Adds profiling flags, computes train metrics average.

examples/controlnet/README.md

@@ -284,9 +284,9 @@ TPU_TYPE=v4-8
 VM_NAME=hg_flax
 
 gcloud alpha compute tpus tpu-vm create $VM_NAME \
-    --zone $ZONE \
-    --accelerator-type $TPU_TYPE \
-    --version  tpu-vm-v4-base
+ --zone $ZONE \
+ --accelerator-type $TPU_TYPE \
+ --version  tpu-vm-v4-base
 
 gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \
 ```
@@ -326,6 +326,7 @@ If you want to use Weights and Biases logging, you should also install `wandb` n
 pip install wandb
 ```
 
+
 Now let's downloading two conditioning images that we will use to run validation during the training in order to track our progress
 
 ```
@@ -343,8 +344,8 @@ Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment v
 
 ```bash
 export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="control_out"
-export HUB_MODEL_ID="fill-circle-controlnet"
+export OUTPUT_DIR="runs/fill-circle-{timestamp}"
+export HUB_MODEL_ID="controlnet-fill-circle"
 ```
 
 And finally start the training
@@ -363,32 +364,36 @@ python3 train_controlnet_flax.py \
  --revision="non-ema" \
  --from_pt \
  --report_to="wandb" \
- --max_train_steps=10000 \
+ --tracker_project_name=$HUB_MODEL_ID \
+ --num_train_epochs=11 \
  --push_to_hub \
  --hub_model_id=$HUB_MODEL_ID
  ```
 
 Since we passed the `--push_to_hub` flag, it will automatically create a model repo under your huggingface account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet).
 
-Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`.  Here is an example command:
+Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`.  Here is an example command (from [this blog article](https://huggingface.co/blog/train-your-controlnet)):
 
 ```bash
+export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export OUTPUT_DIR="runs/uncanny-faces-{timestamp}"
+export HUB_MODEL_ID="controlnet-uncanny-faces"
+
 python3 train_controlnet_flax.py \
-	--pretrained_model_name_or_path=$MODEL_DIR \
-	--output_dir=$OUTPUT_DIR \
-	--dataset_name=multimodalart/facesyntheticsspigacaptioned \
-	--streaming \
-	--conditioning_image_column=spiga_seg \
-	--image_column=image \
-	--caption_column=image_caption \
-	--resolution=512 \
-	--max_train_samples 50 \
-	--max_train_steps 5 \
-	--learning_rate=1e-5 \
-	--validation_steps=2 \
-	--train_batch_size=1 \
-	--revision="flax" \
-	--report_to="wandb"
+ --pretrained_model_name_or_path=$MODEL_DIR \
+ --output_dir=$OUTPUT_DIR \
+ --dataset_name=multimodalart/facesyntheticsspigacaptioned \
+ --streaming \
+ --conditioning_image_column=spiga_seg \
+ --image_column=image \
+ --caption_column=image_caption \
+ --resolution=512 \
+ --max_train_samples 100000 \
+ --learning_rate=1e-5 \
+ --train_batch_size=1 \
+ --revision="flax" \
+ --report_to="wandb" \
+ --tracker_project_name=$HUB_MODEL_ID
 ```
 
 Note, however, that the performance of the TPUs might get bottlenecked as streaming with `datasets` is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options:
@@ -400,16 +405,35 @@ Note, however, that the performance of the TPUs might get bottlenecked as stream
 When work with a larger dataset, you may need to run training process for a long time and it’s useful to save regular checkpoints during the process. You can use the following argument to enable intermediate checkpointing:
 
 ```bash
-  --checkpointing_steps=500
+ --checkpointing_steps=500
 ```
 This will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500 
 
 You can then start your training from this saved checkpoint with 
 
 ```bash
-   --controlnet_model_name_or_path="./control_out/500" 
+ --controlnet_model_name_or_path="./control_out/500" 
 ```
 
 We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. To use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is `5.0`.
 
