diff --git a/.coveragerc b/.coveragerc deleted file mode 100644 index 29de6ff8a3..0000000000 --- a/.coveragerc +++ /dev/null @@ -1,5 +0,0 @@ -[html] -directory = coverage - -[run] -data_file = .coverage_$LOCAL_RANK diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000000..1e35e0c496 --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +max-line-length = 100 +extend-ignore = E203,E501,F401,E402,E714 +per-file-ignores = __init__.py:F401 \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md new file mode 100644 index 0000000000..b639acd3c0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug.md @@ -0,0 +1,32 @@ +--- +name: BUG +about: Report a bug that needs attention +title: "[BUG]" +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior. The easier it is to reproduce the faster it will get maintainer attention. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Stack trace/logs** +If applicable, add the stack trace or logs from the time of the error. + +**Environment (please complete the following information):** + - Megatron-LM commit ID + - PyTorch version + - CUDA version + - NCCL version + +**Proposed fix** +If you have a proposal for how to fix the issue state it here or link to a PR. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md new file mode 100644 index 0000000000..076f7195ba --- /dev/null +++ b/.github/ISSUE_TEMPLATE/enhancement.md @@ -0,0 +1,23 @@ +--- +name: ENHANCEMENT +about: Suggest an idea to improve this project +title: "[ENHANCEMENT]" +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Proposed implementation** +If you have a proposed implementation for the feature state it here or link to a PR. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000000..b3d89a0ac1 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,12 @@ +--- +name: QUESTION +about: Ask a question about Megatron-LM that is not a bug, regression or enhancement + request +title: "[QUESTION]" +labels: '' +assignees: '' + +--- + +**Your question** +Ask a clear and concise question about Megatron-LM. diff --git a/.github/ISSUE_TEMPLATE/regression.md b/.github/ISSUE_TEMPLATE/regression.md new file mode 100644 index 0000000000..10078d23a6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/regression.md @@ -0,0 +1,39 @@ +--- +name: REGRESSION +about: Report a regression in speed or accuracy due to a Megatron-LM update +title: "[REGRESSION]" +labels: '' +assignees: '' + +--- + +**Describe the regression** +A clear and concise description of what the regression is. + +**To Reproduce** +Steps to reproduce the behavior. The easier it is to reproduce the faster it will get maintainer attention. + +**Previous performance** +What speed or accuracy did you previously see. + +**New performance** +What speed or accuracy do you see after the update. + +**Stack trace/logs** +If applicable, add the stack trace or logs related to the regression. + +**Environment (please complete the following information):** + - Previous Megatron-LM commit ID + - New Megatron-LM commit ID + - Previous PyTorch version + - New PyTorch version + - Previous CUDA version + - New CUDA version + - Previous NCCL version + - New NCCL version + +**Proposed fix** +If you have a proposal for how to fix the issue state it here or link to a PR. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml new file mode 100644 index 0000000000..58ba38e060 --- /dev/null +++ b/.github/workflows/stale.yml @@ -0,0 +1,31 @@ +# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time. +# +# You can adjust the behavior by modifying this file. +# For more information, see: +# https://github.com/actions/stale +name: Mark stale issues and pull requests + +on: + schedule: + - cron: '15 18 * * *' + +jobs: + stale: + + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + + steps: + - uses: actions/stale@v5 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + days-before-stale: 60 + stale-issue-message: 'Marking as stale. No activity in 60 days.' + stale-pr-message: 'Marking as stale. No activity in 60 days.' + stale-issue-label: 'stale' + stale-pr-label: 'stale' + remove-stale-when-updated: true + operations-per-run: 1000 + days-before-close: -1 diff --git a/.gitignore b/.gitignore index cac3499524..6cb5f1ec46 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,10 @@ build *~ slurm* logs +.vscode +local/ +.gitmodules +wandb/ +onelogger.log +onelogger.err +.venv diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 3cd1c2f2e6..f6b1a0e0e7 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -1,299 +1,257 @@ -image: gitlab-master.nvidia.com:5005/adlr/megatron-lm/ngc/pytorch:22.12-py3_pytest-cov +.merge_train_rule: &merge_train_rule + UNIT_TEST: "yes" + UNIT_TEST_REPEAT: 1 + UNIT_TEST_TIMEOUT: 30 + INTEGRATION_TEST: "yes" + INTEGRATION_TEST_SCOPE: mr + FUNCTIONAL_TEST: "yes" + FUNCTIONAL_TEST_SCOPE: mr-slim + FUNCTIONAL_TEST_REPEAT: 5 + FUNCTIONAL_TEST_TIME_LIMIT: 2700 + CLUSTER_A100: "" + CLUSTER_H100: "" + PUBLISH: "no" -stages: - - test - - cleanup +workflow: + rules: + # Do not trigger for forks + - if: $CI_PROJECT_NAMESPACE != "ADLR" || ($CI_PIPELINE_SOURCE == "merge_request_event" && $CI_MERGE_REQUEST_PROJECT_PATH != "ADLR/megatron-lm") + when: never -variables: &VARS - SELENE_ADLR_CI_PATH: "/lustre/fsw/adlr/adlr-nlp/adlr_ci/megatron" - DATA_DIR: "/lustre/fsw/adlr/adlr-nlp/adlr_ci/megatron/data" - PYTORCH_IMAGE: gitlab-master.nvidia.com:5005/adlr/megatron-lm/ngc/pytorch:22.12-py3_pytest-cov - PYTHON_VIRTUAL_ENV: /lustre/fsw/adlr/adlr-nlp/adlr_ci/cienv/bin/activate - TESTS_TO_RUN_AFTER_MERGE_REQ_APPROVED: L0 # Can specify levels - TESTS_TO_RUN_AFTER_MERGING: L0 # Can specify levels - TESTS_TO_RUN_ON_THIS_COMMIT: unit_tests - TEST_REGEX_ON_THIS_COMMIT: NONE #https://github.com/google/re2/wiki/Syntax (Can define regex as in this spec) e.g /.*gpt3.*/ - DISPLAY_OUTPUT: "True" # Set to true for new tests to copy the logs for creating golden truth file + # ci-branches only for schedule + - if: $CI_COMMIT_BRANCH =~ /ci-/ && $CI_PIPELINE_SOURCE != "schedule" + when: never -unit_tests: - tags: - - docker_local_runner - stage: test - script: - - torchrun --nproc_per_node=8 -m pytest --cov-report=term --cov-report=html --cov=megatron/core tests/unit_tests - coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/' - artifacts: - paths: - - coverage - expire_in: 30 days - only: - - merge_requests + # For schedules pipelines + - if: $CI_PIPELINE_SOURCE == "schedule" + auto_cancel: + on_new_commit: none -.selene_test_resume_checkpoint_launcher: &selene-test-resume-checkpoint-launcher - tags: - - ssh_selene_runner - stage: test - script: &selene-test-resume-launcher-script - - echo "Running selene resume from checkpoint test. " - - pwd - - export BUILD_DIR=`pwd` - - export RUN_NAME=resume_${RUN_MODEL}_tp${TP_SIZE}_pp${PP_SIZE}_${NUM_NODES}nodes - - echo "In case of error check ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${RUN_NAME}/results directory for result logs." - - export TP_SIZE PP_SIZE NUM_NODES MAX_STEPS - - export DATA_DIR=$DATA_DIR - - echo "Run name is $RUN_NAME" - - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/checkpoints - - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/logs - - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/results - - rm -rf $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/checkpoints/* - - rm -rf $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/logs/* - - rm -rf $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/results/* - - export BASE_DIR=$SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME - - export LOGS_DIR=$BASE_DIR/logs - - export RESULTS_DIR=$BASE_DIR/results - - export CHECKPOINTS_DIR=$BASE_DIR/checkpoints - - echo "Submitting job" - - sbatch_submission=`sbatch $BUILD_DIR/tests/functional_tests/test_scripts/$RUN_MODEL/sbatch_${RUN_MODEL}_distributed_resume_checkpoint_test.sh --export=BASE_DIR,BUILD_DIR,DATA_DIR,TP_SIZE,PP_SIZE,NUM_NODES` - - export SLURM_JOBID=$(echo $sbatch_submission| grep 'Submitted batch job' | awk '{ print $4 }'); - - bash $BUILD_DIR/tests/functional_tests/shell_test_utils/jobwait.sh $SLURM_JOBID - - \[ ! -z ${SLURM_JOBID} \] && echo -e " --------------------------------------------------\n" - "----------WAITING FOR SLURM JOB TO BEGIN-----------\n" - "---------------------------------------------------\n" - "$(scontrol