_index, + beta=beta, + ).to(device) + liger_lm_head_jsd = LigerLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + ignore_index=ignore_index, + beta=beta, + ).to(device) + + # init the linear in all FusedLinearJSDs with the same weights + torch_lm_head_jsd.student_lin.weight.data = ( + liger_lm_head_jsd.student_lin.weight.data + ) = torch.rand(V, H // 2, device=device, dtype=dtype) + torch_lm_head_jsd.teacher_lin.weight.data = ( + liger_lm_head_jsd.teacher_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + + _tensor = torch.rand(B * T, H // 2, device=device, dtype=dtype) * scalar + _input1 = _tensor.detach().clone().requires_grad_(True) + _input2 = _tensor.detach().clone().requires_grad_(True) + + teacher_input = torch.rand(B * T, H, device=device, dtype=dtype) * scalar + + label = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + label[indices_to_assign] = ignore_index + + output1 = torch_lm_head_jsd(_input1, teacher_input, label) + output2 = liger_lm_head_jsd(_input2, teacher_input, label) + + assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol) + + output1.backward() + output2.backward() + + assert_verbose_allclose(_input1.grad, _input2.grad, atol=atol, rtol=rtol) + + assert_verbose_allclose( + torch_lm_head_jsd.student_lin.weight.grad, + liger_lm_head_jsd.student_lin.weight.grad, + atol=atol, + rtol=rtol, + ) + + +@pytest.mark.parametrize( + "B, T, H, V", + [ + (2, 2, 8, 8), + # weird shapes + (9, 7, 41, 41), + ], +) +@pytest.mark.parametrize( + "scalar, dtype, atol, rtol", + [ + (0.5, torch.bfloat16, 5e-3, 5e-2), + (0.5, torch.float32, 1e-5, 5e-4), + ], +) +@pytest.mark.parametrize( + "temperature, beta, ignore_index", [(1.0, 0.5, -100), (2.0, 0.1, 42)] +) +def test_correctness_functional( + B, T, H, V, scalar, dtype, beta, ignore_index, temperature, atol, rtol +): + device = "cuda" + + # init the linear in all FusedLinearJSDs with the same weights + _weight = torch.rand(V, H // 2, device=device, dtype=dtype) + _weight1 = _weight.detach().clone().requires_grad_(True) + _weight2 = _weight.detach().clone().requires_grad_(True) + teacher_weight = torch.rand(V, H, device=device, dtype=dtype) + + _tensor = torch.rand(B * T, H // 2, device=device, dtype=dtype) * scalar + _input1 = _tensor.detach().clone().requires_grad_(True) + _input2 = _tensor.detach().clone().requires_grad_(True) + teacher_input = torch.rand(B * T, H, device=device, dtype=dtype) * scalar + + label = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + label[indices_to_assign] = ignore_index + + output1 = liger_fused_linear_jsd( + _input1, + _weight1, + teacher_input, + teacher_weight, + label, + beta, + ignore_index, + temperature, + ) + output2 = LigerFusedLinearJSDFunction.apply( + _input2, + _weight2, + teacher_input, + teacher_weight, + label, + beta, + ignore_index, + temperature, + ) + + assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol) + + output1.backward() + output2.backward() + + assert_verbose_allclose(_input1.grad, _input2.grad, atol=atol, rtol=rtol) + + assert_verbose_allclose(_weight1.grad, _weight2.grad, atol=atol, rtol=rtol) + + +@pytest.mark.parametrize( + "B, T, H, V", + [ + (8, 128, 1024, 4096), + (4, 423, 167, 1423), # random shape + ], +) +@pytest.mark.parametrize( + "scalar, dtype, atol, rtol", + [ + (1.0, torch.bfloat16, 5e-3, 5e-2), + (1.0, torch.float32, 1e-5, 5e-4), + ], +) +@pytest.mark.parametrize( + "temperature, beta, ignore_index", + [ + (1.0, 0.5, 2), + (2.0, 0.1, 42), + ], +) +def test_correctness_all_ignored( + B, T, H, V, scalar, dtype, beta, ignore_index, temperature, atol, rtol +): + device = "cuda" + torch_lm_head_jsd = TorchLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + ignore_index=ignore_index, + beta=beta, + ).to(device) + liger_lm_head_jsd = LigerLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + ignore_index=ignore_index, + beta=beta, + ).to(device) + + # init the linear in all FusedLinearJSDs with the same weights + torch_lm_head_jsd.student_lin.weight.data = ( + liger_lm_head_jsd.student_lin.weight.data + ) = torch.rand(V, H // 2, device=device, dtype=dtype) + torch_lm_head_jsd.teacher_lin.weight.data = ( + liger_lm_head_jsd.teacher_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + + _tensor = torch.rand(B * T, H // 2, device=device, dtype=dtype) * scalar + _input1 = _tensor.detach().clone().requires_grad_(True) + _input2 = _tensor.detach().clone().requires_grad_(True) + + teacher_input = torch.rand(B * T, H, device=device, dtype=dtype) * scalar + + label = torch.full((B * T,), ignore_index, device=device, dtype=torch.long) + + output1 = torch_lm_head_jsd(_input1, teacher_input, label) + output2 = liger_lm_head_jsd(_input2, teacher_input, label) + + assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol) + assert_verbose_allclose(output2, torch.zeros_like(output2), atol=atol, rtol=rtol) + + output2.backward() + + assert_verbose_allclose( + torch.zeros_like(_input2.grad), _input2.grad, atol=atol, rtol=rtol + ) + + +@pytest.mark.parametrize( + "autocast_dtype, atol, rtol", + [ + (torch.bfloat16, 5e-3, 5e-2), + (torch.float16, 5e-3, 5e-2), + ], +) +def test_amp(autocast_dtype, atol, rtol): + B = 2 + T = 4 + H = 2048 + V = 3200 + scalar = 1.0 + ignore_index = -100 + temperature = 1.0 + beta = 0.5 + device = "cuda" + dtype = torch.float32 + torch_lm_head_jsd = TorchLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + ignore_index=ignore_index, + beta=beta, + ).to(device) + liger_lm_head_jsd = LigerLMHeadJSD( + H=H, + V=V, + dtype=dtype, + device=device, + temperature=temperature, + ignore_index=ignore_index, + beta=beta, + ).to(device) + # init the linear in all FusedLinearJSDs with the same weights + torch_lm_head_jsd.student_lin.weight.data = ( + liger_lm_head_jsd.student_lin.weight.data + ) = torch.rand(V, H // 2, device=device, dtype=dtype) + torch_lm_head_jsd.teacher_lin.weight.data = ( + liger_lm_head_jsd.teacher_lin.weight.data + ) = torch.rand(V, H, device=device, dtype=dtype) + + _tensor = torch.rand(B * T, H // 2, device=device, dtype=autocast_dtype) * scalar + _input1 = _tensor.detach().clone().requires_grad_(True) + _input2 = _tensor.detach().clone().requires_grad_(True) + + teacher_input = torch.rand(B * T, H, device=device, dtype=autocast_dtype) * scalar + + label = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + label[indices_to_assign] = ignore_index + + with torch.autocast(device_type="cuda", dtype=autocast_dtype): + output1 = torch_lm_head_jsd(_input1, teacher_input, label) + output2 = liger_lm_head_jsd(_input2, teacher_input, label) + + assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol) + + output1.backward() + output2.backward() + + assert_verbose_allclose(_input1.grad, _input2.grad, atol=atol, rtol=rtol) + assert_verbose_allclose( + torch_lm_head_jsd.student_lin.weight.grad, + liger_lm_head_jsd.student_lin.weight.grad, + atol=atol, + rtol=rtol, + ) diff --git a/test/transformers/test_geglu.py b/test/transformers/test_geglu.py index 4fa744656..cf7c5a3c5 100644 --- a/test/transformers/test_geglu.py +++ b/test/transformers/test_geglu.py @@ -20,11 +20,9 @@ @pytest.mark.parametrize( "bsz, seq_len, hidden_size, intermediate_size", [ - (2, 2048, 4096, 11008), (2, 2048, 2048, 4096), # weird shapes (9, 41, 341, 4231), - (6, 42, 256, 2048), ], ) @pytest.mark.parametrize( diff --git a/test/transformers/test_jsd.py b/test/transformers/test_jsd.py new file mode 100644 index 000000000..388b3a5c3 --- /dev/null +++ b/test/transformers/test_jsd.py @@ -0,0 +1,329 @@ +from test.utils import assert_verbose_allclose, set_seed, supports_bfloat16 +from typing import Optional + +import pytest +import torch +from torch.nn import KLDivLoss + +from liger_kernel.transformers.functional import liger_jsd +from liger_kernel.transformers.jsd import LigerJSD, LigerJSDFunction + +set_seed(42) + + +class JSD(torch.nn.Module): + def __init__( + self, + beta: float = 0.5, + ignore_index: int = -100, + dtype: torch.dtype = torch.float, + ): + super(JSD, self).__init__() + self.kl = KLDivLoss(reduction="none", log_target=True) + self.beta = beta + self.ignore_index = ignore_index + self.dtype = dtype + + def forward( + self, + log_q: torch.Tensor, # input + log_p: torch.Tensor, # target + label: Optional[torch.Tensor] = None, + ): + log_p, log_q = log_p.to(torch.float), log_q.to(torch.float) + log_p, log_q = log_p.view(-1, log_p.size(-1)), log_q.view(-1, log_q.size(-1)) + m = torch.lerp(torch.exp(log_q), torch.exp(log_p), self.beta) + loss = self.beta * self.kl(torch.log(m), log_p).sum(dim=-1) + ( + 1 - self.beta + ) * self.kl(torch.log(m), log_q).sum(dim=-1) + + if label is not None: + loss = torch.where(label != self.ignore_index, loss, 0.0) + n_non_ignore = (label != self.ignore_index).sum().item() + if n_non_ignore == 0: + loss = torch.tensor(0.0).to(loss.device) + else: + loss = (loss / n_non_ignore).sum() + else: + loss = (loss / log_q.shape[0]).sum() + return loss.to(self.dtype) + + +_SHAPE_PARAMS = ( + "B, T, V", + [ + (2, 1024, 3200), + # weird shape + (41, 401, 1271), + ], +) + +_DTYPE_PARAMS = ( + "dtype, atol, rtol", + [ + pytest.param( + torch.bfloat16, + 1e-8, + 5e-2, + marks=pytest.mark.skipif( + not supports_bfloat16(), reason="bfloat16 not supported on this GPU" + ), + ), + (torch.float32, 1e-8, 1e-6), + (torch.float16, 1e-3, 1e-3), + ], +) + + +def _test_correctness_once( + target_jsd, + B, + T, + V, + dtype, + atol, + rtol, + is_last_layer=True, + device="cuda", +): + torch_jsd = JSD(dtype=dtype) + + input = torch.randn( + B * T, V, device=device, dtype=dtype, requires_grad=True + ).log_softmax(dim=-1) + + x1 = input.detach().clone().requires_grad_(True) + x2 = input.detach().clone().requires_grad_(True) + x3 = input.detach().clone().requires_grad_(True) + + with torch.no_grad(): + target = torch.randn(B * T, V, dtype=dtype, device=device).log_softmax(dim=-1) + + output = torch_jsd(x1, target) + output2 = target_jsd(x2, target) + assert torch.allclose(output, output2, atol=atol, rtol=rtol) + # symmetry + output3 = target_jsd(target, x3) + assert torch.allclose(output3, output2, atol=atol, rtol=rtol) + if ( + not is_last_layer + ): # if the loss is the last layer, grad_output is 1.0 and mul op is skipped, testing for that reason + output = output * 2.0 + output2 = output2 * 2.0 + + output.backward() + output2.backward() + assert_verbose_allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) + + +def _test_correctness_with_beta_once( + target_jsd, + beta, + B, + T, + V, + dtype, + atol, + rtol, + is_last_layer=True, + device="cuda", +): + torch_jsd = JSD(beta=beta, dtype=dtype) + + input = torch.randn( + B * T, V, device=device, dtype=dtype, requires_grad=True + ).log_softmax(dim=-1) + + x1 = input.detach().clone().requires_grad_(True) + x2 = input.detach().clone().requires_grad_(True) + + with torch.no_grad(): + target = torch.randn(B * T, V, dtype=dtype, device=device).log_softmax(dim=-1) + + output = torch_jsd(x1, target) + output2 = target_jsd(x2, target) + assert_verbose_allclose(output, output2, atol=atol, rtol=rtol) + if ( + not is_last_layer + ): # if the loss is the last layer, grad_output is 1.0 and mul op is skipped, testing for that reason + output = output * 2.0 + output2 = output2 * 2.0 + + output.backward() + output2.backward() + assert_verbose_allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) + + +def _test_correctness_with_ignore_index_once( + target_jsd, + ignore_index, + B, + T, + V, + dtype, + atol, + rtol, + device="cuda", +): + torch_jsd = JSD(ignore_index=ignore_index, dtype=dtype) + + input = torch.randn( + B * T, V, device=device, dtype=dtype, requires_grad=True + ).log_softmax(dim=-1) + + x1 = input.detach().clone().requires_grad_(True) + x2 = input.detach().clone().requires_grad_(True) + + with torch.no_grad(): + target = torch.randn(B * T, V, dtype=dtype, device=device).log_softmax(dim=-1) + + label = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + label[indices_to_assign] = ignore_index + + output = torch_jsd(x1, target, label) + output2 = target_jsd(x2, target, label) + assert_verbose_allclose(output, output2, atol=atol, rtol=rtol) + + output.backward() + output2.backward() + assert_verbose_allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) + + +def _test_correctness_functional( + B, T, V, beta, ignore_index, is_last_layer, dtype, atol, rtol, device="cuda" +): + input = torch.randn( + B * T, V, device=device, dtype=dtype, requires_grad=True + ).log_softmax(dim=-1) + + x1 = input.detach().clone().requires_grad_(True) + x2 = input.detach().clone().requires_grad_(True) + + with torch.no_grad(): + target = torch.randn(B * T, V, dtype=dtype, device=device).log_softmax(dim=-1) + + label = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + label[indices_to_assign] = ignore_index + + output = LigerJSDFunction.apply(x1, target, label, beta, ignore_index) + output2 = liger_jsd(x2, target, label, beta, ignore_index) + assert torch.allclose(output, output2, atol=atol, rtol=rtol) + if ( + not is_last_layer + ): # if the loss is the last layer, grad_output is 1.0 and mul op is skipped, testing for that reason + output = output * 2.0 + output2 = output2 * 2.0 + output.backward() + output2.backward() + assert_verbose_allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) + + +@pytest.mark.parametrize(*_SHAPE_PARAMS) +@pytest.mark.parametrize(*_DTYPE_PARAMS) +def test_correctness(B, T, V, dtype, atol, rtol): + liger_jsd = LigerJSD() + _test_correctness_once(liger_jsd, B, T, V, dtype, atol, rtol) + + +@pytest.mark.parametrize(*_SHAPE_PARAMS) +@pytest.mark.parametrize(*_DTYPE_PARAMS) +def test_correctness_not_last(B, T, V, dtype, atol, rtol): + liger_jsd = LigerJSD() + + _test_correctness_once(liger_jsd, B, T, V, dtype, atol, rtol, is_last_layer=False) + + +@pytest.mark.parametrize(*_SHAPE_PARAMS) +@pytest.mark.parametrize(*_DTYPE_PARAMS) +@pytest.mark.parametrize("beta", [0.1, 0.5, 0.9]) +def test_correctness_with_beta(B, T, V, beta, dtype, atol, rtol): + liger_jsd = LigerJSD(beta=beta) + _test_correctness_with_beta_once(liger_jsd, beta, B, T, V, dtype, atol, rtol) + + +@pytest.mark.parametrize(*_SHAPE_PARAMS) +@pytest.mark.parametrize(*_DTYPE_PARAMS) +@pytest.mark.parametrize("ignore_index", [2, 42]) +def test_correctness_with_ignore_index(B, T, V, ignore_index, dtype, atol, rtol): + liger_jsd = LigerJSD(ignore_index=ignore_index) + _test_correctness_with_ignore_index_once( + liger_jsd, ignore_index, B, T, V, dtype, atol, rtol + ) + + +@pytest.mark.parametrize(*_SHAPE_PARAMS) +@pytest.mark.parametrize(*_DTYPE_PARAMS) +@pytest.mark.parametrize( + "beta, ignore_index, is_last_layer", + [ + (0.5, 2, False), + (0.1, 42, True), + ], +) +def test_correctness_functional( + B, T, V, beta, ignore_index, is_last_layer, dtype, atol, rtol +): + _test_correctness_functional( + B, T, V, beta, ignore_index, is_last_layer, dtype, atol, rtol + ) + + +# @pytest.mark.parametrize(*_SHAPE_PARAMS) +def test_correctness_with_all_indices_ignored( + B=2, + T=10, + V=32, + dtype=torch.bfloat16, + atol=1e-3, + rtol=1e-3, + device="cuda", +): + ignore_index = -100 + torch_jsd = JSD(ignore_index=ignore_index, dtype=dtype) + liger_jsd = LigerJSD(ignore_index=ignore_index) + + inp = torch.randn( + B * T, V, device=device, dtype=dtype, requires_grad=True + ).log_softmax(dim=-1) + + x1 = inp.detach().clone().requires_grad_(True) + x2 = inp.detach().clone().requires_grad_(True) + + with torch.no_grad(): + target = torch.randn(B * T, V, dtype=dtype, device=device).log_softmax(dim=-1) + + # label = torch.randint(0, V, (B * T,), device=device, dtype=torch.long) + label = torch.full((B * T,), ignore_index, device=device, dtype=torch.long) + + # Assign some random number of elements as ignore_index + num_elements_to_assign = torch.randint( + 1, B * T // 2, (1,) + ).item() # Random number of elements to set to ignore_index + indices_to_assign = torch.randperm(B * T)[ + :num_elements_to_assign + ] # Randomly select indices + label[indices_to_assign] = ignore_index + + output = torch_jsd(x1, target, label) + output2 = liger_jsd(x2, target, label) + assert_verbose_allclose(output, output2, atol=atol, rtol=rtol) + assert_verbose_allclose(torch.zeros_like(output2), output2, atol=atol, rtol=rtol) + + output2.backward() + assert_verbose_allclose(torch.zeros_like(x2.grad), x2.grad, atol=atol, rtol=rtol) diff --git a/test/transformers/test_kl_div.py b/test/transformers/test_kl_div.py index db29047c7..5cc3eba6a 100644 --- a/test/transformers/test_kl_div.py +++ b/test/transformers/test_kl_div.py @@ -1,4 +1,4 @@ -from test.utils import assert_verbose_allclose, supports_bfloat16 +from test.utils import supports_bfloat16 import pytest import torch @@ -10,20 +10,8 @@ "B, T, V", [ (1, 4096, 32000), - (32, 4096, 1024), # weird shape (41, 401, 1271), - pytest.param( - 1, - 4096, - 128256, - marks=pytest.mark.skipif( - torch.cuda.get_device_properties(0).total_memory - < 36 * 1000 * 1000 * 1000, - reason="This test requires a GPU with at least 36GB of memory", - ), - ), - (3, 423, 32000), ], ) @@ -72,7 +60,7 @@ def _test_correctness_once( output = torch_kldiv(x1, target) output2 = target_kldiv(x2, target) - assert_verbose_allclose(output, output2, atol=atol, rtol=rtol) + assert torch.allclose(output, output2, atol=atol, rtol=rtol) if ( not is_last_layer @@ -85,12 +73,12 @@ def _test_correctness_once( output.backward() output2.backward() - assert_verbose_allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) + assert torch.allclose(x1.grad, x2.grad, atol=atol, rtol=rtol) @pytest.mark.parametrize(*_SHAPE_PARAMS) -@pytest.mark.parametrize("log_target", [False, True]) -@pytest.mark.parametrize("reduction", ["none", "batchmean", "mean", "sum"]) +@pytest.mark.parametrize("log_target", [True, False]) +@pytest.mark.parametrize("reduction", ["batchmean", "sum", "mean", "none"]) @pytest.mark.parametrize(*_DTYPE_PARAMS) def test_correctness(B, T, V, log_target, reduction, dtype, atol, rtol): liger_kldiv = LigerKLDIVLoss(reduction=reduction, log_target=log_target) @@ -100,8 +88,8 @@ def test_correctness(B, T, V, log_target, reduction, dtype, atol, rtol): @pytest.mark.parametrize(*_SHAPE_PARAMS) -@pytest.mark.parametrize("log_target", [False, True]) -@pytest.mark.parametrize("reduction", ["none", "batchmean", "mean", "sum"]) +@pytest.mark.parametrize("log_target", [True, False]) +@pytest.mark.parametrize("reduction", ["batchmean", "sum", "mean", "none"]) @pytest.mark.parametrize(*_DTYPE_PARAMS) def