diff --git a/.github/workflows/python-tests.yaml b/.github/workflows/python-tests.yaml index 8f928da1..a4b8fe49 100644 --- a/.github/workflows/python-tests.yaml +++ b/.github/workflows/python-tests.yaml @@ -13,17 +13,23 @@ jobs: runs-on: ubuntu-latest steps: + - name: Maximize build space + uses: AdityaGarg8/remove-unwanted-software@v4.1 + with: + remove-android: 'true' + - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: - python-version: 3.8 + python-version: 3.11 + + - name: Install Poetry + run: python -m pip install --upgrade pip poetry - name: Install Dependencies - run: | - python -m pip install --upgrade pip poetry - poetry install + run: poetry -C install/apple install - name: Run Tests - run: poetry run python -m unittest discover tests/ + run: poetry -C install/apple run python -m unittest discover tests/ \ No newline at end of file diff --git a/OPTIONS.md b/OPTIONS.md index 62d2b24b..67f1b1ec 100644 --- a/OPTIONS.md +++ b/OPTIONS.md @@ -25,7 +25,7 @@ The script `configure.py` in the project root can be used via `python configure. - **Default**: lora - If lora is used, `--lora_type` dictates whether PEFT or LyCORIS are in use. Some models (PixArt) work only with LyCORIS adapters. -## `--model_family` +### `--model_family` - **What**: Determines which model architecture is being trained. - **Choices**: pixart_sigma, flux, sd3, sdxl, kolors, legacy @@ -40,20 +40,70 @@ The script `configure.py` in the project root can be used via `python configure. - **What**: Path to the pretrained T5 model or its identifier from https://huggingface.co/models. - **Why**: When training PixArt, you might want to use a specific source for your T5 weights so that you can avoid downloading them multiple times when switching the base model you train from. -### `--hub_model_id` +### `--refiner_training` -- **What**: The name of the Huggingface Hub model and local results directory. -- **Why**: This value is used as the directory name under the location specified as `--output_dir`. If `--push_to_hub` is provided, this will become the name of the model on Huggingface Hub. +- **What**: Enables training a custom mixture-of-experts model series. See [Mixture-of-Experts](/documentation/MIXTURE_OF_EXPERTS.md) for more information on these options. + +## Precision + +### `--quantize_via` + +- **Choices**: `cpu`, `accelerator` + - On `accelerator`, it may work moderately faster at the risk of possibly OOM'ing on 24G cards for a model as large as Flux. + - On `cpu`, quantisation takes about 30 seconds. (**Default**) + + +### `--base_model_precision` + +- **What**: Reduce model precision and train using less memory. There are currently two supported quantisation backends: quanto and torchao. + +#### Optimum Quanto + +Provided by Hugging Face, the optimum-quanto library has robust support across all supported platforms. + +- `int8-quanto` is the most broadly compatible and probably produces the best results + - uses hardware-accelerated matmul on CUDA devices for int8, int4 + - works with `TRAINING_DYNAMO_BACKEND=inductor` (`torch.compile()`) +- `fp8uz-quanto` is an experimental fp8 variant for CUDA and ROCm devices. + - better-supported on AMD silicon such as Instinct or newer architecture + - can be slightly faster than `int8-quanto` on a 4090 for training, but not inference (1 second slower) + - works with `TRAINING_DYNAMO_BACKEND=inductor` (`torch.compile()`) +- `fp8-quanto` will not (currently) use fp8 matmul, does not work on Apple systems. + - does not have hardware fp8 matmul yet on CUDA or ROCm devices, so it will possibly be noticeably slower than int8 + - incompatible with dynamo, will automatically switch to `int8-quanto` for you and keep dynamo enabled for speedup. + +#### TorchAO + +A newer library from Pytorch, AO allows us to replace the linears and 2D convolutions (eg. unet style models) with quantised counterparts. + + +- `int8-torchao` will reduce memory consumption to the same level as any of Quanto's precision levels + - at the time of writing, runs slightly slower (11s/iter) than Quanto does (9s/iter) on Apple MPS + - Same speed and memory use as `int8-quanto` on CUDA devices, unknown speed profile on ROCm +- `fp8-torchao` is not enabled for use due to bugs in the implementation. + +#### Torch Dynamo + +To enable `torch.compile()`, add the following line to `config/config.env`: +```bash +TRAINING_DYNAMO_BACKEND=inductor +``` + +Note that the first several steps of training will be slower than usual because of compilation occuring in the background. --- +## 📰 Publishing + ### `--push_to_hub` - **What**: If provided, your model will be uploaded to [Huggingface Hub](https://huggingface.co) once training completes. Using `--push_checkpoints_to_hub` will additionally push every intermediary checkpoint. -### `--refiner_training` +### `--hub_model_id` + +- **What**: The name of the Huggingface Hub model and local results directory. +- **Why**: This value is used as the directory name under the location specified as `--output_dir`. If `--push_to_hub` is provided, this will become the name of the model on Huggingface Hub. -- **What**: Enables training a custom mixture-of-experts model series. See [Mixture-of-Experts](/documentation/MIXTURE_OF_EXPERTS.md) for more information on these options. ### `--disable_benchmark` @@ -122,26 +172,19 @@ A lot of settings are instead set through the [dataloader config](/documentation - All images in the dataset will have their smaller edge resized to this resolution for training, which could result in a lot of VRAM use due to the size of the resulting images. - Example resulting sizes for `1024`: 1024x1024, 1766x1024, 1024x1766 - `resolution_type=area` - - An internal option that isn't user-friendly. Use `pixel_area` instead. + - **Deprecated**. Use `pixel_area` instead. ### `--resolution` - **What**: Input image resolution expressed in pixel edge length - **Default**: 1024 +- **Note**: This is the global default, if a dataset does not have a resolution set. ### `--validation_resolution` - **What**: Output image resolution, measured in pixels, or, formatted as: `widthxheight`, as in `1024x1024`. Multiple resolutions can be defined, separated by commas. - **Why**: All images generated during validation will be this resolution. Useful if the model is being trained with a different resolution. -### `--caption_strategy` - -- **What**: Strategy for deriving image captions. **Choices**: `textfile`, `filename`, `parquet`, `instanceprompt` -- **Why**: Determines how captions are generated for training images. - - `textfile` will use the contents of a `.txt` file with the same filename as the image - - `filename` will apply some cleanup to the filename before using it as the caption. - - `parquet` requires a parquet file to be present in the dataset, and will use the `caption` column as the caption unless `parquet_caption_column` is provided. All captions must be present unless a `parquet_fallback_caption_column` is provided. - - `instanceprompt` will use the value for `instance_prompt` in the dataset config as the prompt for every image in the dataset. ### `--crop` @@ -162,6 +205,15 @@ A lot of settings are instead set through the [dataloader config](/documentation - `crop_aspect=random` will use a random aspect value from `crop_aspect_buckets` without going too far - it will use square crops if your aspects are incompatible - `crop_aspect=square` will use the standard square crop style +### `--caption_strategy` + +- **What**: Strategy for deriving image captions. **Choices**: `textfile`, `filename`, `parquet`, `instanceprompt` +- **Why**: Determines how captions are generated for training images. + - `textfile` will use the contents of a `.txt` file with the same filename as the image + - `filename` will apply some cleanup to the filename before using it as the caption. + - `parquet` requires a parquet file to be present in the dataset, and will use the `caption` column as the caption unless `parquet_caption_column` is provided. All captions must be present unless a `parquet_fallback_caption_column` is provided. + - `instanceprompt` will use the value for `instance_prompt` in the dataset config as the prompt for every image in the dataset. + --- ## 🎛 Training Parameters @@ -176,11 +228,27 @@ A lot of settings are instead set through the [dataloader config](/documentation - **What**: Number of training steps to exit training after. If set to 0, will allow `--num_train_epochs` to take priority. - **Why**: Useful for shortening the length of training. +### `--learning_rate` + +- **What**: Initial learning rate after potential warmup. +- **Why**: The learning rate behaves as a sort of "step size" for gradient updates - too high, and we overstep the solution. Too low, and we never reach the ideal solution. A minimal value for a `full` tune might be as low as `1e-7` to a max of `1e-6` while for `lora` tuning a minimal value might be `1e-5` with a maximal value as high as `1e-3`. When a higher learning rate is used, it's advantageous to use an EMA network with a learning rate warmup - see `--use_ema`, `--lr_warmup_steps`, and `--lr_scheduler`. + +### `--lr_scheduler` + +- **What**: How to scale the learning rate over time. +- **Choices**: constant, constant_with_warmup, cosine, cosine_with_restarts, **polynomial** (recommended), linear +- **Why**: Models benefit from continual learning rate adjustments to further explore the loss landscape. A cosine schedule is used as the default; this allows the training to smoothly transition between two extremes. If using a constant learning rate, it is common to select a too-high or too-low value, causing divergence (too high) or getting stuck in a local minima (too low). A polynomial schedule is best paired with a warmup, where it will gradually approach the `learning_rate` value before then slowing down and approaching `--lr_end` by the end. + ### `--train_batch_size` - **What**: Batch size for the training data loader. - **Why**: Affects the model's memory consumption, convergence quality, and training speed. The higher the batch size, the better the results will be, but a very high batch size might result in overfitting or destabilized training, as well as increasing the duration of the training session unnecessarily. Experimentation is warranted, but in general, you want to try to max out your video memory while not decreasing the training speed. +### `--gradient_accumulation_steps` + +- **What**: Number of update steps to accumulate before performing a backward/update pass, essentially splitting the work over multiple batches to save memory at the cost of a higher training runtime. +- **Why**: Useful for handling larger models or datasets. + --- ## 🛠 Advanced Optimizations @@ -206,21 +274,6 @@ A lot of settings are instead set through the [dataloader config](/documentation - **What**: Reduce the update interval of your EMA shadow parameters. - **Why**: Updating the EMA weights on every step could be an unnecessary waste of resources. Providing `--ema_update_interval=100` will update the EMA weights only once every 100 optimizer steps. -### `--gradient_accumulation_steps` - -- **What**: Number of update steps to accumulate before performing a backward/update pass, essentially splitting the work over multiple batches to save memory at the cost of a higher training runtime. -- **Why**: Useful for handling larger models or datasets. - -### `--learning_rate` - -- **What**: Initial learning rate after potential warmup. -- **Why**: The learning rate behaves as a sort of "step size" for gradient updates - too high, and we overstep the solution. Too low, and we never reach the ideal solution. A minimal value for a `full` tune might be as low as `1e-7` to a max of `1e-6` while for `lora` tuning a minimal value might be `1e-5` with a maximal value as high as `1e-3`. When a higher learning rate is used, it's advantageous to use an EMA network with a learning rate warmup - see `--use_ema`, `--lr_warmup_steps`, and `--lr_scheduler`. - -### `--lr_scheduler` - -- **What**: How to scale the learning rate over time. -- **Choices**: constant, constant_with_warmup, cosine, cosine_with_restarts, **polynomial** (recommended), linear -- **Why**: Models benefit from continual learning rate adjustments to further explore the loss landscape. A cosine schedule is used as the default; this allows the training to smoothly transition between two extremes. If using a constant learning rate, it is common to select a too-high or too-low value, causing divergence (too high) or getting stuck in a local minima (too low). A polynomial schedule is best paired with a warmup, where it will gradually approach the `learning_rate` value before then slowing down and approaching `--lr_end` by the end. ### `--snr_gamma` @@ -260,7 +313,18 @@ A lot of settings are instead set through the [dataloader config](/documentation ### `--report_to` - **What**: Specifies the platform for reporting results and logs. -- **Why**: Enables integration with platforms like TensorBoard, wandb, or comet_ml for monitoring. +- **Why**: Enables integration with platforms like TensorBoard, wandb, or comet_ml for monitoring. Use multiple values separated by a comma to report to multiple trackers; +- **Choices**: wandb, tensorboard, comet_ml + +# Environment configuration variables + +The above options apply for the most part, to `config.json` - but some entries must be set inside `config.env` instead. + +- `TRAINING_NUM_PROCESSES` should be set to the number of GPUs in the system. For most use-cases, this is enough to enable DistributedDataParallel (DDP) training +- `TRAINING_DYNAMO_BACKEND` defaults to `no` but can be set to `inductor` for substantial speed improvements on NVIDIA hardware +- `SIMPLETUNER_LOG_LEVEL` defaults to `INFO` but can be set to `DEBUG` to add more information for issue reports into `debug.log` +- `VENV_PATH` can be set to the location of your python virtual env, if it is not in the typical `.venv` location +- `ACCELERATE_EXTRA_ARGS` can be left unset, or, contain extra arguments to add like `--multi_gpu` or FSDP-specific flags --- @@ -281,7 +345,8 @@ usage: train.py [-h] [--snr_gamma SNR_GAMMA] [--use_soft_min_snr] [--flux_guidance_value FLUX_GUIDANCE_VALUE] [--flux_guidance_min FLUX_GUIDANCE_MIN] [--flux_guidance_max FLUX_GUIDANCE_MAX] - [--flux_attention_masked_training] [--smoldit] + [--flux_attention_masked_training] + [--t5_padding {zero,unmodified}] [--smoldit] [--smoldit_config {smoldit-small,smoldit-swiglu,smoldit-base,smoldit-large,smoldit-huge}] [--flow_matching_loss {diffusers,compatible,diffusion}] [--sd3_t5_mask_behaviour {do-nothing,mask}] @@ -289,7 +354,8 @@ usage: train.py [-h] [--snr_gamma SNR_GAMMA] [--use_soft_min_snr] [--lora_init_type {default,gaussian,loftq,olora,pissa}] [--init_lora INIT_LORA] [--lora_rank LORA_RANK] [--lora_alpha LORA_ALPHA] [--lora_dropout LORA_DROPOUT] - [--lycoris_config LYCORIS_CONFIG] [--controlnet] + [--lycoris_config LYCORIS_CONFIG] + [--init_lokr_norm INIT_LOKR_NORM] [--controlnet] [--controlnet_model_name_or_path] --pretrained_model_name_or_path PRETRAINED_MODEL_NAME_OR_PATH [--pretrained_transformer_model_name_or_path PRETRAINED_TRANSFORMER_MODEL_NAME_OR_PATH] @@ -323,7 +389,7 @@ usage: train.py [-h] [--snr_gamma SNR_GAMMA] [--use_soft_min_snr] [--cache_dir_vae CACHE_DIR_VAE] [--data_backend_config DATA_BACKEND_CONFIG] [--data_backend_sampling {uniform,auto-weighting}] - [--write_batch_size WRITE_BATCH_SIZE] + [--ignore_missing_files] [--write_batch_size WRITE_BATCH_SIZE] [--read_batch_size READ_BATCH_SIZE] [--image_processing_batch_size IMAGE_PROCESSING_BATCH_SIZE] [--enable_multiprocessing] [--max_workers MAX_WORKERS] @@ -364,8 +430,10 @@ usage: train.py [-h] [--snr_gamma SNR_GAMMA] [--use_soft_min_snr] [--ema_update_interval EMA_UPDATE_INTERVAL] [--ema_decay EMA_DECAY] [--non_ema_revision NON_EMA_REVISION] [--offload_param_path OFFLOAD_PARAM_PATH] --optimizer - {adamw_bf16,adamw_schedulefree,adamw_schedulefree+aggressive,adamw_schedulefree+no_kahan,optimi-stableadamw,optimi-adamw,optimi-lion,optimi-radam,optimi-ranger,optimi-adan,optimi-adam,optimi-sgd} + {adamw_bf16,ao-adamw8bit,ao-adamw4bit,ao-adamfp8,ao-adamwfp8,adamw_schedulefree,adamw_schedulefree+aggressive,adamw_schedulefree+no_kahan,optimi-stableadamw,optimi-adamw,optimi-lion,optimi-radam,optimi-ranger,optimi-adan,optimi-adam,optimi-sgd} [--optimizer_config OPTIMIZER_CONFIG] + [--optimizer_cpu_offload_method {none,torchao}] + [--optimizer_offload_gradients] [--fuse_optimizer] [--optimizer_beta1 OPTIMIZER_BETA1] [--optimizer_beta2 OPTIMIZER_BETA2] [--optimizer_release_gradients] [--adam_beta1 ADAM_BETA1] @@ -378,11 +446,12 @@ usage: train.py [-h] [--snr_gamma SNR_GAMMA] [--use_soft_min_snr] [--model_card_safe_for_work] [--logging_dir LOGGING_DIR] [--benchmark_base_model] [--disable_benchmark] [--validation_on_startup] [--validation_seed_source {gpu,cpu}] - [--validation_torch_compile VALIDATION_TORCH_COMPILE] + [--validation_torch_compile] [--validation_torch_compile_mode {max-autotune,reduce-overhead,default}] [--allow_tf32] [--disable_tf32] [--validation_using_datasets] - [--webhook_config WEBHOOK_CONFIG] [--report_to REPORT_TO] - [--tracker_run_name TRACKER_RUN_NAME] + [--webhook_config WEBHOOK_CONFIG] + [--webhook_reporting_interval WEBHOOK_REPORTING_INTERVAL] + [--report_to REPORT_TO] [--tracker_run_name TRACKER_RUN_NAME] [--tracker_project_name TRACKER_PROJECT_NAME] [--tracker_image_layout {gallery,table}] [--validation_prompt VALIDATION_PROMPT] @@ -399,11 +468,12 @@ usage: train.py [-h] [--snr_gamma SNR_GAMMA] [--use_soft_min_snr] [--validation_disable_unconditional] [--enable_watermark] [--mixed_precision {bf16,no}] [--gradient_precision {unmodified,fp32}] - [--base_model_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto}] + [--quantize_via {cpu,accelerator}] + [--base_model_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao}] [--base_model_default_dtype {bf16,fp32}] - [--text_encoder_1_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto}] - [--text_encoder_2_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto}] - [--text_encoder_3_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto}] + [--text_encoder_1_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao}] + [--text_encoder_2_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao}] + [--text_encoder_3_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao}] [--local_rank LOCAL_RANK] [--enable_xformers_memory_efficient_attention] [--set_grads_to_none] [--noise_offset NOISE_OFFSET] @@ -531,6 +601,10 @@ options: --flux_guidance_max FLUX_GUIDANCE_MAX --flux_attention_masked_training Use attention masking while training flux. + --t5_padding {zero,unmodified} + The padding behaviour for Flux. The default is 'zero', + which will pad the input with zeros. The alternative + is 'unmodified', which will not pad the input. --smoldit Use the experimental SmolDiT model architecture. --smoldit_config {smoldit-small,smoldit-swiglu,smoldit-base,smoldit-large,smoldit-huge} The SmolDiT configuration to use. This is a list of @@ -588,6 +662,10 @@ options: --lycoris_config LYCORIS_CONFIG The location for the JSON file of the Lycoris configuration. + --init_lokr_norm INIT_LOKR_NORM + Setting this turns on perturbed normal initialization + of the LyCORIS LoKr PEFT layers. A good value is + between 1e-4 and 1e-2. --controlnet If set, ControlNet style training will be used, where a conditioning input image is required alongside the training data. @@ -802,6 +880,13 @@ options: automatically adjust the sampling weights based on the number of images in each backend. 'uniform' will sample from each backend equally. + --ignore_missing_files + This option will disable the check for files that have + been deleted or removed from your data directory. This + would allow training on large datasets without keeping + the associated images on disk, though it's not + recommended and is not a supported feature. Use with + caution, as it mostly exists for experimentation. --write_batch_size WRITE_BATCH_SIZE When using certain storage backends, it is better to batch smaller writes rather than continuous @@ -1035,12 +1120,22 @@ options: When using DeepSpeed ZeRo stage 2 or 3 with NVMe offload, this may be specified to provide a path for the offload. - --optimizer {adamw_bf16,adamw_schedulefree,adamw_schedulefree+aggressive,adamw_schedulefree+no_kahan,optimi-stableadamw,optimi-adamw,optimi-lion,optimi-radam,optimi-ranger,optimi-adan,optimi-adam,optimi-sgd} + --optimizer {adamw_bf16,ao-adamw8bit,ao-adamw4bit,ao-adamfp8,ao-adamwfp8,adamw_schedulefree,adamw_schedulefree+aggressive,adamw_schedulefree+no_kahan,optimi-stableadamw,optimi-adamw,optimi-lion,optimi-radam,optimi-ranger,optimi-adan,optimi-adam,optimi-sgd} --optimizer_config OPTIMIZER_CONFIG When setting a given optimizer, this allows a comma- separated list of key-value pairs to be provided that will override the optimizer defaults. For example, `-- optimizer_config=decouple_lr=True,weight_decay=0.01`. + --optimizer_cpu_offload_method {none,torchao} + When loading an optimiser, a CPU offload mechanism can + be used. Currently, no offload is used by default, and + only torchao is supported. + --optimizer_offload_gradients + When creating a CPU-offloaded optimiser, the gradients + can be offloaded to the CPU to save more memory. + --fuse_optimizer When creating a CPU-offloaded optimiser, the fused + optimiser could be used to save on memory, while + running slightly slower. --optimizer_beta1 OPTIMIZER_BETA1 The value to use for the first beta value in the optimiser, which is used for the first moment @@ -1107,7 +1202,7 @@ options: validation errors. If so, please set SIMPLETUNER_LOG_LEVEL=DEBUG and submit debug.log to a new Github issue report. - --validation_torch_compile VALIDATION_TORCH_COMPILE + --validation_torch_compile Supply `--validation_torch_compile=true` to enable the use of torch.compile() on the validation pipeline. For some setups, torch.compile() may error out. This is @@ -1134,6 +1229,11 @@ options: should be a JSON file with the following format: {"url": "https://your.webhook.url", "webhook_type": "discord"}} + --webhook_reporting_interval WEBHOOK_REPORTING_INTERVAL + When using 'raw' webhooks that receive structured + data, you can specify a reporting interval here for + training progress updates to be sent at. This does not + impact 'discord' webhook types. --report_to REPORT_TO The integration to report the results and logs to. Supported platforms are `"tensorboard"` (default), @@ -1225,7 +1325,14 @@ options: accumulation steps are enabled is now to use fp32 gradients, which is slower, but provides more accurate updates. - --base_model_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto} + --quantize_via {cpu,accelerator} + When quantising the model, the quantisation process + can be done on the CPU or the accelerator. When done + on the accelerator (default), slightly more VRAM is + required, but the process completes in milliseconds. + When done on the CPU, the process may take upwards of + 60 seconds, but can complete without OOM on 16G cards. + --base_model_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao} When training a LoRA, you might want to quantise the base model to a lower precision to save more VRAM. The default value, 'no_change', does not quantise any @@ -1243,7 +1350,7 @@ options: optimizers than adamw_bf16. However, this uses marginally more memory, and may not be necessary for your use case. - --text_encoder_1_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto} + --text_encoder_1_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao} When training a LoRA, you might want to quantise text encoder 1 to a lower precision to save more VRAM. The default value is to follow base_model_precision @@ -1251,7 +1358,7 @@ options: Bits n Bytes for quantisation (NVIDIA, maybe AMD). Using 'fp8-quanto' will require Quanto for quantisation (Apple Silicon, NVIDIA, AMD). - --text_encoder_2_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto} + --text_encoder_2_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao} When training a LoRA, you might want to quantise text encoder 2 to a lower precision to save more VRAM. The default value is to follow base_model_precision @@ -1259,7 +1366,7 @@ options: Bits n Bytes for quantisation (NVIDIA, maybe AMD). Using 'fp8-quanto' will require Quanto for quantisation (Apple Silicon, NVIDIA, AMD). - --text_encoder_3_precision {no_change,fp8-quanto,int8-quanto,int4-quanto,int2-quanto} + --text_encoder_3_precision {no_change,fp8-quanto,fp8uz-quanto,int8-quanto,int4-quanto,int2-quanto,int8-torchao} When training a LoRA, you might want to quantise text encoder 3 to a lower precision to save more VRAM. The default value is to follow base_model_precision diff --git a/configure.py b/configure.py index 51b1c53e..fe961475 100644 --- a/configure.py +++ b/configure.py @@ -5,10 +5,14 @@ from helpers.training.optimizer_param import optimizer_choices bf16_only_optims = [ - key for key, value in optimizer_choices.items() if value["precision"] == "bf16" + key + for key, value in optimizer_choices.items() + if value.get("precision", "any") == "bf16" ] any_precision_optims = [ - key for key, value in optimizer_choices.items() if value["precision"] == "any" + key + for key, value in optimizer_choices.items() + if value.get("precision", "any") == "any" ] model_classes = { "full": [ @@ -17,10 +21,10 @@ "pixart_sigma", "kolors", "sd3", - "stable_diffusion_legacy", + "legacy", ], - "lora": ["flux", "sdxl", "kolors", "sd3", "stable_diffusion_legacy"], - "controlnet": ["sdxl", "stable_diffusion_legacy"], + "lora": ["flux", "sdxl", "kolors", "sd3", "legacy"], + "controlnet": ["sdxl", "legacy"], } default_models = { @@ -30,6 +34,7 @@ "kolors": "kwai-kolors/kolors-diffusers", "terminus": "ptx0/terminus-xl-velocity-v2", "sd3": "stabilityai/stable-diffusion-3-medium-diffusers", + "legacy": "stabilityai/stable-diffusion-2-1-base", } default_cfg = { diff --git a/documentation/quickstart/FLUX.md b/documentation/quickstart/FLUX.md index 34a5bd23..4e4f61c9 100644 --- a/documentation/quickstart/FLUX.md +++ b/documentation/quickstart/FLUX.md @@ -2,24 +2,26 @@ ![image](https://github.com/user-attachments/assets/6409d790-3bb4-457c-a4b4-a51a45fc91d1) -In this example, we'll be training a Flux.1 LoRA model using the SimpleTuner toolkit. +In this example, we'll be training a Flux.1 LoRA. ### Hardware requirements Flux requires a lot of **system RAM** in addition to GPU memory. Simply quantising the model at startup requires about 50GB of system memory. If it takes an excessively long time, you may need to assess your hardware's capabilities and whether any changes are needed. When you're training every component of a rank-16 LoRA (MLP, projections, multimodal blocks), it ends up using: -- a bit more than 32G VRAM when not quantising the base model -- a bit more than 20G VRAM when quantising to int8 + bf16 base/LoRA weights -- a bit more than 13G VRAM when quantising to int2 + bf16 base/LoRA weights +- a bit more than 30G VRAM when not quantising the base model +- a bit more than 18G VRAM when quantising to int8 + bf16 base/LoRA weights +- a bit more than 13G VRAM when quantising to int4 + bf16 base/LoRA weights +- a bit more than 9G VRAM when quantising to int2 + bf16 base/LoRA weights -To have reliable results, you'll need: -- **at minimum** a single 3090 or V100 GPU -- **ideally** multiple A6000s +You'll need: +- **the absolute minimum** is a single 4060 Ti 16GB +- **a realistic minimum** is a single 3090 or V100 GPU +- **ideally** multiple 4090, A6000, L40S, or better Luckily, these are readily available through providers such as [LambdaLabs](https://lambdalabs.com) which provides the lowest available rates, and localised clusters for multi-node training. -**Unlike other models, AMD and Apple GPUs do not work for training Flux.** +**Unlike other models, Apple GPUs do not currently work for training Flux.