show job=${SLURM_JOBID})\n" - "---------------------------------------------------\n" - # Gitlab logs collapsible section markers - - echo -e "\e[0Ksection_end:`date +%s`:slurm_setup\r\e[0K" - # Follow output of the job - - echo "Finished job" - - export SLURM_STATE=$(sacct -j "${SLURM_JOBID}" --format State --parsable2 --noheader |& head -n 1) - - echo "Slurm job state $SLURM_STATE" - - if [[ "$SLURM_STATE" != "COMPLETED" ]]; then echo "Slurm job did not complete. See ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${RUN_NAME}/results directory for result logs. Skipping pytest."; exit 1; fi - - source $PYTHON_VIRTUAL_ENV - - pytest $BUILD_DIR/tests/functional_tests/python_test_utils/test_resume_checkpoint_pipeline.py || echo "Pytest failed. See ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${RUN_NAME}/results directory for result logs." - - echo "Completed the job" - rules: - - if: $TEST_LEVEL =~ $TESTS_TO_RUN_ON_THIS_COMMIT || $CI_JOB_NAME =~ $TESTS_TO_RUN_ON_THIS_COMMIT || $CI_JOB_NAME =~ $TEST_REGEX_ON_THIS_COMMIT - when: always - - if: '$CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH && $TEST_LEVEL =~ $TESTS_TO_RUN_AFTER_MERGING' - when: always - - if: $CI_MERGE_REQUEST_APPROVED && $TEST_LEVEL =~ $TESTS_TO_RUN_AFTER_MERGE_REQ_APPROVED - when: always - allow_failure: false + # For manual pipelines + - if: $CI_PIPELINE_SOURCE == "web" -.selene_test_launcher: &selene-test-launcher - tags: - - ssh_selene_runner - stage: test - script: &selene-test-launcher-script - - echo "Running selene test" - - echo "$CI_MERGE_REQUEST_APPROVED" - - pwd - - export BUILD_DIR=`pwd` - - RUN_NAME=${RUN_MODEL}_tp${TP_SIZE}_pp${PP_SIZE}_${NUM_NODES}nodes_${MAX_STEPS}steps - - if [[ $USE_TE == 1 ]]; then RUN_NAME=${RUN_NAME}_te_enabled; fi - - export $RUN_NAME - - echo "In case of error check ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${RUN_NAME}/results directory for result logs." - - export USE_TE TP_SIZE PP_SIZE NUM_NODES MAX_STEPS VP_SIZE - - export MBS GBS - - export DATA_DIR=$DATA_DIR - - echo "Run name is $RUN_NAME" - - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/checkpoints - - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/logs - - mkdir -p $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/results - - rm -rf $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/checkpoints/* - - rm -rf $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/logs/* - - rm -rf $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/results/* - - export BASE_DIR=$SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME - - export LOGS_DIR=$BASE_DIR/logs - - export RESULTS_DIR=$BASE_DIR/results - - export CHECKPOINTS_DIR=$BASE_DIR/checkpoints - - echo "Submitting job" - - sbatch_submission=`sbatch $BUILD_DIR/tests/functional_tests/test_scripts/$RUN_MODEL/sbatch_${RUN_MODEL}_distributed_test.sh --export=BASE_DIR,BUILD_DIR,DATA_DIR,USE_TE,TP_SIZE,PP_SIZE,NUM_NODES,MAX_STEPS,VP_SIZE,MBS,GBS` - - export SLURM_JOBID=$(echo $sbatch_submission| grep 'Submitted batch job' | awk '{ print $4 }'); - - bash $BUILD_DIR/tests/functional_tests/shell_test_utils/jobwait.sh $SLURM_JOBID - - \[ ! -z ${SLURM_JOBID} \] && echo -e " --------------------------------------------------\n" - "----------WAITING FOR SLURM JOB TO BEGIN-----------\n" - "---------------------------------------------------\n" - "$(scontrol show job=${SLURM_JOBID})\n" - "---------------------------------------------------\n" - # Gitlab logs collapsible section markers - - echo -e "\e[0Ksection_end:`date +%s`:slurm_setup\r\e[0K" - # Follow output of the job - - echo "Finished job" - - echo "Slurm log dump start ------------------------------------------------------------" - - cat $SELENE_ADLR_CI_PATH/$CI_PIPELINE_ID/$RUN_NAME/results/* - - echo "Slurm log dump end --------------------------------------------------------------" - - python3 $BUILD_DIR/tests/functional_tests/python_test_utils/check_slurm_job_completion.py $SLURM_JOBID - - if [ $? -ne 0 ]; then echo "Slurm job did not complete. See ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${RUN_NAME}/results directory for result logs. Skipping pytest."; exit 1; fi - - source $PYTHON_VIRTUAL_ENV - - | - if [[ "$DISPLAY_OUTPUT" == "True" ]]; then - python3 $BUILD_DIR/tests/functional_tests/python_test_utils/get_test_results_from_tensorboard_logs.py $LOGS_DIR $RUN_NAME - fi - - | - if [[ $USE_TE -ne 1 ]]; then - echo "Checking against ground truth file" - export EXPECTED_METRICS_FILE=$BUILD_DIR/tests/functional_tests/test_results/$RUN_MODEL/$RUN_NAME.json - pytest $BUILD_DIR/tests/functional_tests/python_test_utils/test_ci_pipeline.py || echo "Pytest failed. See ${SELENE_ADLR_CI_PATH}/${CI_PIPELINE_ID}/${RUN_NAME}/results directory for result logs." - fi - - echo "Completed the job" - rules: - - if: $TEST_LEVEL =~ $TESTS_TO_RUN_ON_THIS_COMMIT || $CI_JOB_NAME =~ $TESTS_TO_RUN_ON_THIS_COMMIT || $CI_JOB_NAME =~ $TEST_REGEX_ON_THIS_COMMIT - when: always - - if: '$CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH && $TEST_LEVEL =~ $TESTS_TO_RUN_AFTER_MERGING' - when: always - - if: $CI_MERGE_REQUEST_APPROVED && $TEST_LEVEL =~ $TESTS_TO_RUN_AFTER_MERGE_REQ_APPROVED - when: always - allow_failure: false + # For push to main + - if: $CI_PIPELINE_SOURCE == 'push' && $CI_COMMIT_REF_PROTECTED == "true" + variables: + UNIT_TEST: "no" + INTEGRATION_TEST: "no" + FUNCTIONAL_TEST: "yes" + FUNCTIONAL_TEST_SCOPE: mr + FUNCTIONAL_TEST_REPEAT: 5 + FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no" + FUNCTIONAL_TEST_TIME_LIMIT: 2700 + CLUSTER_A100: "" + CLUSTER_H100: "" + PUBLISH: "no" + auto_cancel: + on_new_commit: none -train.te_gpt3.345m_tp2_pp2_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: gpt3 - USE_TE: 1 - TP_SIZE: 2 - PP_SIZE: 2 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "50:00" - TEST_LEVEL: L0 + # For merge-trains that need to be fast-tracked + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train' && $CI_MERGE_REQUEST_LABELS =~ /fast-track/ + variables: + UNIT_TEST: "yes" + UNIT_TEST_REPEAT: 1 + UNIT_TEST_TIMEOUT: 30 + INTEGRATION_TEST: "no" + FUNCTIONAL_TEST: "no" + CLUSTER_A100: "" + CLUSTER_H100: "" + PUBLISH: "no" -train.gpt3.345m_tp4_pp1_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: gpt3 - USE_TE: 0 - TP_SIZE: 4 - PP_SIZE: 1 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + # For normal merge-trains + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train' + variables: *merge_train_rule -train.gpt3.345m_tp2_pp2_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: gpt3 - USE_TE: 0 - TP_SIZE: 2 - PP_SIZE: 2 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + # For MRs with integration suite + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run tests/ + variables: *merge_train_rule -train.gpt3.345m_tp1_pp2_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: gpt3 - USE_TE: 0 - TP_SIZE: 1 - PP_SIZE: 2 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + # For MRs with nightly + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run nightly/ + variables: + UNIT_TEST: "yes" + UNIT_TEST_REPEAT: 1 + UNIT_TEST_TIMEOUT: 30 + INTEGRATION_TEST: "no" + FUNCTIONAL_TEST: "yes" + FUNCTIONAL_TEST_SCOPE: nightly + FUNCTIONAL_TEST_REPEAT: 5 + FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no" + FUNCTIONAL_TEST_TIME_LIMIT: 2700 + CLUSTER_A100: "" + CLUSTER_H100: "" + PUBLISH: "no" -train.gpt3.345m_tp1_pp4_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: gpt3 - USE_TE: 0 - TP_SIZE: 1 - PP_SIZE: 4 - VP_SIZE: 1 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + # For MRs with weekly + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run weekly/ + variables: + UNIT_TEST: "yes" + UNIT_TEST_REPEAT: 1 + UNIT_TEST_TIMEOUT: 30 + INTEGRATION_TEST: "no" + FUNCTIONAL_TEST: "yes" + FUNCTIONAL_TEST_SCOPE: weekly + FUNCTIONAL_TEST_REPEAT: 1 + FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no" + FUNCTIONAL_TEST_TIME_LIMIT: 9000 + CLUSTER_A100: "" + CLUSTER_H100: "" + PUBLISH: "no" -resume.checkpoint.gpt3.345m_tp1_pp2_1node: - <<: *selene-test-resume-checkpoint-launcher - variables: - <<: [*VARS] - RUN_MODEL: gpt3 - TP_SIZE: 1 - PP_SIZE: 2 - NUM_NODES: 1 - TIME_LIMIT: "30:00" - TEST_LEVEL: L0 + # For MRs with heavy suite + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run functional