Merge pull request #310 from jianhan-amd:jianhan/synthetic_data_iterator

PiperOrigin-RevId: 877546452

Author: Google-ML-Automation

README.md

@@ -262,6 +262,17 @@ After installation completes, run the training script.
   - For use on GPU it is recommended to enable the cudnn_te_flash attention kernel for optimal performance.
     - Best performance is achieved with the use of batch parallelism, which can be enabled by using the ici_fsdp_batch_parallelism axis. Note that this parallelism strategy does not support fractional batch sizes.
     - ici_fsdp_batch_parallelism and ici_fsdp_parallelism can be combined to allow for fractional batch sizes. However, padding is not currently supported for the cudnn_te_flash attention kernel and it is therefore required that the sequence length is divisible by the number of devices in the ici_fsdp_parallelism axis.
+  - For benchmarking training performance on multiple data dimension input without downloading/re-processing the dataset, the synthetic data iterator is supported.
+    - Set dataset_type='synthetic' and synthetic_num_samples=null to enable the synthetic data iterator.
+    - The following overrides on data dimensions are supported:
+      - synthetic_override_height: 720
+      - synthetic_override_width: 1280
+      - synthetic_override_num_frames: 85
+      - synthetic_override_max_sequence_length: 512
+      - synthetic_override_text_embed_dim: 4096
+      - synthetic_override_num_channels_latents: 16
+      - synthetic_override_vae_scale_factor_spatial: 8
+      - synthetic_override_vae_scale_factor_temporal: 4
 
   You should eventually see a training run as:
 

src/maxdiffusion/configs/base_flux_dev.yml

@@ -179,7 +179,20 @@ allow_split_physical_axes: False
 # Replace with dataset path or train_data_dir. One has to be set.
 dataset_name: 'diffusers/pokemon-gpt4-captions'
 train_split: 'train'
-dataset_type: 'tf'
+dataset_type: 'tfrecord'  # Options: 'tfrecord', 'hf', 'tf', 'grain', 'synthetic'
+# ==============================================================================
+# Synthetic Data Configuration (only used when dataset_type='synthetic')
+# ==============================================================================
+# To use synthetic data for testing/debugging without real datasets:
+# 1. Set dataset_type: 'synthetic' above
+# 2. Optionally set synthetic_num_samples (null=infinite, or a number like 10000)
+# 3. Optionally override dimensions
+#
+# synthetic_num_samples: null  # null for infinite, or set a number
+#
+# Optional dimension overrides:
+# resolution: 512
+# ==============================================================================
 cache_latents_text_encoder_outputs: True
 # cache_latents_text_encoder_outputs only apply to dataset_type="tf",
 # only apply to small dataset that fits in memory

src/maxdiffusion/configs/base_wan_14b.yml

@@ -201,7 +201,28 @@ allow_split_physical_axes: False
 # Replace with dataset path or train_data_dir. One has to be set.
 dataset_name: 'diffusers/pokemon-gpt4-captions'
 train_split: 'train'
-dataset_type: 'tfrecord'
+dataset_type: 'tfrecord'  # Options: 'tfrecord', 'hf', 'tf', 'grain', 'synthetic'
+# ==============================================================================
+# Synthetic Data Configuration (only used when dataset_type='synthetic')
+# ==============================================================================
+# To use synthetic data for testing/debugging without real datasets:
+# 1. Set dataset_type: 'synthetic' above
+# 2. Optionally set synthetic_num_samples (null=infinite, or a number like 10000)
+# 3. Optionally override dimensions with synthetic_override_* flags below
+#
+# synthetic_num_samples: null  # null for infinite, or set a number
+#
+# Optional dimension overrides (comment out to use pipeline/config values):
+# synthetic_override_height: 720
+# synthetic_override_width: 1280
+# synthetic_override_num_frames: 121
+# synthetic_override_max_sequence_length: 512
+# synthetic_override_text_embed_dim: 4096
+# synthetic_override_num_channels_latents: 16
+# synthetic_override_vae_scale_factor_spatial: 8
+# synthetic_override_vae_scale_factor_temporal: 4
+# ==============================================================================
+
 cache_latents_text_encoder_outputs: True
 # cache_latents_text_encoder_outputs only apply to dataset_type="tf",
 # only apply to small dataset that fits in memory

src/maxdiffusion/input_pipeline/input_pipeline_interface.py

@@ -23,6 +23,7 @@
 from maxdiffusion.input_pipeline import _hf_data_processing
 from maxdiffusion.input_pipeline import _grain_data_processing
 from maxdiffusion.input_pipeline import _tfds_data_processing
+from maxdiffusion.input_pipeline import synthetic_data_iterator
 from maxdiffusion import multihost_dataloading
 from maxdiffusion.maxdiffusion_utils import tokenize_captions, transform_images, vae_apply
 from maxdiffusion.dreambooth.dreambooth_constants import (
@@ -54,8 +55,9 @@ def make_data_iterator(
     feature_description=None,
     prepare_sample_fn=None,
     is_training=True,
+    pipeline=None,
 ):
-  """Make data iterator for SD1, 2, XL, dataset_types in (hf, tf, tfrecord)"""
+  """Make data iterator for SD1, 2, XL, dataset_types in (hf, tf, tfrecord, grain, synthetic)"""
 
   if config.dataset_type == "hf" or config.dataset_type == "tf":
     if tokenize_fn is None or image_transforms_fn is None:
@@ -110,8 +112,16 @@ def make_data_iterator(
         prepare_sample_fn,
         is_training,
     )
+  elif config.dataset_type == "synthetic":
+    return synthetic_data_iterator.make_synthetic_iterator(
+        config=config,
+        mesh=mesh,
+        global_batch_size=global_batch_size,
+        pipeline=pipeline,
+        is_training=is_training,
+    )
   else:
-    assert False, f"Unknown dataset_type {config.dataset_type}, dataset_type must be in (tf, tfrecord, hf, grain)"
+    assert False, f"Unknown dataset_type {config.dataset_type}, dataset_type must be in (tf, tfrecord, hf, grain, synthetic)"
 
 
 def make_dreambooth_train_iterator(config, mesh, global_batch_size, tokenizer, vae, vae_params):