From 8f360e3f19742635477830def2920eb020438ed6 Mon Sep 17 00:00:00 2001 From: Jinman Xie Date: Wed, 10 Jun 2026 03:58:31 -0700 Subject: [PATCH 1/5] perf(liger/cutile): tune cross_entropy BLOCK_SIZE by GPU arch --- src/tilegym/suites/liger/cutile/cross_entropy.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/tilegym/suites/liger/cutile/cross_entropy.py b/src/tilegym/suites/liger/cutile/cross_entropy.py index 5785287e..f919a2ff 100644 --- a/src/tilegym/suites/liger/cutile/cross_entropy.py +++ b/src/tilegym/suites/liger/cutile/cross_entropy.py @@ -23,8 +23,10 @@ LOG2E = 1.4426950408889634 -def _select_block_size(vocab_size: int) -> int: - return min(4096, min(MAX_FUSED_SIZE, next_power_of_2(vocab_size))) +def _select_block_size(vocab_size: int, device=None) -> int: + cap = torch.cuda.get_device_capability(device) + max_block = 4096 if cap[0] <= 10 else 8192 + return min(max_block, min(MAX_FUSED_SIZE, next_power_of_2(vocab_size))) @ct.kernel(occupancy=4) @@ -321,7 +323,7 @@ def forward( input_requires_grad = _input.requires_grad num_rows, vocab_size = _input.shape - BLOCK_SIZE = _select_block_size(vocab_size) + BLOCK_SIZE = _select_block_size(vocab_size, _input.device) loss_1d = torch.zeros(num_rows, dtype=_input.dtype, device=_input.device) z_loss_1d = torch.zeros(num_rows, dtype=_input.dtype, device=_input.device) if return_z_loss else None From f3fb7f87436415521dd18ed6d50d12c6a07887c3 Mon Sep 17 00:00:00 2001 From: Rundong Li Date: Fri, 12 Jun 2026 02:13:47 -0700 Subject: [PATCH 2/5] Modularize transformer HF benchmark Signed-off-by: Rundong Li --- modeling/transformers/Dockerfile | 127 +- modeling/transformers/Dockerfile.dockerignore | 165 + modeling/transformers/README.md | 288 +- modeling/transformers/bench_deepseek.sh | 71 - modeling/transformers/bench_gemma3.sh | 71 - modeling/transformers/bench_gpt_oss.sh | 71 - modeling/transformers/bench_llama.sh | 71 - modeling/transformers/bench_mistral.sh | 71 - modeling/transformers/bench_olmo3.sh | 74 - modeling/transformers/bench_olmoe.sh | 74 - modeling/transformers/bench_phi3.sh | 71 - modeling/transformers/bench_qwen.sh | 76 - modeling/transformers/bench_qwen3_5.sh | 80 - modeling/transformers/build_docker.sh | 47 - modeling/transformers/infer.py | 871 +--- modeling/transformers/pyproject.toml | 67 + .../scripts/benchmark_hf_model.sh | 239 + .../src/tilegym_hf_bench/__init__.py | 5 + .../src/tilegym_hf_bench/__main__.py | 8 + .../transformers/src/tilegym_hf_bench/_cli.py | 236 + .../src/tilegym_hf_bench/forward.py | 40 + .../src/tilegym_hf_bench/hf_shim.py | 97 + .../kernel_filters/__init__.py | 7 + .../kernel_filters/filters.py | 28 + .../tilegym_kernel_prefixes.yaml | 50 + .../tilegym_hf_bench/profiling/__init__.py | 3 + .../src/tilegym_hf_bench/profiling/nsys.py | 236 + .../profiling/torch_profiler.py | 128 + .../src/tilegym_hf_bench/tilegym_patch.py | 46 + modeling/transformers/uv.lock | 3983 +++++++++++++++++ .../references/auto-kernelize.md | 19 +- .../references/kernel-integration.md | 12 +- .../transformers/qwen3_5/modeling_qwen3_5.py | 38 +- 33 files changed, 5454 insertions(+), 2016 deletions(-) create mode 100644 modeling/transformers/Dockerfile.dockerignore delete mode 100755 modeling/transformers/bench_deepseek.sh delete mode 100644 modeling/transformers/bench_gemma3.sh delete mode 100644 modeling/transformers/bench_gpt_oss.sh delete mode 100755 modeling/transformers/bench_llama.sh delete mode 100755 modeling/transformers/bench_mistral.sh delete mode 100755 modeling/transformers/bench_olmo3.sh delete mode 100644 modeling/transformers/bench_olmoe.sh delete mode 100755 modeling/transformers/bench_phi3.sh delete mode 100755 modeling/transformers/bench_qwen.sh delete mode 100755 modeling/transformers/bench_qwen3_5.sh delete mode 100755 modeling/transformers/build_docker.sh create mode 100644 modeling/transformers/pyproject.toml create mode 100755 modeling/transformers/scripts/benchmark_hf_model.sh create mode 100644 modeling/transformers/src/tilegym_hf_bench/__init__.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/__main__.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/_cli.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/forward.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/hf_shim.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/kernel_filters/__init__.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/kernel_filters/filters.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml create mode 100644 modeling/transformers/src/tilegym_hf_bench/profiling/__init__.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/profiling/nsys.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/profiling/torch_profiler.py create mode 100644 modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py create mode 100644 modeling/transformers/uv.lock diff --git a/modeling/transformers/Dockerfile b/modeling/transformers/Dockerfile index 32397061..388e4bfd 100644 --- a/modeling/transformers/Dockerfile +++ b/modeling/transformers/Dockerfile @@ -2,122 +2,49 @@ # # SPDX-License-Identifier: MIT -# TileGym Transformers Inference Container -# Multi-stage build supporting both wheel (CI) and source (development) installs -# -# Usage: -# CI (wheel): docker build --target wheel -f modeling/transformers/Dockerfile . -# Dev (source): docker build --target source -f modeling/transformers/Dockerfile . -# Default: docker build -f modeling/transformers/Dockerfile . (uses source) - +# Keep the runtime image at CUDA 13.2 for host-driver compatibility. +# The uv environment installs CUDA 13.3 compiler/user-space wheels from pyproject.toml. ARG BASE_IMAGE=nvcr.io/nvidia/cuda:13.2.0-devel-ubuntu22.04 -#################### -# Stage 1: Base dependencies -#################### FROM ${BASE_IMAGE} AS base -ARG TORCH_VERSION=2.9.1 - ENV PYTHONUNBUFFERED=1 -# Install system dependencies and NSight Systems CLI. -RUN \ - apt-get update && \ - apt-get install -y --no-install-recommends gnupg && \ - echo "deb https://developer.download.nvidia.com/devtools/repos/ubuntu2204/amd64/ /" | tee /etc/apt/sources.list.d/nvidia-devtools.list && \ - apt-key adv --fetch-keys https://developer.download.nvidia.com/devtools/repos/ubuntu2204/amd64/nvidia.pub && \ - apt-get update && \ +RUN apt-get update && \ apt-get install -y --no-install-recommends \ - python3-pip \ - python3-dev \ - python-is-python3 \ - git \ - wget \ - curl \ - build-essential \ - nsight-systems-cli && \ - rm -rf /var/lib/apt/lists/* - -# Upgrade pip -RUN python -m pip install --upgrade pip setuptools wheel - -# Install PyTorch with CUDA 13.0 support -RUN pip install --no-cache-dir --pre "torch==${TORCH_VERSION}" --index-url https://download.pytorch.org/whl/cu130 - -# Install additional dependencies not covered by requirements.txt -RUN pip install --no-cache-dir --no-deps accelerate==1.13.0 && \ - pip install --no-cache-dir sentencepiece protobuf - -WORKDIR /workspace/tilegym + ca-certificates \ + curl \ + git \ + gnupg \ + python-is-python3 \ + python3 \ + python3-dev \ + python3-pip && \ + curl -fsSL https://developer.download.nvidia.com/devtools/repos/ubuntu2204/amd64/nvidia.pub | \ + gpg --dearmor -o /usr/share/keyrings/nvidia-devtools.gpg && \ + echo "deb [signed-by=/usr/share/keyrings/nvidia-devtools.gpg] https://developer.download.nvidia.com/devtools/repos/ubuntu2204/amd64/ /" > \ + /etc/apt/sources.list.d/nvidia-devtools.list && \ + apt-get update && \ + apt-get install -y --no-install-recommends nsight-systems-cli && \ + python -m pip install --no-cache-dir uv && \ + apt-get purge -y --auto-remove curl gnupg && \ + rm -rf /var/lib/apt/lists/* /root/.cache/pip -#################### -# Stage 2: Wheel install (for CI) -#################### FROM base AS wheel -# Install runtime dependencies from requirements.txt -COPY requirements.txt /tmp/requirements.txt -RUN pip install --no-cache-dir -r /tmp/requirements.txt && rm -f /tmp/requirements.txt - -# Copy and install pre-built wheel -COPY wheel/*.whl /tmp/ -RUN pip install --no-cache-dir --no-deps /tmp/*.whl && \ - rm -f /tmp/*.whl - -# Copy modeling scripts for inference -# Tests are mounted at runtime in CI -COPY modeling/ /workspace/tilegym/modeling/ -COPY README.md LICENSE /workspace/tilegym/ - -# Set up model cache directory ENV HF_HOME=/workspace/.cache/huggingface \ TILEGYM_MODEL_CACHE_DIR=/workspace/.cache -# Create necessary directories -RUN mkdir -p /workspace/.cache/huggingface/hub \ - /logs \ - /workspace/tilegym/modeling/transformers - -# Set working directory to transformers modeling -WORKDIR /workspace/tilegym/modeling/transformers +WORKDIR /workspace/tilegym-hf-bench -# Expose port for potential web services -EXPOSE 8000 +COPY modeling/transformers/ /workspace/tilegym-hf-bench/ -# Default command -CMD ["/bin/bash"] +RUN uv sync --locked && \ + mkdir -p /workspace/.cache/huggingface/hub /logs -#################### -# Stage 3: Source install (for open source developers) -# This is the default when no --target is specified -#################### -FROM base AS source +FROM wheel AS source -# Copy entire source tree COPY . /workspace/tilegym/ -# Verify repository structure -RUN test -f setup.py || (echo "ERROR: setup.py not found! Please build from tilegym repository root." && exit 1) - -# Install runtime dependencies from requirements.txt, then TileGym in editable mode with dev extras -RUN pip install --no-cache-dir -r requirements.txt && \ - pip install --no-cache-dir -e ".[dev]" - -# Set up model cache directory -ENV HF_HOME=/workspace/.cache/huggingface \ - TILEGYM_MODEL_CACHE_DIR=/workspace/.cache - -# Create necessary directories -RUN mkdir -p /workspace/.cache/huggingface/hub \ - /logs \ - /workspace/tilegym/modeling/transformers - -# Set working directory to transformers modeling -WORKDIR /workspace/tilegym/modeling/transformers - -# Expose port for potential web services -EXPOSE 8000 - -# Default command -CMD ["/bin/bash"] +RUN uv pip uninstall --python /workspace/tilegym-hf-bench/.venv/bin/python tilegym && \ + uv pip install --python /workspace/tilegym-hf-bench/.venv/bin/python --no-deps -e /workspace/tilegym diff --git a/modeling/transformers/Dockerfile.dockerignore b/modeling/transformers/Dockerfile.dockerignore new file mode 100644 index 00000000..2d42931c --- /dev/null +++ b/modeling/transformers/Dockerfile.dockerignore @@ -0,0 +1,165 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +# Dockerfile-specific ignore file for modeling/transformers/Dockerfile. +# Keep hf-bench Docker context small without changing other image builds. + +# Sandbox / scratch +sandbox/ +sandbox/** +**/sandbox/ + +# Git +.git/ +.git/** +**/.git/ +.gitignore +.gitattributes +.gitmodules + +# OS / editor noise +**/.DS_Store +.idea/ +.vscode/ +.cursorignore +.claude/ +.claude/settings.local.json + +# Byte-compiled / optimized / DLL files +**/__pycache__/ +*.py[cod] +*$py.class +*.so + +# Distribution / packaging +.Python +**/build/ +**/develop-eggs/ +**/dist/ +**/downloads/ +**/eggs/ +**/.eggs/ +**/lib/ +**/lib64/ +**/parts/ +**/sdist/ +**/var/ +**/wheels/ +**/share/python-wheels/ +**/*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +**/htmlcov/ +.cache +.cache/ +.cache/** +**/.cache/** +.pytest_cache/ +**/.pytest_cache/ +.ruff_cache/ +**/.ruff_cache/ +.mypy_cache/ +**/.mypy_cache/ +.tox/ +**/.tox/ +.nox/ +**/.nox/ +.coverage +.coverage.* +nosetests.xml +coverage.xml +*.cover +*.py,cover +**/.hypothesis/ +**/cover/ + +# Translations +*.mo +*.pot + +# Logs +*.log + +# Generated / local artifacts +src/tilegym/_cutile_kernels/ +examples/model/ +local_settings.py +*.local +*.local.* +db.sqlite3 +db.sqlite3-journal +prebuild_tools/ +instance/ +.webassets-cache +.scrapy +docs/_build/ +.pybuilder/ +target/ +**/.ipynb_checkpoints/ +profile_default/ +ipython_config.py +celerybeat-schedule +celerybeat.pid +*.sage.py +.ropeproject +/site +.dmypy.json +dmypy.json +.pyre/ +.pytype/ +cython_debug/ +**/dump/ +**/logs/ +**/bench_results/ +**/temp/ +**/cuda-driver/ + +# Profiling / benchmarking artifacts +**/*.nsys-rep +**/*.sqlite +**/*.err +**/*.ptx +**/*.csv +**/*.ncu-rep + +# Python environments +.env +.env.* +.venv +.venv/ +.venv/** +**/.venv/ +env/ +**/env/ +venv/ +**/venv/ +ENV/ +**/ENV/ +env.bak/ +**/env.bak/ +venv.bak/ +**/venv.bak/ +__pypackages__/ + +# Node +**/node_modules/ + +# Docker files +.dockerignore +Dockerfile* + +# hf-bench image installs TileGym from Python source and does not need to +# vendor FlashInfer's full checked-out submodule into the image context. +3rdparty/flashinfer/ diff --git a/modeling/transformers/README.md b/modeling/transformers/README.md index ef4594ee..d9466f64 100644 --- a/modeling/transformers/README.md +++ b/modeling/transformers/README.md @@ -1,84 +1,51 @@ - + -# TileGym Transformers Inference +# TileGym HF Bench -End-to-end inference examples for transformer language models accelerated with TileGym kernels. Optimized for NVIDIA Blackwell Architecture. +`modeling/transformers` is a uv-compatible subproject for Hugging Face inference benchmarks, PyTorch profiler traces, and nsys cuTile kernel coverage reports. -## Supported Models +The distribution name is `tilegym-hf-bench`; the Python package is `tilegym_hf_bench`. -| Model | Model ID | Features | -|-------|----------|----------| -| LLaMA-3.1-8B | `meta-llama/Meta-Llama-3.1-8B` | RoPE, SwiGLU, RMSNorm, Attention*, Flash Decoding* | -| DeepSeek-V2-Lite-Chat | `deepseek-ai/DeepSeek-V2-Lite-Chat` | RoPE, SwiGLU, RMSNorm, MoE, MLADecoding*, Attention* | -| Qwen2-7B | `Qwen/Qwen2-7B` | RoPE, SwiGLU, RMSNorm, Attention* | -| Gemma-3-4B-IT | `google/gemma-3-4b-it` | RoPE, GEGLU, RMSNorm, Attention* | -| GPT-OSS | `openai/gpt-oss-20b` | RoPE, RMSNorm, Attention Sink* | -| Mistral-7B-Instruct-v0.3 | `mistralai/Mistral-7B-Instruct-v0.3` | RoPE, SwiGLU, RMSNorm, Attention* | -| Phi-3-mini-4k-instruct | `microsoft/Phi-3-mini-4k-instruct` | RoPE, SwiGLU, RMSNorm, Attention* | - -*Optional: Enable with `--use_attn`, we can use attention provided in TileGym - -B200 can support both models. Due to memory constraints, RTX 5090 GPUs only support LLaMA-3.1-8B models. DeepSeek-V2-Lite-Chat requires higher memory capacity. - -## Docker Support +## Setup ```bash -# Option 1: Use the build script cd modeling/transformers -./build_docker.sh +uv sync --locked +``` -# Option 2: Build manually (must run from tilegym repository root) -cd /path/to/tilegym -docker build -t tilegym-transformers -f modeling/transformers/Dockerfile . +The legacy command remains available: -# Enter interactive mode -docker run --gpus all -it tilegym-transformers bash - -# Or run inference directly -docker run --gpus all -it tilegym-transformers \ - python infer.py --model_id deepseek-ai/DeepSeek-V2-Lite-Chat --use_tilegym --use_cutile --use_attn --show_outputs +```bash +python infer.py --help ``` -## Quick Start - -### Basic Inference +The preferred CLI is: ```bash -# Transformer baseline -python infer.py --model_id meta-llama/Meta-Llama-3.1-8B --show_outputs - -# With CUTILE backend -python infer.py --model_id meta-llama/Meta-Llama-3.1-8B --use_tilegym --use_cutile --use_attn --show_outputs +uv run tilegym-hf-bench --help ``` -### Using Custom Inputs +## Basic Inference ```bash -# From file -python infer.py \ +uv run tilegym-hf-bench \ --model_id meta-llama/Meta-Llama-3.1-8B \ - --use_tilegym \ - --use_attn \ - --sentence_file sample_inputs/input_prompt_32K.txt \ - --output_length 100 + --show_outputs -# From command line -python infer.py \ +uv run tilegym-hf-bench \ --model_id meta-llama/Meta-Llama-3.1-8B \ --use_tilegym \ --use_cutile \ --use_attn \ - --input_text "Explain machine learning" \ --show_outputs ``` -### Performance Profiling +## Profiling -Will provide results using Torch Profiler. ```bash -python infer.py \ +uv run tilegym-hf-bench \ --model_id meta-llama/Meta-Llama-3.1-8B \ --sentence_file sample_inputs/input_prompt_32K.txt \ --use_tilegym \ @@ -88,12 +55,12 @@ python infer.py \ --num_runs 5 ``` -### Kernel Coverage Report +## Kernel Coverage -Report the fraction of GPU time and kernel launches covered by TileGym cuTile kernels. Runs the model under NSight Systems (`nsys profile`) and analyzes the trace automatically. +`--report_kernel_coverage` runs the model under `nsys profile` and reports the fraction of GPU time and launches attributed to TileGym/cuTile kernels. ```bash -python infer.py \ +uv run tilegym-hf-bench \ --model_id meta-llama/Meta-Llama-3.1-8B \ --use_tilegym \ --use_cutile \ @@ -103,208 +70,39 @@ python infer.py \ --output_length 100 ``` -Example output: -```text -===== NSYS KERNEL GPU TIME ANALYSIS ===== - -Kernel Name # Calls GPU Time (ms) % of Total ------------------------------------------------------------- -------- ------------- ---------- -fmha_kernel ... 