tests/ + variables: + UNIT_TEST: "yes" + UNIT_TEST_REPEAT: 1 + UNIT_TEST_TIMEOUT: 30 + INTEGRATION_TEST: "no" + FUNCTIONAL_TEST: "yes" + FUNCTIONAL_TEST_SCOPE: mr + FUNCTIONAL_TEST_REPEAT: 5 + FUNCTIONAL_TEST_TIME_LIMIT: 2700 + CLUSTER_A100: "" + CLUSTER_H100: "" + PUBLISH: "no" -train.bert.345m_tp4_pp1_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: bert - TP_SIZE: 4 - PP_SIZE: 1 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + # Default MRs + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' + variables: + UNIT_TEST: "yes" + UNIT_TEST_REPEAT: 1 + UNIT_TEST_TIMEOUT: 30 + INTEGRATION_TEST: "no" + FUNCTIONAL_TEST: "no" + PUBLISH: "no" -train.bert.345m_tp2_pp2_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: bert - TP_SIZE: 2 - PP_SIZE: 2 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + - when: never -train.bert.345m_tp1_pp2_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: bert - TP_SIZE: 1 - PP_SIZE: 2 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 + auto_cancel: + on_new_commit: interruptible -train.bert.345m_tp1_pp4_1node_50steps: - <<: *selene-test-launcher - variables: - <<: [*VARS] - RUN_MODEL: bert - TP_SIZE: 1 - PP_SIZE: 4 - VP_SIZE: 2 - NUM_NODES: 1 - MAX_STEPS: 50 - TIME_LIMIT: "20:00" - TEST_LEVEL: L0 +stages: + - build + - test + - integration_tests + - functional_tests + - publish -resume.checkpoint.bert.345m_tp1_pp2_1node: - <<: *selene-test-resume-checkpoint-launcher - variables: - <<: [*VARS] - RUN_MODEL: bert - TP_SIZE: 1 - PP_SIZE: 2 - NUM_NODES: 1 - TIME_LIMIT: "30:00" - TEST_LEVEL: L0 +default: + interruptible: true + retry: + max: 2 + when: runner_system_failure -cleanup.selene: - tags: - - ssh_selene_runner - stage: cleanup - variables: - <<: [*VARS] - script: - - set +e - - NUM_CLEANUP=`find ${SELENE_ADLR_CI_PATH}/* -type d -ctime +20 | grep -v data | wc -l` - - find ${SELENE_ADLR_CI_PATH}/* -type d -ctime +20 | grep -v data | xargs rm -rf - - echo "Finished cleaning $NUM_CLEANUP directories older than 20 days everything in Selene" - allow_failure: true - rules: - - when: always +variables: + UNIT_TEST: + value: "yes" + options: + - "yes" + - "no" + description: To run the funtional test suite + UNIT_TEST_REPEAT: + value: "1" + description: "Number of repetitions" + UNIT_TEST_TIMEOUT: + value: "30" + description: Timeout (minutes) for Unit tests (all repeats) + INTEGRATION_TEST: + value: "yes" + options: + - "yes" + - "no" + description: To run the integration test suite + INTEGRATION_TEST_SCOPE: + value: "mr" + options: + - "mr" + - "nightly" + - "weekly" + - "pre-release" + - "release" + description: "Testsuite to run (only for INTEGRATION_TEST=yes)" + INTEGRATION_TEST_TIME_LIMIT: + value: "900" + description: "Timeout in seconds per test" + INTEGRATION_TEST_CASES: + value: "all" + description: "Comma-separated list of test_cases to run. Use 'all' to run the full suite." + FUNCTIONAL_TEST: + value: "yes" + options: + - "yes" + - "no" + description: To run the funtional test suite + FUNCTIONAL_TEST_SCOPE: + value: "mr" + options: + - "mr" + - "nightly" + - "weekly" + - "pre-release" + - "release" + description: "Testsuite to run (only for FUNCTIONAL_TEST=yes)" + FUNCTIONAL_TEST_REPEAT: + value: "5" + description: "Number of repetitions per test" + FUNCTIONAL_TEST_TIME_LIMIT: + value: "2700" + description: "Timeout in seconds per test" + FUNCTIONAL_TEST_CASES: + value: "all" + description: "Comma-separated list of test_cases to run. Use 'all' to run the full suite." + FUNCTIONAL_TEST_NAME: + description: "Name of functional test run (only for pre-release and release)" + value: "$$CI_COMMIT_SHA" + FUNCTIONAL_TEST_RECORD_CHECKPOINTS: + value: "no" + description: "Record golden checkpoints" + options: + - "yes" + - "no" + CLUSTER_A100: + value: "dgxa100_dracooci" + options: + - "dgxa100_dracooci" + - "dgxa100_dracooci-ord" + description: "Cluster for A100 workloads" + CLUSTER_H100: + value: "dgxh100_coreweave" + options: + - "dgxh100_coreweave" + - "dgxh100_eos" + description: "Cluster for H100 workloads" + PUBLISH: + value: "no" + options: + - "yes" + - "no" + description: Build and publish a wheel to PyPi + PUBLISH_COMMIT: + value: "$$CI_COMMIT_SHA" + description: Which commit to publish + PUBLISH_VERSION_BUMP_BRANCH: + value: "$$CI_COMMIT_BRANCH" + description: Which branch to target for version bump + PUBLISH_SCOPE: + value: "code-freeze" + options: + - "code-freeze" + - "release" + - "review-reminder" + - "upgrade-dependencies" + description: Type of publish (freeze or final release) + + # CI wide variables + CI_MCORE_LTS_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_ci_lts + CI_MCORE_DEV_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_ci_dev + CI_NEMO_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/nemo_ci + UTILITY_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_utility + TE_GIT_REF: "" + +include: + - .gitlab/stages/00.pre.yml + - .gitlab/stages/01.build.yml + - .gitlab/stages/02.test.yml + - .gitlab/stages/03.integration-tests.yml + - .gitlab/stages/04.functional-tests.yml + - .gitlab/stages/05.publish.yml diff --git a/.gitlab/labeler-config.yml b/.gitlab/labeler-config.yml new file mode 100644 index 0000000000..0e218e4bae --- /dev/null +++ b/.gitlab/labeler-config.yml @@ -0,0 +1,36 @@ +CI: + - .gitlab-ci.yml + - Dockerfile.ci.lts + - Dockerfile.ci.dev + - .github/** + - .gitlab/** + +Datasets: + - megatron/core/datasets/** + +BERT: + - megatron/core/models/bert/** + +GPT: + - megatron/core/models/gpt/** + +RETRO: + - megatron/core/models/retro/** + +Dist-Ckpt: + - megatron/core/dist_checkpointing + +Dist-Opt: + - megatron/core/optimizer/distrib_optimizer + +Inference: + - megatron/core/inference + +MoE: + - megatron/core/transformer/moe + +Tests: + - tests/** + +ParallelState: + - megatron/core/parallel_state.py diff --git a/.gitlab/scripts/build.sh b/.gitlab/scripts/build.sh new file mode 100644 index 0000000000..4a4f33007b --- /dev/null +++ b/.gitlab/scripts/build.sh @@ -0,0 +1,47 @@ +#! /bin/bash + +set -x +env +eval "IMAGE=\$$IMAGE" + +docker context create tls-environment +docker buildx create --name container --driver=docker-container --use tls-environment + +ADDITIONAL_PARAMS=() + +if [[ "$CI_COMMIT_BRANCH" == "ci-rebuild-mcore-nemo-image" || "$CI_COMMIT_BRANCH" == "main" ]]; then + ADDITIONAL_PARAMS+=("--pull") + ADDITIONAL_PARAMS+=("--cache-to type=registry,ref=${IMAGE}-buildcache:main,mode=max") + ADDITIONAL_PARAMS+=("-t ${IMAGE}:main") +elif [[ -n "$CI_MERGE_REQUEST_IID" ]]; then + ADDITIONAL_PARAMS+=("--cache-to type=registry,ref=${IMAGE}-buildcache:${CI_MERGE_REQUEST_IID},mode=max") + ADDITIONAL_PARAMS+=("-t ${IMAGE}:${CI_MERGE_REQUEST_IID}") +fi + +if [[ "$CI_COMMIT_BRANCH" == "ci-nightly" ]]; then + ADDITIONAL_PARAMS+=("-t ${IMAGE}:nightly") +fi + +if [[ -n "$TE_GIT_REF" ]]; then + ADDITIONAL_PARAMS+=("--build-arg TE_COMMIT=${TE_GIT_REF}") +fi + +echo $(git rev-parse HEAD) + +JET_API_VERSION=$(curl -s -u "$ARTIFACTORY_USER:$ARTIFACTORY_TOKEN" "https://sc-hw-artf.nvidia.com/artifactory/api/pypi/hw-joc-pypi/simple/jet-api/" | grep -o 'href="../../jet-api/[0-9.]*/' | sed 's|href="../../jet-api/||;s|/||' | sort -V -r | head -n1) + +DOCKER_BUILDKIT=1 docker build \ + --secret id=JET_INDEX_URLS \ + --secret id=LOGGER_INDEX_URL \ + --secret id=EXPERIMENTAL_FLASH_ATTN \ + --target $STAGE \ + -f docker/$FILE \ + -t ${IMAGE}:${CI_PIPELINE_ID} \ + --builder=container \ + --build-arg JET_API_VERSION=$JET_API_VERSION \ + --cache-from type=registry,ref=${IMAGE}-buildcache:${CI_MERGE_REQUEST_IID} \ + --cache-from type=registry,ref=${IMAGE}-buildcache:main \ + --build-arg FROM_IMAGE_NAME=$BASE_IMAGE \ + --push \ + --progress plain \ + ${ADDITIONAL_PARAMS[@]} . diff --git a/.gitlab/scripts/check_imports.py b/.gitlab/scripts/check_imports.py new file mode 100644 index 0000000000..f46987d8d8 --- /dev/null +++ b/.gitlab/scripts/check_imports.py @@ -0,0 +1,208 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +#!