** ### Prerequisites @@ -123,7 +125,7 @@ There, you will possibly need to modify the following variables: - `model_type` - Set this to `lora`. - `model_family` - Set this to `flux`. - `pretrained_model_name_or_path` - Set this to `black-forest-labs/FLUX.1-dev`. - - Note that you will *probably* need to log in to Huggingface and be granted access to download this model. We will go over logging in to Huggingface later in this tutorial. + - Note that you will need to log in to Huggingface and be granted access to download this model. We will go over logging in to Huggingface later in this tutorial. - `output_dir` - Set this to the directory where you want to store your checkpoints and validation images. It's recommended to use a full path here. - `train_batch_size` - this should be kept at 1, especially if you have a very small dataset. - `validation_resolution` - As Flux is a 1024px model, you can set this to `1024x1024`. @@ -139,6 +141,8 @@ There, you will possibly need to modify the following variables: - `optimizer` - Beginners are recommended to stick with adamw_bf16, though optimi-lion and optimi-stableadamw are also good choices. - `mixed_precision` - Beginners should keep this in `bf16` +Multi-GPU users can reference [this document](/OPTIONS.md#environment-configuration-variables) for information on configuring the number of GPUs to use. + #### Validation prompts Inside `config/config.json` is the "primary validation prompt", which is typically the main instance_prompt you are training on for your single subject or style. Additionally, a JSON file may be created that contains extra prompts to run through during validations. @@ -185,7 +189,7 @@ A set of diverse prompt will help determine whether the model is collapsing as i #### Quantised model training -Tested on Apple and NVIDIA systems, Hugging Face Optimum-Quanto can be used to reduce the precision and VRAM requirements, training Flux on just 20GB. +Tested on Apple and NVIDIA systems, Hugging Face Optimum-Quanto can be used to reduce the precision and VRAM requirements, training Flux on just 16GB. Inside your SimpleTuner venv: @@ -203,9 +207,7 @@ For `config.json` users: "base_model_default_dtype": "bf16" ``` -################################################# -# Below guidance is for LoRA, not LyCORIS. # -################################################# +##### LoRA-specific settings (not LyCORIS) ```bash @@ -215,6 +217,7 @@ For `config.json` users: # - This mode has been reported to lack portability, and platforms such as ComfyUI might not be able to load the LoRA. # The option to train only the 'context' blocks is offered as well, but its impact is unknown, and is offered as an experimental choice. # - An extension to this mode, 'context+ffs' is also available, which is useful for pretraining new tokens into a LoRA before continuing finetuning it via `--init_lora`. +# Other options include 'tiny' and 'nano' which train just 1 or 2 layers. "--flux_lora_target": "all", # If you want to use LoftQ initialisation, you can't use Quanto to quantise the base model. @@ -254,7 +257,8 @@ Create a `--data_backend_config` (`config/multidatabackend.json`) document conta "disabled": false, "skip_file_discovery": "", "caption_strategy": "filename", - "metadata_backend": "discovery" + "metadata_backend": "discovery", + "repeats": 0 }, { "id": "dreambooth-subject", @@ -269,7 +273,8 @@ Create a `--data_backend_config` (`config/multidatabackend.json`) document conta "instance_data_dir": "datasets/dreambooth-subject", "caption_strategy": "instanceprompt", "instance_prompt": "the name of your subject goes here", - "metadata_backend": "discovery" + "metadata_backend": "discovery", + "repeats": 1000 }, { "id": "dreambooth-subject-512", @@ -284,7 +289,8 @@ Create a `--data_backend_config` (`config/multidatabackend.json`) document conta "instance_data_dir": "datasets/dreambooth-subject", "caption_strategy": "instanceprompt", "instance_prompt": "the name of your subject goes here", - "metadata_backend": "discovery" + "metadata_backend": "discovery", + "repeats": 1000 }, { "id": "text-embeds", @@ -390,35 +396,32 @@ We can partially reintroduce distillation to a de-distilled model by continuing - Inference workflows for ComfyUI or other applications (eg. AUTOMATIC1111) will need to be modified to also enable "true" CFG, which might not be currently possible out of the box. ### Quantisation -- Minimum 8bit quantisation is required for a 24G card to train this model - but 32G (V100) cards suffer a more tragic fate. - - Without quantising the model, a rank-1 LoRA sits at just over 32GB of mem use, in a way that prevents a 32G V100 from actually working - - Using the optimi-lion optimiser may reduce training just enough to make the V100 work. -- Quantising the model doesn't harm training +- Minimum 8bit quantisation is required for a 16G card to train this model + - In bfloat16/float16, a rank-1 LoRA sits at just over 30GB of mem use +- Quantising the model to 8bit doesn't harm training - It allows you to push higher batch sizes and possibly obtain a better result - Behaves the same as full-precision training - fp32 won't make your model any better than bf16+int8. -- As usual, **fp8 quantisation runs more slowly** than **int8** and might have a worse result due to the use of `e4m3fn` in Quanto - - fp16 training similarly is bad for Flux; this model wants the range of bf16 - - `e5m2` level precision is better at fp8 but haven't looked into how to enable it yet. Sorry, H100 owners. We weep for you. +- **int8** has hardware acceleration and `torch.compile()` support on newer NVIDIA hardware (3090 or better) +- **nf4** does not seem to benefit training as much as it benefits inference - When loading the LoRA in ComfyUI later, you **must** use the same base model precision as you trained your LoRA on. -- `int4` is weird and really only works on A100 and H100 cards due to a reliance on custom bf16 kernels +- **int4** is weird and really only works on A100 and H100 cards due to a reliance on custom bf16 kernels ### Crashing - If you get SIGKILL after the text encoders are unloaded, this means you do not have enough system memory to quantise Flux. - Try loading the `--base_model_precision=bf16` but if that does not work, you might just need more memory.. + - Try `--quantize_via=accelerator` to use the GPU instead ### Schnell -- Direct Schnell training really needs a bit more time in the oven - currently, the results do not look good - - If you absolutely must train Schnell, try the x-flux trainer from X-Labs - - Ostris' ai-toolkit uses a low-rank adapter probably pulled from OpenFLUX.1 as a source of CFG that can be inverted from the final result - this will probably be implemented here eventually after results are more widely available and tests have completed -- Training a LoRA on Dev will however, run just fine on Schnell -- Dev+Schnell merge 50/50 just fine, and the LoRAs can possibly be trained from that, which will then run on Schnell **or** Dev +- If you train a LyCORIS LoKr on Dev, it **generally** works very well on Schnell at just 4 steps later. + - Direct Schnell training really needs a bit more time in the oven - currently, the results do not look good > ℹī¸ When merging Schnell with Dev in any way, the license of Dev takes over and it becomes non-commercial. This shouldn't really matter for most users, but it's worth noting. ### Learning rates #### LoRA (--lora_type=standard) -- It's been reported that Flux trains similarly to SD 1.5 LoRAs +- LoRA has overall worse performance than LoKr for larger datasets +- It's been reported that Flux LoRA trains similarly to SD 1.5 LoRAs - However, a model as large as 12B has empirically performed better with **lower learning rates.** - LoRA at 1e-3 might totally roast the thing. LoRA at 1e-5 does nearly nothing. - Ranks as large as 64 through 128 might be undesirable on a 12B model due to general difficulties that scale up with the size of the base model. diff --git a/helpers/caching/memory.py b/helpers/caching/memory.py index 7e7160ce..87cb66cc 100644 --- a/helpers/caching/memory.py +++ b/helpers/caching/memory.py @@ -3,11 +3,11 @@ def reclaim_memory(): import torch if torch.cuda.is_available(): + gc.collect() torch.cuda.empty_cache() torch.cuda.ipc_collect() if torch.backends.mps.is_available(): torch.mps.empty_cache() torch.mps.synchronize() - - gc.collect() + gc.collect() diff --git a/helpers/caching/text_embeds.py b/helpers/caching/text_embeds.py index bc850224..b8e2b0f4 100644 --- a/helpers/caching/text_embeds.py +++ b/helpers/caching/text_embeds.py @@ -767,11 +767,23 @@ def compute_embeddings_for_sdxl_prompts( self.debug_log(f"Adding embed to write queue: {filename}") self.save_to_cache(filename, (prompt_embeds, add_text_embeds)) - if self.webhook_handler is not None and int(self.write_thread_bar.n % self.webhook_progress_interval) < 10: - last_reported_index = int(self.write_thread_bar.n % self.webhook_progress_interval) + if ( + self.webhook_handler is not None + and int( + self.write_thread_bar.n % self.webhook_progress_interval + ) + < 10 + ): + last_reported_index = int( + self.write_thread_bar.n % self.webhook_progress_interval + ) self.send_progress_update( type="init_cache_text_embeds_status_update", - progress=int(self.write_thread_bar.n // len(local_caption_split) * 100), + progress=int( + self.write_thread_bar.n + // len(local_caption_split) + * 100 + ), total=len(local_caption_split), current=0, ) @@ -947,11 +959,23 @@ def compute_embeddings_for_legacy_prompts( self.save_to_cache(filename, prompt_embeds) - if self.webhook_handler is not None and int(self.write_thread_bar.n % self.webhook_progress_interval) < 10: - last_reported_index = int(self.write_thread_bar.n % self.webhook_progress_interval) + if ( + self.webhook_handler is not None + and int( + self.write_thread_bar.n % self.webhook_progress_interval + ) + < 10 + ): + last_reported_index = int( + self.write_thread_bar.n % self.webhook_progress_interval + ) self.send_progress_update( type="init_cache_text_embeds_status_update", - progress=int(self.write_thread_bar.n // len(local_caption_split) * 100), + progress=int( + self.write_thread_bar.n + // len(local_caption_split) + * 100 + ), total=len(local_caption_split), current=0, ) @@ -1118,15 +1142,27 @@ def compute_embeddings_for_flux_prompts( self.save_to_cache( filename, (prompt_embeds, add_text_embeds, time_ids, masks) ) - if self.webhook_handler is not None and int(self.write_thread_bar.n % self.webhook_progress_interval) < 10: - last_reported_index = int(self.write_thread_bar.n % self.webhook_progress_interval) + if ( + self.webhook_handler is not None + and int( + self.write_thread_bar.n % self.webhook_progress_interval + ) + < 10 + ): + last_reported_index = int( + self.write_thread_bar.n % self.webhook_progress_interval + ) self.send_progress_update( type="init_cache_text_embeds_status_update", - progress=int(self.write_thread_bar.n // len(local_caption_split) * 100), + progress=int( + self.write_thread_bar.n + // len(local_caption_split) + * 100 + ), total=len(local_caption_split), current=0, ) - + if return_concat: prompt_embeds = prompt_embeds.to(self.accelerator.device) add_text_embeds = add_text_embeds.to(self.accelerator.device) @@ -1292,11 +1328,23 @@ def compute_embeddings_for_sd3_prompts( self.debug_log(f"Adding embed to write queue: {filename}") self.save_to_cache(filename, (prompt_embeds, add_text_embeds)) - if self.webhook_handler is not None and int(self.write_thread_bar.n % self.webhook_progress_interval) < 10: - last_reported_index = int(self.write_thread_bar.n % self.webhook_progress_interval) + if ( + self.webhook_handler is not None + and int( + self.write_thread_bar.n % self.webhook_progress_interval + ) + < 10 + ): + last_reported_index = int( + self.write_thread_bar.n % self.webhook_progress_interval + ) self.send_progress_update( type="init_cache_text_embeds_status_update", - progress=int(self.write_thread_bar.n // len(local_caption_split) * 100), + progress=int( + self.write_thread_bar.n + // len(local_caption_split) + * 100 + ), total=len(local_caption_split), current=0, ) diff --git a/helpers/caching/vae.py b/helpers/caching/vae.py index 35fcc7b4..3f12d600 100644 --- a/helpers/caching/vae.py +++ b/helpers/caching/vae.py @@ -918,12 +918,14 @@ def process_buckets(self): shuffle(shuffled_keys) if self.webhook_handler is not None: - total_count = len([item for sublist in aspect_bucket_cache.values() for item in sublist]) + total_count = len( + [item for sublist in aspect_bucket_cache.values() for item in sublist] + ) self.send_progress_update( type="init_cache_vae_processing_started", progress=int(len(processed_images) / total_count * 100), total=total_count, - current=len(processed_images) + current=len(processed_images), ) with ThreadPoolExecutor(max_workers=self.max_workers) as executor: @@ -940,7 +942,7 @@ def process_buckets(self): "total": 0, } last_reported_index = 0 - + for raw_filepath in tqdm( relevant_files, desc=f"Processing bucket {bucket}", @@ -985,13 +987,25 @@ def process_buckets(self): self._encode_images_in_batch ) futures.append(future_to_process) - if self.webhook_handler is not None and int(statistics["total"] // self.webhook_progress_interval) > last_reported_index: - last_reported_index = statistics["total"] // self.webhook_progress_interval + if ( + self.webhook_handler is not None + and int( + statistics["total"] + // self.webhook_progress_interval + ) + > last_reported_index + ): + last_reported_index = ( + statistics["total"] + // self.webhook_progress_interval + ) self.send_progress_update( type="vaecache", - progress=int(statistics["total"] / len(relevant_files) * 100), + progress=int( + statistics["total"] / len(relevant_files) * 100 + ), total=len(relevant_files), - current=statistics["total"] + current=statistics["total"], ) # If we have accumulated enough write objects, we can write them to disk at once. @@ -1055,8 +1069,8 @@ def process_buckets(self): self.send_progress_update( type="init_cache_vae_processing_complete", progress=100, - total=statistics['total'], - current=statistics['total'] + total=statistics["total"], + current=statistics["total"], ) self.debug_log( "Completed process_buckets, all futures have been returned." diff --git a/helpers/configuration/cmd_args.py b/helpers/configuration/cmd_args.py index f72a4883..4be1ffce 100644 --- a/helpers/configuration/cmd_args.py +++ b/helpers/configuration/cmd_args.py @@ -13,7 +13,7 @@ from helpers.training import quantised_precision_levels from helpers.training.optimizer_param import ( is_optimizer_deprecated, - is_optimizer_bf16, + is_optimizer_grad_fp32, map_deprecated_optimizer_parameter, optimizer_choices, ) @@ -1148,6 +1148,30 @@ def get_argument_parser(): " For example, `--optimizer_config=decouple_lr=True,weight_decay=0.01`." ), ) + parser.add_argument( + "--optimizer_cpu_offload_method", + choices=["none"], # , "torchao"], + default="none", + help=( + "This option is a placeholder. In the future, it will allow for the selection of different CPU offload methods." + ), + ) + parser.add_argument( + "--optimizer_offload_gradients", + action="store_true", + default=False, + help=( + "When creating a CPU-offloaded optimiser, the gradients can be offloaded to the CPU to save more memory." + ), + ) + parser.add_argument( + "--fuse_optimizer", + action="store_true", + default=False, + help=( + "When creating a CPU-offloaded optimiser, the fused optimiser could be used to save on memory, while running slightly