54.507 10.5% -rms_norm_kernel_gather ... 9.788 1.9% -... ------------------------------------------------------------- -------- ------------- ---------- -TileGym Total 9676 95.147 18.3% -All Kernels Total 104858 520.725 100.0% - ->>> cuTile Kernel Coverage (GPU Time): 18.3% <<< ->>> cuTile Kernel Coverage (# Launches): 9.2% <<< -``` - -## Performance Benchmark - -Benchmark TileGym's CUTILE-optimized kernels against standard PyTorch implementation. The `--profile` flag enables detailed performance metrics including throughput (tokens/sec) and generation latency. - -### Quick Start - -Run benchmark scripts for automated comparison: - -```bash -# LLaMA-3.1-8B benchmark -./bench_llama.sh - -# DeepSeek-V2-Lite benchmark -./bench_deepseek.sh +Kernel-name matching is configured in `src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml`. -# Qwen2-7B benchmark -./bench_qwen.sh - -# Gemma-3-4B-IT benchmark -./bench_gemma3.sh - -# GPT-OSS benchmark -./bench_gpt_oss.sh - -# Mistral-7B benchmark -./bench_mistral.sh - -# Phi-3-mini-4k-instruct benchmark -./bench_phi3.sh -``` - -### Manual Benchmark - -#### LLaMA-3.1-8B Benchmark - -```bash -# PyTorch baseline -python infer.py \ - --model_id meta-llama/Meta-Llama-3.1-8B \ - --profile \ - --sentence_file sample_inputs/input_prompt_32K.txt \ - --output_length 100 - -# TileGym CUTILE backend -python infer.py \ - --model_id meta-llama/Meta-Llama-3.1-8B \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file sample_inputs/input_prompt_32K.txt \ - --output_length 100 -``` - -#### DeepSeek-V2-Lite Benchmark - -```bash -# PyTorch baseline -python infer.py \ - --model_id deepseek-ai/DeepSeek-V2-Lite-Chat \ - --profile \ - --sentence_file sample_inputs/input_prompt_small.txt \ - --output_length 100 +## Benchmark Script -# TileGym CUTILE backend -python infer.py \ - --model_id deepseek-ai/DeepSeek-V2-Lite-Chat \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file sample_inputs/input_prompt_small.txt \ - --output_length 100 -``` +Use one consolidated script for model presets: -#### Qwen2-7B Benchmark ```bash -# PyTorch baseline -python infer.py \ - --model_id Qwen/Qwen2-7B \ - --profile \ - --sentence_file sample_inputs/input_prompt_small.txt \ - --batch_size 16 \ - --output_length 100 - -# TileGym CUTILE backend -python infer.py \ - --model_id Qwen/Qwen2-7B \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file sample_inputs/input_prompt_small.txt \ - --batch_size 16 \ - --output_length 100 +./scripts/benchmark_hf_model.sh --model-key llama +./scripts/benchmark_hf_model.sh --model-key deepseek +./scripts/benchmark_hf_model.sh --model-key qwen +./scripts/benchmark_hf_model.sh --model-key qwen3_5 +./scripts/benchmark_hf_model.sh --model-key gemma3 +./scripts/benchmark_hf_model.sh --model-key gpt_oss +./scripts/benchmark_hf_model.sh --model-key mistral +./scripts/benchmark_hf_model.sh --model-key phi3 ``` -#### Gemma-3-4B-IT Benchmark -```bash -# PyTorch baseline -python infer.py \ - --model_id google/gemma-3-4b-it \ - --profile \ - --sentence_file sample_inputs/input_prompt_small.txt \ - --output_length 100 - -# TileGym CUTILE backend -python infer.py \ - --model_id google/gemma-3-4b-it \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file sample_inputs/input_prompt_small.txt \ - --output_length 100 -``` +Useful overrides: -#### Mistral-7B Benchmark ```bash -# PyTorch baseline -python infer.py \ - --model_id mistralai/Mistral-7B-Instruct-v0.3 \ - --profile \ - --sentence_file sample_inputs/input_prompt_32K.txt \ - --output_length 100 - -# TileGym CUTILE backend -python infer.py \ - --model_id mistralai/Mistral-7B-Instruct-v0.3 \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file sample_inputs/input_prompt_32K.txt \ - --output_length 100 +./scripts/benchmark_hf_model.sh \ + --model-key qwen3_5 \ + --output-length 16 \ + --batch-size 1 \ + --log-dir /logs/qwen3_5 ``` -## Command Line Options - -| Option | Description | Default | -|--------|-------------|---------| -| `--model_id` | HuggingFace model ID or local path | `meta-llama/Meta-Llama-3.1-8B` | -| `--use_tilegym` | Enable TileGym optimization | `False` | -| `--use_cutile` | Use CUTILE backend | `False` | -| `--use_attn` | Enable attention optimization | `False` | -| `--input_text` | Input prompt text | - | -| `--sentence_file` | Input file path | - | -| `--output_length` | Number of tokens to generate | `100` | -| `--batch_size` | Batch size | `1` | -| `--precision` | `bfloat16` or `float32` | `bfloat16` | -| `--num_runs` | Benchmark iterations | `5` | -| `--warmup_runs` | Warmup iterations | `2` | -| `--profile` | Enable profiling | `False` | -| `--report_kernel_coverage` | Report cuTile kernel GPU time and launch count coverage via nsys | `False` | -| `--show_outputs` | Print generated text | `False` | - - -## Using Local Models - -You can use locally cached models by specifying the path directly: +## Docker ```bash -# Use local model path -python infer.py \ - --model_id /path/to/local/model \ - --use_tilegym \ - --use_attn \ - --use_cutile \ - --show_outputs +cd /path/to/tilegym +docker build -t tilegym-transformers -f modeling/transformers/Dockerfile . +docker run --gpus all -it tilegym-transformers bash ``` -## Troubleshooting - -**CUDA Out of Memory** -- Reduce `--batch_size` or use smaller inputs -- Use `--precision bfloat16` - -**Model Download Issues** -- Set up [HuggingFace authentication](https://huggingface.co/docs/huggingface_hub/quick-start#authentication) -- Ensure your account has access to the model (e.g., [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)) -- Check network connectivity - -**Slow Performance** -- Disable `--use_tilegym` flag to see whether naive version has output -- Sometimes, it may take more than one minute to get the output when your output sentence is too long. Try to use shorter input and reduce `--output_length` - -**Import Errors** -- Install TileGym: `pip install -e .` (from repo root) +The Docker image installs this subproject through uv and keeps `tilegym-hf-bench` on `PATH`. diff --git a/modeling/transformers/bench_deepseek.sh b/modeling/transformers/bench_deepseek.sh deleted file mode 100755 index c86e38dc..00000000 --- a/modeling/transformers/bench_deepseek.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for DeepSeek-V2-Lite-Chat model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="deepseek-ai/DeepSeek-V2-Lite-Chat" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=100 -SUMMARY_FILE="deepseek_benchmark_summary.txt" - -echo "========================================" -echo " DeepSeek-V2-Lite Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_gemma3.sh b/modeling/transformers/bench_gemma3.sh deleted file mode 100644 index 688e51ef..00000000 --- a/modeling/transformers/bench_gemma3.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for Gemma3 model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="google/gemma-3-4b-it" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=100 -SUMMARY_FILE="gemma3_benchmark_summary.txt" - -echo "========================================" -echo " Gemma3 Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_gpt_oss.sh b/modeling/transformers/bench_gpt_oss.sh deleted file mode 100644 index 798bcee4..00000000 --- a/modeling/transformers/bench_gpt_oss.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for GPT-OSS model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="openai/gpt-oss-20b" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=100 -SUMMARY_FILE="gpt_oss_benchmark_summary.txt" - -echo "========================================" -echo " GPT-OSS Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_llama.sh b/modeling/transformers/bench_llama.sh deleted file mode 100755 index 4704184f..00000000 --- a/modeling/transformers/bench_llama.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for LLaMA-3.1-8B model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="meta-llama/Meta-Llama-3.1-8B" -INPUT_FILE="sample_inputs/input_prompt_32K.txt" -OUTPUT_LENGTH=50 -SUMMARY_FILE="llama_benchmark_summary.txt" - -echo "========================================" -echo " LLaMA-3.1-8B Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_mistral.sh b/modeling/transformers/bench_mistral.sh deleted file mode 100755 index 5f274ab7..00000000 --- a/modeling/transformers/bench_mistral.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for Mistral-7B-Instruct-v0.3 model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="mistralai/Mistral-7B-Instruct-v0.3" -INPUT_FILE="sample_inputs/input_prompt_32K.txt" -OUTPUT_LENGTH=50 -SUMMARY_FILE="mistral_benchmark_summary.txt" - -echo "========================================" -echo " Mistral-7B Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym cuTile backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_olmo3.sh b/modeling/transformers/bench_olmo3.sh deleted file mode 100755 index 266d63a3..00000000 --- a/modeling/transformers/bench_olmo3.sh +++ /dev/null @@ -1,74 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for OLMo-3-1025-7B model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="allenai/Olmo-3-1025-7B" -INPUT_FILE="sample_inputs/input_prompt_32K.txt" -OUTPUT_LENGTH=50 -LOG_DIR="${LOG_DIR:-/logs}" -SUMMARY_FILE="${LOG_DIR}/olmo3_benchmark_summary.txt" - -echo "========================================" -echo " OLMo-3-1025-7B Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --log_dir ${LOG_DIR} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --log_dir ${LOG_DIR} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --log_dir ${LOG_DIR} -echo "========================================" diff --git a/modeling/transformers/bench_olmoe.sh b/modeling/transformers/bench_olmoe.sh deleted file mode 100644 index f3d73aab..00000000 --- a/modeling/transformers/bench_olmoe.sh +++ /dev/null @@ -1,74 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for OLMoE-1B-7B-0924 (sparse MoE) model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="allenai/OLMoE-1B-7B-0924" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=50 -LOG_DIR="${LOG_DIR:-/logs}" -SUMMARY_FILE="${LOG_DIR}/olmoe_benchmark_summary.txt" - -echo "========================================" -echo " OLMoE-1B-7B-0924 Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --log_dir ${LOG_DIR} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --log_dir ${LOG_DIR} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --log_dir ${LOG_DIR} -echo "========================================" diff --git a/modeling/transformers/bench_phi3.sh b/modeling/transformers/bench_phi3.sh deleted file mode 100755 index 186bb754..00000000 --- a/modeling/transformers/bench_phi3.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for Phi-3-mini-4k-instruct model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="microsoft/Phi-3-mini-4k-instruct" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=50 -SUMMARY_FILE="phi3_benchmark_summary.txt" - -echo "========================================" -echo " Phi-3-mini-4k-instruct Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_qwen.sh b/modeling/transformers/bench_qwen.sh deleted file mode 100755 index b90ef6b8..00000000 --- a/modeling/transformers/bench_qwen.sh +++ /dev/null @@ -1,76 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for Qwen2-7B model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="Qwen/Qwen2-7B" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=50 -SUMMARY_FILE="qwen_benchmark_summary.txt" -BATCH_SIZE=16 - -echo "========================================" -echo " Qwen2-7B Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "Batch size: ${BATCH_SIZE}" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --batch_size ${BATCH_SIZE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --sentence_file ${INPUT_FILE} \ - --batch_size ${BATCH_SIZE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --sentence_file ${INPUT_FILE} \ - --batch_size ${BATCH_SIZE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/bench_qwen3_5.sh b/modeling/transformers/bench_qwen3_5.sh deleted file mode 100755 index aa82417b..00000000 --- a/modeling/transformers/bench_qwen3_5.sh +++ /dev/null @@ -1,80 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: MIT - -# Benchmark script for Qwen3.5-0.8B model -# Compares PyTorch baseline vs TileGym CUTILE backend - -set -e - -MODEL_ID="Qwen/Qwen3.5-0.8B" -INPUT_FILE="sample_inputs/input_prompt_small.txt" -OUTPUT_LENGTH=50 -SUMMARY_FILE="qwen3_5_benchmark_summary.txt" -BATCH_SIZE=1 -LOG_DIR="${LOG_DIR:-${TMPDIR:-/tmp}/tilegym_bench}" - -echo "========================================" -echo " Qwen3.5-0.8B Performance Benchmark" -echo "========================================" -echo "" -echo "Model: ${MODEL_ID}" -echo "Input: ${INPUT_FILE}" -echo "Output length: ${OUTPUT_LENGTH} tokens" -echo "Batch size: ${BATCH_SIZE}" -echo "" - -# Clean previous results -rm -f ${SUMMARY_FILE} - -echo "Running PyTorch baseline..." -python infer.py \ - --model_id ${MODEL_ID} \ - --profile \ - --log_dir ${LOG_DIR} \ - --sentence_file ${INPUT_FILE} \ - --batch_size ${BATCH_SIZE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "Running TileGym CUTILE backend..." -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --profile \ - --log_dir ${LOG_DIR} \ - --sentence_file ${INPUT_FILE} \ - --batch_size ${BATCH_SIZE} \ - --output_length ${OUTPUT_LENGTH} \ - --summary_file ${SUMMARY_FILE} - -echo "" -echo "========================================" -echo " Benchmark Results" -echo "========================================" -if [ -f ${SUMMARY_FILE} ]; then - cat ${SUMMARY_FILE} - rm -f ${SUMMARY_FILE} -else - echo "Summary file not found." -fi -echo "========================================" - -echo "" -echo "========================================" -echo " TileGym Kernel Coverage" -echo "========================================" -python infer.py \ - --model_id ${MODEL_ID} \ - --use_tilegym \ - --use_cutile \ - --use_attn \ - --report_kernel_coverage \ - --log_dir ${LOG_DIR} \ - --sentence_file ${INPUT_FILE} \ - --batch_size ${BATCH_SIZE} \ - --output_length ${OUTPUT_LENGTH} -echo "========================================" diff --git a/modeling/transformers/build_docker.sh b/modeling/transformers/build_docker.sh deleted file mode 100755 index 3d6238f7..00000000 --- a/modeling/transformers/build_docker.