Merge pull request #3189 from AI-Hypercomputer:bvandermoon-restructure

PiperOrigin-RevId: 872774667

Author: Google-ML-Automation

.vscode/launch.json

@@ -51,7 +51,7 @@
       "console": "integratedTerminal",
       "justMyCode": false,
       "python": "python3",
-      "module": "MaxText.train",
+      "module": "maxtext.trainers.pre_train.train",
       "args": ["src/maxtext/configs/base.yml",
                "run_name=runner_$(date +%Y-%m-%d-%H-%M)",
                "base_output_directory=gs://test-maxtext-output",

PREFLIGHT.md

@@ -7,12 +7,12 @@ Before you run ML workload on Multihost with GCE or GKE, simply apply `bash pref
 
 Here is an example for GCE:
 ```
-bash preflight.sh PLATFORM=GCE && python3 -m MaxText.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
+bash preflight.sh PLATFORM=GCE && python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
 ```
 
 Here is an example for GKE:
 ```
-bash preflight.sh PLATFORM=GKE && python3 -m MaxText.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
+bash preflight.sh PLATFORM=GKE && python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
 ```
 
 # Optimization 2: Numa binding (You can only apply this to v4 and v5p)
@@ -22,14 +22,14 @@ For GCE,
 [preflight.sh](https://github.com/google/maxtext/blob/main/preflight.sh) will help you install `numactl` dependency, so you can use it directly, here is an example:
 
 ```
-bash preflight.sh PLATFORM=GCE && numactl --membind 0 --cpunodebind=0 python3 -m MaxText.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
+bash preflight.sh PLATFORM=GCE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
 ```
 
 For GKE,
 `numactl` should be built into your docker image from [maxtext_tpu_dependencies.Dockerfile](https://github.com/google/maxtext/blob/main/dependencies/dockerfiles/maxtext_tpu_dependencies.Dockerfile), so you can use it directly if you built the maxtext docker image. Here is an example
 
 ```
-bash preflight.sh PLATFORM=GKE && numactl --membind 0 --cpunodebind=0 python3 -m MaxText.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
+bash preflight.sh PLATFORM=GKE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=$YOUR_JOB_NAME
 ```
 
 1. `numactl`: This is the command-line tool used for controlling NUMA policy for processes or shared memory. It's particularly useful on multi-socket systems where memory locality can impact performance.

benchmarks/maxtext_xpk_runner.py

@@ -440,7 +440,7 @@ def build_user_command(
           f"export JAX_PLATFORMS={jax_platforms} &&",
           "export ENABLE_PJRT_COMPATIBILITY=true &&",
           "export MAXTEXT_ASSETS_ROOT=/deps/src/maxtext/assets MAXTEXT_PKG_DIR=/deps/src/MaxText MAXTEXT_REPO_ROOT=/deps &&"
-          f'{hlo_dump} python3 -m MaxText.train {os.path.join(MAXTEXT_CONFIGS_DIR, "base.yml")}',
+          f'{hlo_dump} python3 -m maxtext.trainers.pre_train.train {os.path.join(MAXTEXT_CONFIGS_DIR, "base.yml")}',
           f"{config_tuning_params}",
           f"steps={wl_config.num_steps}",
           f"model_name={wl_config.model.model_type}",

docs/guides/checkpointing_solutions/emergency_checkpointing.md

@@ -157,5 +157,5 @@ The flags below would give the user access to the ramdisk in their workload:
    --num-slices=${NUM_SLICES} \
    --ramdisk-directory=${RAMDISK_DIRECTORY} \
    --mtc-enabled \
-   --command "python3 src/MaxText/train.py src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH steps=120 per_device_batch_size=6 enable_checkpoint_cloud_logger=True checkpoint_period=${CHECKPOINT_PEROID} enable_emergency_checkpoint=True local_checkpoint_period=${LOCAL_CHECKPOINT_PERIOD} local_checkpoint_directory=/${RAMDISK_DIRECTORY}"
+   --command "python3 src/maxtext/trainers/pre_train/train.py src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH steps=120 per_device_batch_size=6 enable_checkpoint_cloud_logger=True checkpoint_period=${CHECKPOINT_PEROID} enable_emergency_checkpoint=True local_checkpoint_period=${LOCAL_CHECKPOINT_PERIOD} local_checkpoint_directory=/${RAMDISK_DIRECTORY}"
    ```