/usr/bin/env python3 +""" +Import checker script for megatron.hub package. + +This script recursively discovers all Python modules in the specified package +and attempts to import them, reporting any import errors. +""" + +import importlib +import os +import sys +import traceback +from typing import Dict, List, Tuple + +import click + + +class ImportChecker: + """Check imports for all modules in a package.""" + + def __init__(self, package_name: str = "megatron.core", verbose: bool = False): + self.package_name = package_name + self.success_count = 0 + self.failure_count = 0 + self.graceful_count = 0 + self.skipped_count = 0 + self.failures: Dict[str, str] = {} + self.successes: List[str] = [] + self.graceful_failures: Dict[str, str] = {} + self.skipped: List[str] = [] + + # Modules to skip (known problematic ones) + self.skip_patterns = { + "__pycache__", + ".pytest_cache", + ".git", + "test_", + "_test", + } + + # Add current directory to Python path if not already there + current_dir = os.getcwd() + if current_dir not in sys.path: + sys.path.insert(0, current_dir) + + def should_skip_module(self, module_name: str) -> bool: + """Check if a module should be skipped.""" + for pattern in self.skip_patterns: + if pattern in module_name: + return True + return False + + def discover_modules(self, package_path: str) -> List[str]: + """Discover all Python modules in the given package path.""" + modules = [] + + package = importlib.import_module(package_path) + package_path = package.__path__[0] + + # Walk through all Python files + for root, dirs, files in os.walk(package.__path__[0]): + # Skip hidden directories and __pycache__ + dirs[:] = [d for d in dirs if not d.startswith(".") and d != "__pycache__"] + + for file in files: + if file.endswith(".py") and not file.startswith("."): + # Convert file path to module name + rel_path = os.path.relpath(os.path.join(root, file), package_path) + module_parts = rel_path.replace(os.sep, ".").replace(".py", "") + + # Handle __init__.py files + if module_parts.endswith(".__init__"): + module_parts = module_parts[:-9] # Remove .__init__ + + full_module_name = ( + f"{self.package_name}.{module_parts}" + if module_parts + else self.package_name + ) + + if not self.should_skip_module(full_module_name): + modules.append(full_module_name) + + # Remove duplicates and sort + modules = sorted(list(set(modules))) + + return modules + + def import_module(self, module_name: str) -> Tuple[str, str]: + """ + Try to import a module and return success status and error message. + + Returns: + Tuple of (status: str, error_message: str) + status can be: "success", "graceful", or "failed" + """ + try: + if module_name in sys.modules: + del sys.modules[module_name] + + importlib.import_module(module_name) + return "success", "" + + except Exception: + tb = traceback.format_exc() + if "UnavailableError" in tb: + return "graceful", "UnavailableError detected during import" + return "failed", f"{str(tb)}" + + def check_all_imports(self): + """Check imports for all discovered modules.""" + print(f"Discovering modules in package '{self.package_name}'...") + modules = self.discover_modules(self.package_name) + + if not modules: + print("No modules found!") + return + + print(f"Found {len(modules)} modules to check") + print("=" * 60) + + for i, module_name in enumerate(modules, 1): + status, error_msg = self.import_module(module_name) + + if status == "success": + self.success_count += 1 + self.successes.append(module_name) + elif status == "graceful": + self.graceful_count += 1 + self.graceful_failures[module_name] = error_msg + else: # failed + self.failure_count += 1 + self.failures[module_name] = error_msg + + """Print a summary of the import check results.""" + total = ( + self.success_count + + self.failure_count + + self.graceful_count + + self.skipped_count + ) + + print("\n" + "=" * 60) + print("IMPORT CHECK SUMMARY") + print("=" * 60) + print(f"Total modules checked: {total}") + print( + f"Successful imports: {self.success_count} ({self.success_count / total * 100:.1f}%)" + ) + print( + f"Gracefully handled: {self.graceful_count} ({self.graceful_count / total * 100:.1f}%)" + ) + print( + f"Failed imports: {self.failure_count} ({self.failure_count / total * 100:.1f}%)" + ) + if self.skipped_count > 0: + print( + f"Skipped modules: {self.skipped_count} ({self.skipped_count / total * 100:.1f}%)" + ) + + if self.graceful_failures: + print(f"\nšŸŸ” GRACEFULLY HANDLED ({len(self.graceful_failures)}):") + print("-" * 40) + + if self.failures: + print(f"\nāŒ FAILED IMPORTS ({len(self.failures)}):") + print("-" * 40) + for module_name, error_msg in self.failures.items(): + print(f"\nā€¢ {module_name}") + # Show only the first few lines of error to keep output manageable + error_lines = error_msg.split("\n") + for line in error_lines: + # if self.package_name.replace(".", os.sep) not in line: + # continue + if line.strip(): + print(f" {line}") + + return self.failure_count == 0 + + +@click.command() +@click.option( + "--package-name", + required=True, + help="Package name to check imports for", +) +def main(package_name: str): + """Main entry point.""" + checker = ImportChecker(package_name=package_name) + successful = checker.check_all_imports() + exit(0 if successful else 1) + + +if __name__ == "__main__": + main() diff --git a/.gitlab/scripts/fetch-legacy-suite.sh b/.gitlab/scripts/fetch-legacy-suite.sh new file mode 100644 index 0000000000..cde6af6afb --- /dev/null +++ b/.gitlab/scripts/fetch-legacy-suite.sh @@ -0,0 +1,69 @@ +#!/bin/bash +set -euxo pipefail + +# Default values +MCORE_REPO="https://github.com/nvidia/megatron-lm.git" +MCORE_MR_COMMIT="main" +MCORE_BACKWARDS_COMMIT="" + +# Parse command line arguments +usage() { + cat < labels + - gitlab-mr-labeler -f .gitlab/labeler-config.yml -t ${PROJECT_ACCESS_TOKEN_MCORE} --debug true + - cat labels + after_script: + - | + source labels + curl --header "PRIVATE-TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE}" --url "https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests/${CI_MERGE_REQUEST_IID}" --data-urlencode "add_labels=$LABELS" -X PUT + +pre:maybe_cherry_pick_commit: + rules: + - if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH && $CI_PIPELINE_SOURCE == "push"' + - when: never + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + stage: .pre + image: nentangso/alpine-git-curl-jq + variables: + GIT_STRATEGY: "clone" + script: + - set -x + - set +e + - SHA=$(git rev-list --no-merges -n 1 HEAD) + - MESSAGE=$(git log -n 1 --pretty=format:%s $SHA) + - MR_ID=$(echo $MESSAGE | awk -F'!' '{print $2}' | awk '{print $1}' ) + - git remote set-url origin "https://gitlab-ci-token:${PROJECT_ACCESS_TOKEN_MCORE}@${GITLAB_ENDPOINT}/$CI_PROJECT_NAMESPACE/megatron-lm.git" + - git config --global user.email "mcore-bot@nvidia.com" + - git config --global user.name "Mcore Bot" + - | + MR=$(curl --header "PRIVATE-TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE}" --url "https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests/${MR_ID}") + + LABELS=$(echo -E $MR | jq '.labels | join(",")' | tr -d '"') + AUTHOR_ID=$(echo -E $MR | jq '.author.id' | tr -d '"') + AUTHOR_NAME=$(echo -E $MR | jq '.author.username' | tr -d '"') + TITLE=$(echo -E $MR | jq '.title' | tr -d '"') + MILESTONE_ID=$(echo -E $MR | jq '.milestone.id' | tr -d '"') + TARGET_BRANCHES=$(echo "$LABELS" | grep -o 'core_[^,]*') + + if [[ $TARGET_BRANCHES == "" ]]; then + echo Nothing to cherry pick + exit 0 + fi + + echo $TARGET_BRANCHES | while read -r RELEASE_BRANCH ; do + TARGET_BRANCH_EXISTS_OK=$([[ "$(git ls-remote --heads origin refs/heads/$RELEASE_BRANCH)" != "" ]] && echo true || echo false) + + if [[ "$TARGET_BRANCH_EXISTS_OK" == "false" ]]; then + echo Release branch does not yet exist, will not cherry-pick + continue + fi + + ( + git fetch origin $RELEASE_BRANCH:$RELEASE_BRANCH + git switch --force-create cherry-pick-$MR_ID-$RELEASE_BRANCH $RELEASE_BRANCH + git cherry-pick $SHA + git push -u origin --force cherry-pick-$MR_ID-$RELEASE_BRANCH + git checkout ${CI_DEFAULT_BRANCH:-main} + ) + + CHERRYPICK_SUCCESSFUL=$? + + if [[ $CHERRYPICK_SUCCESSFUL -eq 0 ]]; then + curl \ + --header "PRIVATE-TOKEN: $PAT" \ + --url https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests \ + -d "source_branch=cherry-pick-$MR_ID-$RELEASE_BRANCH" \ + -d "target_branch=$RELEASE_BRANCH" \ + -d "title=Cherry pick \`$TITLE ($MR_ID)\` into \`$RELEASE_BRANCH\`" \ + -d "labels=cherry-pick" \ + -d "reviewer_ids=$AUTHOR_ID" \ + -d "milestone_id=$MILESTONE_ID" \ + -d "description=[šŸ¤–]: Hi @$AUTHOR_NAME šŸ‘‹,

we've cherry picked \`$TITLE ($MR_ID)\` into \`$RELEASE_BRANCH\` for you! šŸš€