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# SPDX-License-Identifier: MIT - -# Helper script to build TileGym Transformers Docker container from any location - -set -e - -# Colors for output -GREEN='\033[0;32m' -RED='\033[0;31m' -YELLOW='\033[1;33m' -NC='\033[0m' # No Color - -# Find tilegym repository root -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -TILEGYM_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)" - -echo -e "${GREEN}TileGym Transformers Docker Build${NC}" -echo "================================" -echo "" - -# Verify we're in the right place -if [ ! -f "${TILEGYM_ROOT}/setup.py" ]; then - echo -e "${RED}ERROR: Cannot find setup.py at ${TILEGYM_ROOT}${NC}" - echo "Please ensure this script is in modeling/transformers/ directory" - exit 1 -fi - -echo -e "${GREEN}✓${NC} Found TileGym repository at: ${TILEGYM_ROOT}" -echo "" - -# Build the container -echo -e "${YELLOW}Building Docker container...${NC}" -cd "${TILEGYM_ROOT}" -docker build -t tilegym-transformers -f modeling/transformers/Dockerfile . - -echo "" -echo -e "${GREEN}✓ Build successful!${NC}" -echo "" -echo "Enter interactive mode:" -echo " docker run --gpus all -it tilegym-transformers bash" -echo "" -echo "Or run inference directly:" -echo " docker run --gpus all -it tilegym-transformers \\" -echo " python infer.py --model_id deepseek-ai/DeepSeek-V2-Lite-Chat --use_tilegym --use_cutile --show_outputs" diff --git a/modeling/transformers/infer.py b/modeling/transformers/infer.py index 2eeeef14..920681bf 100644 --- a/modeling/transformers/infer.py +++ b/modeling/transformers/infer.py @@ -1,874 +1,17 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # SPDX-License-Identifier: MIT -import argparse -import datetime -import glob -import os -import shlex -import sqlite3 -import subprocess +"""Backward-compatible shim for the TileGym HF benchmark CLI.""" + import sys -import zipfile -from collections import defaultdict from pathlib import Path -import numpy as np -import torch -from torch.profiler import ProfilerActivity -from torch.profiler import profile -from torch.profiler import record_function -from transformers import AutoModelForCausalLM -from transformers import AutoTokenizer - -from tilegym.transformers import apply_tilegym_kernel_to_deepseek_v2 -from tilegym.transformers import apply_tilegym_kernel_to_gemma3 -from tilegym.transformers import apply_tilegym_kernel_to_gpt_oss -from tilegym.transformers import apply_tilegym_kernel_to_llama -from tilegym.transformers import apply_tilegym_kernel_to_mistral -from tilegym.transformers import apply_tilegym_kernel_to_olmo3 -from tilegym.transformers import apply_tilegym_kernel_to_olmoe -from tilegym.transformers import apply_tilegym_kernel_to_phi3 -from tilegym.transformers import apply_tilegym_kernel_to_qwen2 -from tilegym.transformers import apply_tilegym_kernel_to_qwen3 - - -def check_and_setup_model_cache(model_id): - """ - Check if model exists in local cache, set up cache directories if needed. - Returns the effective cache directory for the model. - """ - # Get cache directory from environment or use default - cache_base = os.environ.get("TILEGYM_MODEL_CACHE_DIR", os.path.expanduser("~/.cache")) - - # Set up HuggingFace cache directories - hf_home = os.path.join(cache_base, "huggingface") - hf_hub = os.path.join(hf_home, "hub") - - # Set environment variables for transformers - os.environ.setdefault("HF_HOME", hf_home) - os.environ.setdefault("HF_HUB_CACHE", hf_hub) - - # Create cache directories if they don't exist - Path(hf_hub).mkdir(parents=True, exist_ok=True) - - return str(cache_base) - - -def _check_cache_complete(model_id): - """Check if cache directory exists with snapshots. Actual completeness verified during load.""" - hf_home = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/huggingface")) - hf_hub = os.path.join(hf_home, "hub") - model_cache_path = Path(hf_hub) / f"models--{model_id.replace('/', '--')}" - snapshots_dir = model_cache_path / "snapshots" - - # Check if snapshots directory exists and has content - return snapshots_dir.exists() and any(snapshots_dir.iterdir()) - - -def _load_with_fallback(model_id, loader_class, resource_name, **kwargs): - """ - Generic loader with automatic fallback from local cache to download. - Supports both HuggingFace model IDs and local filesystem paths. - """ - # Check if model_id is a local path - model_path = Path(model_id) - if model_path.exists() and model_path.is_dir(): - print(f"Loading {resource_name} from local path: {model_id}") - try: - result = loader_class.from_pretrained(model_id, **kwargs) - print(f"Successfully loaded {resource_name} from local path") - return result - except Exception as e: - print(f"Error loading {resource_name} from local path: {e}") - raise - - # Otherwise, use HuggingFace Hub logic - check_and_setup_model_cache(model_id) - print(f"Loading {resource_name} {model_id}...") - - # Try local cache first if available - if _check_cache_complete(model_id): - print("Found cached files, attempting to use local cache") - try: - kwargs_local = kwargs.copy() - kwargs_local["local_files_only"] = True - result = loader_class.from_pretrained(model_id, **kwargs_local) - print(f"Successfully loaded {resource_name} from local cache") - return result - except Exception: - print("Failed to load from local cache, will download from HuggingFace") - - # Fallback to download - try: - result = loader_class.from_pretrained(model_id, **kwargs) - print(f"Successfully loaded {resource_name}") - return result - except Exception as e: - print(f"Error loading {resource_name}: {e}") - raise - - -def load_model_with_cache(model_id, **kwargs): - """Load model with cache checking. Downloads if not available locally.""" - return _load_with_fallback(model_id, AutoModelForCausalLM, "model", **kwargs) - - -def _fix_tokenizer_decoder_if_needed(tokenizer, model_id): - """Fix tokenizer decoder for models whose tokenizer.json uses ByteLevel BPE. - - In transformers 5.3.0, LlamaTokenizer.convert_to_native_format() extracts vocab/merges - from tokenizer.json but hardcodes a ByteFallback decoder, discarding the original decoder. - For models like DeepSeek-V2 that use ByteLevel BPE (GPT-2 style), this causes garbled - output for non-ASCII characters (e.g., Chinese text appears as corrupted Latin-1). - - Fix: when tokenizer.json specifies ByteLevel decoder, replace the backend decoder with - the correct one loaded directly from the file. - """ - import json - from pathlib import Path - - if not hasattr(tokenizer, "_tokenizer"): - return - - tokenizer_json_path = Path(model_id) / "tokenizer.json" - if not tokenizer_json_path.exists(): - return - - with open(tokenizer_json_path) as f: - decoder_config = json.load(f).get("decoder", {}) - - if decoder_config.get("type") != "ByteLevel": - return - - # Current decoder is ByteFallback but tokenizer.json requires ByteLevel. - # Load the correct decoder from the serialized tokenizer and apply it. - import tokenizers as hf_tokenizers - - fast_tok = hf_tokenizers.Tokenizer.from_file(str(tokenizer_json_path)) - tokenizer._tokenizer.decoder = fast_tok.decoder - print(f"Fixed tokenizer decoder: replaced ByteFallback with ByteLevel (from tokenizer.json)") - - -def load_tokenizer_with_cache(model_id, **kwargs): - """Load tokenizer with cache checking.""" - tokenizer = _load_with_fallback(model_id, AutoTokenizer, "tokenizer", **kwargs) - _fix_tokenizer_decoder_if_needed(tokenizer, model_id) - return tokenizer - - -class NaiveForwardWrapper: - def __init__( - self, - model, - tokenizer, - messages_list, - args, - device="cuda", - **default_kwargs, - ): - self.model = model - self.default_kwargs = default_kwargs - self.tokenizer = tokenizer - self.tokenized_inputs = tokenizer(messages_list, return_tensors="pt").to(device) - self.input_seq_len = self.tokenized_inputs.input_ids.shape[1] - - self.default_kwargs.update( - { - "input_ids": self.tokenized_inputs.input_ids, - "attention_mask": self.tokenized_inputs.attention_mask, - "max_new_tokens": args.output_length, - "min_new_tokens": args.output_length, # Force to generate exactly output_length tokens - } - ) - - def get_input_seq_len(self): - return self.input_seq_len - - def update_params(self, **kwargs): - self.default_kwargs.update(kwargs) - - def forward(self): - return self.model.generate(**self.default_kwargs) - - def post_process(self, outputs): - return outputs - - -def parse_args(): - parser = argparse.ArgumentParser(description="Benchmark LLM inference") - parser.add_argument("--use_tilegym", action="store_true", help="Use tilegym kernel") - parser.add_argument("--model_id", type=str, default="meta-llama/Meta-Llama-3.1-8B", help="Model ID to load") - parser.add_argument("--batch_size", type=int, default=1, help="Batch size for inference") - parser.add_argument( - "--input_text", - type=str, - default="What is the capital of France?", - help="Input text for generation", - ) - parser.add_argument("--num_runs", type=int, default=5, help="Number of runs for averaging performance") - parser.add_argument("--warmup_runs", type=int, default=2, help="Number of warmup runs") - parser.add_argument("--show_outputs", action="store_true", help="Show full model outputs") - parser.add_argument("--summary_file", type=str, default=None, help="File to append summary lines") - # use-attn: True or False - parser.add_argument("--use_attn", action="store_true", help="Use attention") - # use-cutile: True or False - parser.add_argument("--use_cutile", action="store_true", help="Use cutile") - # profile: True or False - parser.add_argument("--profile", action="store_true", help="Profile the model") - parser.add_argument( - "--log_dir", - type=str, - default="/logs", - help="Directory to save profiler logs (default: /logs)", - ) - # precision: bfloat16 or float32 - parser.add_argument("--precision", type=str, default="bfloat16", help="Precision") - parser.add_argument("--sentence_file", type=str, default=None) - # output length - parser.add_argument("--output_length", type=int, default=100, help="Output length") - # Only used in benchmark - # If mock_input_len > 0, then the input length will be set to mock_input_len, this is used to mock the input length - # If you use this, you may not get the correct answer of your sentence - parser.add_argument("--mock_input_len", type=int, default=0, help="Mock input length") - parser.add_argument( - "--report_kernel_coverage", - action="store_true", - help="Run under nsys profiler and report cuTile kernel coverage (GPU time and launch count ratios)", - ) - return parser.parse_args() - - -def get_messages_list(args): - messages_list = [] - if args.mock_input_len > 0: - line = " Hello" * (args.mock_input_len - 1) # Because `<|begin_of_text|>` token will be added by the tokenizer - for _ in range(args.batch_size): - messages_list.append(line) - elif args.sentence_file is not None: - with open(args.sentence_file, "r") as f: - lines = [line.strip() for line in f.readlines()] - for _ in range(args.batch_size): - messages_list.append("\n".join(lines)) - else: - for _ in range(args.batch_size): - messages_list.append(args.input_text) - print(messages_list) - return messages_list - - -def apply_tilegym_patch(model_id, use_attn=False, use_cutile=False): - model_name = model_id.lower() - if "llama" in model_name: - apply_tilegym_kernel_to_llama(rope=True, swiglu=True, rms_norm=True, attn=use_attn, use_cutile=use_cutile) - elif "deepseek" in model_name: - apply_tilegym_kernel_to_deepseek_v2( - rope=True, rms_norm=True, swiglu=True, attn=use_attn, moe=True, use_cutile=use_cutile - ) - elif "gpt-oss" in model_name: - apply_tilegym_kernel_to_gpt_oss(rope=True, rms_norm=True, swiglu=False, attn=use_attn, use_cutile=use_cutile) - elif "mistral" in model_name: - apply_tilegym_kernel_to_mistral(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) - elif "qwen3.5" in model_name or "qwen3_5" in model_name: - apply_tilegym_kernel_to_qwen3( - rope=True, rms_norm=True, swiglu=True, attn=use_attn, gated_delta_rule=True, use_cutile=use_cutile - ) - elif "qwen" in model_name: - apply_tilegym_kernel_to_qwen2(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) - elif "gemma" in model_name: - apply_tilegym_kernel_to_gemma3(rope=True, rms_norm=True, mlp=True, attn=use_attn, use_cutile=use_cutile) - elif "phi-3" in model_name or "phi3" in model_name: - apply_tilegym_kernel_to_phi3(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) - elif "olmoe" in model_name: - apply_tilegym_kernel_to_olmoe(rope=True, rms_norm=True, attn=use_attn, moe=True, use_cutile=use_cutile) - elif "olmo-3" in model_name or "olmo3" in model_name: - apply_tilegym_kernel_to_olmo3(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) - else: - print(f"Warning: Model {model_id} is not supported in tilegym patch. No optimizations will be applied.") - - -class KernelFilter: - def __init__(self): - self.kernel_names_prefix = [ - # Rope kernels - "rope", - "tile_rope_kernel", - # Attention kernels - "prefill_fmha", - "prefill_mla", - "fmha_kernel", - "attention_decode_kernel", - # MoE kernels - "moe", - "fused_moe_kernel", - "moe_align_block_size", - # SwiGLU/activation kernels - "silu_and_mul", - "_silu_and_mul_kernel", - "swiglu_forward", - # General cutile prefixes - "tile_", - "cutile", - # RMS norm kernels - "rms_norm_kernel", - # MLA kernels - "naive_absorb_mla", - "_mla_decoding", - # Gated delta rule kernels (Qwen3.5 linear attention) - "recurrent_gated_delta_rule", - "chunk_gated_delta_rule", - "_ct_chunk_inter_recurrence_kernel", - "_ct_intra_chunk_prepare_kernel", - # Partial RoPE kernel (Qwen3.5) - "rope_partial_kernel", - # Fused Qwen3.5 cuTile kernels - "_sigmoid_mul_kernel", - "_gdr_preprocess_kernel", - "_rms_norm_gated_silu_kernel", - "_causal_conv1d_update_silu_kernel", - "_silu_and_mul_separate_kernel", - "_causal_conv1d_prefill_silu_kernel", - "_residual_add_rms_norm_kernel", - # Fused OLMo-3 cuTile kernels - "_rms_norm_residual_add_kernel", - "_dual_rms_norm_kernel", - # Fused OLMoE cuTile kernels - "_olmoe_residual_add_rms_norm_kernel", - "_olmoe_dual_rms_norm_kernel", - # Reduce kernels - "splitk_reduce_kernel", - # GEMM kernels - "static_persistent_gemm_kernel", - "static_persistent_matmul_kernel", - "group_gemm", - "matmul", - "gemm_kernel", - ] - self.blacklist_kernel_names = [ - "aten::matmul", - ] - - def get_kernel_names(self): - return self.kernel_names_prefix - - def contains(self, key): - for k in self.kernel_names_prefix: - if k in key and key not in self.blacklist_kernel_names: - return True - return False - - -class NsysKernelCoverageReporter: - """Runs nsys profiling and reports cuTile kernel coverage (GPU time and launch count ratios).""" - - def __init__(self, args): - self.args = args - self.kernel_filter = KernelFilter() - self.log_dir = args.log_dir - self.model_name = args.model_id.split("/")[-1] - - def run(self): - """Launch infer.py under nsys profile and report kernel coverage.""" - inner_args = self._build_inner_args() - nsys_output_base = self._build_output_path() - nsys_cmd = self._build_nsys_command(inner_args, nsys_output_base) - - print(f"Running nsys profile command:\n {shlex.join(nsys_cmd)}\n") - - env = os.environ.copy() - # nsys writes scratch files to TMPDIR (defaults to /tmp/nvidia/nsight_systems). - # Use log_dir as TMPDIR so nsys works in environments where /tmp is read-only. - env["TMPDIR"] = self.log_dir - proc = subprocess.Popen(nsys_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, env=env) - for line in proc.stdout: - print(line, end="") - proc.wait() - - nsys_rep_path = self._find_nsys_report(nsys_output_base, proc.returncode) - self._compute_and_report_ratio(nsys_rep_path) - - def _build_inner_args(self): - """Reconstruct inner CLI args: strip --report_kernel_coverage, ensure --profile.""" - inner_args = [] - skip_next = False - for arg in sys.argv[1:]: - if skip_next: - skip_next = False - continue - if arg == "--report_kernel_coverage": - continue - inner_args.append(arg) - if "--profile" not in inner_args: - inner_args.append("--profile") - return inner_args - - def _build_output_path(self): - """Build the nsys output base path.""" - os.makedirs(self.log_dir, exist_ok=True) - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - return os.path.join(self.log_dir, f"nsys_{self.model_name}_{timestamp}") - - def _build_nsys_command(self, inner_args, output_base): - """Build the nsys profile command.""" - return [ - "nsys", - "profile", - "-c", - "cudaProfilerApi", - "--capture-range-end=stop-shutdown", - "-o", - output_base, - "--trace=cuda", - "--force-overwrite=true", - "--", - sys.executable, - os.path.abspath(__file__), - ] + inner_args - - def _find_nsys_report(self, output_base, returncode): - """Find the generated .nsys-rep file (nsys may add numeric suffixes).""" - pattern = f"{output_base}*.nsys-rep" - matches = sorted(glob.glob(pattern)) - - if returncode != 0 and not matches: - print(f"\nnsys profile exited with code {returncode} and no report was generated.") - sys.exit(returncode) - elif returncode != 0: - print(f"\nWarning: nsys exited with code {returncode}, but report was generated. Proceeding.") - - if not matches: - print(f"Error: No .nsys-rep file found matching {pattern}") - sys.exit(1) - - nsys_rep_path = matches[-1] - print(f"\nFound nsys report: {nsys_rep_path}") - return nsys_rep_path - - @staticmethod - def _resolve_sqlite_path(path): - """Resolve an .nsys-rep or .sqlite path to a .sqlite file path.""" - if path.endswith(".sqlite"): - if not os.path.isfile(path): - raise FileNotFoundError(f"SQLite file not found: {path}") - return path - - if path.endswith(".nsys-rep"): - if not os.path.isfile(path): - raise FileNotFoundError(f"nsys-rep file not found: {path}") - sibling = path.replace(".nsys-rep", ".sqlite") - if os.path.isfile(sibling): - return sibling - # Export using nsys CLI - output_path = sibling - try: - subprocess.run( - ["nsys", "export", "--type=sqlite", "-o", output_path, path], - check=True, - capture_output=True, - text=True, - ) - except FileNotFoundError: - raise RuntimeError("nsys CLI not found. Install NVIDIA Nsight Systems or provide a .sqlite file.") - except subprocess.CalledProcessError as e: - raise RuntimeError(f"nsys export failed: {e.stderr}") - return output_path - - raise ValueError(f"Unsupported file type: {path} (expected .nsys-rep or .sqlite)") - - def _extract_kernel_durations(self, path): - """Extract GPU kernel durations from an NSight Systems report. - - Args: - path: Path to an .nsys-rep or .sqlite file. - - Returns: - Dict mapping (kernel_idx, kernel_name) to GPU duration in nanoseconds. - """ - sqlite_path = self._resolve_sqlite_path(path) - conn = sqlite3.connect(sqlite_path) - try: - cursor = conn.cursor() - cursor.execute( - """ - SELECT k.start, k.end, s.value AS name - FROM CUPTI_ACTIVITY_KIND_KERNEL k - JOIN StringIds s ON k.demangledName = s.id - ORDER BY k.start ASC - """ - ) - result = {} - for idx, (start, end, name) in enumerate(cursor): - duration_ns = float(end - start) - result[(idx, name)] = duration_ns - return result - finally: - conn.close() - - def _classify_kernels(self, durations): - """Classify kernel durations into TileGym and other categories.""" - tilegym_by_name = defaultdict(float) - tilegym_count_by_name = defaultdict(int) - other_by_name = defaultdict(float) - other_count_by_name = defaultdict(int) - for (_idx, name), dur_ns in durations.items(): - if self.kernel_filter.contains(name): - tilegym_by_name[name] += dur_ns - tilegym_count_by_name[name] += 1 - else: - other_by_name[name] += dur_ns - other_count_by_name[name] += 1 - return tilegym_by_name, tilegym_count_by_name, other_by_name, other_count_by_name - - def _compute_and_report_ratio(self, nsys_rep_path): - """Compute and print the TileGym kernel GPU time ratio from an nsys report.""" - durations = self._extract_kernel_durations(nsys_rep_path) - if not durations: - print("No kernel durations found in the nsys report.") - return - - tilegym_by_name, tilegym_count_by_name, other_by_name, other_count_by_name = self._classify_kernels(durations) - - tilegym_total_ns = sum(tilegym_by_name.values()) - other_total_ns = sum(other_by_name.values()) - all_total_ns = tilegym_total_ns + other_total_ns - tilegym_total_count = sum(tilegym_count_by_name.values()) - other_total_count = sum(other_count_by_name.values()) - all_total_count = tilegym_total_count + other_total_count - - self._print_report( - tilegym_by_name, - tilegym_count_by_name, - tilegym_total_ns, - tilegym_total_count, - all_total_ns, - all_total_count, - ) - - tilegym_time_pct = (tilegym_total_ns / all_total_ns * 100) if all_total_ns > 0 else 0 - tilegym_count_pct = (tilegym_total_count / all_total_count * 100) if all_total_count > 0 else 0 - time_ratio_str = f"{tilegym_time_pct:.2f}%" - count_ratio_str = f"{tilegym_count_pct:.2f}%" - - if self.args.summary_file: - self._append_summary(time_ratio_str, count_ratio_str) - - def _print_report( - self, - tilegym_by_name, - tilegym_count_by_name, - tilegym_total_ns, - tilegym_total_count, - all_total_ns, - all_total_count, - ): - """Print the formatted kernel coverage report table.""" - print("\n===== NSYS KERNEL GPU TIME ANALYSIS =====\n") - header_fmt = "{:<60} {:>8} {:>15} {:>12}" - row_fmt = "{:<60} {:>8} {:>15.3f} {:>11.1f}%" - sep = "-" * 60 - print(header_fmt.format("Kernel Name", "# Calls", "GPU Time (ms)", "% of Total")) - print(f"{sep} {'--------':>8} {'-------------':>15} {'----------':>10}") - - for name, dur_ns in sorted(tilegym_by_name.items(), key=lambda x: -x[1]): - dur_ms = dur_ns / 1e6 - pct = (dur_ns / all_total_ns * 100) if all_total_ns > 0 else 0 - count = tilegym_count_by_name[name] - print(row_fmt.format(name[:60], count, dur_ms, pct)) - - print(f"{sep} {'--------':>8} {'-------------':>15} {'----------':>10}") - tilegym_ms = tilegym_total_ns / 1e6 - all_ms = all_total_ns / 1e6 - tilegym_time_pct = (tilegym_total_ns / all_total_ns * 100) if all_total_ns > 0 else 0 - tilegym_count_pct = (tilegym_total_count / all_total_count * 100) if all_total_count > 0 else 0 - print(row_fmt.format("TileGym Total", tilegym_total_count, tilegym_ms, tilegym_time_pct)) - print(row_fmt.format("All Kernels Total", all_total_count, all_ms, 100.0)) - - time_ratio_str = f"{tilegym_time_pct:.2f}%" - count_ratio_str = f"{tilegym_count_pct:.2f}%" - print(f"\n>>> cuTile Kernel Coverage (GPU Time): {time_ratio_str} <<<") - print(f">>> cuTile Kernel Coverage (# Launches): {count_ratio_str} <<<\n") - - def _append_summary(self, time_ratio_str, count_ratio_str): - """Append coverage ratio to the summary file.""" - with open(self.args.summary_file, "a") as f: - f.write( - f"nsys_cutile_coverage | {self.model_name:<40} | time={time_ratio_str} | launches={count_ratio_str}\n" - ) - print(f"Coverage ratio appended to {self.args.summary_file}") - - -def main(): - args = parse_args() - - if args.report_kernel_coverage: - NsysKernelCoverageReporter(args).run() - return - - # Check if GPU is available - device = "cuda" if torch.cuda.is_available() else "cpu" - print(f"Using device: {device}") - print( - f"Benchmark settings: model={args.model_id}, batch_size={args.batch_size}, output_length={args.output_length}" - ) - - # Load tokenizer and model with cache support - print(f"Loading model {args.model_id}...") - tokenizer_kwargs = {} - if "qwen3.5" in args.model_id.lower() or "qwen3_5" in args.model_id.lower(): - # Qwen3.5 slow tokenizer produces empty outputs; use fast tokenizer - tokenizer_kwargs["use_fast"] = True - tokenizer = load_tokenizer_with_cache(args.model_id, **tokenizer_kwargs) - backend = "base" - if args.use_tilegym: - if args.use_cutile: - backend = "cutile" - print("########################") - print("#######Use TileGym#########") - print("########################") - apply_tilegym_patch(args.model_id, args.use_attn, use_cutile=(backend == "cutile")) - - # Load model with cache support - model_kwargs = { - "trust_remote_code": False, - "device_map": "cuda", - "torch_dtype": torch.bfloat16 if args.precision == "bfloat16" else torch.float32, - } - - model = load_model_with_cache(args.model_id, **model_kwargs) - - if args.show_outputs or args.profile: - args.warmup_runs = 1 - if args.show_outputs: - args.num_runs = 1 - do_sample = False - else: - do_sample = True - - forward_wrapper = NaiveForwardWrapper( - model, - tokenizer=tokenizer, - messages_list=get_messages_list(args), - args=args, - pad_token_id=tokenizer.eos_token_id, - do_sample=do_sample, - early_stopping=False, - ) - - # Warmup runs - print("Performing warmup runs...") - for i in range(args.warmup_runs): - with torch.no_grad(): - _ = forward_wrapper.forward() - print(f" Warmup run {i + 1}/{args.warmup_runs} completed") - - print(f"\nRunning benchmark with {args.num_runs} iterations...") - generation_times = [] - tokens_per_second = [] - torch.cuda.synchronize() - for run in range(args.num_runs): - torch.cuda.synchronize() - start_event = torch.cuda.Event(enable_timing=True) - end_event = torch.cuda.Event(enable_timing=True) - - total_tokens = 0 - outputs_list = [] - input_length = forward_wrapper.get_input_seq_len() - - start_event.record() - with torch.no_grad(): - outputs = forward_wrapper.forward() - end_event.record() - - outputs = forward_wrapper.post_process(outputs) - outputs_list.append(outputs) - # Calculate only newly generated tokens - generated_tokens = outputs.shape[1] - input_length - print(f"generated_tokens: {generated_tokens}") - total_tokens += generated_tokens * args.batch_size - - torch.cuda.synchronize() - elapsed_time = start_event.elapsed_time(end_event) / 1000.0 # Convert ms to seconds - generation_times.append(elapsed_time) - tokens_per_second.append(total_tokens / elapsed_time) - - print( - f"Run {run + 1}: Generated {generated_tokens} tokens in {elapsed_time:.4f}s ({generated_tokens / elapsed_time:.2f} tokens/sec)" - ) - - # Print results - avg_time = np.mean(generation_times) - avg_tokens_per_sec = np.mean(tokens_per_second) - std_tokens_per_sec = np.std(tokens_per_second) - - print("\n===== BENCHMARK RESULTS =====") - print(f"Model: {args.model_id}") - print(f"Device: {device}") - print(f"Use TileGym: {args.use_tilegym}") - print(f"Backend: {backend}") - print(f"Use attention: {args.use_attn}") - print(f"Batch size: {args.batch_size}") - print(f"Output length: {args.output_length}") - print(f"Input length: {input_length}") - print(f"Average generation time: {avg_time:.4f}s") - print(f"Average throughput: {avg_tokens_per_sec:.2f} ± {std_tokens_per_sec:.2f} tokens/sec") - - # Display generated outputs if needed - if args.show_outputs: - print("\n===== GENERATED OUTPUTS =====") - for batch_idx, outputs in enumerate(outputs_list): - for i in range(outputs.shape[0]): - decoded_output = tokenizer.decode(outputs[i][input_length:], skip_special_tokens=True) - print(f"\nBatch {batch_idx + 1}, Output {i + 1}:") - print(decoded_output) - print("-" * 50) - - # Generate case identifier - case_id = f"{args.model_id.split('/')[-1]}" - - # Write summary to file if specified - if args.summary_file: - if args.use_tilegym: - if args.use_cutile: - case_id += "_cutile" - if args.use_attn: - case_id += "_attn" - else: - case_id += "_naive" - case_id += f"_{args.precision}" - - summary_line = f"{case_id:<40} | {avg_tokens_per_sec:>10.2f} | {avg_time:>9.4f}" - # Note: CUDA kernel time will be added after profiling if --profile is used - # For now, write the summary line without CUDA kernel time - with open(args.summary_file, "a") as f: - f.write(summary_line + "\n") - print(f"Summary written to {args.summary_file}: {summary_line}") - - if args.profile: - print("Profile the model...") - with profile( - activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], - with_stack=False, - record_shapes=False, - ) as prof: - with record_function("model_inference"): - with torch.no_grad(): - _ = forward_wrapper.forward() - - # Also run a trace with cudaProfilerAPI. - with torch.no_grad(): - torch.cuda.cudart().cudaProfilerStart() - _ = forward_wrapper.forward() - torch.cuda.cudart().cudaProfilerStop() - - # prof.export_chrome_trace("trace.json") - filtered_results = [] - kernel_filter = KernelFilter() - - for item in prof.key_averages(): - if kernel_filter.contains(item.key): - filtered_results.append(item) - - # Create a custom table with just the filtered results - if filtered_results: - print("\n===== FILTERED PROFILER RESULTS =====") - headers = [ - "Name", - "CPU time total (us)", - "CPU time avg (us)", - "CUDA time total (us)", - "CUDA time avg (us)", - "Count", - ] - row_format = "{:<80} {:<20} {:<20} {:<20} {:<20} {:<10}" - - print(row_format.format(*headers)) - print("-" * 140) - total_device_time = 0.0 - calculated_key = {} - for item in filtered_results: - if kernel_filter.contains(item.key): - calculated_key[item.key] = item.device_time_total - total_device_time += item.device_time_total - print( - row_format.format( - item.key[:50], - f"{item.cpu_time_total:.2f}", - f"{item.cpu_time:.2f}", - f"{item.device_time_total:.2f}", - f"{item.device_time:.2f}", - item.count, - ) - ) - print(f"Calculated key: {calculated_key}") - print(prof.key_averages().table(row_limit=1)) - - # Save profiler results to CSV file - all_results = prof.key_averages() - - # Sort results by device_time_total in descending order - all_results = sorted(all_results, key=lambda x: x.device_time_total, reverse=True) - - # Generate timestamp for filename - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - log_dir = args.log_dir - os.makedirs(log_dir, exist_ok=True) - filename = f"{log_dir}/profiler_results_{case_id}_{timestamp}.csv" - - with open(filename, "w") as f: - # Write CSV header with self-time columns and device type - f.write( - "Name,CPU_time_total_us,CPU_time_avg_us,CUDA_time_total_us,CUDA_time_avg_us,Self_CPU_time_total_us,Self_CUDA_time_total_us,Count,Filtered,DeviceType\n" - ) - - # Write CSV rows