docs/guides/checkpointing_solutions/multi_tier_checkpointing.md

@@ -186,5 +186,5 @@ The flags below would give the user access to the ramdisk in their workload:
    --num-slices=${NUM_SLICES} \
    --ramdisk-directory=${RAMDISK_DIRECTORY} \
    --mtc-enabled  \
-   --command "python3 src/MaxText/train.py src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH steps=120 per_device_batch_size=6 enable_checkpoint_cloud_logger=True checkpoint_period=${CHECKPOINT_PEROID} enable_multi_tier_checkpointing=True local_checkpoint_period=${LOCAL_CHECKPOINT_PERIOD} local_checkpoint_directory=/${RAMDISK_DIRECTORY} multi_tier_checkpointing_backup_interval_minutes=${MULTI_TIER_CHECKPOINTING_BACKUP_INT_MIN}"
+   --command "python3 src/maxtext/trainers/pre_train/train.py src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH steps=120 per_device_batch_size=6 enable_checkpoint_cloud_logger=True checkpoint_period=${CHECKPOINT_PEROID} enable_multi_tier_checkpointing=True local_checkpoint_period=${LOCAL_CHECKPOINT_PERIOD} local_checkpoint_directory=/${RAMDISK_DIRECTORY} multi_tier_checkpointing_backup_interval_minutes=${MULTI_TIER_CHECKPOINTING_BACKUP_INT_MIN}"
    ```

docs/guides/data_input_pipeline/data_input_grain.md

@@ -112,7 +112,7 @@ Note that `FILE_PATH` is optional; when provided, the script runs `ls -R` for pr
 bash tools/setup/setup_gcsfuse.sh \
 DATASET_GCS_BUCKET=maxtext-dataset \
 MOUNT_PATH=/tmp/gcsfuse && \
-python3 -m MaxText.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
 run_name=<RUN_NAME> base_output_directory=gs://<MY_BUCKET>  \
 dataset_type=grain \
 grain_file_type=arrayrecord # or parquet \ 

docs/guides/monitoring_and_debugging/features_and_diagnostics.md

@@ -84,7 +84,7 @@ To load the compiled train_step, you just need to pass `compiled_trainstep_file=
 ```sh
 # Run the below on each host of the target hardware, e.g. each host on 2 slices of v5e-256
 export LIBTPU_INIT_ARGS="--xla_enable_async_all_gather=true"
-python3 -m MaxText.train src/maxtext/configs/base.yml run_name=example_load_compile \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=example_load_compile \
   compiled_trainstep_file=my_compiled_train.pickle \
   global_parameter_scale=16 per_device_batch_size=4 steps=10000 learning_rate=1e-3 \
   base_output_directory=gs://my-output-bucket dataset_path=gs://my-dataset-bucket
@@ -122,7 +122,7 @@ To load the compiled `train_step`, you just need to pass `compiled_trainstep_fil
 ```sh
 # Run the below on each of the 4 target A3 hosts.
 export XLA_FLAGS="--xla_gpu_enable_async_collectives=true"
-python3 -m MaxText.train src/maxtext/configs/base.yml run_name=example_load_compile \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=example_load_compile \
   compiled_trainstep_file=my_compiled_train.pickle \
   attention=dot_product global_parameter_scale=16  per_device_batch_size=4 steps=10000 learning_rate=1e-3 \
   base_output_directory=gs://my-output-bucket dataset_path=gs://my-dataset-bucket

docs/guides/monitoring_and_debugging/ml_workload_diagnostics.md

@@ -35,7 +35,7 @@ MaxText has integrated the ML Diagnostics [SDK](https://github.com/AI-Hypercompu
 1. Enable ML Diagnostics to just capture Maxtext metrics and configs
 