slower." + ), + ) parser.add_argument( "--optimizer_beta1", type=float, @@ -1282,8 +1306,8 @@ def get_argument_parser(): ) parser.add_argument( "--validation_torch_compile", - type=str, - default="false", + action="store_true", + default=False, help=( "Supply `--validation_torch_compile=true` to enable the use of torch.compile() on the validation pipeline." " For some setups, torch.compile() may error out. This is dependent on PyTorch version, phase of the moon," @@ -1529,6 +1553,13 @@ def get_argument_parser(): " Using 'fp8-quanto' will require Quanto for quantisation (Apple Silicon, NVIDIA, AMD)." ), ) + parser.add_argument( + "--quantize_activations", + action="store_true", + help=( + "(EXPERIMENTAL) This option is currently unsupported, and exists solely for development purposes." + ), + ) parser.add_argument( "--base_model_default_dtype", type=str, @@ -1957,12 +1988,6 @@ def parse_cmdline_args(input_args=None): f"When using --resolution_type=pixel, --target_downsample_size must be at least 512 pixels. You may have accidentally entered {args.target_downsample_size} megapixels, instead of pixels." ) - if "int4" in args.base_model_precision and torch.cuda.is_available(): - print_on_main_thread( - "WARNING: int4 precision is ONLY supported on A100 and H100 or newer devices. Waiting 10 seconds to continue.." - ) - time.sleep(10) - model_is_bf16 = ( args.base_model_precision == "no_change" and (args.mixed_precision == "bf16" or torch.backends.mps.is_available()) @@ -1984,6 +2009,15 @@ def parse_cmdline_args(input_args=None): raise ValueError( f"Model is not using bf16 precision, but the optimizer {chosen_optimizer} requires it." ) + if is_optimizer_grad_fp32(args.optimizer): + warning_log( + "Using an optimizer that requires fp32 gradients. Training will potentially run more slowly." + ) + if args.gradient_precision != "fp32": + args.gradient_precision = "fp32" + else: + if args.gradient_precision == "fp32": + args.gradient_precision = "unmodified" if torch.backends.mps.is_available(): if ( @@ -2001,6 +2035,12 @@ def parse_cmdline_args(input_args=None): ) sys.exit(1) + if args.quantize_via == "accelerator": + error_log( + "MPS does not benefit from models being quantized on the accelerator device. Overriding --quantize_via to 'cpu'." + ) + args.quantize_via = "cpu" + if ( args.max_train_steps is not None and args.max_train_steps > 0 @@ -2091,10 +2131,6 @@ def parse_cmdline_args(input_args=None): if args.metadata_update_interval < 60: raise ValueError("Metadata update interval must be at least 60 seconds.") - if args.validation_torch_compile == "true": - args.validation_torch_compile = True - else: - args.validation_torch_compile = False if args.model_family == "sd3": args.pretrained_vae_model_name_or_path = None @@ -2124,7 +2160,7 @@ def parse_cmdline_args(input_args=None): or args.flux_fast_schedule ): if not args.flux_fast_schedule: - logger.error("Schnell requires --flux_fast_schedule.") + error_log("Schnell requires --flux_fast_schedule.") sys.exit(1) flux_version = "schnell" model_max_seq_length = 256 @@ -2161,11 +2197,11 @@ def parse_cmdline_args(input_args=None): ) if args.flux_guidance_mode == "mobius": - logger.warning( + warning_log( "Mobius training is only for the most elite. Pardon my English, but this is not for those who don't like to destroy something beautiful every now and then. If you feel perhaps this is not for you, please consider using a different guidance mode." ) if args.flux_guidance_min < 1.0: - logger.warning( + warning_log( "Flux minimum guidance value for Mobius training is 1.0. Updating value.." ) args.flux_guidance_min = 1.0 @@ -2247,9 +2283,7 @@ def parse_cmdline_args(input_args=None): ) args.disable_accelerator = os.environ.get("SIMPLETUNER_DISABLE_ACCELERATOR", False) - if "lora" not in args.model_type: - args.base_model_precision = "no_change" - elif "lycoris" == args.lora_type.lower(): + if "lycoris" == args.lora_type.lower(): from lycoris import create_lycoris if args.lycoris_config is None: @@ -2300,7 +2334,7 @@ def parse_cmdline_args(input_args=None): ) args.use_dora = False else: - logger.warning( + warning_log( "DoRA support is experimental and not very thoroughly tested." ) args.lora_initialisation_style = "default" @@ -2311,7 +2345,7 @@ def parse_cmdline_args(input_args=None): args.data_backend_config = os.path.join( StateTracker.get_config_path(), "multidatabackend.json" ) - logger.warning( + warning_log( f"No data backend config provided. Using default config at {args.data_backend_config}." ) diff --git a/helpers/data_backend/csv_url_list.py b/helpers/data_backend/csv_url_list.py index 4a275649..7e3c8070 100644 --- a/helpers/data_backend/csv_url_list.py +++ b/helpers/data_backend/csv_url_list.py @@ -38,7 +38,9 @@ def path_to_hashed_path(path: Path, hash_filenames: bool) -> Path: def html_to_file_loc(parent_directory: Path, url: str, hash_filenames: bool) -> str: filename = url_to_filename(url) - cached_loc = path_to_hashed_path(parent_directory.joinpath(filename), hash_filenames) + cached_loc = path_to_hashed_path( + parent_directory.joinpath(filename), hash_filenames + ) return str(cached_loc.resolve()) @@ -94,12 +96,14 @@ def read(self, location, as_byteIO: bool = False): data = requests.get(location, stream=True).raw.data if not location.startswith("http"): # read from local file - hashed_location = path_to_hashed_path(location, hash_filenames=self.hash_filenames and not already_hashed) + hashed_location = path_to_hashed_path( + location, hash_filenames=self.hash_filenames and not already_hashed + ) try: with open(hashed_location, "rb") as file: data = file.read() except FileNotFoundError as e: - tqdm.write(f'ask was for file {location} bound to {hashed_location}') + tqdm.write(f"ask was for file {location} bound to {hashed_location}") raise e if not as_byteIO: return data diff --git a/helpers/data_backend/factory.py b/helpers/data_backend/factory.py index da998af6..56cc599a 100644 --- a/helpers/data_backend/factory.py +++ b/helpers/data_backend/factory.py @@ -1112,7 +1112,7 @@ def get_csv_backend( caption_column: str, compress_cache: bool = False, hash_filenames: bool = False, - shorten_filenames: bool = False + shorten_filenames: bool = False, ) -> CSVDataBackend: from pathlib import Path diff --git a/helpers/models/flux/__init__.py b/helpers/models/flux/__init__.py index 8cc855f3..271c738e 100644 --- a/helpers/models/flux/__init__.py +++ b/helpers/models/flux/__init__.py @@ -11,10 +11,7 @@ def apply_flux_schedule_shift(args, noise_scheduler, sigmas, noise): # Resolution-dependent shifting of timestep schedules as per section 5.3.2 of SD3 paper shift = None - if ( - args.flux_schedule_shift is not None - and args.flux_schedule_shift > 0 - ): + if args.flux_schedule_shift is not None and args.flux_schedule_shift > 0: # Static shift value for every resolution shift = args.flux_schedule_shift elif args.flux_schedule_auto_shift: @@ -29,9 +26,7 @@ def apply_flux_schedule_shift(args, noise_scheduler, sigmas, noise): ) shift = math.exp(mu) if shift is not None: - sigmas = (sigmas * shift) / ( - 1 + (shift - 1) * sigmas - ) + sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas) return sigmas diff --git a/helpers/models/flux/transformer.py b/helpers/models/flux/transformer.py index 07b5511a..be6d1cd6 100644 --- a/helpers/models/flux/transformer.py +++ b/helpers/models/flux/transformer.py @@ -261,7 +261,7 @@ def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: 0, dim, 2, - dtype=torch.float32 if torch.backends.mps.is_available() else torch.float64, + dtype=torch.float32, device=pos.device, ) / dim diff --git a/helpers/sd3/expanded.py b/helpers/sd3/expanded.py index f1a6b0d2..02f35aed 100644 --- a/helpers/sd3/expanded.py +++ b/helpers/sd3/expanded.py @@ -12,12 +12,22 @@ from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin from diffusers.models.attention import FeedForward, _chunked_feed_forward -from diffusers.models.attention_processor import Attention, AttentionProcessor, JointAttnProcessor2_0 +from diffusers.models.attention_processor import ( + Attention, + AttentionProcessor, + JointAttnProcessor2_0, +) from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed from diffusers.models.modeling_utils import ModelMixin from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput -from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers +from diffusers.utils import ( + USE_PEFT_BACKEND, + is_torch_version, + logging, + scale_lora_layers, + unscale_lora_layers, +) from diffusers.utils.torch_utils import maybe_allow_in_graph @@ -54,13 +64,20 @@ def __init__( super().__init__() self.context_pre_only = context_pre_only - context_norm_type = "ada_norm_continous" if context_pre_only else "ada_norm_zero" + context_norm_type = ( + "ada_norm_continous" if context_pre_only else "ada_norm_zero" + ) self.norm1 = AdaLayerNormZero(dim) if context_norm_type == "ada_norm_continous": self.norm1_context = AdaLayerNormContinuous( - dim, dim, elementwise_affine=False, eps=1e-6, bias=True, norm_type="layer_norm" + dim, + dim, + elementwise_affine=False, + eps=1e-6, + bias=True, + norm_type="layer_norm", ) elif context_norm_type == "ada_norm_zero": self.norm1_context = AdaLayerNormZero(dim) @@ -92,7 +109,9 @@ def __init__( if not context_pre_only: self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) - self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate") + self.ff_context = FeedForward( + dim=dim, dim_out=dim, activation_fn="gelu-approximate" + ) else: self.norm2_context = None self.ff_context = None @@ -108,20 +127,30 @@ def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): self._chunk_dim = dim def forward( - self, hidden_states: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor, temb: torch.FloatTensor + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor, + temb: torch.FloatTensor, ): - norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb) + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, emb=temb + ) if self.context_pre_only: norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb) else: - norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context( - encoder_hidden_states, emb=temb - ) + ( + norm_encoder_hidden_states, + c_gate_msa, + c_shift_mlp, + c_scale_mlp, + c_gate_mlp, + ) = self.norm1_context(encoder_hidden_states, emb=temb) # Attention. attn_output, context_attn_output = self.attn( - hidden_states=norm_hidden_states, encoder_hidden_states=norm_encoder_hidden_states + hidden_states=norm_hidden_states, + encoder_hidden_states=norm_encoder_hidden_states, ) # Process attention outputs for the `hidden_states`. @@ -129,10 +158,14 @@ def forward( hidden_states = hidden_states + attn_output norm_hidden_states = self.norm2(hidden_states) - norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + norm_hidden_states = ( + norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ) if self._chunk_size is not None: # "feed_forward_chunk_size" can be used to save memory - ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size) + ff_output = _chunked_feed_forward( + self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size + ) else: ff_output = self.ff(norm_hidden_states) ff_output = gate_mlp.unsqueeze(1) * ff_output @@ -147,20 +180,30 @@ def forward( encoder_hidden_states = encoder_hidden_states + context_attn_output norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states) - norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + norm_encoder_hidden_states = ( + norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + + c_shift_mlp[:, None] + ) if self._chunk_size is not None: # "feed_forward_chunk_size" can be used to save memory context_ff_output = _chunked_feed_forward( - self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size + self.ff_context, + norm_encoder_hidden_states, + self._chunk_dim, + self._chunk_size, ) else: context_ff_output = self.ff_context(norm_encoder_hidden_states) - encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output + encoder_hidden_states = ( + encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output + ) return encoder_hidden_states, hidden_states -class SD3TransformerQKNorm2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin): +class SD3TransformerQKNorm2DModel( + ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin +): """ The Transformer model introduced in Stable Diffusion 3. @@ -200,12 +243,16 @@ def __init__( pooled_projection_dim: int = 2048, out_channels: int = 16, pos_embed_max_size: int = 96, - qk_norm: str|None="layer_norm", + qk_norm: str | None = "layer_norm", ): super().__init__() default_out_channels = in_channels - self.out_channels = out_channels if out_channels is not None else default_out_channels - self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim + self.out_channels = ( + out_channels if out_channels is not None else default_out_channels + ) + self.inner_dim = ( + self.config.num_attention_heads * self.config.attention_head_dim + ) self.pos_embed = PatchEmbed( height=self.config.sample_size, @@ -216,9 +263,12 @@ def __init__( pos_embed_max_size=pos_embed_max_size, # hard-code for now. ) self.time_text_embed = CombinedTimestepTextProjEmbeddings( - embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim + embedding_dim=self.inner_dim, + pooled_projection_dim=self.config.pooled_projection_dim, + ) + self.context_embedder = nn.Linear( + self.config.joint_attention_dim, self.config.caption_projection_dim ) - self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.config.caption_projection_dim) # `attention_head_dim` is doubled to account for the mixing. # It needs to crafted when we get the actual checkpoints. @@ -235,13 +285,19 @@ def __init__( ] ) - self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6) - self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True) + self.norm_out = AdaLayerNormContinuous( + self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6 + ) + self.proj_out = nn.Linear( + self.inner_dim, patch_size * patch_size * self.out_channels, bias=True + ) self.gradient_checkpointing = False # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking - def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: + def enable_forward_chunking( + self, chunk_size: Optional[int] = None, dim: int = 0 + ) -> None: """ Sets the attention processor to use [feed forward chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). @@ -260,7 +316,9 @@ def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0 # By default chunk size is 1 chunk_size = chunk_size or 1 - def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): + def fn_recursive_feed_forward( + module: torch.nn.Module, chunk_size: int, dim: int + ): if hasattr(module, "set_chunk_feed_forward"): module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) @@ -281,9 +339,15 @@ def attn_processors(self) -> Dict[str, AttentionProcessor]: # set recursively processors = {} - def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + processors[f"{name}.processor"] = module.get_processor( + return_deprecated_lora=True + ) for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) @@ -296,7 +360,9 @@ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: return processors # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor - def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): r""" Sets the attention processor to use to compute attention. @@ -346,7 +412,9 @@ def fuse_qkv_projections(self): for _, attn_processor in self.attn_processors.items(): if "Added" in str(attn_processor.