with self-time data and device type (include all operations for inspection) - for item in all_results: - # Get device type string (e.g., "DeviceType.CUDA" or "DeviceType.CPU") - device_type_str = str(item.device_type) if hasattr(item, "device_type") else "" - f.write( - f'"{item.key}",{item.cpu_time_total:.2f},{item.cpu_time:.2f},{item.device_time_total:.2f},{item.device_time:.2f},{item.self_cpu_time_total:.2f},{item.self_device_time_total:.2f},{item.count},{kernel_filter.contains(item.key)},{device_type_str}\n' - ) - trace_filename = f"{log_dir}/trace_{case_id}_{timestamp}.json" - prof.export_chrome_trace(trace_filename) - - # Zip the trace file - zip_filename = f"{log_dir}/trace_{case_id}_{timestamp}.zip" - with zipfile.ZipFile(zip_filename, "w", zipfile.ZIP_DEFLATED) as zipf: - zipf.write(trace_filename) - # Remove the original trace file to save space - os.remove(trace_filename) - - print(f"Trace file zipped to {zip_filename}") - print(f"Profiler results saved to {filename}") - print(prof.key_averages().table(row_limit=1)) - - # Calculate CUDA kernel time from profiler results (for summary update) - if args.summary_file: - cuda_kernel_time_us = 0.0 - for item in all_results: - # Only count DeviceType.CUDA operations (actual kernels), exclude model_inference - device_type_str = str(item.device_type) if hasattr(item, "device_type") else "" - if device_type_str == "DeviceType.CUDA" and item.key != "model_inference": - cuda_kernel_time_us += item.self_device_time_total - - cuda_kernel_time_ms = cuda_kernel_time_us / 1000.0 - - # Update the summary line with CUDA kernel time - updated_summary_line = f"{summary_line} | {cuda_kernel_time_ms:>10.1f}\n" - - # Read the file, replace the last line, and write back - with open(args.summary_file, "r") as f: - lines = f.readlines() - - if lines: - # Replace the last line (which should be our summary) - lines[-1] = updated_summary_line - - with open(args.summary_file, "w") as f: - f.writelines(lines) - - print(f"Updated summary with CUDA kernel time: {cuda_kernel_time_ms:.1f} ms") +_SRC = Path(__file__).resolve().parent / "src" +if str(_SRC) not in sys.path: + sys.path.insert(0, str(_SRC)) +from tilegym_hf_bench._cli import main # noqa: E402 if __name__ == "__main__": main() diff --git a/modeling/transformers/pyproject.toml b/modeling/transformers/pyproject.toml new file mode 100644 index 00000000..481e699e --- /dev/null +++ b/modeling/transformers/pyproject.toml @@ -0,0 +1,67 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +[build-system] +requires = ["setuptools>=69", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "tilegym-hf-bench" +version = "0.1.0" +description = "Hugging Face inference benchmarks and profiler tooling for TileGym" +requires-python = ">=3.10" +dependencies = [ + "accelerate==1.13.0", + "cuda-bindings>=13.2.0", + "cuda-core>=0.7.0", + "cuda-tile[tileiras]>=1.3.0", + "filelock>=3.20.3", + "huggingface-hub", + "matplotlib", + "numpy", + "pandas", + "pillow>=12.1.1", + "protobuf", + "pyyaml", + "sentencepiece", + "tilegym @ git+https://github.com/NVIDIA/TileGym.git@v1.3.0", + "tokenizers==0.22.2", + "torch==2.9.1", + "transformers==5.10.2", +] + +[project.optional-dependencies] +eval = [ + "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@1dd931087362abba74e0375c8c631295559f48b2", + "peft", +] +liger = [ + "liger-kernel", +] + +[project.scripts] +tilegym-hf-bench = "tilegym_hf_bench._cli:main" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.setuptools.package-data] +tilegym_hf_bench = ["kernel_filters/*.yaml"] + +[tool.uv] +managed = true +override-dependencies = [ + "transformers==5.10.2", + "cuda-toolkit[tileiras,nvcc,nvvm]==13.3.0", + "nvidia-cuda-runtime>=13.3,<14", + "nvidia-nvjitlink>=13.3,<14", +] + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true + +[tool.uv.sources] +torch = { index = "pytorch-cu130" } diff --git a/modeling/transformers/scripts/benchmark_hf_model.sh b/modeling/transformers/scripts/benchmark_hf_model.sh new file mode 100755 index 00000000..b8e310e3 --- /dev/null +++ b/modeling/transformers/scripts/benchmark_hf_model.sh @@ -0,0 +1,239 @@ +#!/bin/bash +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: MIT + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)" +PYTHON_BIN="${PYTHON_BIN:-python}" +RUN_BASELINE=1 +RUN_CUTILE=1 +RUN_COVERAGE=1 +MODEL_KEY="" +MODEL_ID="" +INPUT_FILE="" +OUTPUT_LENGTH="" +BATCH_SIZE="" +SUMMARY_FILE="" +LOG_DIR="${LOG_DIR:-/logs}" + +usage() { + cat < 0: + line = " Hello" * (args.mock_input_len - 1) + for _ in range(args.batch_size): + messages_list.append(line) + elif args.sentence_file is not None: + with open(args.sentence_file, "r") as f: + lines = [line.strip() for line in f.readlines()] + for _ in range(args.batch_size): + messages_list.append("\n".join(lines)) + else: + for _ in range(args.batch_size): + messages_list.append(args.input_text) + print(messages_list) + return messages_list + + +def _detect_backend(args): + backend = "base" + if args.use_tilegym: + if args.use_cutile: + backend = "cutile" + return backend + + +def _model_name_for_case(model_id): + model_name = Path(model_id.rstrip("/\\")).name + if model_name: + return model_name + return model_id.replace("/", "_") + + +def _build_case_id(args, backend): + case_id = _model_name_for_case(args.model_id) + if args.use_tilegym: + if args.use_cutile: + case_id += "_cutile" + if args.use_attn: + case_id += "_attn" + else: + case_id += "_naive" + case_id += f"_{args.precision}" + return case_id + + +def _build_summary_line(case_id, avg_tokens_per_sec, avg_time): + return f"{case_id:<40} | {avg_tokens_per_sec:>10.2f} | {avg_time:>9.4f}" + + +def main(argv=None): + raw_argv = list(sys.argv[1:] if argv is None else argv) + args = parse_args(raw_argv) + + if args.report_kernel_coverage: + NsysKernelCoverageReporter(args, raw_argv).run() + return + + device = "cuda" if torch.cuda.is_available() else "cpu" + print(f"Using device: {device}") + print( + f"Benchmark settings: model={args.model_id}, batch_size={args.batch_size}, output_length={args.output_length}" + ) + + print(f"Loading model {args.model_id}...") + tokenizer_kwargs = {} + if "qwen3.5" in args.model_id.lower() or "qwen3_5" in args.model_id.lower(): + tokenizer_kwargs["use_fast"] = True + tokenizer = load_tokenizer_with_cache(args.model_id, **tokenizer_kwargs) + + backend = _detect_backend(args) + if args.use_tilegym: + print("########################") + print("#######Use TileGym#########") + print("########################") + apply_tilegym_patch(args.model_id, args.use_attn, use_cutile=(backend == "cutile")) + + model_kwargs = { + "trust_remote_code": False, + "device_map": "cuda", + "torch_dtype": torch.bfloat16 if args.precision == "bfloat16" else torch.float32, + } + + model = load_model_with_cache(args.model_id, **model_kwargs) + + if args.show_outputs or args.profile: + args.warmup_runs = 1 + if args.show_outputs: + args.num_runs = 1 + do_sample = False + else: + do_sample = True + + forward_wrapper = NaiveForwardWrapper( + model, + tokenizer=tokenizer, + messages_list=get_messages_list(args), + args=args, + pad_token_id=tokenizer.eos_token_id, + do_sample=do_sample, + early_stopping=False, + ) + + print("Performing warmup runs...") + for i in range(args.warmup_runs): + with torch.no_grad(): + _ = forward_wrapper.forward() + print(f" Warmup run {i + 1}/{args.warmup_runs} completed") + + print(f"\nRunning benchmark with {args.num_runs} iterations...") + generation_times = [] + tokens_per_second = [] + torch.cuda.synchronize() + for run in range(args.num_runs): + torch.cuda.synchronize() + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + + total_tokens = 0 + outputs_list = [] + input_length = forward_wrapper.get_input_seq_len() + + start_event.record() + with torch.no_grad(): + outputs = forward_wrapper.forward() + end_event.record() + + outputs = forward_wrapper.post_process(outputs) + outputs_list.append(outputs) + generated_tokens = outputs.shape[1] - input_length + print(f"generated_tokens: {generated_tokens}") + total_tokens += generated_tokens * args.batch_size + + torch.cuda.synchronize() + elapsed_time = start_event.elapsed_time(end_event) / 1000.0 + generation_times.append(elapsed_time) + tokens_per_second.append(total_tokens / elapsed_time) + + print( + f"Run {run + 1}: Generated {generated_tokens} tokens in {elapsed_time:.4f}s ({generated_tokens / elapsed_time:.2f} tokens/sec)" + ) + + avg_time = np.mean(generation_times) + avg_tokens_per_sec = np.mean(tokens_per_second) + std_tokens_per_sec = np.std(tokens_per_second) + + print("\n===== BENCHMARK RESULTS =====") + print(f"Model: {args.model_id}") + print(f"Device: {device}") + print(f"Use TileGym: {args.use_tilegym}") + print(f"Backend: {backend}") + print(f"Use attention: {args.use_attn}") + print(f"Batch size: {args.batch_size}") + print(f"Output length: {args.output_length}") + print(f"Input length: {input_length}") + print(f"Average generation time: {avg_time:.4f}s") + print(f"Average throughput: {avg_tokens_per_sec:.2f} ± {std_tokens_per_sec:.2f} tokens/sec") + + if args.show_outputs: + print("\n===== GENERATED OUTPUTS =====") + for batch_idx, outputs in enumerate(outputs_list): + for i in range(outputs.shape[0]): + decoded_output = tokenizer.decode(outputs[i][input_length:], skip_special_tokens=True) + print(f"\nBatch {batch_idx + 1}, Output {i + 1}:") + print(decoded_output) + print("-" * 50) + + case_id = _build_case_id(args, backend) + summary_line = _build_summary_line(case_id, avg_tokens_per_sec, avg_time) + + if args.summary_file: + with open(args.summary_file, "a") as f: + f.write(summary_line + "\n") + print(f"Summary written to {args.summary_file}: {summary_line}") + + if args.profile: + run_torch_profiler(forward_wrapper, args, case_id, avg_time, summary_line) + + +if __name__ == "__main__": + main() diff --git a/modeling/transformers/src/tilegym_hf_bench/forward.py b/modeling/transformers/src/tilegym_hf_bench/forward.py new file mode 100644 index 00000000..f85f88a3 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/forward.py @@ -0,0 +1,40 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + + +class NaiveForwardWrapper: + def __init__( + self, + model, + tokenizer, + messages_list, + args, + device="cuda", + **default_kwargs, + ): + self.model = model + self.default_kwargs = default_kwargs + self.tokenizer = tokenizer + self.tokenized_inputs = tokenizer(messages_list, return_tensors="pt").to(device) + self.input_seq_len = self.tokenized_inputs.input_ids.shape[1] + self.default_kwargs.update( + { + "input_ids": self.tokenized_inputs.input_ids, + "attention_mask": self.tokenized_inputs.attention_mask, + "max_new_tokens": args.output_length, + "min_new_tokens": args.output_length, + } + ) + + def get_input_seq_len(self): + return self.input_seq_len + + def update_params(self, **kwargs): + self.default_kwargs.update(kwargs) + + def forward(self): + return self.model.generate(**self.default_kwargs) + + def post_process(self, outputs): + return outputs diff --git a/modeling/transformers/src/tilegym_hf_bench/hf_shim.py b/modeling/transformers/src/tilegym_hf_bench/hf_shim.py new file mode 100644 index 00000000..e6146f69 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/hf_shim.py @@ -0,0 +1,97 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +import json +import os +from pathlib import Path + +from transformers import AutoModelForCausalLM +from transformers import AutoTokenizer + + +def check_and_setup_model_cache(model_id): + """Set Hugging Face cache env vars and return the cache base directory.""" + cache_base = os.environ.get("TILEGYM_MODEL_CACHE_DIR", os.path.expanduser("~/.cache")) + hf_home = os.path.join(cache_base, "huggingface") + hf_hub = os.path.join(hf_home, "hub") + os.environ.setdefault("HF_HOME", hf_home) + os.environ.setdefault("HF_HUB_CACHE", hf_hub) + Path(hf_hub).mkdir(parents=True, exist_ok=True) + return str(cache_base) + + +def _check_cache_complete(model_id): + hf_home = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/huggingface")) + hf_hub = os.path.join(hf_home, "hub") + model_cache_path = Path(hf_hub) / f"models--{model_id.replace('/', '--')}" + snapshots_dir = model_cache_path / "snapshots" + return snapshots_dir.exists() and any(snapshots_dir.iterdir()) + + +def _load_with_fallback(model_id, loader_class, resource_name, **kwargs): + """Load a local path or HF Hub resource, preferring complete local cache.""" + model_path = Path(model_id) + if model_path.exists() and model_path.is_dir(): + print(f"Loading {resource_name} from local path: {model_id}") + try: + result = loader_class.from_pretrained(model_id, **kwargs) + print(f"Successfully loaded {resource_name} from local path") + return result + except Exception as e: + print(f"Error loading {resource_name} from local path: {e}") + raise + + check_and_setup_model_cache(model_id) + print(f"Loading {resource_name} {model_id}...") + + if _check_cache_complete(model_id): + print("Found cached files, attempting to use local cache") + try: + kwargs_local = kwargs.copy() + kwargs_local["local_files_only"] = True + result = loader_class.from_pretrained(model_id, **kwargs_local) + print(f"Successfully loaded {resource_name} from local cache") + return result + except Exception: + print("Failed to load from local cache, will download from HuggingFace") + + try: + result = loader_class.from_pretrained(model_id, **kwargs) + print(f"Successfully loaded {resource_name}") + return result + except Exception as e: + print(f"Error loading {resource_name}: {e}") + raise + + +def load_model_with_cache(model_id, **kwargs): + return _load_with_fallback(model_id, AutoModelForCausalLM, "model", **kwargs) + + +def _fix_tokenizer_decoder_if_needed(tokenizer, model_id): + """Fix ByteLevel BPE tokenizer decoders lost by native-format conversion.""" + if not hasattr(tokenizer, "_tokenizer"): + return + + tokenizer_json_path = Path(model_id) / "tokenizer.json" + if not tokenizer_json_path.exists(): + return + + with open(tokenizer_json_path) as f: + decoder_config = json.load(f).get("decoder", {}) + + if decoder_config.get("type") != "ByteLevel": + return + + import tokenizers as hf_tokenizers + + fast_tok = hf_tokenizers.Tokenizer.from_file(str(tokenizer_json_path)) + tokenizer._tokenizer.decoder = fast_tok.decoder + print("Fixed tokenizer decoder: replaced ByteFallback with ByteLevel (from tokenizer.json)") + + +def load_tokenizer_with_cache(model_id, **kwargs): + tokenizer = _load_with_fallback(model_id, AutoTokenizer, "tokenizer", **kwargs) + _fix_tokenizer_decoder_if_needed(tokenizer, model_id) + return tokenizer diff --git a/modeling/transformers/src/tilegym_hf_bench/kernel_filters/__init__.py b/modeling/transformers/src/tilegym_hf_bench/kernel_filters/__init__.py new file mode 100644 index 00000000..f02d2eb7 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/kernel_filters/__init__.py @@ -0,0 +1,7 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +from tilegym_hf_bench.kernel_filters.filters import KernelFilter + +__all__ = ["KernelFilter"] diff --git a/modeling/transformers/src/tilegym_hf_bench/kernel_filters/filters.py b/modeling/transformers/src/tilegym_hf_bench/kernel_filters/filters.py new file mode 100644 index 