test_correctness_not_last(B, T, V, log_target, reduction, dtype, atol, rtol): liger_kldiv = LigerKLDIVLoss(reduction=reduction, log_target=log_target) diff --git a/test/transformers/test_layer_norm.py b/test/transformers/test_layer_norm.py index 840fd1155..3132c0d50 100644 --- a/test/transformers/test_layer_norm.py +++ b/test/transformers/test_layer_norm.py @@ -7,20 +7,10 @@ @pytest.mark.parametrize( - "hidden_size", + "batch_size, seq_len, hidden_size", [ - 64, - 128, - 256, - 512, - ], -) -@pytest.mark.parametrize( - "batch_size, seq_len", - [ - (2, 8), - (4, 16), - (8, 32), + (2, 8, 64), + (4, 16, 128), ], ) @pytest.mark.parametrize( @@ -33,9 +23,11 @@ def test_liger_layer_norm(batch_size, seq_len, hidden_size, dtype, atol, rtol): torch.manual_seed(0) - x = torch.randn( - batch_size, seq_len, hidden_size, dtype=dtype, device="cuda", requires_grad=True - ) + x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda") + + liger_x = x.clone().requires_grad_(True) + torch_x = x.clone().requires_grad_(True) + liger_ln = LigerLayerNorm(hidden_size, eps=1e-6).to(dtype).cuda() torch_ln = torch.nn.LayerNorm(hidden_size, eps=1e-6).to(dtype).cuda() @@ -43,8 +35,8 @@ def test_liger_layer_norm(batch_size, seq_len, hidden_size, dtype, atol, rtol): torch_ln.weight.copy_(liger_ln.weight) torch_ln.bias.copy_(liger_ln.bias) - liger_output = liger_ln(x) - torch_output = torch_ln(x) + liger_output = liger_ln(liger_x) + torch_output = torch_ln(torch_x) assert torch.allclose(liger_output, torch_output, atol=atol, rtol=rtol) @@ -52,7 +44,7 @@ def test_liger_layer_norm(batch_size, seq_len, hidden_size, dtype, atol, rtol): liger_output.backward(grad_output, retain_graph=True) torch_output.backward(grad_output, retain_graph=True) - assert torch.allclose(x.grad, x.grad, atol=atol, rtol=rtol) + assert torch.allclose(liger_x.grad, torch_x.grad, atol=atol, rtol=rtol) assert torch.allclose( liger_ln.weight.grad, torch_ln.weight.grad, atol=atol, rtol=rtol ) @@ -60,14 +52,10 @@ def test_liger_layer_norm(batch_size, seq_len, hidden_size, dtype, atol, rtol): @pytest.mark.parametrize( - "hidden_size", - [8, 41], -) -@pytest.mark.parametrize( - "batch_size, seq_len", + "batch_size, seq_len, hidden_size", [ - (2, 2), - (9, 7), + (2, 8, 64), + (4, 16, 128), ], ) @pytest.mark.parametrize( diff --git a/test/transformers/test_mm_int8int2.py b/test/transformers/test_mm_int8int2.py new file mode 100644 index 000000000..d7d13a958 --- /dev/null +++ b/test/transformers/test_mm_int8int2.py @@ -0,0 +1,106 @@ +import pytest +import torch + +from liger_kernel.ops.experimental.mm_int8int2 import ( + matmul, + pack_weights, + unpack_weights, +) + + +# input_features = size*4 when the weight matrix is unpacked +@pytest.mark.skip(reason="mm_int8int2 is under experimentation") +@pytest.mark.parametrize( + "size", + [ + 2048, + 1024, + 512, + ], +) +@pytest.mark.parametrize( + "batch_size", + [1, 2, 3, 8], +) +@pytest.mark.parametrize( + "seq_len", + [1, 7, 16, 2048], +) +@pytest.mark.parametrize( + "out_features", + [ + 1024, + 2048, + 4096, + 10000, + ], +) +@pytest.mark.parametrize( + "atol, rtol, device", + [ + (1e-2, 1e-2, "cuda"), + ], +) +def test_kernel_correctness( + batch_size, seq_len, out_features, size, atol, rtol, device +): + print(f"\nTesting kernel with size: {size}, atol: {atol}, rtol: {rtol}") + + # Generate the random tensors + ht = torch.randint( + -127, 127, (batch_size, seq_len, size * 4), device=device, dtype=torch.int8 + ) + u = torch.randint(0, 255, (out_features, size), device=device, dtype=torch.uint8) + + # Calculate dimensions + B, M, N = ht.size() + + # Compute triton output + triton_output = matmul(ht.view(B * M, N), u.T.contiguous()).view(B, M, -1) + + # Unpack weights and compute torch output + unpacked = unpack_weights(u.T, bits=2).T + torch_output = torch.matmul( + ht.to(torch.float32), unpacked.T.contiguous().to(torch.float32) + ) + + # Print the results (optional, can be commented out) + print("triton_output =", triton_output) + print("torch_output =", torch_output) + + # Check if outputs are close within the given tolerances + assert torch.allclose( + triton_output, torch_output.to(torch.int32), atol=atol, rtol=rtol + ), "Results differ" + + +@pytest.mark.skip(reason="mm_int8int2 is under experimentation") +@pytest.mark.parametrize( + "size", + [ + 2048, + 1024, + 512, + ], +) +@pytest.mark.parametrize( + "out_features", + [ + 1024, + 2048, + 4096, + 10000, + ], +) +@pytest.mark.parametrize( + "device", + [ + "cuda", + ], +) +def test_unpack_pack_correctness(out_features, size, device): + u = torch.randint(0, 255, (out_features, size), device=device, dtype=torch.uint8) + + assert ( + pack_weights(unpack_weights(u.T), 2) == u.T + ).all(), "Packed weights do not match original weights." diff --git a/test/transformers/test_monkey_patch.py b/test/transformers/test_monkey_patch.py index bdb6ee11e..c62ea3575 100644 --- a/test/transformers/test_monkey_patch.py +++ b/test/transformers/test_monkey_patch.py @@ -15,6 +15,7 @@ LigerSwiGLUMLP, monkey_patch, ) +from liger_kernel.transformers.layer_norm import LigerLayerNorm from liger_kernel.transformers.monkey_patch import ( MODEL_TYPE_TO_APPLY_LIGER_FN, _apply_liger_kernel, @@ -31,6 +32,7 @@ def test_import_from_root(): apply_liger_kernel_to_llama, apply_liger_kernel_to_mistral, apply_liger_kernel_to_mixtral, + apply_liger_kernel_to_mllama, apply_liger_kernel_to_phi3, apply_liger_kernel_to_qwen2, apply_liger_kernel_to_qwen2_vl, @@ -81,16 +83,90 @@ def dummy_apply_liger_kernal_to_llama( with patch.dict(MODEL_TYPE_TO_APPLY_LIGER_FN, {"llama": mock_llama}): mock_llama.