__class__.__name__): - raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") + raise ValueError( + "`fuse_qkv_projections()` is not supported for models having added KV projections." + ) self.original_attn_processors = self.attn_processors @@ -415,14 +483,19 @@ def forward( # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) else: - if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None: + if ( + joint_attention_kwargs is not None + and joint_attention_kwargs.get("scale", None) is not None + ): logger.warning( "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective." ) height, width = hidden_states.shape[-2:] - hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too. + hidden_states = self.pos_embed( + hidden_states + ) # takes care of adding positional embeddings too. temb = self.time_text_embed(timestep, pooled_projections) encoder_hidden_states = self.context_embedder(encoder_hidden_states) @@ -438,7 +511,9 @@ def custom_forward(*inputs): return custom_forward - ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, @@ -449,7 +524,9 @@ def custom_forward(*inputs): else: encoder_hidden_states, hidden_states = block( - hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=temb, ) hidden_states = self.norm_out(hidden_states, temb) @@ -461,11 +538,23 @@ def custom_forward(*inputs): width = width // patch_size hidden_states = hidden_states.reshape( - shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels) + shape=( + hidden_states.shape[0], + height, + width, + patch_size, + patch_size, + self.out_channels, + ) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( - shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size) + shape=( + hidden_states.shape[0], + self.out_channels, + height * patch_size, + width * patch_size, + ) ) if USE_PEFT_BACKEND: @@ -507,35 +596,44 @@ def verify_all_parameters_offset_copy( # Retrieve the corresponding parameter from the destination layer if isinstance(operator.attrgetter(param_name)(dest_layer), torch.Tensor): dest_param = operator.attrgetter(param_name)(dest_layer) - + # Check if the parameters are close enough (considering floating-point arithmetic) if not torch.allclose(source_param, dest_param, atol=1e-6): - raise AssertionError(f'Parameter mismatch for {layer_name_prefix}.{source_idx}.{param_name} (original) -> {layer_name_prefix}.{dest_idx}.{param_name} (new).') + raise AssertionError( + f"Parameter mismatch for {layer_name_prefix}.{source_idx}.{param_name} (original) -> {layer_name_prefix}.{dest_idx}.{param_name} (new)." + ) else: - raise AssertionError(f'Missing parameter {layer_name_prefix}.{dest_idx}.{param_name} in the new model.') - - print(f"All parameters from {source_start_idx} to {source_start_idx + num_layers_to_check - 1} ({num_layers_to_check} layers) in {layer_name_prefix} have been verified to be correctly copied to {dest_start_idx} to {dest_start_idx + num_layers_to_check - 1}.") + raise AssertionError( + f"Missing parameter {layer_name_prefix}.{dest_idx}.{param_name} in the new model." + ) + + print( + f"All parameters from {source_start_idx} to {source_start_idx + num_layers_to_check - 1} ({num_layers_to_check} layers) in {layer_name_prefix} have been verified to be correctly copied to {dest_start_idx} to {dest_start_idx + num_layers_to_check - 1}." + ) + def expand_existing_sd3_model(model_old): # This model is 36 layers deep, versus 24 layers deep from the original model. # We will prune 12 layers off from the end and the start of the merged weights. - model_new = SD3TransformerQKNorm2DModel.from_config({ - "_class_name": "SD3Transformer2DModel", - "_diffusers_version": "0.30.0.dev0", - "_name_or_path": "stabilityai/stable-diffusion-3-medium-diffusers", - "attention_head_dim": 64, - "caption_projection_dim": 1536, - "in_channels": 16, - "joint_attention_dim": 4096, - "num_attention_heads": 24, - "num_layers": FINAL_DEPTH, - "out_channels": 16, - "patch_size": 2, - "pooled_projection_dim": 2048, - "pos_embed_max_size": 192, - "qk_norm": "layer_norm", - "sample_size": 128, - }) + model_new = SD3TransformerQKNorm2DModel.from_config( + { + "_class_name": "SD3Transformer2DModel", + "_diffusers_version": "0.30.0.dev0", + "_name_or_path": "stabilityai/stable-diffusion-3-medium-diffusers", + "attention_head_dim": 64, + "caption_projection_dim": 1536, + "in_channels": 16, + "joint_attention_dim": 4096, + "num_attention_heads": 24, + "num_layers": FINAL_DEPTH, + "out_channels": 16, + "patch_size": 2, + "pooled_projection_dim": 2048, + "pos_embed_max_size": 192, + "qk_norm": "layer_norm", + "sample_size": 128, + } + ) # Copy in layers 0...23 and all other layers. with torch.no_grad(): @@ -548,10 +646,13 @@ def expand_existing_sd3_model(model_old): try: model_new.state_dict()[name].copy_(param) except RuntimeError as e: - if 'The size of tensor a (9216) must match the size of tensor b (3072) at non-singleton dimension 0' in str(e): + if ( + "The size of tensor a (9216) must match the size of tensor b (3072) at non-singleton dimension 0" + in str(e) + ): pass else: - print(f'Got {str(e)} on layer {name}') + print(f"Got {str(e)} on layer {name}") raise # We now need to deal with [18:] for both transformer_blocks. @@ -562,11 +663,11 @@ def expand_existing_sd3_model(model_old): range(ORIG_DEPTH - M_VALUE, FINAL_DEPTH), ): for name, param in model_old.named_parameters(): - if 'transformer_blocks' in name: - if f'transformer_blocks.{layer_idx}.' in name: + if "transformer_blocks" in name: + if f"transformer_blocks.{layer_idx}." in name: name_to_inject_into = name.replace( - f'transformer_blocks.{layer_idx}.', - f'transformer_blocks.{injection_idx}.', + f"transformer_blocks.{layer_idx}.", + f"transformer_blocks.{injection_idx}.", ) model_new.state_dict()[name_to_inject_into].copy_(param) @@ -575,39 +676,46 @@ def expand_existing_sd3_model(model_old): # should do nothing to the model. with torch.no_grad(): for name, param in model_new.named_parameters(): - if 'transformer_blocks' in name and ('norm_q' in name or 'norm_k' in name): - if 'norm_q.weight' in name: + if "transformer_blocks" in name and ("norm_q" in name or "norm_k" in name): + if "norm_q.weight" in name: param.fill_(1) - elif 'norm_q.bias' in name: + elif "norm_q.bias" in name: param.fill_(0) - verify_all_parameters_offset_copy(model_old, model_new, 'transformer_blocks', 0, 0, ORIG_DEPTH - M_VALUE) # Adjust the index as needed - verify_all_parameters_offset_copy(model_old, model_new, 'transformer_blocks', 6, 18, ORIG_DEPTH - M_VALUE) # Adjust the last parameter as needed based on the number of layers you're checking + verify_all_parameters_offset_copy( + model_old, model_new, "transformer_blocks", 0, 0, ORIG_DEPTH - M_VALUE + ) # Adjust the index as needed + verify_all_parameters_offset_copy( + model_old, model_new, "transformer_blocks", 6, 18, ORIG_DEPTH - M_VALUE + ) # Adjust the last parameter as needed based on the number of layers you're checking orig_params = sum(p.numel() for p in model_old.parameters()) expanded_params = sum(p.numel() for p in model_new.parameters()) - print(f'Model has been successfully expanded from {orig_params / 1e6:.2f}M to {expanded_params / 1e6:.2f}M.') + print( + f"Model has been successfully expanded from {orig_params / 1e6:.2f}M to {expanded_params / 1e6:.2f}M." + ) model_new.save_pretrained((os.path.join(args.output_model, "transformer"))) return model_new -if __name__ == '__main__': +if __name__ == "__main__": from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel + parser = argparse.ArgumentParser( - description='Make a 24 block deep SD3 2B into a 36 block deep version', + description="Make a 24 block deep SD3 2B into a 36 block deep version", ) parser.add_argument( - 'input_model', - action='store', + "input_model", + action="store", type=str, - help='The input pretrained model', + help="The input pretrained model", ) parser.add_argument( - 'output_model', - action='store', + "output_model", + action="store", type=str, - help='The output pretrained model location', + help="The output pretrained model location", ) args = parser.parse_args() @@ -619,12 +727,14 @@ def expand_existing_sd3_model(model_old): model_new = expand_existing_sd3_model(model_old) del model_old gc.collect() - model_new = model_new.to('cuda', dtype=torch.bfloat16) + model_new = model_new.to("cuda", dtype=torch.bfloat16) with torch.no_grad(), torch.inference_mode(): model_new( - hidden_states=torch.rand((1, 16, 64, 64)).to('cuda', dtype=torch.bfloat16), - encoder_hidden_states=torch.rand((1, 144, 4096)).to('cuda', dtype=torch.bfloat16), - pooled_projections=torch.rand((1, 2048)).to('cuda', dtype=torch.bfloat16), - timestep=torch.tensor([500]).to('cuda', dtype=torch.bfloat16), + hidden_states=torch.rand((1, 16, 64, 64)).to("cuda", dtype=torch.bfloat16), + encoder_hidden_states=torch.rand((1, 144, 4096)).to( + "cuda", dtype=torch.bfloat16 + ), + pooled_projections=torch.rand((1, 2048)).to("cuda", dtype=torch.bfloat16), + timestep=torch.tensor([500]).to("cuda", dtype=torch.bfloat16), ) - print('Successfully expanded and tested model.') + print("Successfully expanded and tested model.") diff --git a/helpers/training/__init__.py b/helpers/training/__init__.py index 362fd19c..d5386028 100644 --- a/helpers/training/__init__.py +++ b/helpers/training/__init__.py @@ -3,9 +3,13 @@ # "fp4-bnb", # "fp8-bnb", "fp8-quanto", + "fp8uz-quanto", "int8-quanto", "int4-quanto", "int2-quanto", + # currently does not work. + # "fp8-torchao", + "int8-torchao", ] image_file_extensions = set(["jpg", "jpeg", "png", "webp", "bmp", "tiff", "tif"]) diff --git a/helpers/training/deepspeed.py b/helpers/training/deepspeed.py index 32a453e3..d13d7ea0 100644 --- a/helpers/training/deepspeed.py +++ b/helpers/training/deepspeed.py @@ -24,8 +24,9 @@ def prepare_model_for_deepspeed(accelerator, args): use_deepspeed_optimizer = False use_deepspeed_scheduler = False if ( - hasattr(accelerator, 'state') and hasattr(accelerator.state, "deepspeed_plugin") - and getattr(accelerator.state, 'deepspeed_plugin') is not None + hasattr(accelerator, "state") + and hasattr(accelerator.state, "deepspeed_plugin") + and getattr(accelerator.state, "deepspeed_plugin") is not None ): offload_param = accelerator.state.deepspeed_plugin.deepspeed_config[ "zero_optimization" diff --git a/helpers/training/default_settings/safety_check.py b/helpers/training/default_settings/safety_check.py index 93fa97bf..180ab479 100644 --- a/helpers/training/default_settings/safety_check.py +++ b/helpers/training/default_settings/safety_check.py @@ -1,10 +1,14 @@ import logging, sys, os from os import environ from diffusers.utils import is_wandb_available +from helpers.training.multi_process import _get_rank as get_rank from helpers.training.state_tracker import StateTracker logger = logging.getLogger(__name__) -logger.setLevel(environ.get("SIMPLETUNER_LOG_LEVEL", "INFO")) +if get_rank() == 0: + logger.setLevel(environ.get("SIMPLETUNER_LOG_LEVEL", "INFO")) +else: + logger.setLevel(logging.ERROR) from helpers.training.error_handling import validate_deepspeed_compat_from_args @@ -13,11 +17,22 @@ def safety_check(args, accelerator): # mulit-gpu safety checks & warnings if args.model_type == "lora" and args.lora_type == "standard": # multi-gpu PEFT checks & warnings - if "quanto" in args.base_model_precision: - print( - "Quanto is incompatible with multi-GPU training on PEFT adapters. Use LORA_TYPE (--lora_type) lycoris for quantised multi-GPU training of LoKr models." + if args.base_model_precision in ["fp8-quanto"]: + logger.error( + f"{args.base_model_precision} is incompatible with multi-GPU training on PEFT LoRA." + " Use LORA_TYPE (--lora_type) lycoris for quantised multi-GPU training of LoKr models in FP8." ) - sys.exit(1) + args.base_model_precision = "int8-quanto" + + if ( + (args.base_model_precision in ["fp8-quanto", "int4-quanto"] or (args.base_model_precision != "no_change" and args.quantize_activations)) + and (accelerator is not None and accelerator.state.dynamo_plugin.backend.lower() == "inductor") + ): + logger.warning( + f"{args.base_model_precision} is not supported with Dynamo backend. Disabling Dynamo." + ) + from accelerate.utils import DynamoBackend + accelerator.state.dynamo_plugin.backend = DynamoBackend.NO if args.report_to == "wandb": if not is_wandb_available(): raise ImportError( @@ -40,11 +55,40 @@ def safety_check(args, accelerator): if "lora" in args.model_type and args.train_text_encoder: if args.lora_type.lower() == "lycoris": - print( + logger.error( "LyCORIS training is not meant to be combined with --train_text_encoder. It is powerful enough on its own!" ) sys.exit(1) if args.user_prompt_library and not os.path.exists(args.user_prompt_library): raise FileNotFoundError( f"User prompt library not found at {args.user_prompt_library}. Please check the path and try again." - ) \ No newline at end of file + ) + + # optimizer memory limit check for SOAP w/ 24G + if ( + accelerator is not None + and accelerator.device.type == "cuda" + and accelerator.is_main_process + ): + import subprocess + + output = subprocess.check_output( + [ + "nvidia-smi", + "--query-gpu=memory.total", + "--format=csv,noheader,nounits", + ] + ).split(b"\n")[get_rank()] + total_memory = int(output.decode().strip()) / 1024 + from math import ceil + + total_memory_gb = ceil(total_memory) + if total_memory_gb < 32 and total_memory_gb > 16 and args.optimizer == "soap": + logger.warning( + f"Your GPU has {total_memory_gb}GB of memory. The SOAP optimiser may require more than this. Setting --accelerator_cache_clear_interval=10 may help to eliminate OOM." + ) + elif total_memory_gb < 24 and args.optimizer == "soap": + logger.error( + f"Your GPU has {total_memory_gb}GB of memory. The SOAP optimiser requires a GPU with at least 24G of memory." + ) + sys.exit(1) diff --git a/helpers/training/error_handling.py b/helpers/training/error_handling.py index 19704f17..8eda39b3 100644 --- a/helpers/training/error_handling.py +++ b/helpers/training/error_handling.py @@ -7,6 +7,7 @@ target_level = os.environ.get("SIMPLETUNER_LOG_LEVEL", "INFO") logger.setLevel(target_level) + def validate_deepspeed_compat_from_args(accelerator, args): if "lora" in args.model_type: logger.error( diff --git a/helpers/training/optimizer_param.py b/helpers/training/optimizer_param.py index 95922c3c..7e3b8bc4 100644 --- a/helpers/training/optimizer_param.py +++ b/helpers/training/optimizer_param.py @@ -11,12 +11,30 @@ is_optimi_available = False from helpers.training.optimizers.adamw_bfloat16 import AdamWBF16 from helpers.training.optimizers.adamw_schedulefree import AdamWScheduleFreeKahan +from helpers.training.optimizers.soap import SOAP try: from optimum.quanto import QTensor except: pass +try: + from torchao.prototype.low_bit_optim import ( + AdamW8bit as AOAdamW8Bit, + Adam4bit as AOAdamW4Bit, + AdamFp8 as AOAdamFp8, + AdamWFp8 as AOAdamWFp8, + CPUOffloadOptimizer as AOCPUOffloadOptimizer, + ) + + if torch.backends.mps.is_available(): + import torch._dynamo + + torch._dynamo.config.suppress_errors = True +except Exception as e: + print("You need torchao installed for its low-precision optimizers.") + raise e + try: import optimi @@ -36,6 +54,46 @@ }, "class": AdamWBF16, }, + "ao-adamw8bit": { + "gradient_precision": "bf16", + "precision": "any", + "default_settings": { + "betas": (0.9, 0.999), + "weight_decay": 1e-2, + "eps": 1e-6, + }, + "class": AOAdamW8Bit, + }, + "ao-adamw4bit": { + "gradient_precision": "bf16", + "precision": "any", + "default_settings": { + "betas": (0.9, 0.999), + "weight_decay": 1e-2, + "eps": 1e-6, + }, + "class": AOAdamW4Bit, + }, + "ao-adamfp8": { + "gradient_precision": "bf16", + "precision": "any", + "default_settings": { + "betas": (0.9, 0.999), + "weight_decay": 1e-2, + "eps": 1e-6, + }, + "class": AOAdamFp8, + }, + "ao-adamwfp8": { + "gradient_precision": "bf16", + "precision": "any", + "default_settings": { + "betas": (0.9, 0.999), + "weight_decay": 1e-2, + "eps": 1e-6, + }, + "class": AOAdamWFp8, + }, "adamw_schedulefree": { "precision": "any", "override_lr_scheduler": True, @@ -178,8 +236,25 @@ }, "class": optimi.SGD, }, + "soap": { + "precision": "any", + "gradient_precision": "fp32", + "default_settings": { + "betas": (0.95, 0.95), + "shampoo_beta": -1, + "eps": 1e-8, + "weight_decay": 0.01, + "precondition_frequency": 10, + "max_precond_dim": 10000, + "merge_dims": False, + "precondition_1d": False, + "normalize_grads": False, + "data_format": "channels_first", + "correct_bias": True, + }, + "class": SOAP, + }, } - args_to_optimizer_mapping = { "use_adafactor_optimizer": "adafactor", "use_prodigy_optimizer": "prodigy", @@ -276,6 +351,40 @@ def is_optimizer_bf16(optimizer: str) -> bool: return False +def is_optimizer_grad_fp32(optimizer: str) -> bool: + optimizer_precision = optimizer_choices.get(optimizer, {}).get( + "gradient_precision", None + ) + if optimizer_precision == "fp32": + return True + return False + + +def cpu_offload_optimizer( + params_to_optimize, + optimizer_cls, + optimizer_parameters: dict, + offload_gradients: bool = True, + fused: bool = True, + offload_mechanism: str = None, +): + if not offload_mechanism or offload_mechanism == "none": + return optimizer_cls(params_to_optimize, **optimizer_parameters) + if offload_mechanism != "torchao": + raise ValueError( + f"Unknown CPU optimiser offload mechanism: {offload_mechanism}" + ) + + if offload_gradients: + optimizer_parameters["offload_gradients"] = offload_gradients + if fused: + optimizer_parameters["fused"] = fused + + optimizer_parameters["optimizer_class"] = optimizer_cls + + return AOCPUOffloadOptimizer(params_to_optimize, **optimizer_parameters) + + def determine_optimizer_class_with_config( args, use_deepspeed_optimizer, is_quantized, enable_adamw_bf16 ) -> tuple: diff --git a/helpers/training/optimizers/soap/__init__.py b/helpers/training/optimizers/soap/__init__.py new file mode 100644 index 00000000..3bd9dc58 --- /dev/null +++ b/helpers/training/optimizers/soap/__init__.py @@ -0,0 +1,476 @@ +import torch +import torch.nn as nn +import torch.optim as optim + +from itertools import chain + +# Parts of the code are modifications of Pytorch's AdamW optimizer +# Parts of the code are modifications of code from https://github.com/jiaweizzhao/GaLore/blob/master/galore_torch/galore_projector.py + + +class SOAP(optim.Optimizer): + """ + Implements SOAP algorithm (https://arxiv.org/abs/2409.11321). + + Parameters: + params (`Iterable[nn.parameter.Parameter]`): + Iterable of parameters to optimize or dictionaries defining parameter groups. + lr (`float`, *optional*, defaults to 0.003): + The learning rate to use. + betas (`Tuple[float,float]`, *optional*, defaults to `(0.95, 0.95)`): + Adam's betas parameters (b1, b2). + shampoo_beta (`float`, *optional*, defaults to -1): + If >= 0, use this beta for the preconditioner (L and R in paper, state['GG'] below) moving average instead of betas[1]. + eps (`float`, *optional*, defaults to 1e-08): + Adam's epsilon for numerical stability. + weight_decay (`float`, *optional*, defaults to 0.01): weight decay coefficient. + precondition_frequency (`int`, *optional*, defaults to 10): + How often to update the preconditioner. + max_precond_dim (`int`, *optional*, defaults to 10000): + Maximum dimension of the preconditioner. + Set to 10000, so that we exclude most common vocab sizes while including layers. + merge_dims (`bool`, *optional*, defaults to `False`): + Whether or not to merge dimensions of the preconditioner. + precondition_1d (`bool`, *optional*, defaults to `False`): + Whether or not to precondition 1D gradients. + normalize_grads (`bool`, *optional*, defaults to `False`): + Whether or not to normalize gradients per layer. + Helps at large precondition_frequency (~100 in our experiments), + but hurts performance at small precondition_frequency (~10 in our experiments). + data_format (`str`, *optional*, defaults to `channels_first`): + Data format of the input for convolutional layers. + Should be "channels_last" for data_format of NHWC and "channels_first" for NCHW. + correct_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use bias correction in Adam. + """ + + def __init__( + self, + params, + lr: float = 3e-3, + betas=(0.95, 0.95), + shampoo_beta: float = -1, + eps: float = 1e-8, + weight_decay: float = 0.01, + precondition_frequency: int = 10, + max_precond_dim: int = 10000, # + merge_dims: bool = False, # Merge dimensions till the product of the dimensions is less than or equal to max_precond_dim. + precondition_1d: bool = False, + normalize_grads: bool = False, + data_format: str = "channels_first", + correct_bias: bool = True, + ): + defaults = { + "lr": lr, + "betas": betas, + "shampoo_beta": shampoo_beta, + "eps": eps, + "weight_decay": weight_decay, + "precondition_frequency": precondition_frequency, + "max_precond_dim": max_precond_dim, + "merge_dims": merge_dims, + "precondition_1d": precondition_1d, + "normalize_grads": normalize_grads, + "correct_bias": correct_bias, + } + super().__init__(params, defaults) + self._data_format = data_format + + def merge_dims(self, grad, max_precond_dim): + """ + Merges dimensions of the gradient tensor till the product of the dimensions is less than or equal to max_precond_dim. + """ + assert self._data_format in ["channels_first", "channels_last"] + if self._data_format == "channels_last" and grad.dim() == 4: + grad = grad.permute(0, 3, 1, 2) + shape = grad.shape + new_shape = [] + + curr_shape = 1 + for sh in shape: + temp_shape = curr_shape * sh + if temp_shape > max_precond_dim: + if curr_shape > 1: + new_shape.append(curr_shape) + curr_shape = sh + else: + new_shape.append(sh) + curr_shape = 1 + else: + curr_shape = temp_shape + + if curr_shape > 1 or len(new_shape) == 0: + new_shape.append(curr_shape) + + new_grad = grad.reshape(new_shape) + return new_grad + + @torch.no_grad() + def step(self, closure=None): + """ + Performs a single optimization step. + + Arguments: + closure (`Callable`, *optional*): A closure that reevaluates the model and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad + + state = self.state[p] + + if "step" not in state: + state["step"] = 0 + + # State initialization + if "exp_avg" not in state: + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(grad) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like(grad) + + if "Q" not in state: + self.init_preconditioner( + grad, + state, + precondition_frequency=group["precondition_frequency"], + precondition_1d=group["precondition_1d"], + shampoo_beta=( + group["shampoo_beta"] + if group["shampoo_beta"] >= 0 + else group["betas"][1] + ), + max_precond_dim=group["max_precond_dim"], + merge_dims=group["merge_dims"], + ) + self.update_preconditioner( + grad, + state, + max_precond_dim=group["max_precond_dim"], + merge_dims=group["merge_dims"], + precondition_1d=group["precondition_1d"], + ) + continue # first step is skipped so that we never use the current gradients in the projection. + + # Projecting gradients to the eigenbases of Shampoo's preconditioner + # i.e. projecting to the eigenbases of matrices in state['GG'] + grad_projected = self.project( + grad, + state, + merge_dims=group["merge_dims"], + max_precond_dim=group["max_precond_dim"], + ) + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + beta1, beta2 = group["betas"] + + state["step"] += 1 + + # Decay the first and second moment running average coefficient + # In-place operations to update the averages at the same time + exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1)) + exp_avg_sq.mul_(beta2).add_( + grad_projected.square(), alpha=(1.0 - beta2) + ) + + denom = exp_avg_sq.sqrt().add_(group["eps"]) + + # Projecting the exponential moving average of gradients to the eigenbases of Shampoo's preconditioner + # i.e. projecting to the eigenbases of matrices in state['GG'] + exp_avg_projected = self.project( + exp_avg, + state, + merge_dims=group["merge_dims"], + max_precond_dim=group["max_precond_dim"], + ) + + step_size = group["lr"] + if group["correct_bias"]: + bias_correction1 = 1.0 - beta1 ** (state["step"]) + bias_correction2 = 1.0 - beta2 ** (state["step"]) + step_size = step_size * (bias_correction2**0.5) / bias_correction1 + + # Projecting back the preconditioned (by Adam) exponential moving average of gradients + # to the original space + norm_grad = self.project_back( + exp_avg_projected / denom, + state, + merge_dims=group["merge_dims"], + max_precond_dim=group["max_precond_dim"], + ) + + if group["normalize_grads"]: + norm_grad = norm_grad / (1e-30 + torch.mean(norm_grad**2) ** 0.5) + + p.add_(norm_grad, alpha=-step_size) + + # From AdamW code: Just adding the square of the weights to the loss function is *not* + # the correct way of using L2 regularization/weight decay with Adam, + # since that will interact with the m and v parameters in strange ways. + # + # Instead we want to decay the weights in a manner that doesn't interact + # with the m/v parameters. This is equivalent to adding the square + # of the weights to the loss with plain (non-momentum) SGD. + # Add weight decay at the end (fixed version) + if group["weight_decay"] > 0.0: + p.add_(p, alpha=(-group["lr"] * group["weight_decay"])) + + # Update is done after the gradient step to avoid using current gradients in the projection. + self.update_preconditioner( + grad, + state, + max_precond_dim=group["max_precond_dim"], + merge_dims=group["merge_dims"], + precondition_1d=group["precondition_1d"], + ) + + return loss + + def init_preconditioner( + self, + grad, + state, + precondition_frequency=10, + shampoo_beta=0.95, + max_precond_dim=10000, + precondition_1d=False, + merge_dims=False, + ): + """ + Initializes the preconditioner matrices (L and R in the paper). + """ + state["GG"] = ( + [] + ) # Will hold all the preconditioner matrices (L and R in the paper). + if grad.dim() == 1: + if not precondition_1d or grad.shape[0] > max_precond_dim: + state["GG"].append([]) + else: + state["GG"].append( + torch.zeros(grad.shape[0], grad.shape[0], device=grad.device) + ) + else: + if merge_dims: + grad = self.merge_dims(grad, max_precond_dim) + + for sh in grad.shape: + if sh > max_precond_dim: + state["GG"].append([]) + else: + state["GG"].append(torch.zeros(sh, sh, device=grad.device)) + + state["Q"] = None # Will hold all the eigenbases of the preconditioner. + state["precondition_frequency"] = precondition_frequency + state["shampoo_beta"] = shampoo_beta + + def project(self, grad, state, merge_dims=False, max_precond_dim=10000): + """ + Projects the gradient to the eigenbases of the preconditioner. + """ + original_shape = grad.shape + if merge_dims: + if grad.dim() == 4 and self._data_format == "channels_last": + permuted_shape = grad.permute(0, 3, 1, 2).shape + grad = self.merge_dims(grad, max_precond_dim) + + for mat in state["Q"]: + if len(mat) > 0: + grad = torch.tensordot( + grad, + mat.to(grad.dtype), + dims=[[0], [0]], + ) + else: + permute_order = list(range(1, len(grad.shape))) + [0] + grad = grad.permute(permute_order) + + if merge_dims: + if self._data_format == "channels_last" and len(original_shape) == 4: + grad = grad.reshape(permuted_shape).permute(0, 2, 3, 1) + else: + grad = grad.reshape(original_shape) + return grad + + def update_preconditioner( + self, + grad, + state, + max_precond_dim=10000, + merge_dims=False, + precondition_1d=False, + ): + """ + Updates the preconditioner matrices and the eigenbases (L, R, Q_L, Q_R in the paper). + """ + if grad.dim() == 1: + if precondition_1d and grad.shape[0] <= max_precond_dim: + state["GG"][0].lerp_( + grad.unsqueeze(1) @ grad.unsqueeze(0), 1 - state["shampoo_beta"] + ) + else: + if merge_dims: + new_grad = self.merge_dims(grad, max_precond_dim) + for idx, sh in enumerate(new_grad.shape): + if sh <= max_precond_dim: + outer_product = torch.tensordot( + new_grad, + new_grad, + dims=[ + [ + *chain( + range(idx), range(idx + 1, len(new_grad.shape)) + ) + ] + ] + * 2, + ) + state["GG"][idx].lerp_(outer_product, 1 - state["shampoo_beta"]) + else: + for