00000000..859dee86 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/kernel_filters/filters.py @@ -0,0 +1,28 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +from importlib import resources + +import yaml + + +def _load_list_yaml(resource_name): + text = resources.files(__package__).joinpath(resource_name).read_text(encoding="utf-8") + return yaml.safe_load(text) or {} + + +class KernelFilter: + def __init__(self, config_resource="tilegym_kernel_prefixes.yaml"): + config = _load_list_yaml(config_resource) + self.kernel_names_prefix = config.get("prefixes", []) + self.blacklist_kernel_names = config.get("blacklist", []) + + def get_kernel_names(self): + return self.kernel_names_prefix + + def contains(self, key): + for prefix in self.kernel_names_prefix: + if prefix in key and key not in self.blacklist_kernel_names: + return True + return False diff --git a/modeling/transformers/src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml b/modeling/transformers/src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml new file mode 100644 index 00000000..49642352 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml @@ -0,0 +1,50 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +prefixes: + - "rope" + - "tile_rope_kernel" + - "prefill_fmha" + - "prefill_mla" + - "fmha_kernel" + - "fmha_varlen_kernel" + - "fmha_variant_kernel" + - "attention_decode_kernel" + - "moe" + - "fused_moe_kernel" + - "moe_align_block_size" + - "silu_and_mul" + - "_silu_and_mul_kernel" + - "swiglu_forward" + - "tile_" + - "cutile" + - "rms_norm_kernel" + - "naive_absorb_mla" + - "_mla_decoding" + - "recurrent_gated_delta_rule" + - "chunk_gated_delta_rule" + - "_chunk_inter_recurrence_kernel" + - "_intra_chunk_prepare_kernel" + - "_sigmoid_mul_kernel" + - "_gdr_preprocess_kernel" + - "_rms_norm_gated_silu_kernel" + - "_causal_conv1d_update_silu_kernel" + - "_silu_and_mul_separate_kernel" + - "_causal_conv1d_prefill_silu_kernel" + - "_residual_add_rms_norm_kernel" + - "_rms_norm_residual_add_kernel" + - "_dual_rms_norm_kernel" + - "_olmoe_residual_add_rms_norm_kernel" + - "_olmoe_dual_rms_norm_kernel" + - "splitk_reduce_kernel" + - "static_persistent_matmul_kernel" + - "group_gemm" + - "matmul" + # Legacy profiler substrings intentionally omitted after checking current kernel names: + # rope_partial_kernel -> rope/tile_rope_kernel + # static_persistent_gemm_kernel -> static_persistent_matmul_kernel + # gemm_kernel -> matmul/group_gemm + +blacklist: + - "aten::matmul" diff --git a/modeling/transformers/src/tilegym_hf_bench/profiling/__init__.py b/modeling/transformers/src/tilegym_hf_bench/profiling/__init__.py new file mode 100644 index 00000000..8476bc52 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/profiling/__init__.py @@ -0,0 +1,3 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT diff --git a/modeling/transformers/src/tilegym_hf_bench/profiling/nsys.py b/modeling/transformers/src/tilegym_hf_bench/profiling/nsys.py new file mode 100644 index 00000000..341fb24a --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/profiling/nsys.py @@ -0,0 +1,236 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +import datetime +import glob +import os +import shlex +import sqlite3 +import subprocess +import sys +from collections import defaultdict + +from tilegym_hf_bench.kernel_filters import KernelFilter + + +class NsysKernelCoverageReporter: + """Runs nsys profiling and reports cuTile kernel coverage.""" + + def __init__(self, args, argv=None): + self.args = args + self.argv = list(sys.argv[1:] if argv is None else argv) + self.kernel_filter = KernelFilter() + self.log_dir = args.log_dir + self.model_name = args.model_id.split("/")[-1] + + def run(self): + inner_args = self._build_inner_args() + nsys_output_base = self._build_output_path() + nsys_cmd = self._build_nsys_command(inner_args, nsys_output_base) + + print(f"Running nsys profile command:\n {shlex.join(nsys_cmd)}\n") + + env = os.environ.copy() + env["TMPDIR"] = self.log_dir + proc = subprocess.Popen(nsys_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, env=env) + for line in proc.stdout: + print(line, end="") + proc.wait() + + nsys_rep_path = self._find_nsys_report(nsys_output_base, proc.returncode) + self._compute_and_report_ratio(nsys_rep_path) + + def _build_inner_args(self): + inner_args = [] + skip_next = False + for arg in self.argv: + if skip_next: + skip_next = False + continue + if arg == "--report_kernel_coverage": + continue + inner_args.append(arg) + if "--profile" not in inner_args: + inner_args.append("--profile") + return inner_args + + def _build_output_path(self): + os.makedirs(self.log_dir, exist_ok=True) + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + return os.path.join(self.log_dir, f"nsys_{self.model_name}_{timestamp}") + + @staticmethod + def _build_nsys_command(inner_args, output_base): + return [ + "nsys", + "profile", + "-c", + "cudaProfilerApi", + "--capture-range-end=stop-shutdown", + "-o", + output_base, + "--trace=cuda", + "--force-overwrite=true", + "--", + sys.executable, + "-m", + "tilegym_hf_bench._cli", + ] + inner_args + + @staticmethod + def _find_nsys_report(output_base, returncode): + pattern = f"{output_base}*.nsys-rep" + matches = sorted(glob.glob(pattern)) + + if returncode != 0 and not matches: + print(f"\nnsys profile exited with code {returncode} and no report was generated.") + sys.exit(returncode) + if returncode != 0: + print(f"\nWarning: nsys exited with code {returncode}, but report was generated. Proceeding.") + + if not matches: + print(f"Error: No .nsys-rep file found matching {pattern}") + sys.exit(1) + + nsys_rep_path = matches[-1] + print(f"\nFound nsys report: {nsys_rep_path}") + return nsys_rep_path + + @staticmethod + def _resolve_sqlite_path(path): + if path.endswith(".sqlite"): + if not os.path.isfile(path): + raise FileNotFoundError(f"SQLite file not found: {path}") + return path + + if path.endswith(".nsys-rep"): + if not os.path.isfile(path): + raise FileNotFoundError(f"nsys-rep file not found: {path}") + sibling = path.replace(".nsys-rep", ".sqlite") + if os.path.isfile(sibling): + return sibling + output_path = sibling + try: + subprocess.run( + ["nsys", "export", "--type=sqlite", "-o", output_path, path], + check=True, + capture_output=True, + text=True, + ) + except FileNotFoundError: + raise RuntimeError("nsys CLI not found. Install NVIDIA Nsight Systems or provide a .sqlite file.") + except subprocess.CalledProcessError as e: + raise RuntimeError(f"nsys export failed: {e.stderr}") + return output_path + + raise ValueError(f"Unsupported file type: {path} (expected .nsys-rep or .sqlite)") + + def _extract_kernel_durations(self, path): + sqlite_path = self._resolve_sqlite_path(path) + conn = sqlite3.connect(sqlite_path) + try: + cursor = conn.cursor() + cursor.execute( + """ + SELECT k.start, k.end, s.value AS name + FROM CUPTI_ACTIVITY_KIND_KERNEL k + JOIN StringIds s ON k.demangledName = s.id + ORDER BY k.start ASC + """ + ) + result = {} + for idx, (start, end, name) in enumerate(cursor): + duration_ns = float(end - start) + result[(idx, name)] = duration_ns + return result + finally: + conn.close() + + def _classify_kernels(self, durations): + tilegym_by_name = defaultdict(float) + tilegym_count_by_name = defaultdict(int) + other_by_name = defaultdict(float) + other_count_by_name = defaultdict(int) + for (_idx, name), dur_ns in durations.items(): + if self.kernel_filter.contains(name): + tilegym_by_name[name] += dur_ns + tilegym_count_by_name[name] += 1 + else: + other_by_name[name] += dur_ns + other_count_by_name[name] += 1 + return tilegym_by_name, tilegym_count_by_name, other_by_name, other_count_by_name + + def _compute_and_report_ratio(self, nsys_rep_path): + durations = self._extract_kernel_durations(nsys_rep_path) + if not durations: + print("No kernel durations found in the nsys report.") + return + + tilegym_by_name, tilegym_count_by_name, other_by_name, other_count_by_name = self._classify_kernels(durations) + + tilegym_total_ns = sum(tilegym_by_name.values()) + other_total_ns = sum(other_by_name.values()) + all_total_ns = tilegym_total_ns + other_total_ns + tilegym_total_count = sum(tilegym_count_by_name.values()) + other_total_count = sum(other_count_by_name.values()) + all_total_count = tilegym_total_count + other_total_count + + self._print_report( + tilegym_by_name, + tilegym_count_by_name, + tilegym_total_ns, + tilegym_total_count, + all_total_ns, + all_total_count, + ) + + tilegym_time_pct = (tilegym_total_ns / all_total_ns * 100) if all_total_ns > 0 else 0 + tilegym_count_pct = (tilegym_total_count / all_total_count * 100) if all_total_count > 0 else 0 + time_ratio_str = f"{tilegym_time_pct:.2f}%" + count_ratio_str = f"{tilegym_count_pct:.2f}%" + + if self.args.summary_file: + self._append_summary(time_ratio_str, count_ratio_str) + + @staticmethod + def _print_report( + tilegym_by_name, + tilegym_count_by_name, + tilegym_total_ns, + tilegym_total_count, + all_total_ns, + all_total_count, + ): + print("\n===== NSYS KERNEL GPU TIME ANALYSIS =====\n") + header_fmt = "{:<60} {:>8} {:>15} {:>12}" + row_fmt = "{:<60} {:>8} {:>15.3f} {:>11.1f}%" + sep = "-" * 60 + print(header_fmt.format("Kernel Name", "# Calls", "GPU Time (ms)", "% of Total")) + print(f"{sep} {'--------':>8} {'-------------':>15} {'----------':>10}") + + for name, dur_ns in sorted(tilegym_by_name.items(), key=lambda x: -x[1]): + dur_ms = dur_ns / 1e6 + pct = (dur_ns / all_total_ns * 100) if all_total_ns > 0 else 0 + count = tilegym_count_by_name[name] + print(row_fmt.format(name[:60], count, dur_ms, pct)) + + print(f"{sep} {'--------':>8} {'-------------':>15} {'----------':>10}") + tilegym_ms = tilegym_total_ns / 1e6 + all_ms = all_total_ns / 1e6 + tilegym_time_pct = (tilegym_total_ns / all_total_ns * 100) if all_total_ns > 0 else 0 + tilegym_count_pct = (tilegym_total_count / all_total_count * 100) if all_total_count > 0 else 0 + print(row_fmt.format("TileGym Total", tilegym_total_count, tilegym_ms, tilegym_time_pct)) + print(row_fmt.format("All Kernels Total", all_total_count, all_ms, 100.0)) + + time_ratio_str = f"{tilegym_time_pct:.2f}%" + count_ratio_str = f"{tilegym_count_pct:.2f}%" + print(f"\n>>> cuTile Kernel Coverage (GPU Time): {time_ratio_str} <<<") + print(f">>> cuTile Kernel Coverage (# Launches): {count_ratio_str} <<<\n") + + def _append_summary(self, time_ratio_str, count_ratio_str): + with open(self.args.summary_file, "a") as f: + f.write( + f"nsys_cutile_coverage | {self.model_name:<40} | time={time_ratio_str} | launches={count_ratio_str}\n" + ) + print(f"Coverage ratio appended to {self.args.summary_file}") diff --git a/modeling/transformers/src/tilegym_hf_bench/profiling/torch_profiler.py b/modeling/transformers/src/tilegym_hf_bench/profiling/torch_profiler.py new file mode 100644 index 00000000..e983e0eb --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/profiling/torch_profiler.py @@ -0,0 +1,128 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +import datetime +import os +import zipfile + +import torch +from torch.profiler import ProfilerActivity +from torch.profiler import profile +from torch.profiler import record_function + +from tilegym_hf_bench.kernel_filters import KernelFilter + + +def run_torch_profiler(forward_wrapper, args, case_id, avg_time, summary_line): + print("Profile the model...") + with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + with_stack=False, + record_shapes=False, + ) as prof: + with record_function("model_inference"): + with torch.no_grad(): + _ = forward_wrapper.forward() + + with torch.no_grad(): + torch.cuda.cudart().cudaProfilerStart() + _ = forward_wrapper.forward() + torch.cuda.cudart().cudaProfilerStop() + + filtered_results = [] + kernel_filter = KernelFilter() + + for item in prof.key_averages(): + if kernel_filter.contains(item.key): + filtered_results.append(item) + + if filtered_results: + print("\n===== FILTERED PROFILER RESULTS =====") + headers = [ + "Name", + "CPU time total (us)", + "CPU time avg (us)", + "CUDA time total (us)", + "CUDA time avg (us)", + "Count", + ] + row_format = "{:<80} {:<20} {:<20} {:<20} {:<20} {:<10}" + + print(row_format.format(*headers)) + print("-" * 140) + total_device_time = 0.0 + for item in filtered_results: + if kernel_filter.contains(item.key): + total_device_time += item.device_time_total + print( + row_format.format( + item.key[:50], + f"{item.cpu_time_total:.2f}", + f"{item.cpu_time:.2f}", + f"{item.device_time_total:.2f}", + f"{item.device_time:.2f}", + item.count, + ) + ) + total_device_time_pct = 0.0 if avg_time <= 0 else (total_device_time / 1_000_000.0) / avg_time * 100.0 + print( + f"Total device time: {total_device_time:.2f} us", + f"Total device time %: {total_device_time_pct:.2f}%", + ) + print(prof.key_averages().table(row_limit=1)) + + all_results = prof.key_averages() + all_results = sorted(all_results, key=lambda x: x.device_time_total, reverse=True) + + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + log_dir = args.log_dir + os.makedirs(log_dir, exist_ok=True) + filename = f"{log_dir}/profiler_results_{case_id}_{timestamp}.csv" + + with open(filename, "w") as f: + f.write( + "Name,CPU_time_total_us,CPU_time_avg_us,CUDA_time_total_us,CUDA_time_avg_us,Self_CPU_time_total_us,Self_CUDA_time_total_us,Count,Filtered,DeviceType\n" + ) + + for item in all_results: + device_type_str = str(item.device_type) if hasattr(item, "device_type") else "" + f.write( + f'"{item.key}",{item.cpu_time_total:.2f},{item.cpu_time:.2f},{item.device_time_total:.2f},{item.device_time:.2f},{item.self_cpu_time_total:.2f},{item.self_device_time_total:.2f},{item.count},{kernel_filter.contains(item.key)},{device_type_str}\n' + ) + trace_filename = f"{log_dir}/trace_{case_id}_{timestamp}.json" + prof.export_chrome_trace(trace_filename) + + zip_filename = f"{log_dir}/trace_{case_id}_{timestamp}.zip" + with zipfile.ZipFile(zip_filename, "w", zipfile.ZIP_DEFLATED) as zipf: + zipf.write(trace_filename) + os.remove(trace_filename) + + print(f"Trace file zipped to {zip_filename}") + print(f"Profiler results saved to {filename}") + print(prof.key_averages().table(row_limit=1)) + + if args.summary_file: + cuda_kernel_time_us = 0.0 + for item in all_results: + device_type_str = str(item.device_type) if hasattr(item, "device_type") else "" + if device_type_str == "DeviceType.CUDA" and item.key != "model_inference": + cuda_kernel_time_us += item.self_device_time_total + + cuda_kernel_time_ms = cuda_kernel_time_us / 1000.0 + updated_summary_line = f"{summary_line} | {cuda_kernel_time_ms:>10.1f}\n" + + lines = [] + if os.path.exists(args.summary_file): + with open(args.summary_file, encoding="utf-8") as f: + lines = f.readlines() + + if lines: + lines[-1] = updated_summary_line + else: + lines = [updated_summary_line] + + with open(args.summary_file, "w", encoding="utf-8") as f: + f.writelines(lines) + + print(f"Updated summary with CUDA kernel time: {cuda_kernel_time_ms:.1f} ms") diff --git a/modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py b/modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py new file mode 100644 index 00000000..c0eae5e5 --- /dev/null +++ b/modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py @@ -0,0 +1,46 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# SPDX-License-Identifier: MIT + +from tilegym.transformers import apply_tilegym_kernel_to_deepseek_v2 +from tilegym.transformers import apply_tilegym_kernel_to_gemma3 +from tilegym.transformers import apply_tilegym_kernel_to_gpt_oss +from tilegym.transformers import apply_tilegym_kernel_to_llama +from tilegym.transformers import apply_tilegym_kernel_to_mistral +from tilegym.transformers import apply_tilegym_kernel_to_olmo3 +from tilegym.transformers import apply_tilegym_kernel_to_olmoe +from tilegym.transformers import apply_tilegym_kernel_to_phi3 +from tilegym.transformers import apply_tilegym_kernel_to_qwen2 +from tilegym.transformers import apply_tilegym_kernel_to_qwen3 + + +def apply_tilegym_patch(model_id, use_attn=False, use_cutile=False): + model_name = model_id.lower() + if "llama" in model_name: + apply_tilegym_kernel_to_llama(rope=True, swiglu=True, rms_norm=True, attn=use_attn, use_cutile=use_cutile) + elif "deepseek" in model_name: + apply_tilegym_kernel_to_deepseek_v2( + rope=True, rms_norm=True, swiglu=True, attn=use_attn, moe=True, use_cutile=use_cutile + ) + elif "gpt-oss" in model_name: + apply_tilegym_kernel_to_gpt_oss(rope=True, rms_norm=True, swiglu=False, attn=use_attn, use_cutile=use_cutile) + elif "mistral" in model_name: + apply_tilegym_kernel_to_mistral(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) + elif "qwen3.5" in model_name or "qwen3_5" in model_name: + apply_tilegym_kernel_to_qwen3( + rope=True, rms_norm=True, swiglu=True, attn=use_attn, gated_delta_rule=True, use_cutile=use_cutile + ) + elif "qwen" in model_name: + apply_tilegym_kernel_to_qwen2(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) + elif "gemma-3" in model_name or "gemma3" in model_name: + apply_tilegym_kernel_to_gemma3(rope=True, rms_norm=True, mlp=True, attn=use_attn, use_cutile=use_cutile) + elif "gemma" in model_name: + print(f"Warning: Gemma variant {model_id} is not supported in tilegym patch. No optimizations will be applied.") + elif "phi-3" in model_name or "phi3" in model_name: + apply_tilegym_kernel_to_phi3(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) + elif "olmoe" in model_name: + apply_tilegym_kernel_to_olmoe(rope=True, rms_norm=True, attn=use_attn, moe=True, use_cutile=use_cutile) + elif "olmo-3" in model_name or "olmo3" in model_name: + apply_tilegym_kernel_to_olmo3(rope=True, rms_norm=True, swiglu=True, attn=use_attn, use_cutile=use_cutile) + else: + print(f"Warning: Model {model_id} is not supported in tilegym patch. 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b/skills/tilegym-monkey-patch-kernels-to-transformers/references/auto-kernelize.md @@ -6,17 +6,19 @@ Work with user to prepare experiment environment: 1. Check Git branch status. The previous commit should only contain monkey-patching existing TileGym OPs to the target transformers model. No other unstaged/uncommitted modifications 2. Check GPU available and UUID match; Docker container has been built 3. Study code relating to the target transformers model: - - modeling/transformers/bench_.sh: end-to-end benchmark entrance, run PyTorch baseline perf, cuTile kernelize perf, cuTile kernel coverage + - modeling/transformers/scripts/benchmark_hf_model.sh: end-to-end benchmark entrance, run PyTorch baseline perf, cuTile kernelize perf, cuTile kernel coverage - src/tilegym/transformers//modeling_.py: Target model specific OP adapters and wrappers - src/tilegym/transformers//kernel_definitions/*.json and kernel_solutions/*.json: Existing reusable kernel inventory - src/tilegym/transformers//kernels/*.py: Dedicated reusable transformer-local kernel implementations - @src/tilegym/transformers/monkey_patch.py: Study apply_tilegym_kernel_to_ to understand how to kernelize - - @modeling/transformers/infer.py: End-to-end benchmark and kernel coverage script + - @modeling/transformers/src/tilegym_hf_bench/_cli.py: End-to-end benchmark and kernel coverage CLI + - @modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py: model-id dispatch to TileGym monkey-patch functions + - @modeling/transformers/src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml: cuTile kernel coverage filter prefixes 4. Create sandbox/_results.md to track progress. The first run will write a baseline 5. Confirm and go: Once you get confirmation, kick off the experimentation ## Experimentation -Every experiment run on a NVIDIA GPU that [TileIR supported](https://docs.nvidia.com/cuda/tile-ir/latest/sections/stability.html#supported-architectures) (currently Ampere, Ada, and Blackwell architectures). Each experiment should be enforced to finish in 15 minutes. Every command should be executed within the experiment Docker container. `cd` to @modeling/transformers/ first, then `bash bench_.sh` to launch one experiment. +Every experiment must run on an NVIDIA GPU [supported by TileIR](https://docs.nvidia.com/cuda/tile-ir/latest/sections/stability.html#supported-architectures) (currently Ampere, Ada, and Blackwell). Each experiment should be enforced to finish in 15 minutes. Every command should be executed within the experiment Docker container. `cd` to @modeling/transformers/ first, then `bash scripts/benchmark_hf_model.sh --model-key ` to launch one experiment. Reusable generated kernels must follow the kernel inventory schema linked from `SKILL.md`: start each experiment with a draft FlashInfer Definition, keep verified kernels in dedicated `kernels/.py` files, and record matching Definition and Solution JSON metadata. The Definition `reference` must begin with `# Source:` comment(s) pointing to precise upstream `transformers` or Hugging Face remote-code regions. @@ -30,17 +32,16 @@ Reusable generated kernels must follow the kernel inventory schema linked from ` - @src/tilegym/transformers//kernel_definitions/.json: FlashInfer Definition metadata for kept reusable kernels - @src/tilegym/transformers//kernel_solutions/.json: FlashInfer Solution metadata for kept reusable kernels - @src/tilegym/transformers/monkey_patch.py: Only change the `apply_tilegym_kernel_to_` function -- @modeling/transformers/infer.py: Only change - * `apply_tilegym_kernel_to_` arguments - * `KernelFilter.kernel_names_prefix` for new cuTile kernels -- @modeling/transformers/bench_.sh: Optionally comment out line 30-38 to skip PyTorch throughput to accelerate experiment iterations. If so, ensure to restore on each experiment end +- @modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py: Only change `apply_tilegym_kernel_to_` dispatch arguments +- @modeling/transformers/src/tilegym_hf_bench/kernel_filters/tilegym_kernel_prefixes.yaml: Only add kernel name substrings for new cuTile kernels after checking current nsys names +- @modeling/transformers/scripts/benchmark_hf_model.sh: Optionally run with `--skip-baseline` to accelerate experiment iterations. Restore full baseline + cuTile + coverage at each experiment end - @sandbox/: Feel free to add new files or modify files created by you, but don't check to git ### What you can NOT change - Anything not listed above ### What to expect from experiment outputs -`bench_.sh` prints ~300 lines of plain text. Use this command to grep core metrics: `grep -E "Average throughput|cuTile Kernel Coverage \(GPU Time\)" `. Example output: +`scripts/benchmark_hf_model.sh --model-key ` prints ~300 lines of plain text. Use this command to grep core metrics: `grep -E "Average throughput|cuTile Kernel Coverage \(GPU Time\)" `. Example output: ```text Average throughput: 25.93 ± 3.20 tokens/sec @@ -69,7 +70,7 @@ Example content: Create the tabular header if the file was empty. Append one line for currently experiment. ### The baseline -The first experiment will not change any code and simply run bench_.sh. Results will list at first row as baseline. +The first experiment will not change any code and simply run `scripts/benchmark_hf_model.sh --model-key `. Results will list at first row as baseline. ## The experiment loop Core methodology is to create new cuTile kernels to replace uncovered PyTorch code while keeping performant and correctness. Try one piece of code at a time, and have clean experiment records. diff --git a/skills/tilegym-monkey-patch-kernels-to-transformers/references/kernel-integration.md b/skills/tilegym-monkey-patch-kernels-to-transformers/references/kernel-integration.md index 65c6ede5..3378a623 100644 --- a/skills/tilegym-monkey-patch-kernels-to-transformers/references/kernel-integration.md +++ b/skills/tilegym-monkey-patch-kernels-to-transformers/references/kernel-integration.md @@ -98,7 +98,7 @@ flowchart TD 3. Execute-and-verify phase: Check develop environment, launch subagents to implement monkey-patch for each of the items in integration plan once-a-time, verify it on develop environment, and accept/reject that monkey-patch. Specific sub-steps: 1. The orchestrator agent (i.e., you) checks the Docker container is available, GPU UUID reported inside the Docker container is expected, and current git branch does not have unstaged/uncommitted changes 2. For each unverified integration plan item (i.e., a mapping of Transformer model compute <-> one or more TileGym implementation candidates), launch a code subagent **sequentially, one at a time** — purpose is context isolation, not parallelism; concurrent subagents race on `src/tilegym/transformers//` and on the Docker container. Subagent needs filesystem read/write + Bash (in-container test runs); web access not required. Tell this subagent how to invoke command in our Docker environment and its workflow: - 1. Study @src/tilegym/transformers/monkey_patch.py and @modeling/transformers/infer.py to understand how to monkey-patch a transformer model with TileGym implementation; + 1. Study @src/tilegym/transformers/monkey_patch.py, @modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py, and @modeling/transformers/src/tilegym_hf_bench/_cli.py to understand how to monkey-patch a transformer model with TileGym implementation; 2. Locate the integration point at `transformers` library. E.g., It could be a `nn.Module` subclass that corresponds to a layer in the transformer model, or an utility function that applies certain modification to transformer models' intermediate variables/tensors; 3. Collect inputs and outputs around integration point to serve as subsequent verifications' references. You can create a simple debug Python script that calls `transformers` library's `.generate()` API to prompt the Transformer model to output "The capital of France is", and add code before and after the integration point to save intermediate PyTorch tensors and other necessary variables to disk as future references. *Critical: unoptimized `.generate()` is slow, collect as less data as possible*; 4. Select the next unverified TileGym implementation candidate. If no unverified candidate is available, exit current subagent and let the orchestrator agent know that the current Transformer compute is unsuitable for TileGym to patch. Otherwise, implement a monkey-patch function following the convention studied at sub-step 3.2.1. If the candidate is a reusable new kernel, place the kernel implementation in src/tilegym/transformers//kernels/.py and create matching `kernel_definitions/.json` and `kernel_solutions/.json`. Before keeping the Definition, revisit its `reference` snippet and verify every `# Source:` permalink points to a precise corresponding code region in upstream `transformers` or remote model code. The patch function of current compute goes to src/tilegym/transformers//monkey_patch_.py. If additional modifications are need for the current transformer model (similar to the scenario of @src/tilegym/transformers/deepseek2/modeling_deepseek.py), check existence (create by other subagents) or create a self-contained Python submodule src/tilegym/transformers//modeling_.py and place model-specific patching glue there; @@ -118,7 +118,7 @@ flowchart TD ``` 7. Exit the current subagent and let orchestrator agent know that the assigned Transformer compute can be patched by TileGym implementation verified at sub-step 3.2.5 and 3.2.6 and the patch function is available at src/tilegym/transformers//monkey_patch_.py. 3. Aggregate all verified computes and corresponding patches. If none of the compute can be faithfully integrated, exit the workflow and let users know; Otherwise, aggregate all patching logic to a main monkey-patch function `def apply_tilegym_kernel_to_(...)` and place it at @src/tilegym/transformers/monkey_patch.py. Each compute has a corresponding boolean flag as function argument; - 4. Update @modeling/transformers/infer.py to include the main monkey-patch function in the inference and benchmark flow. Create a Bash script modeling/transformers/bench_.sh similar to other bench scripts in that directory. Ensure to use `--use_cutile` at 2nd infer.py call, as we focus on cuTile backend; + 4. Update @modeling/transformers/src/tilegym_hf_bench/tilegym_patch.py to include the main monkey-patch function in the inference and benchmark flow. If a new model preset is needed, add it to @modeling/transformers/scripts/benchmark_hf_model.sh. Ensure the cuTile benchmark path passes `--use_cutile`, as we focus on cuTile backend; 5. Run the end-to-end inference script created at sub-step 3.4. It should print ~300 lines of plain text. Collect baseline throughput, cuTile kernelized throughput, and cuTile kernel coverage by `grep -E "Average throughput|cuTile Kernel Coverage \(GPU Time\)" `. Example output: ```text Average throughput: 25.93 ± 3.20 tokens/sec @@ -138,7 +138,9 @@ flowchart TD |- test_monkey_patch_.py # don't check to git |- # Optional [monkey_patch_.py, test_monkey_patch_.py] pairs created by other subagents assigned with s --- don't check to git |- modeling_.py # check to git - |- modeling/transformers/ - |- bench_.sh # check to git - |- infer.py # check modifications to git +modeling/transformers/ +|- scripts/benchmark_hf_model.sh # update model presets if needed +|- src/tilegym_hf_bench/ + |- tilegym_patch.py # check model dispatch modifications to git + |- kernel_filters/tilegym_kernel_prefixes.yaml # check kernel filter updates to git ``` diff --git a/src/tilegym/transformers/qwen3_5/modeling_qwen3_5.py b/src/tilegym/transformers/qwen3_5/modeling_qwen3_5.py index a96ca99e..48f81ce2 100644 --- a/src/tilegym/transformers/qwen3_5/modeling_qwen3_5.py +++ b/src/tilegym/transformers/qwen3_5/modeling_qwen3_5.py @@ -61,13 +61,11 @@ def _gated_delta_net_forward_tilegym(self, hidden_states, cache_params=None, cac hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask) batch_size, seq_len, _ = hidden_states.shape - use_precomputed_states = ( - cache_params is not None and cache_params.has_previous_state and seq_len == 1 and cache_position is not None - ) + use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) - if cache_params is not None: - conv_state = cache_params.conv_states[self.layer_idx] - recurrent_state = cache_params.recurrent_states[self.layer_idx] + if use_precomputed_states: + conv_state = cache_params.layers[self.layer_idx].conv_states + recurrent_state = cache_params.layers[self.layer_idx].recurrent_states mixed_qkv = self.in_proj_qkv(hidden_states) mixed_qkv = mixed_qkv.transpose(1, 2) @@ -78,7 +76,7 @@ def _gated_delta_net_forward_tilegym(self, hidden_states, cache_params=None, cac b = self.in_proj_b(hidden_states) a = self.in_proj_a(hidden_states) - if use_precomputed_states: + if use_precomputed_states and seq_len == 1: mixed_qkv = self.causal_conv1d_update( mixed_qkv, conv_state, @@ -87,9 +85,11 @@ def _gated_delta_net_forward_tilegym(self, hidden_states, cache_params=None, cac self.activation, ) else: + if use_precomputed_states: + mixed_qkv = torch.cat([conv_state, mixed_qkv], dim=-1) if cache_params is not None: - conv_state_val = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0)) - cache_params.conv_states[self.layer_idx] = conv_state_val + new_conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0)) + cache_params.update_conv_state(new_conv_state, self.layer_idx) if self.causal_conv1d_fn is not None: mixed_qkv = self.causal_conv1d_fn( x=mixed_qkv, @@ -101,7 +101,13 @@ def _gated_delta_net_forward_tilegym(self, hidden_states, cache_params=None, cac else: # Fused cuTile causal conv1d + SiLU for prefill padded = F.pad(mixed_qkv, (self.conv_kernel_size - 1, 0)) - mixed_qkv = causal_conv1d_prefill_silu_cutile(padded, self.conv1d.weight.squeeze(1), seq_len) + mixed_qkv = causal_conv1d_prefill_silu_cutile( + padded, + self.conv1d.weight.squeeze(1), + mixed_qkv.shape[-1], + ) + if use_precomputed_states: + mixed_qkv = mixed_qkv[:, :, -seq_len:] mixed_qkv = mixed_qkv.transpose(1, 2) query, key, value = torch.split(mixed_qkv, [self.key_dim, self.key_dim, self.value_dim], dim=-1) @@ -117,31 +123,31 @@ def _gated_delta_net_forward_tilegym(self, hidden_states, cache_params=None, cac query = query.