__signature__ = apply_liger_kernal_to_llama_sig - _apply_liger_kernel( - "llama", + ( + _apply_liger_kernel( + "llama", + rope=False, + fused_linear_cross_entropy=False, + cross_entropy=True, + foobar=True, + barbaz=False, + ), + ) + mock_llama.assert_called_once() + mock_llama.assert_called_once_with( rope=False, fused_linear_cross_entropy=False, cross_entropy=True, - foobar=True, - barbaz=False, - ), + ) + + +def test_apply_liger_kernel_to_instance_no_supported_model_type(): + # Test that calling _apply_liger_kernel_to_instance with an unsupported model type is a no-op + mock_mistral = Mock() + mock_unknown_model = MagicMock(spec=PreTrainedModel) + mock_unknown_model.config = {"model_type": "foobar"} + + with patch.dict(MODEL_TYPE_TO_APPLY_LIGER_FN, {"mistral": mock_mistral}): + _apply_liger_kernel_to_instance(model=mock_unknown_model) + MODEL_TYPE_TO_APPLY_LIGER_FN["mistral"].assert_not_called() + + +def test_apply_liger_kernel_to_instance_only_supported_model_type_called(): + # Test that liger kernel is applied only to the specified model + mock_gemma = Mock() + mock_llama = Mock() + mock_mistral = Mock() + + mock_llama_model_instance = MagicMock(spec=PreTrainedModel) + mock_llama_model_instance.config = MagicMock(spec=PretrainedConfig) + mock_llama_model_instance.config.model_type = "llama" + + with patch.dict( + MODEL_TYPE_TO_APPLY_LIGER_FN, + {"gemma": mock_gemma, "llama": mock_llama, "mistral": mock_mistral}, + ): + _apply_liger_kernel_to_instance(model=mock_llama_model_instance) + mock_llama.assert_called_once() + mock_gemma.assert_not_called() + mock_mistral.assert_not_called() + + +def test_apply_liger_kernel_to_instance_only_passes_valid_kwargs(): + # Test that keyword args that are not valid for the apply_liger_* function are not passed + mock_llama = Mock() + + mock_llama_model_instance = MagicMock(spec=PreTrainedModel) + mock_llama_model_instance.config = MagicMock(spec=PretrainedConfig) + mock_llama_model_instance.config.model_type = "llama" + + def dummy_apply_liger_kernel_to_llama( + rope=False, + cross_entropy=False, + fused_linear_cross_entropy=True, + rms_norm=True, + swiglu=True, + model=None, + ): + pass + + apply_liger_kernel_to_llama_sig = signature(dummy_apply_liger_kernel_to_llama) + + with patch.dict(MODEL_TYPE_TO_APPLY_LIGER_FN, {"llama": mock_llama}): + mock_llama.__signature__ = apply_liger_kernel_to_llama_sig + ( + _apply_liger_kernel_to_instance( + model=mock_llama_model_instance, + rope=False, + fused_linear_cross_entropy=False, + cross_entropy=True, + foobar=True, + barbaz=False, + ), + ) mock_llama.assert_called_once() mock_llama.assert_called_once_with( + model=mock_llama_model_instance, rope=False, fused_linear_cross_entropy=False, cross_entropy=True, @@ -199,7 +275,6 @@ def test_patching_apis_support_patching_model_instance(): def test_apply_liger_kernel_to_instance_for_llama(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.llama.modeling_llama"): - # Instantiate a dummy model config = transformers.models.llama.configuration_llama.LlamaConfig( torch_dtype=torch.bfloat16, @@ -211,28 +286,213 @@ def test_apply_liger_kernel_to_instance_for_llama(): ) dummy_model_instance = AutoModelForCausalLM.from_config(config) + # Check that model instance variables are not yet patched with Liger modules + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + for layer in dummy_model_instance.model.layers: + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + + # Test applying kernels to the model instance + _apply_liger_kernel_to_instance(model=dummy_model_instance) + + # Check that the model's instance variables were correctly patched with Liger modules + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + for layer in dummy_model_instance.model.layers: + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + + +def test_apply_liger_kernel_to_instance_for_mllama_for_conditional_generation(): + # Ensure any monkey patching is cleaned up for subsequent tests + with patch("transformers.models.mllama.modeling_mllama"): + from transformers.models.mllama.modeling_mllama import ( + MllamaForConditionalGeneration, + ) + + # Instantiate a dummy model + config = transformers.models.mllama.configuration_mllama.MllamaConfig( + torch_dtype=torch.bfloat16, + text_config=transformers.models.mllama.configuration_mllama.MllamaTextConfig( + rms_norm_eps=1e-5, + hidden_size=32, + intermediate_size=64, + hidden_act="silu", + num_hidden_layers=2, + rope_scaling=dict( + factor=8.0, + high_freq_factor=4.0, + low_freq_factor=1.0, + original_max_position_embeddings=8192, + rope_type="llama3", + ), + ), + vision_config=transformers.models.mllama.configuration_mllama.MllamaVisionConfig( + rms_norm_eps=1e-5, + hidden_size=32, + intermediate_size=64, + hidden_act="gelu", + num_hidden_layers=2, + vision_output_dim=64, + ), + ) + dummy_model_instance = MllamaForConditionalGeneration._from_config(config) + + assert isinstance(dummy_model_instance, MllamaForConditionalGeneration) + + # Check that model instance variables are not yet patched with Liger modules + assert inspect.getsource( + dummy_model_instance.language_model.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + for layer in dummy_model_instance.language_model.model.layers: + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + + assert inspect.getsource( + dummy_model_instance.vision_model.layernorm_pre.forward + ) != inspect.getsource(LigerLayerNorm.forward) + assert inspect.getsource( + dummy_model_instance.vision_model.layernorm_post.forward + ) != inspect.getsource(LigerLayerNorm.forward) + for layer in dummy_model_instance.vision_model.transformer.layers: + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerLayerNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerLayerNorm.forward) + for layer in dummy_model_instance.vision_model.global_transformer.layers: + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerLayerNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerLayerNorm.forward) + + # Test applying kernels to the model instance + _apply_liger_kernel_to_instance(model=dummy_model_instance) + + # Check that the model's instance variables were correctly patched with Liger modules + assert inspect.getsource( + dummy_model_instance.language_model.