Please review and approve this cherry pick by your convenience\!" + + else + URL=https://${GITLAB_ENDPOINT}/ADLR/megatron-lm/-/merge_requests/$MR_ID + + MESSAGE='{ + "blocks": [ + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": "beep boop šŸ¤–: Cherry-pick of <'$URL'|!'$MR_ID'> failed\ncc '$SLACK_ADMIN'" + } + } + ] + }' + + curl -X POST -H "Content-type: application/json" --data "$MESSAGE" ${MCORE_NOTIFICATION_HOOK} + + fi + + done + interruptible: false + +pre:check_milestone: + extends: [.pre_rules] + image: badouralix/curl-jq + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - | + MILESTONE=$(curl --header "PRIVATE-TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE}" --url "https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests/${CI_MERGE_REQUEST_IID}" | jq '.milestone') + - | + if [[ "$MILESTONE" == "null" ]]; then + LATEST_MILESTONE=$(curl --header "PRIVATE-TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE}" --url "https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/milestones?state=active&order_by=due_date&sort=desc" | jq '.[0].id') + curl --request PUT --header "PRIVATE-TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE}" --url "https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests/${CI_MERGE_REQUEST_IID}" --data "milestone_id=${LATEST_MILESTONE}" + echo "Applied latest milestone (ID: ${LATEST_MILESTONE}) to this MR" + fi + +pre:check_status_of_main: + extends: [.pre_rules] + image: python:3.10 + timeout: 7 days + variables: + KUBERNETES_SERVICE_MEMORY_REQUEST: 32Gi + KUBERNETES_SERVICE_MEMORY_LIMIT: 32Gi + KUBERNETES_SERVICE_CPU_REQUEST: 8 + KUBERNETES_SERVICE_CPU_LIMIT: 12 + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - pip install --no-cache-dir python-gitlab click + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - python tests/test_utils/python_scripts/check_status_of_main.py --target-branch "$CI_MERGE_REQUEST_TARGET_BRANCH_NAME" + rules: + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train' && $CI_MERGE_REQUEST_LABELS =~ /fast-track/ + when: never + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train' + when: always + - when: never diff --git a/.gitlab/stages/01.build.yml b/.gitlab/stages/01.build.yml new file mode 100644 index 0000000000..2a7454832a --- /dev/null +++ b/.gitlab/stages/01.build.yml @@ -0,0 +1,71 @@ +.build_rules: + rules: + - when: on_success + stage: test + +.build_image: + extends: [.build_rules, .dind_rules] + stage: build + tags: + - arch/amd64 + - origin/jet-fleet + - env/prod + - ${TAG} + services: + - name: docker:24.0.5-dind + variables: + HEALTHCHECK_TCP_PORT: "2376" + timeout: 180m + variables: + DOCKER_HOST: tcp://docker:2376 + DOCKER_TLS_CERTDIR: "/certs" + DOCKER_TLS_VERIFY: 1 + DOCKER_CERT_PATH: "$DOCKER_TLS_CERTDIR/client" + TAG: purpose/builder-large + STAGE: jet + MCORE_BACKWARDS_REF: core_r0.13.0 + KUBERNETES_SERVICE_MEMORY_REQUEST: 90Gi + KUBERNETES_SERVICE_MEMORY_LIMIT: 90Gi + # KUBERNETES_SERVICE_CPU_REQUEST: 60 + # KUBERNETES_SERVICE_CPU_LIMIT: 60 + script: + - eval PUBLISH_COMMIT=$PUBLISH_COMMIT + - apk add bash curl git + - export TE_GIT_REF=$TE_GIT_REF + - bash .gitlab/scripts/build.sh + + - git fetch origin $MCORE_BACKWARDS_REF + - MCORE_BACKWARDS_COMMIT=$(git rev-parse FETCH_HEAD) + + - echo "MCORE_MR_COMMIT=$CI_COMMIT_SHA" | tee -a build.env + - echo "MCORE_BACKWARDS_COMMIT=$MCORE_BACKWARDS_COMMIT" | tee -a build.env + - cat build.env + retry: + max: 2 + artifacts: + reports: + dotenv: build.env + +test:build_image: + extends: [.build_image] + parallel: + matrix: + - IMAGE: CI_MCORE_LTS_IMAGE + FILE: Dockerfile.ci.lts + BASE_IMAGE: nvcr.io/nvidia/pytorch:24.01-py3 + - IMAGE: CI_MCORE_DEV_IMAGE + FILE: Dockerfile.ci.dev + BASE_IMAGE: nvcr.io/nvidia/pytorch:25.05-py3 + - IMAGE: UTILITY_IMAGE + FILE: Dockerfile.linting + BASE_IMAGE: python:3.10 + +test:build_nemo_image: + extends: [.build_image] + variables: + IMAGE: CI_NEMO_IMAGE + FILE: Dockerfile.ci.nemo + BASE_IMAGE: nvcr.io/nvidian/nemo:nightly + rules: + - if: $FUNCTIONAL_TEST == "yes" || $INTEGRATION_TEST == "yes" || $CI_COMMIT_BRANCH == "ci-rebuild-mcore-nemo-image" + when: on_success diff --git a/.gitlab/stages/02.test.yml b/.gitlab/stages/02.test.yml new file mode 100644 index 0000000000..d4e9e3c0dc --- /dev/null +++ b/.gitlab/stages/02.test.yml @@ -0,0 +1,405 @@ +.test_rules: + rules: + - if: $PUBLISH == "yes" + when: never + - when: on_success + stage: test + +include: + - template: Security/Secret-Detection.gitlab-ci.yml + +wait_for_resources: + extends: [.test_rules] + needs: + - test:linting_formatting + - test:linting_copyright + - job: test:linting_secret_detection + optional: true + - test:build_image + image: python:3.10 + timeout: 7 days + variables: + KUBERNETES_SERVICE_MEMORY_REQUEST: 32Gi + KUBERNETES_SERVICE_MEMORY_LIMIT: 32Gi + KUBERNETES_SERVICE_CPU_REQUEST: 8 + KUBERNETES_SERVICE_CPU_LIMIT: 12 + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - pip install --no-cache-dir python-gitlab click + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - export NUM_CONCURRENT_JOBS + - python tests/test_utils/python_scripts/wait_for_resources.py --pipeline-id $CI_PIPELINE_ID + rules: + - if: $CI_MERGE_REQUEST_LABELS =~ /fast-track/ + when: never + - if: $CI_PIPELINE_SOURCE == "merge_request_event" + when: on_success + - when: never + +test:unit_tests_configure: + extends: [.test_rules] + needs: + - test:build_image + - job: wait_for_resources + optional: true + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + before_script: + - git rm -r tests/test_utils/local_recipes || true + - git submodule add --force https://gitlab-ci-token:${CI_JOB_TOKEN}@${GITLAB_ENDPOINT}/ADLR/megatron-lm-convergence-tests.git tests/test_utils/local_recipes + - ls tests/test_utils/local_recipes + script: + - env + - set -x + - | + A100_CLUSTER=$([[ "$CLUSTER_A100" != "" ]] && echo $CLUSTER_A100 || echo $DEFAULT_A100_CLUSTER) + H100_CLUSTER=$([[ "$CLUSTER_H100" != "" ]] && echo $CLUSTER_H100 || echo $DEFAULT_H100_CLUSTER) + - | + ARGS=( + "--scope unit-tests" + "--n-repeat ${UNIT_TEST_REPEAT}" + "--time-limit $(( UNIT_TEST_TIMEOUT * 60 ))" + "--test-cases all" + "--cluster dgxh100_coreweave" + "--platform dgx_h100" + "--partition batch_short,batch" + "--container-image ${UTILITY_IMAGE}" + "--container-tag ${CI_PIPELINE_ID}" + "--dependent-job test:unit_tests_configure" + "--slurm-account ${CI_SLURM_ACCOUNT}" + "--no-enable-warmup" + ) + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment "lts" \ + --tag "legacy" \ + --output-path "unit-test-job-lts-legacy.yaml" + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment "lts" \ + --tag "latest" \ + --output-path "unit-test-job-lts-latest.yaml" + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment "dev" \ + --tag "legacy" \ + --output-path "unit-test-job-dev-legacy.yaml" + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment "dev" \ + --tag "latest" \ + --output-path "unit-test-job-dev-latest.yaml" + rules: + - if: $UNIT_TEST == 'yes' && $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED != "true" + allow_failure: true + when: on_success + - if: $UNIT_TEST == 'yes' && $UNIT_TEST_REPEAT != '0' + when: on_success + artifacts: + paths: + - unit-test-job-dev-legacy.yaml + - unit-test-job-dev-latest.yaml + - unit-test-job-lts-legacy.yaml + - unit-test-job-lts-latest.yaml + - tests/test_utils/local_recipes + +.unit_tests_run: + needs: + - test:linting_formatting + - test:linting_copyright + - job: test:linting_secret_detection + optional: true + - test:unit_tests_configure + - test:build_image + extends: [.test_rules] + trigger: + include: + - artifact: unit-test-job-$ENVIRONMENT-$TAG.yaml + job: test:unit_tests_configure + strategy: depend + variables: + RO_API_TOKEN: $PAT + CONTAINER_TAG: $CI_PIPELINE_ID + CI_MCORE_LTS_IMAGE: $CI_MCORE_LTS_IMAGE + GITLAB_ENDPOINT: $GITLAB_ENDPOINT + PARENT_PIPELINE_ID: $CI_PIPELINE_ID + MCORE_MR_COMMIT: $MCORE_MR_COMMIT + MCORE_BACKWARDS_COMMIT: $MCORE_BACKWARDS_COMMIT + + inherit: + variables: true + rules: + - if: $UNIT_TEST == 'yes' && $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED != "true" + allow_failure: true + when: on_success + - if: $UNIT_TEST == 'yes' && $UNIT_TEST_REPEAT != '0' + when: on_success + +test:unit_tests_pyt(DEV)_mcore(legacy): + extends: [.unit_tests_run] + variables: + ENVIRONMENT: dev + TAG: legacy + rules: + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME != 'main' + when: never + - if: $UNIT_TEST == 'yes' && $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED != "true" + allow_failure: true + when: on_success + - if: $UNIT_TEST == 'yes' && $UNIT_TEST_REPEAT != '0' + when: on_success + +test:unit_tests_pyt(LTS)_mcore(legacy): + extends: [.unit_tests_run] + variables: + ENVIRONMENT: lts + TAG: legacy + rules: + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME != 'main' + when: never + - if: $UNIT_TEST == 'yes' && $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED != "true" + allow_failure: true + when: on_success + - if: $UNIT_TEST == 'yes' && $UNIT_TEST_REPEAT != '0' + when: on_success + +test:unit_tests_pyt(DEV)_mcore(latest): + extends: [.unit_tests_run] + variables: + ENVIRONMENT: dev + TAG: latest + +test:unit_tests_pyt(LTS)_mcore(latest): + extends: [.unit_tests_run] + variables: + ENVIRONMENT: lts + TAG: latest + +test:unit_tests_notify: + extends: [.test_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + needs: + - test:unit_tests_pyt(DEV)_mcore(latest) + - test:unit_tests_pyt(LTS)_mcore(latest) + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - export WEBHOOK_URL=${MCORE_NOTIFICATION_HOOK} + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - export TAG_TEAM=$([[ "$CI_COMMIT_BRANCH" == "main" ]] && echo "1" || "0") + - export TEAM_SLUG=$SLACK_ADMIN + - | + python tests/test_utils/python_scripts/notify.py \ + --pipeline-id "${CI_PIPELINE_ID}" \ + --check-for unit-tests \ + --pipeline-context "unit-tests-extended" \ + --pipeline-created-at "${CI_PIPELINE_CREATED_AT}" + artifacts: + when: always + paths: + - scripts + rules: + - if: $CI_PIPELINE_SOURCE == "schedule" && $CI_COMMIT_BRANCH == "ci-unit-test-extended" + when: always + - when: never + +test:linting_docs_build: + extends: [.test_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + needs: [test:build_image] + script: + - cd .. + - rm -rf documentation && git clone https://gitlab-ci-token:${CI_JOB_TOKEN}@${GITLAB_ENDPOINT}/nemo-megatron-core-tme/documentation.git + - mv megatron-lm/ documentation/ + - cd documentation/ + - ./repo docs + +test:linting_formatting: + extends: [.test_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + needs: [test:build_image] + variables: + GIT_STRATEGY: "clone" + script: + - | + if [[ "$CI_PIPELINE_SOURCE" != "merge_request_event" ]]; then + exit 0 + fi + - set +e + - git fetch origin main:main + - | + if [[ "$CI_MERGE_REQUEST_PROJECT_PATH" == "$CI_MERGE_REQUEST_SOURCE_PROJECT_PATH" ]]; then + bash tools/autoformat.sh + set -e + git fetch origin $CI_MERGE_REQUEST_SOURCE_BRANCH_NAME + git checkout $CI_MERGE_REQUEST_SOURCE_BRANCH_NAME + git config --global user.email "mcore-bot@nvidia.com" + git config --global user.name "Mcore Bot" + git remote set-url origin "https://gitlab-ci-token:${PAT}@${GITLAB_ENDPOINT}/$CI_PROJECT_NAMESPACE/megatron-lm.git" + git add -A . + git commit -m "chore: Format files" || true + git push -u origin $CI_MERGE_REQUEST_SOURCE_BRANCH_NAME + fi + - env + - BASE_REF="$CI_MERGE_REQUEST_TARGET_BRANCH_NAME" CHECK_ONLY=true SKIP_DOCS=$([[ "$CI_MERGE_REQUEST_LABELS" == *"Skip docs"* ]] && echo "true" || echo "false") bash tools/autoformat.sh + +test:linting_copyright: + extends: [.test_rules] + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + needs: [test:build_image] + script: + - git fetch origin main + - bash tools/copyright.sh + +# Override from template +secret_detection: + rules: + - when: never + +# Inherit and modify template +test:linting_secret_detection: + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + extends: [".secret-analyzer"] + needs: [test:build_image] + variables: + GIT_DEPTH: 0 + SECRET_DETECTION_LOG_OPTIONS: ${CI_MERGE_REQUEST_DIFF_BASE_SHA}..${CI_COMMIT_SHA} + allow_failure: false + rules: + - if: $CI_PIPELINE_SOURCE == "merge_request_event" + - when: never + script: + - apk add jq + - /analyzer run + - | + if [[ $(cat gl-secret-detection-report.json | jq '.vulnerabilities | length > 0') == true ]]; then + echo "Atleast one vulnerability has been found" + cat gl-secret-detection-report.json | jq '.' + exit 1 + fi + +test:unit_tests_x_coverage_report: + extends: [.test_rules] + needs: + - job: test:unit_tests_pyt(DEV)_mcore(latest) + - job: test:unit_tests_pyt(LTS)_mcore(latest) + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - python tests/test_utils/python_scripts/download_coverage_results.py --pipeline-id ${CI_PIPELINE_ID} + - coverage combine --keep $(ls coverage_results/*/coverage_report) + - coverage report + - coverage xml + coverage: "/TOTAL.+ ([0-9]{1,3}%)/" + artifacts: + reports: + coverage_report: + coverage_format: cobertura + path: coverage.xml + rules: + - if: $UNIT_TEST == 'yes' && $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED != "true" + allow_failure: true + when: on_success + - if: $UNIT_TEST == 'yes' && $UNIT_TEST_REPEAT != '0' + when: on_success + +test:safe_imports: + extends: [.test_rules] + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/builder-large + - team/megatron + services: + - name: docker:24.0.5-dind + variables: + HEALTHCHECK_TCP_PORT: "2376" + variables: + KUBERNETES_SERVICE_MEMORY_REQUEST: 32Gi + KUBERNETES_SERVICE_MEMORY_LIMIT: 32Gi + KUBERNETES_SERVICE_CPU_REQUEST: 8 + KUBERNETES_SERVICE_CPU_LIMIT: 12 + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + needs: [test:build_image] + script: + - env + - python -m ensurepip --upgrade + - python -m pip install -e . + - python .gitlab/scripts/check_imports.py --package-name megatron.core + rules: + - if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME != 'main' + when: never + - if: $UNIT_TEST == 'yes' && $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED != "true" + allow_failure: true + when: on_success + - if: $UNIT_TEST == 'yes' && $UNIT_TEST_REPEAT != '0' + when: on_success + retry: + max: 2 diff --git a/.gitlab/stages/03.integration-tests.yml b/.gitlab/stages/03.integration-tests.yml new file mode 100644 index 0000000000..56a65da27e --- /dev/null +++ b/.gitlab/stages/03.integration-tests.yml @@ -0,0 +1,142 @@ +.integration_tests_rules: + stage: integration_tests + rules: + - if: $INTEGRATION_TEST == "yes" + when: on_success + - when: never + +default: + id_tokens: + VAULT_JWT_TOKEN: + aud: https://stg.vault.nvidia.com + +include: + - project: dl/jet/gitlab-templates + ref: main + file: downstreams.yml + +integration:configure: + needs: + - test:build_image + - job: test:unit_tests_pyt(DEV)_mcore(latest) + optional: true + - job: test:unit_tests_pyt(LTS)_mcore(latest) + optional: true + - job: test:build_nemo_image + extends: [.integration_tests_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + before_script: + - git rm -r tests/test_utils/local_recipes || true + - git submodule add --force https://gitlab-ci-token:${CI_JOB_TOKEN}@${GITLAB_ENDPOINT}/ADLR/megatron-lm-convergence-tests.git tests/test_utils/local_recipes + - ls tests/test_utils/local_recipes + script: + - set -x + - | + A100_CLUSTER=$([[ "$CLUSTER_A100" != "" ]] && echo $CLUSTER_A100 || echo $DEFAULT_A100_CLUSTER) + H100_CLUSTER=$([[ "$CLUSTER_H100" != "" ]] && echo $CLUSTER_H100 || echo $DEFAULT_H100_CLUSTER) + - | + ARGS=( + "--scope $INTEGRATION_TEST_SCOPE" + "--n-repeat 1" + "--time-limit $INTEGRATION_TEST_TIME_LIMIT" + "--test-cases $INTEGRATION_TEST_CASES" + "--container-image ${UTILITY_IMAGE}" + "--container-tag ${CI_PIPELINE_ID}" + "--slurm-account ${CI_SLURM_ACCOUNT}" + "--no-enable-warmup" + "--dependent-job integration:configure" + "--enable-lightweight-mode" + ) + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment dev \ + --platform dgx_a100 \ + --cluster $A100_CLUSTER \ + --output-path "functional-test-job-dev-A100.yaml" + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment dev \ + --platform dgx_h100 \ + --cluster $H100_CLUSTER \ + --output-path "functional-test-job-dev-H100.yaml" + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment lts \ + --platform dgx_a100 \ + --cluster $A100_CLUSTER \ + --output-path "functional-test-job-lts-A100.yaml" + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment lts \ + --platform dgx_h100 \ + --cluster $H100_CLUSTER \ + --output-path "functional-test-job-lts-H100.yaml" + artifacts: + paths: + - functional-test-job-lts-A100.yaml + - functional-test-job-lts-H100.yaml + - functional-test-job-dev-H100.yaml + - functional-test-job-dev-A100.yaml + - tests/test_utils/local_recipes + +.integration_run: + needs: + - integration:configure + - test:build_image + - wait_for_resources + extends: [.integration_tests_rules] + trigger: + include: + - artifact: functional-test-job-$ENVIRONMENT-$CLUSTER.yaml + job: integration:configure + strategy: depend + variables: + RO_API_TOKEN: $PAT + CONTAINER_TAG: $CI_PIPELINE_ID + CI_MCORE_LTS_IMAGE: $CI_MCORE_LTS_IMAGE + GITLAB_ENDPOINT: $GITLAB_ENDPOINT + PARENT_PIPELINE_ID: $CI_PIPELINE_ID + DASHBOARD_ENDPOINT: $DASHBOARD_ENDPOINT + MCORE_MR_COMMIT: $MCORE_MR_COMMIT + MCORE_BACKWARDS_COMMIT: $MCORE_BACKWARDS_COMMIT + inherit: + variables: true + +integration:run_lts_dgx_a100: + extends: [.integration_run] + variables: + ENVIRONMENT: lts + CLUSTER: A100 + +integration:run_lts_dgx_h100: + extends: [.integration_run] + variables: + ENVIRONMENT: lts + CLUSTER: H100 + +integration:run_dev_dgx_a100: + extends: [.integration_run] + variables: + ENVIRONMENT: dev + CLUSTER: A100 + +integration:run_dev_dgx_h100: + extends: [.integration_run] + variables: + ENVIRONMENT: dev + CLUSTER: H100 diff --git a/.gitlab/stages/04.functional-tests.yml b/.gitlab/stages/04.functional-tests.yml new file mode 100644 index 0000000000..a8575e921e --- /dev/null +++ b/.gitlab/stages/04.functional-tests.yml @@ -0,0 +1,254 @@ +.functional_tests_rules: + stage: functional_tests + rules: + - if: $FUNCTIONAL_TEST == "yes" + when: on_success + - when: never +default: + id_tokens: + VAULT_JWT_TOKEN: + aud: https://stg.vault.nvidia.com + +include: + - project: dl/jet/gitlab-templates + ref: main + file: downstreams.yml + +functional:configure: + needs: + - test:build_image + - test:build_nemo_image + - job: test:unit_tests_pyt(DEV)_mcore(latest) + optional: true + - job: test:unit_tests_pyt(LTS)_mcore(latest) + optional: true + - job: integration:run_lts_dgx_a100 + optional: true + - job: integration:run_dev_dgx_a100 + optional: true + - job: integration:run_lts_dgx_h100 + optional: true + - job: integration:run_dev_dgx_h100 + optional: true + extends: [.functional_tests_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + before_script: + - git rm -r tests/test_utils/local_recipes || true + - git submodule add --force https://gitlab-ci-token:${CI_JOB_TOKEN}@${GITLAB_ENDPOINT}/ADLR/megatron-lm-convergence-tests.git tests/test_utils/local_recipes + - ls tests/test_utils/local_recipes + script: + - set -x + - | + A100_CLUSTER=$([[ "$CLUSTER_A100" != "" ]] && echo $CLUSTER_A100 || echo $DEFAULT_A100_CLUSTER) + H100_CLUSTER=$([[ "$CLUSTER_H100" != "" ]] && echo $CLUSTER_H100 || echo $DEFAULT_H100_CLUSTER) + - | + RECORD_CHECKPOINTS=$([[ "$CI_MERGE_REQUEST_LABELS" == *"Record checkpoints"* || "$FUNCTIONAL_TEST_RECORD_CHECKPOINTS" == "yes" ]] && echo "true" || echo "false") + - | + if [[ "$FUNCTIONAL_TEST_SCOPE" == "release" || "$FUNCTIONAL_TEST_SCOPE" == "pre-release" ]]; then + FUNCTIONAL_TEST_NAME=$(eval echo $FUNCTIONAL_TEST_NAME) + RELEASE_ARGS=( + "--run-name" + $FUNCTIONAL_TEST_NAME + "--wandb-experiment" + $(echo $FUNCTIONAL_TEST_NAME | tr '/' '-') + ) + else + RELEASE_ARGS=() + fi + - | + ARGS=( + "--scope $FUNCTIONAL_TEST_SCOPE" + "--n-repeat $FUNCTIONAL_TEST_REPEAT" + "--time-limit $FUNCTIONAL_TEST_TIME_LIMIT" + "--test-cases $FUNCTIONAL_TEST_CASES" + "--container-image ${UTILITY_IMAGE}" + "--container-tag ${CI_PIPELINE_ID}" + "--dependent-job functional:configure" + "--record-checkpoints ${RECORD_CHECKPOINTS}" + "--slurm-account ${CI_SLURM_ACCOUNT}" + "--no-enable-warmup" + ) + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment dev \ + --platform dgx_a100 \ + --cluster $A100_CLUSTER \ + --output-path "functional-test-job-dev-A100.yaml" \ + ${RELEASE_ARGS[@]} + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment dev \ + --platform dgx_h100 \ + --cluster $H100_CLUSTER \ + --output-path "functional-test-job-dev-H100.yaml" \ + ${RELEASE_ARGS[@]} + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment lts \ + --platform dgx_a100 \ + --cluster $A100_CLUSTER \ + --output-path "functional-test-job-lts-A100.yaml" \ + ${RELEASE_ARGS[@]} + - | + export PYTHONPATH=$(pwd) + python tests/test_utils/python_scripts/generate_jet_trigger_job.py \ + ${ARGS[@]} \ + --environment lts \ + --platform dgx_h100 \ + --cluster $H100_CLUSTER \ + --output-path "functional-test-job-lts-H100.yaml" \ + ${RELEASE_ARGS[@]} + artifacts: + paths: + - functional-test-job-lts-A100.yaml + - functional-test-job-lts-H100.yaml + - functional-test-job-dev-A100.yaml + - functional-test-job-dev-H100.yaml + - tests/test_utils/local_recipes + +.functional_run: + needs: + - functional:configure + - test:build_image + extends: [.functional_tests_rules] + trigger: + include: + - artifact: functional-test-job-$ENVIRONMENT-$CLUSTER.yaml + job: functional:configure + strategy: depend + variables: + RO_API_TOKEN: $PAT + CONTAINER_TAG: $CI_PIPELINE_ID + CI_MCORE_LTS_IMAGE: $CI_MCORE_LTS_IMAGE + GITLAB_ENDPOINT: $GITLAB_ENDPOINT + PARENT_PIPELINE_ID: $CI_PIPELINE_ID + DASHBOARD_ENDPOINT: $DASHBOARD_ENDPOINT + MCORE_MR_COMMIT: $MCORE_MR_COMMIT + MCORE_BACKWARDS_COMMIT: $MCORE_BACKWARDS_COMMIT + CLUSTER: $CLUSTER + + inherit: + variables: true + +functional:run_lts_dgx_a100: + extends: [.functional_run] + variables: + ENVIRONMENT: lts + CLUSTER: A100 + +functional:run_lts_dgx_h100: + extends: [.functional_run] + variables: + ENVIRONMENT: lts + CLUSTER: H100 + +functional:run_dev_dgx_a100: + extends: [.functional_run] + variables: + ENVIRONMENT: dev + CLUSTER: A100 + +functional:run_dev_dgx_h100: + extends: [.functional_run] + variables: + ENVIRONMENT: dev + CLUSTER: H100 + +functional:run_nemo: + extends: [.functional_tests_rules] + trigger: + project: "dl/joc/nemo-ci" + branch: main-mirror + strategy: depend + inherit: + variables: true + variables: + MCORE_COMMIT: $CI_COMMIT_SHA + TEST_NEMO2_MODULE: "True" + ALLOW_FAILURE_DEPENDENCY: "True" + TESTS_TO_RUN_ON_THIS_COMMIT: nightly + rules: + - if: $FUNCTIONAL_TEST == "yes" + when: manual + allow_failure: true + - when: never + +functional:x_notify: + extends: [.functional_tests_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + needs: + - functional:run_lts_dgx_a100 + - functional:run_dev_dgx_a100 + - functional:run_lts_dgx_h100 + - functional:run_dev_dgx_h100 + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + variables: + WEBHOOK_URL: ${MCORE_NOTIFICATION_HOOK} + RO_API_TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE} + CONTEXT: $FUNCTIONAL_TEST_SCOPE + script: + - env + - export WEBHOOK_URL=${MCORE_NOTIFICATION_HOOK} + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - export CONTEXT=$FUNCTIONAL_TEST_SCOPE + - export TAG_TEAM=$([[ "$CI_COMMIT_BRANCH" == "main" ]] && echo "1" || "0") + - export TEAM_SLUG=$SLACK_ADMIN + - | + python tests/test_utils/python_scripts/notify.py \ + --pipeline-id "${CI_PIPELINE_ID}" \ + --check-for functional-tests \ + --pipeline-context $CONTEXT \ + --pipeline-created-at "${CI_PIPELINE_CREATED_AT}" + + artifacts: + when: always + paths: + - scripts + rules: + - if: ($CI_PIPELINE_SOURCE == "schedule" || $CI_COMMIT_BRANCH == "main") && $FUNCTIONAL_TEST == "yes" + when: always + - when: never + +functional:x_download_golden_values: + extends: [.functional_tests_rules] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - python tests/test_utils/python_scripts/download_golden_values.py --pipeline-id ${CI_PIPELINE_ID} + artifacts: + paths: + - tests/ + rules: + - if: $FUNCTIONAL_TEST == "yes" + when: manual + allow_failure: true + - when: never diff --git a/.gitlab/stages/05.publish.yml b/.gitlab/stages/05.publish.yml new file mode 100644 index 0000000000..dcd2eaf2ed --- /dev/null +++ b/.gitlab/stages/05.publish.yml @@ -0,0 +1,583 @@ +.publish_common_freeze: + stage: publish + rules: + - if: ($CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH) && $PUBLISH == "yes" && $PUBLISH_SCOPE == "code-freeze" + when: manual + - when: never + +.publish_common_release: + stage: publish + rules: + - if: $CI_PIPELINE_SOURCE == "web" && $PUBLISH == "yes" && $PUBLISH_SCOPE == "release" + when: manual + - if: $PUBLISH == "yes" && $PUBLISH_SCOPE == "release" + when: on_success + - when: never + +publish:test_release_pypi_build_wheel: + extends: [.test_rules] + stage: publish + image: + name: ${IMAGE} + entrypoint: [""] + services: + - name: docker:24.0.5-dind + variables: + HEALTHCHECK_TCP_PORT: "2376" + needs: [test:build_image] + parallel: + matrix: + - PLATFORM: arm64 + IMAGE: quay.io/pypa/manylinux_2_28_aarch64 + - PLATFORM: amd64 + IMAGE: quay.io/pypa/manylinux_2_28_x86_64 + tags: + - arch/${PLATFORM} + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/builder-small + - team/megatron + variables: + PY_ENV: pytorch_25.03 + KUBERNETES_SERVICE_MEMORY_REQUEST: 16Gi + KUBERNETES_SERVICE_MEMORY_LIMIT: 16Gi + PUBLISH_DRYRUN: "yes" + KUBERNETES_SERVICE_CPU_REQUEST: 4 + KUBERNETES_SERVICE_CPU_LIMIT: 8 + before_script: + - env + - eval PUBLISH_COMMIT=$PUBLISH_COMMIT + - env + - git fetch origin $PUBLISH_COMMIT + - git checkout $PUBLISH_COMMIT + script: + - echo $PUBLISH_DRYRUN + - | + if [ "$PUBLISH_DRYRUN" = "yes" ]; then + PRE_RELEASE=$(sed -n "s/.