idx, sh in enumerate(grad.shape): + if sh <= max_precond_dim: + outer_product = torch.tensordot( + grad, + grad, + # Contracts across all dimensions except for k. + dims=[[*chain(range(idx), range(idx + 1, len(grad.shape)))]] + * 2, + ) + state["GG"][idx].lerp_(outer_product.to(state["GG"][idx].dtype), 1 - state["shampoo_beta"]) + + if state["Q"] is None: + state["Q"] = self.get_orthogonal_matrix(state["GG"]) + if state["step"] > 0 and state["step"] % state["precondition_frequency"] == 0: + state["Q"] = self.get_orthogonal_matrix_QR( + state, max_precond_dim, merge_dims + ) + + def project_back(self, grad, state, merge_dims=False, max_precond_dim=10000): + """ + Projects the gradient back to the original space. + """ + original_shape = grad.shape + if merge_dims: + if self._data_format == "channels_last" and grad.dim() == 4: + permuted_shape = grad.permute(0, 3, 1, 2).shape + grad = self.merge_dims(grad, max_precond_dim) + for mat in state["Q"]: + if len(mat) > 0: + grad = torch.tensordot( + grad, + mat, + dims=[[0], [1]], + ) + else: + permute_order = list(range(1, len(grad.shape))) + [0] + grad = grad.permute(permute_order) + + if merge_dims: + if self._data_format == "channels_last" and len(original_shape) == 4: + grad = grad.reshape(permuted_shape).permute(0, 2, 3, 1) + else: + grad = grad.reshape(original_shape) + return grad + + def get_orthogonal_matrix(self, mat): + """ + Computes the eigenbases of the preconditioner using torch.linalg.eigh decomposition. + """ + matrix = [] + for m in mat: + if len(m) == 0: + matrix.append([]) + continue + if m.data.dtype != torch.float: + float_data = False + original_type = m.data.dtype + original_device = m.data.device + matrix.append(m.data.float()) + else: + float_data = True + matrix.append(m.data) + + final = [] + for m in matrix: + if len(m) == 0: + final.append([]) + continue + try: + _, Q = torch.linalg.eigh( + m + 1e-30 * torch.eye(m.shape[0], device=m.device) + ) + except: + _, Q = torch.linalg.eigh( + m.to(torch.float64) + 1e-30 * torch.eye(m.shape[0], device=m.device) + ) + Q = Q.to(m.dtype) + Q = torch.flip(Q, [1]) + + if not float_data: + Q = Q.to(original_device).type(original_type) + final.append(Q) + return final + + def get_orthogonal_matrix_QR(self, state, max_precond_dim=10000, merge_dims=False): + """ + Computes the eigenbases of the preconditioner using one round of power iteration + followed by torch.linalg.qr decomposition. + """ + precond_list = state["GG"] + orth_list = state["Q"] + + matrix = [] + orth_matrix = [] + for m, o in zip(precond_list, orth_list): + if len(m) == 0: + matrix.append([]) + orth_matrix.append([]) + continue + if m.data.dtype != torch.float: + float_data = False + original_type = m.data.dtype + original_device = m.data.device + matrix.append(m.data.float()) + orth_matrix.append(o.data.float()) + else: + float_data = True + matrix.append(m.data.float()) + orth_matrix.append(o.data.float()) + + orig_shape = state["exp_avg_sq"].shape + if self._data_format == "channels_last" and len(orig_shape) == 4: + permuted_shape = state["exp_avg_sq"].permute(0, 3, 1, 2).shape + if merge_dims: + exp_avg_sq = self.merge_dims(state["exp_avg_sq"], max_precond_dim) + else: + exp_avg_sq = state["exp_avg_sq"] + + final = [] + for ind, (m, o) in enumerate(zip(matrix, orth_matrix)): + if len(m) == 0: + final.append([]) + continue + est_eig = torch.diag(o.T @ m @ o) + sort_idx = torch.argsort(est_eig, descending=True) + exp_avg_sq = exp_avg_sq.index_select(ind, sort_idx) + o = o[:, sort_idx] + power_iter = m @ o + Q, _ = torch.linalg.qr(power_iter) + + if not float_data: + Q = Q.to(original_device).type(original_type) + final.append(Q) + + if merge_dims: + if self._data_format == "channels_last" and len(orig_shape) == 4: + exp_avg_sq = exp_avg_sq.reshape(permuted_shape).permute(0, 2, 3, 1) + else: + exp_avg_sq = exp_avg_sq.reshape(orig_shape) + + state["exp_avg_sq"] = exp_avg_sq + return final diff --git a/helpers/training/quantisation/__init__.py b/helpers/training/quantisation/__init__.py index 447ba294..8d332399 100644 --- a/helpers/training/quantisation/__init__.py +++ b/helpers/training/quantisation/__init__.py @@ -8,72 +8,170 @@ else: logger.setLevel(logging.ERROR) -try: - from optimum.quanto import freeze, quantize, qfloat8, qint8, qint4, qint2, QTensor -except ImportError as e: - raise ImportError( - f"To use Quanto, please install the optimum library: `pip install optimum-quanto`: {e}" + +def _quanto_type_map(model_precision: str): + if model_precision == "no_change": + return None + from optimum.quanto import ( + qfloat8, + qfloat8_e4m3fnuz, + qint8, + qint4, + qint2, ) + if model_precision == "int2-quanto": + quant_level = qint2 + elif model_precision == "int4-quanto": + quant_level = qint4 + elif model_precision == "int8-quanto": + quant_level = qint8 + elif model_precision == "fp8-quanto" or model_precision == "fp8uz-quanto": + if torch.backends.mps.is_available(): + logger.warning( + "MPS doesn't support dtype float8, you must select another precision level such as bf16, int2, int8, or int8." + ) + + return model + if model_precision == "fp8-quanto": + quant_level = qfloat8 + elif model_precision == "fp8uz-quanto": + quant_level = qfloat8_e4m3fnuz + else: + raise ValueError(f"Invalid quantisation level: {model_precision}") + return quant_level + +def _quanto_model(model, model_precision, base_model_precision=None, quantize_activations: bool = False): + try: + from helpers.training.quantisation import quanto_workarounds + from optimum.quanto import ( + freeze, + quantize, + QTensor, + ) + except ImportError as e: + raise ImportError( + f"To use Quanto, please install the optimum library: `pip install optimum-quanto`: {e}" + ) -def _quanto_model(model, model_precision, base_model_precision=None): if model_precision is None: model_precision = base_model_precision if model is None: - return + return model if model_precision == "no_change" or model_precision is None: logger.info(f"...No quantisation applied to {model.__class__.__name__}.") - return + return model logger.info(f"Quantising {model.__class__.__name__}. Using {model_precision}.") - if model_precision == "int2-quanto": - weight_quant = qint2 - elif model_precision == "int4-quanto": - if torch.cuda.is_available(): - logger.error( - "int4-quanto is only supported on A100 and H100 GPUs, but other GPUs would support int2-quanto, int8-quanto or fp8-quanto... waiting 10 seconds for you to cancel." - ) - import time + weight_quant = _quanto_type_map(model_precision) + extra_quanto_args = {} + if quantize_activations: + logger.info("Freezing model weights and activations") + extra_quanto_args["activations"] = weight_quant + extra_quanto_args["exclude"] = [ + "*.norm", + "*.norm1", + "*.norm2", + "*.norm2_context", + "proj_out", + ] + else: + logger.info("Freezing model weights only") - time.sleep(10) - weight_quant = qint4 - elif model_precision == "int8-quanto": - weight_quant = qint8 - elif model_precision == "fp8-quanto": - if torch.backends.mps.is_available(): - logger.warning( - "MPS doesn't support dtype float8_e4m3n, you must select another precision level such as bf16, int2, int8, or int8." + quantize(model, weights=weight_quant, **extra_quanto_args) + freeze(model) + + return model + + +def _torchao_filter_fn(mod: torch.nn.Module, fqn: str): + # don't convert the output module + if fqn == "proj_out": + return False + # don't convert linear modules with weight dimensions not divisible by 16 + if isinstance(mod, torch.nn.Linear): + if mod.in_features % 16 != 0 or mod.out_features % 16 != 0: + return False + return True + + +def _torchao_model(model, model_precision, base_model_precision=None, quantize_activations:bool=False): + if model_precision is None: + model_precision = base_model_precision + if model is None: + return model + if model_precision == "no_change" or model_precision is None: + logger.info(f"...No quantisation applied to {model.__class__.__name__}.") + return model + + try: + from helpers.training.quantisation import torchao_workarounds + from torchao.float8 import convert_to_float8_training, Float8LinearConfig + from torchao.prototype.quantized_training import ( + int8_weight_only_quantized_training, + ) + import torchao + from torchao.quantization import quantize_ + except ImportError as e: + raise ImportError( + f"To use torchao, please install the torchao library: `pip install torchao`: {e}" + ) + logger.info(f"Quantising {model.__class__.__name__}. Using {model_precision}.") + if quantize_activations: + logger.warning("Activation quantisation is not used in TorchAO. This will be ignored.") + + if model_precision == "int8-torchao": + quantize_( + model, + int8_weight_only_quantized_training(), # , filter_fn=_torchao_filter_fn + ) + elif model_precision == "fp8-torchao": + if not torch.cuda.is_available(): + raise ValueError( + "fp8-torchao is only supported on CUDA enabled GPUs. int8-quanto can be used everywhere else." ) + logger.error( + "fp8-torchao requires the latest pytorch nightly build, but int8-torchao, int8-quanto, or fp8-quanto may be used instead." + ) + model = convert_to_float8_training( + model, + module_filter_fn=_torchao_filter_fn, + config=Float8LinearConfig(pad_inner_dim=True), + ) - return - weight_quant = qfloat8 else: raise ValueError(f"Invalid quantisation level: {base_model_precision}") - quantize(model, weights=weight_quant) - logger.info("Freezing model.") - freeze(model) + return model -def quantoise( + +def quantise_model( unet, transformer, text_encoder_1, text_encoder_2, text_encoder_3, controlnet, args ): - logger.info("Loading Quanto for LoRA training. This may take a few minutes.") + if "quanto" in args.base_model_precision.lower(): + logger.info("Loading Quanto. This may take a few minutes.") + quant_fn = _quanto_model + elif "torchao" in args.base_model_precision.lower(): + logger.info("Loading TorchAO. This may take a few minutes.") + quant_fn = _torchao_model if transformer is not None: - _quanto_model(transformer, args.base_model_precision) + transformer = quant_fn(transformer, model_precision=args.base_model_precision, quantize_activations=args.quantize_activations) if unet is not None: - _quanto_model(unet, args.base_model_precision) + unet = quant_fn(unet, model_precision=args.base_model_precision, quantize_activations=args.quantize_activations) if controlnet is not None: - _quanto_model(controlnet, args.base_model_precision) + controlnet = quant_fn(controlnet, model_precision=args.base_model_precision, quantize_activations=args.quantize_activations) if text_encoder_1 is not None: - _quanto_model( - text_encoder_1, args.text_encoder_1_precision, args.base_model_precision + text_encoder_1 = quant_fn( + text_encoder_1, model_precision=args.text_encoder_1_precision, base_model_precision=args.base_model_precision ) if text_encoder_2 is not None: - _quanto_model( - text_encoder_2, args.text_encoder_2_precision, args.base_model_precision + text_encoder_2 = quant_fn( + text_encoder_2, model_precision=args.text_encoder_2_precision, base_model_precision=args.base_model_precision ) if text_encoder_3 is not None: - _quanto_model( - text_encoder_3, args.text_encoder_3_precision, args.base_model_precision + text_encoder_3 = quant_fn( + text_encoder_3, model_precision=args.text_encoder_3_precision, base_model_precision=args.base_model_precision ) + + return unet, transformer, text_encoder_1, text_encoder_2, text_encoder_3, controlnet diff --git a/helpers/training/quantisation/quanto_workarounds.py b/helpers/training/quantisation/quanto_workarounds.py new file mode 100644 index 00000000..d48bd1b2 --- /dev/null +++ b/helpers/training/quantisation/quanto_workarounds.py @@ -0,0 +1,65 @@ +import torch + +if torch.cuda.is_available(): + # the marlin fp8 kernel needs some help with dtype casting for some reason + # see: https://github.com/huggingface/optimum-quanto/pull/296#issuecomment-2380719201 + import optimum + from optimum.quanto.library.extensions.cuda import ext as quanto_ext + + # torch tells us to do this because + torch._dynamo.config.optimize_ddp=False + # Save the original operator + original_gemm_f16f8_marlin = torch.ops.quanto.gemm_f16f8_marlin + + def fp8_marlin_gemm_wrapper( + a: torch.Tensor, + b_q_weight: torch.Tensor, + b_scales: torch.Tensor, + workspace: torch.Tensor, + num_bits: int, + size_m: int, + size_n: int, + size_k: int, + ) -> torch.Tensor: + # Ensure 'a' has the correct dtype + a = a.to(b_scales.dtype) + # Call the original operator + return original_gemm_f16f8_marlin( + a, + b_q_weight, + b_scales, + workspace, + num_bits, + size_m, + size_n, + size_k, + ) + + # Monkey-patch the operator + torch.ops.quanto.gemm_f16f8_marlin = fp8_marlin_gemm_wrapper + + class TinyGemmQBitsLinearFunction( + optimum.quanto.tensor.function.QuantizedLinearFunction + ): + @staticmethod + def forward(ctx, input, other, bias): + ctx.save_for_backward(input, other) + if type(input) is not torch.Tensor: + input = input.dequantize() + in_features = input.shape[-1] + out_features = other.shape[0] + output_shape = input.shape[:-1] + (out_features,) + output = torch._weight_int4pack_mm( + input.view(-1, in_features).to(dtype=other.dtype), + other._data._data, + other._group_size, + other._scale_shift, + ) + output = output.view(output_shape) + if bias is not None: + output = output + bias + return output + + from optimum.quanto.tensor.weights import tinygemm + + tinygemm.qbits.TinyGemmQBitsLinearFunction = TinyGemmQBitsLinearFunction diff --git a/helpers/training/quantisation/torchao_workarounds.py b/helpers/training/quantisation/torchao_workarounds.py new file mode 100644 index 00000000..4887c5b9 --- /dev/null +++ b/helpers/training/quantisation/torchao_workarounds.py @@ -0,0 +1,40 @@ +import torchao, torch + +from torch import Tensor +from typing import Optional +from torchao.prototype.quantized_training.int8 import Int8QuantizedTrainingLinearWeight + + +class _Int8WeightOnlyLinear(torch.autograd.Function): + @staticmethod + def forward( + ctx, + input: Tensor, + weight: Int8QuantizedTrainingLinearWeight, + bias: Optional[Tensor] = None, + ): + ctx.save_for_backward(input, weight) + ctx.bias = bias is not None + + # NOTE: we have to .T before .to(input.dtype) for torch.compile() mixed matmul to work + out = (input @ weight.int_data.T.to(input.dtype)) * weight.scale + out = out + bias if bias is not None else out + return out + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + + grad_input = (grad_output * weight.scale) @ weight.int_data.to( + grad_output.dtype + ) + # print(f"dtypes: grad_output {grad_output.dtype}, input {input.dtype}, weight {weight.dtype}") + # here is the patch: we will cast the input to the grad_output dtype. + grad_weight = grad_output.view(-1, weight.shape[0]).T @ input.to( + grad_output.dtype + ).view(-1, weight.shape[1]) + grad_bias = grad_output.view(-1, weight.shape[0]).sum(0) if ctx.bias else None + return grad_input, grad_weight, grad_bias + + +torchao.prototype.quantized_training.int8._Int8WeightOnlyLinear = _Int8WeightOnlyLinear diff --git a/helpers/training/save_hooks.py b/helpers/training/save_hooks.py index 35197fa5..a847faf6 100644 --- a/helpers/training/save_hooks.py +++ b/helpers/training/save_hooks.py @@ -349,24 +349,34 @@ def _load_lora(self, models, input_dir): while len(models) > 0: model = models.pop() - if isinstance(model, type(unwrap_model(self.accelerator, self.unet))): + if isinstance( + unwrap_model(self.accelerator, model), + type(unwrap_model(self.accelerator, self.unet)), + ): unet_ = model denoiser = unet_ elif isinstance( - model, type(unwrap_model(self.accelerator, self.transformer)) + unwrap_model(self.accelerator, model), + type(unwrap_model(self.accelerator, self.transformer)), ): transformer_ = model denoiser = transformer_ elif isinstance( - model, type(unwrap_model(self.accelerator, self.text_encoder_1)) + unwrap_model(self.accelerator, model), + type(unwrap_model(self.accelerator, self.text_encoder_1)), ): text_encoder_one_ = model elif isinstance( - model, type(unwrap_model(self.accelerator, self.text_encoder_2)) + unwrap_model(self.accelerator, model), + type(unwrap_model(self.accelerator, self.text_encoder_2)), ): text_encoder_two_ = model else: - raise ValueError(f"unexpected save model: {model.