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + for layer in dummy_model_instance.language_model.model.layers: + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + + assert inspect.getsource( + dummy_model_instance.vision_model.layernorm_pre.forward + ) == inspect.getsource(LigerLayerNorm.forward) + assert inspect.getsource( + dummy_model_instance.vision_model.layernorm_post.forward + ) == inspect.getsource(LigerLayerNorm.forward) + for layer in dummy_model_instance.vision_model.transformer.layers: + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerLayerNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerLayerNorm.forward) + for layer in dummy_model_instance.vision_model.global_transformer.layers: + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerLayerNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerLayerNorm.forward) + + +def test_apply_liger_kernel_to_instance_for_mllama_for_causal_lm(): + # Ensure any monkey patching is cleaned up for subsequent tests + with patch("transformers.models.mllama.modeling_mllama"): + from transformers.models.mllama.modeling_mllama import MllamaForCausalLM + + # Instantiate a dummy model + config = transformers.models.mllama.configuration_mllama.MllamaTextConfig( + rms_norm_eps=1e-5, + hidden_size=32, + intermediate_size=64, + hidden_act="silu", + num_hidden_layers=2, + rope_scaling=dict( + factor=8.0, + high_freq_factor=4.0, + low_freq_factor=1.0, + original_max_position_embeddings=8192, + rope_type="llama3", + ), + ) + + dummy_model_instance = MllamaForCausalLM._from_config(config) + + assert isinstance(dummy_model_instance, MllamaForCausalLM) + # Check that model instance variables are not yet patched with Liger modules assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) for layer in dummy_model_instance.model.layers: - assert not isinstance(layer.mlp, LigerSwiGLUMLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert isinstance(layer.mlp, LigerSwiGLUMLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) def test_apply_liger_kernel_to_instance_for_mistral(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.mistral.modeling_mistral"): - # Instantiate a dummy model config = transformers.models.mistral.configuration_mistral.MistralConfig( torch_dtype=torch.bfloat16, @@ -245,27 +505,42 @@ def test_apply_liger_kernel_to_instance_for_mistral(): dummy_model_instance = AutoModelForCausalLM.from_config(config) # Check that model instance variables are not yet patched with Liger modules - assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert not isinstance(layer.mlp, LigerSwiGLUMLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert isinstance(layer.mlp, LigerSwiGLUMLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) def test_apply_liger_kernel_to_instance_for_mixtral(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.mixtral.modeling_mixtral"): - # Instantiate a dummy model config = transformers.models.mixtral.configuration_mixtral.MixtralConfig( torch_dtype=torch.bfloat16, @@ -280,29 +555,44 @@ def test_apply_liger_kernel_to_instance_for_mixtral(): dummy_model_instance = AutoModelForCausalLM.from_config(config) # Check that model instance variables are not yet patched with Liger modules - assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: for expert in layer.block_sparse_moe.experts: - assert not isinstance(expert, LigerBlockSparseTop2MLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(expert.forward) != inspect.getsource( + LigerBlockSparseTop2MLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: for expert in layer.block_sparse_moe.experts: - assert isinstance(expert, LigerBlockSparseTop2MLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(expert.forward) == inspect.getsource( + LigerBlockSparseTop2MLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) def test_apply_liger_kernel_to_instance_for_gemma(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.gemma.modeling_gemma"): - # Instantiate a dummy model config = transformers.models.gemma.configuration_gemma.GemmaConfig( torch_dtype=torch.bfloat16, @@ -315,27 +605,42 @@ def test_apply_liger_kernel_to_instance_for_gemma(): dummy_model_instance = AutoModelForCausalLM.from_config(config) # Check that model instance variables are not yet patched with Liger modules - assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert not isinstance(layer.mlp, LigerGEGLUMLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerGEGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert isinstance(layer.mlp, LigerGEGLUMLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerGEGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) def test_apply_liger_kernel_to_instance_for_gemma2(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.gemma2.modeling_gemma2"): - # Instantiate a dummy model config = transformers.models.gemma2.configuration_gemma2.Gemma2Config( torch_dtype=torch.bfloat16, @@ -348,31 +653,54 @@ def test_apply_liger_kernel_to_instance_for_gemma2(): dummy_model_instance = AutoModelForCausalLM.from_config(config) # Check that model instance variables are not yet patched with Liger modules - assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert not isinstance(layer.mlp, LigerGEGLUMLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) - assert not isinstance(layer.pre_feedforward_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_feedforward_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerGEGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.pre_feedforward_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_feedforward_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert isinstance(layer.mlp, LigerGEGLUMLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) - assert isinstance(layer.pre_feedforward_layernorm, LigerRMSNorm) - assert isinstance(layer.post_feedforward_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerGEGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.pre_feedforward_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_feedforward_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) def test_apply_liger_kernel_to_instance_for_qwen2(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.qwen2.modeling_qwen2"): - # Instantiate