    ```
-      python3 -m MaxText.train src/maxtext/configs/base.yml \
+      python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
          run_name=${USER}-tpu-job \
          base_output_directory="gs://your-output-bucket/" \
          dataset_path="gs://your-dataset-bucket/" \
@@ -47,7 +47,7 @@ MaxText has integrated the ML Diagnostics [SDK](https://github.com/AI-Hypercompu
 2. Enable ML Diagnostics to capture Maxtext metrics, configs and singlehost profiles (on the first TPU device)
 
    ```
-      python3 -m MaxText.train src/maxtext/configs/base.yml \
+      python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
          run_name=${USER}-tpu-job \
          base_output_directory="gs://your-output-bucket/" \
          dataset_path="gs://your-dataset-bucket/" \
@@ -60,7 +60,7 @@ MaxText has integrated the ML Diagnostics [SDK](https://github.com/AI-Hypercompu
 3. Enable ML Diagnostics to capture Maxtext metrics, configs and multihost profiles (on all TPU devices)
 
    ```
-      python3 -m MaxText.train src/maxtext/configs/base.yml \
+      python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
          run_name=${USER}-tpu-job \
          base_output_directory="gs://your-output-bucket/" \
          dataset_path="gs://your-dataset-bucket/" \

docs/guides/monitoring_and_debugging/monitor_goodput.md

@@ -89,7 +89,7 @@ Please use a unique workload name, unless you intend to monitor cumulative Goodp
 MaxText enables Goodput recording and monitoring by default with `enable_goodput_recording=True` and `monitor_goodput=True`. You can configure the goodput upload frequency by setting `goodput_upload_interval_seconds`.
 
 ```bash
-python3 -m MaxText.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH \
   dataset_path=$DATA_PATH run_name=goodput-test-run steps=200 goodput_upload_interval_seconds=30
 ```
 
@@ -98,7 +98,7 @@ python3 -m MaxText.train src/maxtext/configs/base.yml base_output_directory=$OUT
 MaxText enables step time deviation monitoring by default with `monitor_step_time_deviation=True`. You can configure the upload frequency by setting `step_deviation_interval_seconds`.
 
 ```bash
-python3 -m MaxText.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH \
   dataset_path=$DATA_PATH run_name=goodput-test-run steps=200 step_deviation_interval_seconds=30
 ```
 
@@ -111,7 +111,7 @@ Enabling `enable_pathways_goodput` turns on Goodput measurement for Pathways wor
 ```
 
 ```bash
-python3 -m MaxText.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH \
   run_name=goodput-test-run steps=200 goodput_upload_interval_seconds=30 enable_pathways_goodput=True
 ```
 
@@ -168,7 +168,7 @@ and `enable_gcp_step_deviation_metrics` to `False` for disabling step deviation
 metrics.
 
 ```bash
-python3 -m MaxText.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml base_output_directory=$OUTPUT_PATH dataset_path=$DATA_PATH \
   run_name=goodput-test-run steps=200 goodput_upload_interval_seconds=30 enable_gcp_goodput_metrics=False \
   enable_gcp_step_deviation_metrics=False
 ```

docs/guides/monitoring_and_debugging/understand_logs_and_metrics.md

@@ -23,7 +23,7 @@ When you run a training job, MaxText produces detailed output logs. This guide s
 To start, run a simple pretraining job on a single-host TPU. For instance, we can run the following command on TPU v5p-8. The resulting log is used as an example throughout this guide.
 
 ```bash
-python3 -m MaxText.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
 base_output_directory=gs://runner-maxtext-logs run_name=demo \
 model_name=deepseek2-16b \
 per_device_batch_size=24 max_target_length=2048 steps=10 dataset_type=synthetic enable_checkpointing=false
@@ -123,7 +123,7 @@ To generate all optional artifacts in one run, you can set the corresponding fla
 This command enables tensorboard, profiler, text metrics, config saving, and checkpointing:
 
 ```bash
-python3 -m MaxText.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
 base_output_directory=gs://runner-maxtext-logs run_name=demo2 \
 model_name=deepseek2-16b \
 per_device_batch_size=24 max_target_length=2048 steps=10 dataset_type=synthetic \