-We also support gradient accumulation - it is a technique that lets you use a bigger batch size than your machine would normally be able to fit into memory. You can use `gradient_accumulation_steps` argument to set gradient accumulation steps. The ControlNet author recommends using gradient accumulation to achieve better convergence. Read more [here](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#more-consideration-sudden-converge-phenomenon-and-gradient-accumulation).
+We also support gradient accumulation - it is a technique that lets you use a bigger batch size than your machine would normally be able to fit into memory. You can use `gradient_accumulation_steps` argument to set gradient accumulation steps. The ControlNet author recommends using gradient accumulation to achieve better convergence. Read more [here](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#more-consideration-sudden-converge-phenomenon-and-gradient-accumulation).
+
+You can **profile your code** with:
+
+```bash
+ --profile_steps==5
+```
+
+Refer to the [JAX documentation on profiling](https://jax.readthedocs.io/en/latest/profiling.html). To inspect the profile trace, you'll have to install and start Tensorboard with the profile plugin:
+
+```bash
+pip install tensorflow tensorboard-plugin-profile
+tensorboard --logdir runs/fill-circle-100steps-20230411_165612/
+```
+
+The profile can then be inspected at http://localhost:6006/#profile
+
+Sometimes you'll get version conflicts (error messages like `Duplicate plugins for name projector`), which means that you have to uninstall and reinstall all versions of Tensorflow/Tensorboard (e.g. with `pip uninstall tensorflow tf-nightly tensorboard tb-nightly tensorboard-plugin-profile && pip install tf-nightly tbp-nightly tensorboard-plugin-profile`).
+
+Note that the debugging functionality of the Tensorboard `profile` plugin is still under active development. Not all views are fully functional, and for example the `trace_viewer` cuts off events after 1M (which can result in all your device traces getting lost if you for example profile the compilation step by accident).

examples/controlnet/train_controlnet_flax.py

@@ -18,6 +18,7 @@
 import math
 import os
 import random
+import time
 from pathlib import Path
 
 import jax
@@ -220,6 +221,28 @@ def parse_args():
         default=None,
         help="Revision of controlnet model identifier from huggingface.co/models.",
     )
+    parser.add_argument(
+        "--profile_steps",
+        type=int,
+        default=0,
+        help="How many training steps to profile in the beginning.",
+    )
+    parser.add_argument(
+        "--profile_validation",
+        action="store_true",
+        help="Whether to profile the (last) validation.",
+    )
+    parser.add_argument(
+        "--profile_memory",
+        action="store_true",
+        help="Whether to dump an initial (before training loop) and a final (at program end) memory profile.",
+    )
+    parser.add_argument(
+        "--ccache",
+        type=str,
+        default=None,
+        help="Enables compilation cache.",
+    )
     parser.add_argument(
         "--controlnet_from_pt",
         action="store_true",
@@ -234,8 +257,9 @@ def parse_args():
     parser.add_argument(
         "--output_dir",
         type=str,
-        default="controlnet-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
+        default="runs/{timestamp}",
+        help="The output directory where the model predictions and checkpoints will be written. "
+        "Can contain placeholders: {timestamp}.",
     )
     parser.add_argument(
         "--cache_dir",
@@ -317,15 +341,6 @@ def parse_args():
         default=None,
         help="The name of the repository to keep in sync with the local `output_dir`.",
     )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
     parser.add_argument(
         "--logging_steps",
         type=int,
@@ -459,6 +474,8 @@ def parse_args():
     parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
 
     args = parser.parse_args()
+    args.output_dir = args.output_dir.replace("{timestamp}", time.strftime("%Y%m%d_%H%M%S"))
+
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
     if env_local_rank != -1 and env_local_rank != args.local_rank:
         args.local_rank = env_local_rank
@@ -952,6 +969,11 @@ def cumul_grad_step(grad_idx, loss_grad_rng):
         metrics = {"loss": loss}
         metrics = jax.lax.pmean(metrics, axis_name="batch")
 
+        def l2(xs):
+            return jnp.sqrt(sum([jnp.vdot(x, x) for x in jax.tree_util.tree_leaves(xs)]))
+
+        metrics["l2_grads"] = l2(jax.tree_util.tree_leaves(grad))
+
         return new_state, metrics, new_train_rng
 
     # Create parallel version of the train step
@@ -983,32 +1005,38 @@ def cumul_grad_step(grad_idx, loss_grad_rng):
     logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
     logger.info(f"  Total optimization steps = {args.num_train_epochs * num_update_steps_per_epoch}")
 