a dummy model config = transformers.models.qwen2.configuration_qwen2.Qwen2Config( torch_dtype=torch.bfloat16, @@ -385,27 +713,120 @@ def test_apply_liger_kernel_to_instance_for_qwen2(): dummy_model_instance = AutoModelForCausalLM.from_config(config) # Check that model instance variables are not yet patched with Liger modules - assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + for layer in dummy_model_instance.model.layers: + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + + # Test applying kernels to the model instance + _apply_liger_kernel_to_instance(model=dummy_model_instance) + + # Check that the model's instance variables were correctly patched with Liger modules + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert not isinstance(layer.mlp, LigerSwiGLUMLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + + +def test_apply_liger_kernel_to_instance_for_qwen2_vl(): + # Ensure any monkey patching is cleaned up for subsequent tests + with patch("transformers.models.qwen2_vl.modeling_qwen2_vl"): + from transformers.models.qwen2_vl.modeling_qwen2_vl import ( + Qwen2VLForConditionalGeneration, + ) + + # Instantiate a dummy model + config = transformers.models.qwen2_vl.configuration_qwen2_vl.Qwen2VLConfig( + torch_dtype=torch.bfloat16, + rms_norm_eps=1e-5, + hidden_size=32, + intermediate_size=48, + embed_dim=16, + hidden_act="silu", + num_hidden_layers=2, + num_attention_heads=2, + max_position_embeddings=128, + vocab_size=1000, + vision_config={ + "depth": 4, + "embed_dim": 128, + "num_heads": 8, + "hidden_size": 1024, + }, + ) + dummy_model_instance = Qwen2VLForConditionalGeneration._from_config(config) + + assert isinstance(dummy_model_instance, Qwen2VLForConditionalGeneration) + + # Check that model instance variables are not yet patched with Liger modules + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + for layer in dummy_model_instance.model.layers: + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + for vision_block in dummy_model_instance.visual.blocks: + assert inspect.getsource(vision_block.norm1.forward) != inspect.getsource( + LigerLayerNorm.forward + ) + assert inspect.getsource(vision_block.norm2.forward) != inspect.getsource( + LigerLayerNorm.forward + ) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert isinstance(layer.mlp, LigerSwiGLUMLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerSwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + for vision_block in dummy_model_instance.visual.blocks: + assert inspect.getsource(vision_block.norm1.forward) == inspect.getsource( + LigerLayerNorm.forward + ) + assert inspect.getsource(vision_block.norm2.forward) == inspect.getsource( + LigerLayerNorm.forward + ) def test_apply_liger_kernel_to_instance_for_phi3(): # Ensure any monkey patching is cleaned up for subsequent tests with patch("transformers.models.phi3.modeling_phi3"): - # Instantiate a dummy model config = transformers.models.phi3.configuration_phi3.Phi3Config( torch_dtype=torch.bfloat16, @@ -418,18 +839,34 @@ def test_apply_liger_kernel_to_instance_for_phi3(): dummy_model_instance = AutoModelForCausalLM.from_config(config) # Check that model instance variables are not yet patched with Liger modules - assert not isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) != inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert not isinstance(layer.mlp, LigerPhi3SwiGLUMLP) - assert not isinstance(layer.input_layernorm, LigerRMSNorm) - assert not isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) != inspect.getsource( + LigerPhi3SwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) != inspect.getsource(LigerRMSNorm.forward) # Test applying kernels to the model instance _apply_liger_kernel_to_instance(model=dummy_model_instance) # Check that the model's instance variables were correctly patched with Liger modules - assert isinstance(dummy_model_instance.model.norm, LigerRMSNorm) + assert inspect.getsource( + dummy_model_instance.model.norm.forward + ) == inspect.getsource(LigerRMSNorm.forward) for layer in dummy_model_instance.model.layers: - assert isinstance(layer.mlp, LigerPhi3SwiGLUMLP) - assert isinstance(layer.input_layernorm, LigerRMSNorm) - assert isinstance(layer.post_attention_layernorm, LigerRMSNorm) + assert inspect.getsource(layer.mlp.forward) == inspect.getsource( + LigerPhi3SwiGLUMLP.forward + ) + assert inspect.getsource( + layer.input_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) + assert inspect.getsource( + layer.post_attention_layernorm.forward + ) == inspect.getsource(LigerRMSNorm.forward) diff --git a/test/transformers/test_rms_norm.py b/test/transformers/test_rms_norm.py index d9e823e6d..1dd2299b8 100644 --- a/test/transformers/test_rms_norm.py +++ b/test/transformers/test_rms_norm.py @@ -1,5 +1,5 @@ import os -from test.utils import assert_verbose_allclose, supports_bfloat16 +from test.utils import assert_verbose_allclose, set_seed, supports_bfloat16 import pytest import torch @@ -9,6 +9,7 @@ from liger_kernel.transformers.functional import liger_rms_norm from liger_kernel.transformers.rms_norm import LigerRMSNorm +set_seed(42) torch.use_deterministic_algorithms(True) # Only setting torch.use_deterministic_algorithms(True) might throw the following error: @@ -73,14 +74,8 @@ def forward(self, x): "bs, sl, hd", [ (2, 128, 512), - (4, 256, 1024), - (8, 512, 2048), - (16, 1024, 4096), - # # weird shapes - (3, 423, 213), + # weird shapes (5, 123, 123), - (7, 341, 234), - (9, 236, 345), ], ) @pytest.mark.parametrize( @@ -95,7 +90,6 @@ def forward(self, x): not supports_bfloat16(), reason="bfloat16 not supported on this GPU" ), ), - (torch.float16, 2e-1, 2e-2), ], ) @pytest.mark.parametrize( @@ -107,9 +101,6 @@ def forward(self, x): ], ) def test_correctness(bs, sl, hd, dtype, atol, rtol, reference, offset, casting_mode): - if reference == BaseRMSNorm and dtype == torch.bfloat16: - pytest.skip("bfloat16 has larger errors for BaseRMSNorm") - _tensor = torch.randn(bs, sl, hd, device="cuda", dtype=dtype) h1 = _tensor.clone().requires_grad_(True) @@ -121,7 +112,7 @@ def test_correctness(bs, sl, hd, dtype, atol, rtol, reference, offset, casting_m # reference (llama or gemma) ref_rms = reference(hidden_size=hd).to("cuda").to(dtype) ref_o = ref_rms(h1) - ref_o.backward(do.clone(), retain_graph=True) + ref_o.backward(do, retain_graph=True) # triton triton_rms = ( @@ -130,20 +121,22 @@ def test_correctness(bs, sl, hd, dtype, atol, rtol, reference, offset, casting_m .to(dtype) ) triton_o = triton_rms(h2) - triton_o.backward(do.clone(), retain_graph=True) + triton_o.backward(do, retain_graph=True) assert_verbose_allclose(ref_o, triton_o, atol=atol, rtol=rtol) assert_verbose_allclose( ref_rms.weight.grad, triton_rms.weight.grad, atol=atol, rtol=rtol ) - assert_verbose_allclose(h1.grad, h2.grad, atol=atol, rtol=rtol) + print(f"{h1.grad=}") + print(f"{h2.grad=}") + assert_verbose_allclose(h1.grad, h2.grad, atol=atol, rtol=rtol, max_print=20) @pytest.mark.parametrize( "bs, sl, hd", [ (2, 2, 8), - # # weird shapes + # weird shapes (9, 7, 41), ], ) @@ -152,7 +145,6 @@ def test_correctness(bs, sl, hd, dtype, atol, rtol, reference, offset, casting_m [ (torch.float32, 1e-4, 1e-6), (torch.bfloat16, 2e-1, 2e-2), - (torch.float16, 2e-1, 2e-2), ], ) @pytest.mark.parametrize( diff --git a/test/transformers/test_swiglu.py b/test/transformers/test_swiglu.py index ccb395c98..be7aaef42 100644 --- a/test/transformers/test_swiglu.py +++ b/test/transformers/test_swiglu.py @@ -27,11 +27,9 @@ @pytest.mark.parametrize( "bsz, seq_len, hidden_size, intermediate_size", [ - (2, 2048, 4096, 11008), - (2, 2048, 2048, 4096), + (2, 256, 256, 512), # weird shapes - (9, 41, 341, 4231), - (6, 42, 256, 2048), + (6, 42, 123, 431), ], ) @pytest.mark.parametrize( @@ -109,11 +107,9 @@ def test_correctness_llamamlp( @pytest.mark.parametrize( "bsz, seq_len, hidden_size, intermediate_size", [ - (2, 2048, 4096, 11008), - (2, 2048, 2048, 4096), + (2, 256, 256, 512), # weird shapes - (9, 41, 341, 4231), - (6, 42, 256, 2048), + (6, 42, 123, 431), ], ) @pytest.mark.parametrize( diff --git a/test/utils.py b/test/utils.py index 748d84e64..ac9a13190 100644 --- a/test/utils.py +++ b/test/utils.py @@ -1,10 +1,15 @@ import importlib +import json import os import random from dataclasses import dataclass from typing import Any, Dict, List import torch +from tokenizers import AddedToken, Tokenizer +from tokenizers.models import BPE +from tokenizers.pre_tokenizers import Whitespace +from tokenizers.trainers import BpeTrainer from transformers import PretrainedConfig, PreTrainedModel from transformers.tokenization_utils_base import BatchEncoding @@ -55,10 +60,27 @@ def assert_verbose_allclose(tensor1, tensor2, rtol=1e-05, atol=1e-08, max_print= # Determine the tolerance tolerance = atol + rtol * torch.abs(tensor2) - # Find mismatched elements - mismatched = diff > tolerance + # Find tolerance mismatched elements + tol_mismatched = diff > tolerance + + # Find nan mismatched elements + nan_mismatched = torch.logical_xor(torch.isnan(tensor1), torch.isnan(tensor2)) + + # Find +inf mismatched elements + posinf_mismatched = torch.logical_xor( + torch.isposinf(tensor1), torch.isposinf(tensor2) + ) + # Find -inf mismatched elements + neginf_mismatched = torch.logical_xor( + torch.isneginf(tensor1), torch.isneginf(tensor2) + ) + + # Find all mismatched elements + mismatched = torch.logical_or( + torch.logical_or(tol_mismatched, nan_mismatched), + torch.logical_or(posinf_mismatched, neginf_mismatched), + ) - # Get the indices of mismatched elements mismatched_indices = torch.nonzero(mismatched) # Count the number of mismatched elements @@ -68,7 +90,7 @@ def assert_verbose_allclose(tensor1, tensor2, rtol=1e-05, atol=1e-08, max_print= all_close = num_mismatched == 0 # Raise AssertionError with detailed information if there are mismatches - if not all_close and num_mismatched > 1: + if not all_close and num_mismatched >= 1: mismatch_details = [f"Number of mismatched elements: {num_mismatched}"] print_count = min(max_print, num_mismatched) for index in mismatched_indices[:print_count]: @@ -93,6 +115,10 @@ def assert_verbose_allclose(tensor1, tensor2, rtol=1e-05, atol=1e-08, max_print= os.path.dirname(os.path.abspath(__file__)), "resources/tiny_shakespeare.txt" ) +FAKE_CONFIGS_PATH = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "resources/fake_configs" +) + @dataclass class MiniModelConfig: @@ -139,6 +165,41 @@ def multimodal_collate_fn(data: List[Dict[str, Any]]): return BatchEncoding(batch) +def load_tokenizer_config(config_path: str) -> dict: + """Load and process tokenizer configuration from a JSON file.""" + with open(config_path) as reader: + tokenizer_config = json.load(reader) + tokenizer_config["added_tokens_decoder"] = { + k: AddedToken(**v) for k, v in tokenizer_config["added_tokens_decoder"].items() + } + return tokenizer_config + + +def train_bpe_tokenizer(special_tokens: List[str], unk_token: str = "<|unk|>"): + """ + Train a tokenizer using the BPE algorithm. + + Parameters: + unk_token (str): The token to use for unknown tokens. + special_tokens (List[str]): A list of special tokens to use. + + Returns: + Tokenizer: The trained tokenizer. + """ + # Add unk_token to special_tokens if not already present + if unk_token not in special_tokens: + special_tokens.append(unk_token) + + tokenizer = Tokenizer(BPE(unk_token=unk_token)) + trainer = BpeTrainer(special_tokens=special_tokens) + + tokenizer.pre_tokenizer = Whitespace() + file = [UNTOKENIZED_DATASET_PATH] + tokenizer.train(file, trainer) + + return tokenizer + + def supports_bfloat16(): if not torch.cuda.is_available(): return False @@ -156,6 +217,19 @@ def revert_liger_kernel_to_llama(): print("Liger kernel patches have been reverted.") +def revert_liger_kernel_to_mllama(): + """ + Revert all Liger kernel patches applied to MLlama. + """ + + import torch.nn as nn + from transformers.models.mllama import modeling_mllama + + importlib.reload(nn) + importlib.reload(modeling_mllama) + print("Liger kernel patches have been reverted.") + + def revert_liger_kernel_to_mistral(): """ Revert all Liger kernel patches applied to Mistral.