__class__}") + raise ValueError( + f"unexpected save model: {model.__class__}" + f"\nunwrapped: {unwrap_model(self.accelerator, model).__class__}" + f"\nunet: {unwrap_model(self.accelerator, self.unet).__class__}" + ) if self.args.model_family in ["sd3", "flux", "pixart_sigma"]: key_to_replace = "transformer" @@ -422,11 +432,16 @@ def _load_lycoris(self, models, input_dir): if len(state.keys()) > 0: logging.error(f"LyCORIS failed to load: {state}") raise RuntimeError("Loading of LyCORIS model failed") - self.accelerator._lycoris_wrapped_network.to( - device=self.accelerator.device, dtype=self.transformer.dtype - ) - # print(f"transformer dtype: {self.transformer.dtype}") - # print(f"lycoris dtype: {self.accelerator._lycoris_wrapped_network}") + if self.transformer is not None: + self.accelerator._lycoris_wrapped_network.to( + device=self.accelerator.device, dtype=self.transformer.dtype + ) + elif self.unet is not None: + self.accelerator._lycoris_wrapped_network.to( + device=self.accelerator.device, dtype=self.unet.dtype + ) + else: + raise ValueError("No model found to load LyCORIS weights into.") logger.info("LyCORIS weights have been loaded from disk") diff --git a/helpers/training/trainer.py b/helpers/training/trainer.py index 9b53384d..2ef7e143 100644 --- a/helpers/training/trainer.py +++ b/helpers/training/trainer.py @@ -23,7 +23,6 @@ from helpers.training.state_tracker import StateTracker from helpers.training.schedulers import load_scheduler_from_args from helpers.training.custom_schedule import get_lr_scheduler -from helpers.training.optimizer_param import is_lr_scheduler_disabled from helpers.training.adapter import determine_adapter_target_modules, load_lora_weights from helpers.training.diffusion_model import load_diffusion_model from helpers.training.text_encoding import ( @@ -35,6 +34,8 @@ from helpers.training.optimizer_param import ( determine_optimizer_class_with_config, determine_params_to_optimize, + is_lr_scheduler_disabled, + cpu_offload_optimizer, ) from helpers.data_backend.factory import BatchFetcher from helpers.training.deepspeed import ( @@ -730,6 +731,7 @@ def init_precision(self): ) self.config.base_weight_dtype = self.config.weight_dtype self.config.is_quanto = False + self.config.is_torchao = False quantization_device = ( "cpu" if self.config.quantize_via == "cpu" else self.accelerator.device ) @@ -752,27 +754,46 @@ def init_precision(self): self.transformer.to( quantization_device, dtype=self.config.base_weight_dtype ) - if ( - "quanto" in self.config.base_model_precision - and "lora" in self.config.model_type - ): + if "quanto" in self.config.base_model_precision: self.config.is_quanto = True - from helpers.training.quantisation import quantoise + elif "torchao" in self.config.base_model_precision: + self.config.is_torchao = True - self.quantoise = quantoise + if self.config.is_quanto: + from helpers.training.quantisation import quantise_model - # we'll quantise pretty much everything but the adapter, if we execute this here. - if not self.config.controlnet: - with self.accelerator.local_main_process_first(): - quantoise( - unet=self.unet, - transformer=self.transformer, - text_encoder_1=self.text_encoder_1, - text_encoder_2=self.text_encoder_2, - text_encoder_3=self.text_encoder_3, - controlnet=None, - args=self.config, - ) + self.quantise_model = quantise_model + with self.accelerator.local_main_process_first(): + quantise_model( + unet=self.unet, + transformer=self.transformer, + text_encoder_1=self.text_encoder_1, + text_encoder_2=self.text_encoder_2, + text_encoder_3=self.text_encoder_3, + controlnet=None, + args=self.config, + ) + elif self.config.is_torchao: + from helpers.training.quantisation import quantise_model + + self.quantise_model = quantise_model + with self.accelerator.local_main_process_first(): + ( + self.unet, + self.transformer, + self.text_encoder_1, + self.text_encoder_2, + self.text_encoder_3, + self.controlnet, + ) = quantise_model( + unet=self.unet, + transformer=self.transformer, + text_encoder_1=self.text_encoder_1, + text_encoder_2=self.text_encoder_2, + text_encoder_3=self.text_encoder_3, + controlnet=None, + args=self.config, + ) def init_controlnet_model(self): if not self.config.controlnet: @@ -789,7 +810,7 @@ def init_controlnet_model(self): if "quanto" in self.config.base_model_precision: # since controlnet training uses no adapter currently, we just quantise the base transformer here. with self.accelerator.local_main_process_first(): - self.quantoise( + self.quantise_model( unet=self.unet, transformer=self.transformer, text_encoder_1=self.text_encoder_1, @@ -1070,9 +1091,13 @@ def init_optimizer(self): if self.text_encoder_2 is not None: self.params_to_optimize[2]["lr"] = float(self.config.learning_rate) - self.optimizer = optimizer_class( - self.params_to_optimize, - **extra_optimizer_args, + self.optimizer = cpu_offload_optimizer( + params_to_optimize=self.params_to_optimize, + optimizer_cls=optimizer_class, + optimizer_parameters=extra_optimizer_args, + fused=self.config.fuse_optimizer, + offload_gradients=self.config.optimizer_offload_gradients, + offload_mechanism=self.config.optimizer_cpu_offload_method, ) if ( @@ -1768,7 +1793,6 @@ def train(self): current_epoch_step = None self.bf, fetch_thread = None, None iterator_fn = random_dataloader_iterator - for epoch in range(self.state["first_epoch"], self.config.num_train_epochs + 1): if self.state["current_epoch"] > self.config.num_train_epochs + 1: # This might immediately end training, but that's useful for simply exporting the model. @@ -2109,6 +2133,9 @@ def train(self): num_channels_latents=latents.shape[1], height=latents.shape[2], width=latents.shape[3], + ).to( + dtype=self.config.base_weight_dtype, + device=self.accelerator.device, ) if self.config.flux_guidance_mode == "mobius": guidance_scales = get_mobius_guidance( @@ -2180,10 +2207,7 @@ def train(self): ) flux_transformer_kwargs = { - "hidden_states": packed_noisy_latents.to( - dtype=self.config.base_weight_dtype, - device=self.accelerator.device, - ), + "hidden_states": packed_noisy_latents, # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing) "timestep": timesteps, "guidance": guidance, diff --git a/helpers/webhooks/handler.py b/helpers/webhooks/handler.py index 29bfe9c8..51c4ab85 100644 --- a/helpers/webhooks/handler.py +++ b/helpers/webhooks/handler.py @@ -55,6 +55,10 @@ def _send_request( # Prepare Discord-style payload data = {"content": f"{self.message_prefix}{message}"} files = self._prepare_images(images) + request_args = { + "data": data, + "files": files if self.webhook_type == "discord" else None, + } elif self.webhook_type == "raw": # Prepare raw data payload for direct POST if raw_request: @@ -70,16 +74,20 @@ def _send_request( ), } files = None + request_args = { + "json": data, + "files": None, + } else: logger.error(f"Unsupported webhook type: {self.webhook_type}") return # Send request try: + logger.debug(f"Sending webhook request: {request_args}") post_result = requests.post( self.webhook_url, - json=data, - files=files if self.webhook_type == "discord" else None, + **request_args, ) post_result.raise_for_status() except Exception as e: @@ -138,7 +146,11 @@ def send( self._send_request(message, images, store_response=store_response) def send_raw( - self, structured_data: dict, message_type: str, message_level: str = "info", job_id: str = None + self, + structured_data: dict, + message_type: str, + message_level: str = "info", + job_id: str = None, ): """ for sending structured dict to the callback for eg. training step progress updates diff --git a/helpers/webhooks/mixin.py b/helpers/webhooks/mixin.py index 071b1204..f7a3b98b 100644 --- a/helpers/webhooks/mixin.py +++ b/helpers/webhooks/mixin.py @@ -4,13 +4,14 @@ current_rank = get_rank() + class WebhookMixin: webhook_handler: WebhookHandler = None def set_webhook_handler(self, webhook_handler: WebhookHandler): self.webhook_handler = webhook_handler - def send_progress_update(self, type:str, progress: int, total:int, current:int): + def send_progress_update(self, type: str, progress: int, total: int, current: int): if int(current_rank) != 0: return progress = { @@ -23,4 +24,6 @@ def send_progress_update(self, type:str, progress: int, total:int, current:int): }, } - self.webhook_handler.send_raw(progress, "progress_update", job_id=StateTracker.get_job_id()) + self.webhook_handler.send_raw( + progress, "progress_update", job_id=StateTracker.get_job_id() + ) diff --git a/install/apple/poetry.lock b/install/apple/poetry.lock index e70521e2..b3b9a16b 100644 --- a/install/apple/poetry.lock +++ b/install/apple/poetry.lock @@ -1184,7 +1184,7 @@ typing = ["mypy (>=1.0.0)", "types-setuptools"] [[package]] name = "lycoris_lora" -version = "3.0.1.dev13" +version = "3.0.1.dev14" description = "Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion" optional = false python-versions = ">=3.10" @@ -1201,7 +1201,7 @@ tqdm = "*" type = "git" url = "https://github.com/kohakublueleaf/lycoris" reference = "dev" -resolved_reference = "6bea3d061e3730c2274f7f9d035a2757a9b0fd7d" +resolved_reference = "8978355aa43164393736269416956f1974580166" [[package]] name = "markdown" @@ -1759,7 +1759,7 @@ files = [] develop = false 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@patch("helpers.training.trainer.Trainer.init_noise_schedule") - @patch("accelerate.accelerator.Accelerator", return_value=Mock()) + @patch( + "accelerate.accelerator.Accelerator", + return_value=Mock(device=Mock(type="cuda")), + ) @patch("accelerate.state.AcceleratorState", Mock()) @patch( "argparse.ArgumentParser.parse_args", diff --git a/tests/test_webhooks.py b/tests/test_webhooks.py index ffb63d12..7433e3ae 100644 --- a/tests/test_webhooks.py +++ b/tests/test_webhooks.py @@ -9,12 +9,12 @@ class TestWebhookHandler(unittest.TestCase): def setUp(self): # Create a mock for the WebhookConfig - mock_config_instance = MagicMock(spec=WebhookConfig) - mock_config_instance.webhook_url = "http://example.com/webhook" - mock_config_instance.webhook_type = "discord" - mock_config_instance.log_level = "info" - mock_config_instance.message_prefix = "TestPrefix" - mock_config_instance.values = { + self.mock_config_instance = MagicMock(spec=WebhookConfig) + self.mock_config_instance.webhook_url = "http://example.com/webhook" + self.mock_config_instance.webhook_type = "discord" + self.mock_config_instance.log_level = "info" + self.mock_config_instance.message_prefix = "TestPrefix" + self.mock_config_instance.values = { "webhook_url": "http://example.com/webhook", "webhook_type": "discord", "log_level": "info", @@ -30,14 +30,20 @@ def setUp(self): config_path="dummy_path", accelerator=self.mock_accelerator, project_name="TestProject", - mock_webhook_config=mock_config_instance, + mock_webhook_config=self.mock_config_instance, ) @patch("requests.post") def test_send_message_info_level(self, mock_post): # Test sending a simple info level message - self.handler.send("Test message", message_level="info") + message = "Test message" + self.handler.send(message, message_level="info") mock_post.assert_called_once() + # Capture the call arguments + args, kwargs = mock_post.call_args + # Assuming the message is sent in 'data' parameter + self.assertIn("data", kwargs) + self.assertIn(message, kwargs["data"].get("content")) @patch("requests.post") def test_debug_message_wont_send(self, mock_post): @@ -56,12 +62,16 @@ def test_do_not_send_lower_than_configured_level(self, mock_post): def test_send_with_images(self, mock_post): # Test sending messages with images image = Image.new("RGB", (60, 30), color="red") - self.handler.send( - "Test message with image", images=[image], message_level="info" - ) + message = "Test message with image" + self.handler.send(message, images=[image], message_level="info") args, kwargs = mock_post.call_args self.assertIn("files", kwargs) self.assertEqual(len(kwargs["files"]), 1) + # Check that the message is in the 'data' parameter + content = kwargs.get("data", {}).get("content", "") + self.assertIn(self.mock_config_instance.values.get("message_prefix"), content) + self.assertIn("data", kwargs, f"Check data for contents: {kwargs}") + self.assertIn(message, content) @patch("requests.post") def test_response_storage(self, mock_post): @@ -72,6 +82,11 @@ def test_response_storage(self, mock_post): self.handler.send("Test message", message_level="info", store_response=True) self.assertEqual(self.handler.stored_response, mock_response.headers) + # Also check that the message is sent + args, kwargs = mock_post.call_args + content = kwargs.get("data", {}).get("content", "") + self.assertIn(self.mock_config_instance.values.get("message_prefix"), content) + self.assertIn("Test message", content) if __name__ == "__main__":