-    if jax.process_index() == 0:
+    if jax.process_index() == 0 and args.report_to == "wandb":
         wandb.define_metric("*", step_metric="train/step")
+        wandb.define_metric("train/step", step_metric="walltime")
         wandb.config.update(
             {
                 "num_train_examples": args.max_train_samples if args.streaming else len(train_dataset),
                 "total_train_batch_size": total_train_batch_size,
                 "total_optimization_step": args.num_train_epochs * num_update_steps_per_epoch,
                 "num_devices": jax.device_count(),
+                "controlnet_params": sum(np.prod(x.shape) for x in jax.tree_util.tree_leaves(state.params)),
             }
         )
 
-    global_step = 0
+    global_step = step0 = 0
     epochs = tqdm(
         range(args.num_train_epochs),
         desc="Epoch ... ",
         position=0,
         disable=jax.process_index() > 0,
     )
+    if args.profile_memory:
+        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_initial.prof"))
+    t00 = t0 = time.monotonic()
     for epoch in epochs:
         # ======================== Training ================================
 
         train_metrics = []
+        train_metric = None
 
         steps_per_epoch = (
             args.max_train_samples // total_train_batch_size
-            if args.streaming
+            if args.streaming or args.max_train_samples
             else len(train_dataset) // total_train_batch_size
         )
         train_step_progress_bar = tqdm(
@@ -1020,10 +1048,18 @@ def cumul_grad_step(grad_idx, loss_grad_rng):
         )
         # train
         for batch in train_dataloader:
+            if args.profile_steps and global_step == 1:
+                train_metric["loss"].block_until_ready()
+                jax.profiler.start_trace(args.output_dir)
+            if args.profile_steps and global_step == 1 + args.profile_steps:
+                train_metric["loss"].block_until_ready()
+                jax.profiler.stop_trace()
+
             batch = shard(batch)
-            state, train_metric, train_rngs = p_train_step(
-                state, unet_params, text_encoder_params, vae_params, batch, train_rngs
-            )
+            with jax.profiler.StepTraceAnnotation("train", step_num=global_step):
+                state, train_metric, train_rngs = p_train_step(
+                    state, unet_params, text_encoder_params, vae_params, batch, train_rngs
+                )
             train_metrics.append(train_metric)
 
             train_step_progress_bar.update(1)
@@ -1041,13 +1077,19 @@ def cumul_grad_step(grad_idx, loss_grad_rng):
 
             if global_step % args.logging_steps == 0 and jax.process_index() == 0:
                 if args.report_to == "wandb":
+                    train_metrics = jax_utils.unreplicate(train_metrics)
+                    train_metrics = jax.tree_util.tree_map(lambda *m: jnp.array(m).mean(), *train_metrics)
                     wandb.log(
                         {
+                            "walltime": time.monotonic() - t00,
                             "train/step": global_step,
-                            "train/epoch": epoch,
-                            "train/loss": jax_utils.unreplicate(train_metric)["loss"],
+                            "train/epoch": global_step / dataset_length,
+                            "train/steps_per_sec": (global_step - step0) / (time.monotonic() - t0),
+                            **{f"train/{k}": v for k, v in train_metrics.items()},
                         }
                     )
+                t0, step0 = time.monotonic(), global_step
+                train_metrics = []
             if global_step % args.checkpointing_steps == 0 and jax.process_index() == 0:
                 controlnet.save_pretrained(
                     f"{args.output_dir}/{global_step}",
@@ -1058,10 +1100,14 @@ def cumul_grad_step(grad_idx, loss_grad_rng):
         train_step_progress_bar.close()
         epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
 
-    # Create the pipeline using using the trained modules and save it.
+    # Final validation & store model.
     if jax.process_index() == 0:
         if args.validation_prompt is not None:
+            if args.profile_validation:
+                jax.profiler.start_trace(args.output_dir)
             image_logs = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype)
+            if args.profile_validation:
+                jax.profiler.stop_trace()
         else:
             image_logs = None
 
@@ -1084,6 +1130,10 @@ def cumul_grad_step(grad_idx, loss_grad_rng):
                 ignore_patterns=["step_*", "epoch_*"],
             )
 
+    if args.profile_memory:
+        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_final.prof"))
+    logger.info("Finished training.")
+
 
 if __name__ == "__main__":
     main()