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2) key = key.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2) - if not use_precomputed_states: - core_attn_out, last_recurrent_state = self.chunk_gated_delta_rule( + if use_precomputed_states and seq_len == 1: + core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule( query, key, value, g=g, beta=beta, - initial_state=None, + initial_state=recurrent_state, output_final_state=cache_params is not None, use_qk_l2norm_in_kernel=True, ) else: - core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule( + core_attn_out, last_recurrent_state = self.chunk_gated_delta_rule( query, key, value, g=g, beta=beta, - initial_state=recurrent_state, + initial_state=recurrent_state if use_precomputed_states else None, output_final_state=cache_params is not None, use_qk_l2norm_in_kernel=True, ) if cache_params is not None: - cache_params.recurrent_states[self.layer_idx] = last_recurrent_state + cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx) core_attn_out = core_attn_out.reshape(-1, self.head_v_dim) z = z.reshape(-1, self.head_v_dim) From 53220253a72135c7db4c230033948428bf229a52 Mon Sep 17 00:00:00 2001 From: Yifei Song Date: Mon, 15 Jun 2026 16:24:26 +0000 Subject: [PATCH 3/5] Update test_mla_decoding.py --- tests/ops/test_mla_decoding.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tests/ops/test_mla_decoding.py b/tests/ops/test_mla_decoding.py index c8a4b59f..476f11f5 100644 --- a/tests/ops/test_mla_decoding.py +++ b/tests/ops/test_mla_decoding.py @@ -192,6 +192,17 @@ def test_perf( pytest.skip("Skip due to sm80 not support fp8 type") if torch.cuda.get_device_capability() == (12, 0) and S_kv == 8192: pytest.skip("Skip OOM on B20X (sm120): MLA decoding with seqlen=8192 exceeds 32 GiB VRAM") + if framework == "tilecpp": + # All architectures: __nv_fp8_e5m2 is an incomplete type in CUDA Tile; + # pointer arithmetic `T* ptr = base + offset` fails at nvcc compile time + # (mla_decoding.cuh L223-225). Affects sm_90 (H100), sm_103, and others. + if dtype == torch.float8_e5m2: + pytest.skip("tilecpp fp8_e5m2 compilation fails: __nv_fp8_e5m2 incomplete type in pointer arithmetic") + # All architectures: int32 overflow in kernel stride calc. + # (num_batch-1) * S_kv * BLOCK_D = 1023 * 8192 * 512 = 4.3B > INT32_MAX(2.1B). + # Fix: use int64/long long for strides in mla_decoding.py + mla_decoding.cuh. + if num_batch == 1024 and S_kv == 8192: + pytest.skip("tilecpp int32 stride overflow: (B-1)*S_kv*D=4.3B > INT32_MAX on large batch") self.setUp() From a93e230f679a83821044b72a28da9137cd56b1b6 Mon Sep 17 00:00:00 2001 From: Rundong Li Date: Mon, 15 Jun 2026 19:54:05 -0700 Subject: [PATCH 4/5] Use in-tree TileGym for hf-bench --- modeling/transformers/Dockerfile | 15 +-- modeling/transformers/pyproject.toml | 7 +- modeling/transformers/uv.lock | 172 ++++++++++++++++++++++++++- 3 files changed, 179 insertions(+), 15 deletions(-) diff --git a/modeling/transformers/Dockerfile b/modeling/transformers/Dockerfile index 388e4bfd..6ef20841 100644 --- a/modeling/transformers/Dockerfile +++ b/modeling/transformers/Dockerfile @@ -30,21 +30,14 @@ RUN apt-get update && \ apt-get purge -y --auto-remove curl gnupg && \ rm -rf /var/lib/apt/lists/* /root/.cache/pip -FROM base AS wheel +FROM base AS source ENV HF_HOME=/workspace/.cache/huggingface \ TILEGYM_MODEL_CACHE_DIR=/workspace/.cache -WORKDIR /workspace/tilegym-hf-bench +COPY . /workspace/tilegym/ -COPY modeling/transformers/ /workspace/tilegym-hf-bench/ +WORKDIR /workspace/tilegym/modeling/transformers -RUN uv sync --locked && \ +RUN uv sync --locked --extra dev && \ mkdir -p /workspace/.cache/huggingface/hub /logs - -FROM wheel AS source - -COPY . /workspace/tilegym/ - -RUN uv pip uninstall --python /workspace/tilegym-hf-bench/.venv/bin/python tilegym && \ - uv pip install --python /workspace/tilegym-hf-bench/.venv/bin/python --no-deps -e /workspace/tilegym diff --git a/modeling/transformers/pyproject.toml 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a/.github/workflows/tilegym-ci.yml b/.github/workflows/tilegym-ci.yml index 3c31e4cc..f5cdf1a3 100644 --- a/.github/workflows/tilegym-ci.yml +++ b/.github/workflows/tilegym-ci.yml @@ -1,6 +1,6 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # -# SPDX-License-Identifier: MIT +# SPDX-License-Identifier: MIT name: tilegym-ci @@ -59,16 +59,16 @@ jobs: script: | let prBody = ''; let prNumber = ''; - + const branchName = context.ref.replace('refs/heads/', ''); core.info(`Looking for PR for branch: ${branchName}`); - + // Try method 1: Extract PR number from branch name const branchMatch = branchName.match(/^pull-request\/(\d+)/); if (branchMatch) { prNumber = branchMatch[1]; core.info(`Extracted PR #${prNumber} from branch name`); - + // Fetch PR body by number try { const { data: pr } = await github.rest.pulls.get({ @@ -90,7 +90,7 @@ jobs: state: 'open', head: `${context.repo.owner}:${branchName}`, }); - + if (prs.length > 0) { prBody = prs[0].body || ''; prNumber = prs[0].number.toString(); @@ -103,7 +103,7 @@ jobs: core.warning(`Error searching for PR: ${error.message}`); } } - + return { prBody, prNumber }; - name: Parse config and set image tag @@ -118,7 +118,7 @@ jobs: if [[ "$IS_PR" == "true" ]]; then pip install pyyaml --quiet python3 .github/scripts/parse_pr_config.py - + # Set PR-specific image tag if [ -n "$PR_NUMBER" ]; then echo "image_tag=pr-${PR_NUMBER}" >> $GITHUB_OUTPUT @@ -130,15 +130,15 @@ jobs: else # Main/nightly: check if image already exists before building echo "image_tag=${{ github.sha }}" >> $GITHUB_OUTPUT - + # Check if 'latest' already points to current SHA (tests passed previously) OWNER_LOWER=$(echo '${{ github.repository_owner }}' | tr '[:upper:]' '[:lower:]') export REGISTRY_IMAGE="ghcr.io/${OWNER_LOWER}/${{ steps.context.outputs.image_name }}" export IMAGE_TAG="${{ github.sha }}" export IS_PR="false" - + python3 .github/scripts/check_image_exists.py - + # Read the skipped output from check_image_exists.py if [ -f "$GITHUB_OUTPUT" ] && grep -q "skipped=true" "$GITHUB_OUTPUT"; then echo "✅ Image already exists and tests passed, skipping build" @@ -154,7 +154,7 @@ jobs: echo "run_sanity=true" >> $GITHUB_OUTPUT fi fi - + # Pass through image name from context echo "image_name=${{ steps.context.outputs.image_name }}" >> $GITHUB_OUTPUT @@ -187,7 +187,7 @@ jobs: steps: - name: Checkout code uses: actions/checkout@v4 - + - name: Download TileGym wheel (Python 3.10, x86_64) uses: actions/download-artifact@v4 with: @@ -199,7 +199,7 @@ jobs: run: | OWNER_LOWER=$(echo '${{ github.repository_owner }}' | tr '[:upper:]' '[:lower:]') REGISTRY_IMAGE="ghcr.io/${OWNER_LOWER}/${{ needs.config.outputs.image_name }}" - + echo "owner_lower=${OWNER_LOWER}" >> $GITHUB_OUTPUT echo "registry_image=${REGISTRY_IMAGE}" >> $GITHUB_OUTPUT @@ -227,13 +227,13 @@ jobs: run: | TAGS="${{ steps.vars.outputs.registry_image }}:${{ needs.config.outputs.image_tag }}" TAGS="${TAGS},${{ steps.vars.outputs.registry_image }}:${{ github.sha }}" - + # Add datetime tag for nightly builds if [[ "${{ needs.config.outputs.is_pr }}" == "false" ]]; then DATETIME=$(date -u +%Y%m%d-%H%M%S) TAGS="${TAGS},${{ steps.vars.outputs.registry_image }}:nightly-${DATETIME}" fi - + echo "tags=${TAGS}" >> $GITHUB_OUTPUT - name: Build and push Docker image to GHCR @@ -242,7 +242,7 @@ jobs: with: context: . file: ./modeling/transformers/Dockerfile - target: wheel # Use wheel target for CI builds + target: source tags: ${{ steps.tags.outputs.tags }} push: true provenance: false @@ -312,21 +312,22 @@ jobs: run: | OWNER_LOWER=$(echo '${{ github.repository_owner }}' | tr '[:upper:]' '[:lower:]') IMAGE="ghcr.io/${OWNER_LOWER}/${{ needs.config.outputs.image_name }}:${{ needs.config.outputs.image_tag }}" - + docker pull ${IMAGE} docker run --rm \ --gpus all \ -e DISABLE_AUTOTUNE=1 \ -v ${{ github.workspace }}/tests:/workspace/tilegym/tests \ -v ${{ github.workspace }}/test-results:/test-results \ - -w /workspace/tilegym \ + -w /workspace/tilegym/modeling/transformers \ ${IMAGE} \ - bash -c "pip install --no-cache-dir pytest-xdist pytest-html && \ - pytest -s tests/ops tests/suites -v -k test_op \ + uv run --locked --no-sync python -m pytest \ + /workspace/tilegym/tests/ops /workspace/tilegym/tests/suites \ + -v -k test_op \ -n 12 \ --junitxml=/test-results/ops-results.xml \ --html=/test-results/ops-report.html \ - --self-contained-html" + --self-contained-html - name: Upload test results if: always() @@ -349,7 +350,7 @@ jobs: needs: [config, build] timeout-minutes: 70 if: | - always() && + always() && needs.config.outputs.run_benchmark == 'true' && (needs.build.result == 'success' || needs.build.result == 'skipped') runs-on: linux-amd64-gpu-rtxpro6000-latest-1 @@ -375,21 +376,21 @@ jobs: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | echo "Attempting to download baseline benchmark results..." - + # Find the most recent successful workflow run on main with baseline artifact RUN_ID=$(gh api \ -H "Accept: application/vnd.github+json" \ -H "X-GitHub-Api-Version: 2022-11-28" \ "/repos/${{ github.repository }}/actions/workflows/tilegym-ci.yml/runs?branch=main&status=success&per_page=10" \ --jq '.workflow_runs[].id' | head -1) - + if [ -z "$RUN_ID" ]; then echo "⚠️ No successful workflow runs found on main branch" exit 0 fi - + echo "Found workflow run: $RUN_ID" - + # Download baseline artifact from that run mkdir -p ${{ github.workspace }}/baseline-results if gh run download "$RUN_ID" \ @@ -413,15 +414,15 @@ jobs: run: | OWNER_LOWER=$(echo '${{ github.repository_owner }}' | tr '[:upper:]' '[:lower:]') IMAGE="ghcr.io/${OWNER_LOWER}/${{ needs.config.outputs.image_name }}:${{ needs.config.outputs.image_tag }}" - + docker pull ${IMAGE} docker run --rm \ --gpus all \ -v ${{ github.workspace }}/tests/benchmark:/workspace/tilegym/tests/benchmark \ -v ${{ github.workspace }}/test-results:/test-results \ - -w /workspace/tilegym/tests/benchmark \ + -w /workspace/tilegym/modeling/transformers \ ${IMAGE} \ - ./run_all.sh /test-results --json + uv run --locked --no-sync bash /workspace/tilegym/tests/benchmark/run_all.sh /test-results --json # Compare current results against baseline with three zones: # - Regression zone (< -5%): Build fails @@ -437,7 +438,7 @@ jobs: if [ -d "${{ github.workspace }}/baseline-results" ] && [ "$(ls -A ${{ github.workspace }}/baseline-results/*.json 2>/dev/null)" ]; then echo "Baseline results found, checking for regressions..." echo "has_baseline=true" >> $GITHUB_OUTPUT - + if python3 .github/scripts/check_benchmark_regression.py \ --current test-results \ --baseline baseline-results \ @@ -447,11 +448,11 @@ jobs: --fail-on-regression; then echo "✅ No regressions detected" echo "passed=true" >> $GITHUB_OUTPUT - + # Check if we should update baseline (only if significant improvements) SHOULD_UPDATE=$(python3 -c "import json; print(json.load(open('test-results/regression_report.json'))['summary']['should_update_baseline'])" 2>/dev/null || echo "false") echo "should_update_baseline=${SHOULD_UPDATE}" >> $GITHUB_OUTPUT - + if [ "$SHOULD_UPDATE" == "True" ]; then echo "🎉 Significant improvements detected - will update baseline (nightly only)" else @@ -470,7 +471,7 @@ jobs: echo "passed=true" >> $GITHUB_OUTPUT echo "should_update_baseline=true" >> $GITHUB_OUTPUT fi - + # Note: PR builds check for regressions but cannot update the baseline if [ "${{ needs.config.outputs.is_pr }}" == "true" ]; then echo "" @@ -511,13 +512,13 @@ jobs: steps.regression_check.outputs.has_baseline == 'true' run: | mkdir -p ${{ github.workspace }}/merged-baseline - + python3 .github/scripts/merge_baseline_selective.py \ --old-baseline baseline-results \ --new-results test-results \ --regression-report test-results/regression_report.json \ --output merged-baseline - + - name: Update baseline (nightly only - selective or full) if: needs.config.outputs.is_pr == 'false' uses: actions/upload-artifact@v4 @@ -526,7 +527,7 @@ jobs: # Use merged baseline if it exists (partial update), otherwise use all new results (first run or all improved) path: ${{ steps.regression_check.outputs.has_baseline == 'true' && 'merged-baseline/*.json' || 'test-results/*.json' }} retention-days: 90 - + - name: Log baseline decision if: always() run: | @@ -544,13 +545,13 @@ jobs: TOTAL_FILES=$(python3 -c "import json; print(json.load(open('test-results/regression_report.json'))['summary']['total_benchmark_files'])" 2>/dev/null || echo "0") FILES_WITH_REGRESSIONS=$(python3 -c "import json; print(json.load(open('test-results/regression_report.json'))['summary']['files_with_regressions'])" 2>/dev/null || echo "0") FILES_SAFE_TO_UPDATE=$(python3 -c "import json; print(json.load(open('test-results/regression_report.json'))['summary']['files_safe_to_update'])" 2>/dev/null || echo "0") - + echo "📊 Baseline Update Summary:" echo " Total benchmark files: $TOTAL_FILES" echo " Files with regressions: $FILES_WITH_REGRESSIONS" echo " Files updated: $FILES_SAFE_TO_UPDATE" echo "" - + if [ "$FILES_WITH_REGRESSIONS" -gt 0 ]; then echo "⚠️ SELECTIVE UPDATE: Some benchmarks regressed, keeping old baseline for those" echo " ✅ Updated baseline for $FILES_SAFE_TO_UPDATE non-regressing benchmarks" @@ -612,7 +613,7 @@ jobs: OWNER_LOWER=$(echo '${{ github.repository_owner }}' | tr '[:upper:]' '[:lower:]') IMAGE="ghcr.io/${OWNER_LOWER}/${{ needs.config.outputs.image_name }}" SHA="${{ github.sha }}" - + echo "Promoting ${IMAGE}:${SHA} to latest and adding verified tags (tests passed)" docker buildx imagetools create \ -t ${IMAGE}:latest \