*PRE_RELEASE = '\(.*\)'/\1/p" megatron/core/package_info.py) + sed -i "/^PRE_RELEASE/c\PRE_RELEASE = '${PRE_RELEASE}.dev$((RANDOM % 900000 + 100000))'" megatron/core/package_info.py + fi + + - /opt/python/cp310-cp310/bin/python -m build + - /opt/python/cp311-cp311/bin/python -m build + - auditwheel repair dist/*.whl + - rm -rf dist/*.whl + + - pushd megatron/core + - EXPECTED_RELEASE_NUMBER=$(/opt/python/cp311-cp311/bin/python -c "import package_info; print(package_info.__version__)") + - popd + - echo "EXPECTED_RELEASE_NUMBER_$PLATFORM=$EXPECTED_RELEASE_NUMBER" | tee -a build.env + artifacts: + paths: + - megatron/core/package_info.py + - wheelhouse/ + - dist/ + reports: + dotenv: build.env + retry: + max: 2 + +publish:test_release_pypi_test_wheel: + extends: [.test_rules] + stage: publish + image: + name: python:3.11 + entrypoint: [""] + needs: + - job: publish:test_release_pypi_build_wheel + optional: true + parallel: + matrix: + - PLATFORM: arm64 + - PLATFORM: amd64 + services: + - name: docker:24.0.5-dind + variables: + HEALTHCHECK_TCP_PORT: "2376" + tags: + - arch/${PLATFORM} + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/builder-small + - team/megatron + variables: + KUBERNETES_SERVICE_MEMORY_REQUEST: 16Gi + KUBERNETES_SERVICE_MEMORY_LIMIT: 16Gi + KUBERNETES_SERVICE_CPU_REQUEST: 4 + KUBERNETES_SERVICE_CPU_LIMIT: 8 + GIT_STRATEGY: none + PUBLISH_DRYRUN: "yes" + script: + - rm -rf megatron + - pip install -U --no-cache-dir pip + - | + if [[ "$PLATFORM" == "arm64" ]]; then + for file in wheelhouse/*cp311*aarch64.whl; do + pip install --no-cache-dir "$file[dev,mlm]" + done + + else + for file in wheelhouse/*cp311*x86_64.whl; do + pip install --no-cache-dir "$file[dev,mlm]" + done + fi + + - RELEASE_NUMBER=$(python -c "from megatron import core; print(core.__version__)") + + - | + if [[ "$PLATFORM" == "arm64" ]]; then + test "$EXPECTED_RELEASE_NUMBER_arm64" == "$RELEASE_NUMBER" + else + test "$EXPECTED_RELEASE_NUMBER_amd64" == "$RELEASE_NUMBER" + fi + + - echo "RELEASE_NUMBER=$RELEASE_NUMBER" | tee -a build.env + artifacts: + reports: + dotenv: build.env + paths: + - wheelhouse/ + - dist/ + retry: + max: 2 + +publish:test_release_version_bump: + needs: [publish:test_release_pypi_test_wheel] + extends: [.test_rules] + image: nentangso/alpine-git-curl-jq + stage: publish + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + before_script: + - eval PUBLISH_COMMIT=$PUBLISH_COMMIT + - eval PUBLISH_VERSION_BUMP_BRANCH=$PUBLISH_VERSION_BUMP_BRANCH + - git fetch origin $PUBLISH_COMMIT + - git checkout $PUBLISH_COMMIT + variables: + PUBLISH_DRYRUN: "yes" + script: + - env + - echo $PUBLISH_DRYRUN + - MAJOR=$(cat megatron/core/package_info.py | awk '/^MAJOR = /' | awk -F"= " '{print $2}') + - MINOR=$(cat megatron/core/package_info.py | awk '/^MINOR = /' | awk -F"= " '{print $2}') + - PATCH=$(cat megatron/core/package_info.py | awk '/^PATCH = /' | awk -F"= " '{print $2}') + - PRERELEASE=$(cat megatron/core/package_info.py | awk '/^PRE_RELEASE = /' | awk -F"= " '{print $2}' | tr -d '"' | tr -d "'") + + - | + if [[ "$PRERELEASE" != "" ]]; then + NEXT_PATCH=$PATCH + NEXT_PRERELEASE=rc$((${PRERELEASE#rc} + 1)) + else + NEXT_PATCH=$((${PATCH} + 1)) + NEXT_PRERELEASE=$NEXT_PRERELEASE + fi + + - sed -i "/^PATCH/c\PATCH = $NEXT_PATCH" megatron/core/package_info.py + - sed -i "/^PRE_RELEASE/c\PRE_RELEASE = '$NEXT_PRERELEASE'" megatron/core/package_info.py + + - git config --global user.email "mcore-bot@nvidia.com" + - git config --global user.name "Mcore Bot" + - git remote set-url origin "https://gitlab-ci-token:${PROJECT_ACCESS_TOKEN_MCORE}@${GITLAB_ENDPOINT}/$CI_PROJECT_NAMESPACE/megatron-lm.git" + - | + CMD=$( + cat <<'EOF' + git fetch origin $PUBLISH_VERSION_BUMP_BRANCH && \ + git switch $PUBLISH_VERSION_BUMP_BRANCH && \ + git add megatron/core/package_info.py && \ + git commit -m "chore: Version bump" && \ + git push origin $PUBLISH_VERSION_BUMP_BRANCH + EOF + ) + + - | + if [[ "$PUBLISH_DRYRUN" == "yes" ]]; then + echo "$CMD" + else + eval "$CMD" + fi + +publish:test_release_pypi_push_wheel: + extends: [.test_rules] + image: python:3.11 + stage: publish + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + needs: + - job: publish:test_release_pypi_test_wheel + optional: true + - job: publish:test_release_version_bump + optional: true + variables: + GIT_STRATEGY: none + PUBLISH_DRYRUN: "yes" + timeout: 3m + script: + - echo $PUBLISH_DRYRUN + - | + if [ "$PUBLISH_DRYRUN" = "yes" ]; then + REPOSITORY=testpypi + export TWINE_USERNAME=$TWINE_TEST_USERNAME + export TWINE_PASSWORT=$TWINE_TEST_PASSWORD + else + REPOSITORY=pypi + export TWINE_USERNAME=$TWINE_PROD_USERNAME + export TWINE_PASSWORT=$TWINE_PROD_PASSWORD + fi + + - ls -al dist/ + - ls -al wheelhouse/ + - pip install twine + + - | + if [[ "$PUBLISH_DRYRUN" != "yes" ]]; then + twine upload --verbose -u $TWINE_USERNAME -p $TWINE_PASSWORT --repository $REPOSITORY wheelhouse/* dist/* + fi + +publish:test_release_github: + extends: [.test_rules] + needs: + - job: publish:test_release_pypi_test_wheel + optional: true + - job: publish:test_release_version_bump + optional: true + stage: publish + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + image: nentangso/alpine-git-curl-jq + before_script: + - eval PUBLISH_COMMIT=$PUBLISH_COMMIT + - git fetch origin $PUBLISH_COMMIT + - git checkout $PUBLISH_COMMIT + variables: + PUBLISH_DRYRUN: "yes" + script: + - echo $PUBLISH_DRYRUN + - NAME="NVIDIA Megatron Core $RELEASE_NUMBER" + - IS_PRERELEASE=$([[ "$RELEASE_NUMBER" == *rc* ]] && echo "true" || echo "false") + - | + if [[ "$IS_PRERELEASE" == "true" ]]; then + DATE=$(date +"%Y-%m-%d") + CHANGELOG="Prerelease: $NAME ($DATE)" + else + CHANGELOG=$(awk '/^## '"$NAME"'/{flag=1; next} /^## /{flag=0} flag' CHANGELOG.md) + CHANGELOG=$(echo "$CHANGELOG" | sed '/./!d') + fi + + - | + PAYLOAD=$(jq -nc \ + --arg TAG_NAME "core_v${RELEASE_NUMBER}" \ + --arg CI_COMMIT_SHA "$PUBLISH_COMMIT" \ + --arg NAME "$NAME" \ + --arg BODY "$CHANGELOG" \ + --argjson PRERELEASE "$IS_PRERELEASE" \ + '{ + "tag_name": $TAG_NAME, + "target_commitish": $CI_COMMIT_SHA, + "name": $NAME, + "body": $BODY, + "draft": false, + "prerelease": $PRERELEASE, + "generate_release_notes": false + }' + ) + echo -E "$PAYLOAD" | tee -a payload.txt + + - cat payload.txt + - | + CMD=$(echo -E 'curl -L \ + -X POST \ + -H "Accept: application/vnd.github+json" \ + -H "Authorization: Bearer '"$GH_TOKEN"'" \ + -H "X-GitHub-Api-Version: 2022-11-28" \ + https://api.github.com/repos/NVIDIA/Megatron-LM/releases \ + -d @payload.txt + ') + + - | + if [[ "$PUBLISH_DRYRUN" == "yes" ]]; then + echo -E "$CMD" + else + eval "$CMD" + fi + +publish:test_release_notify: + needs: [publish:test_release_pypi_test_wheel, publish:test_release_pypi_push_wheel, publish:test_release_github] + extends: [.test_rules] + image: badouralix/curl-jq + stage: publish + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + variables: + PUBLISH_DRYRUN: "yes" + script: + - echo $PUBLISH_DRYRUN + - URL="https://github.com/NVIDIA/Megatron-LM/releases/tag/core_v$RELEASE_NUMBER" + - | + cat << EOF > message.json + { + "blocks": [ + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": "Releasebot šŸ¤–: Megatron-Core released <${URL}|core_v${RELEASE_NUMBER}> šŸš€" + } + } + ] + } + EOF + + - cat message.json + + - | + CMD=$(echo curl \ + -X POST \ + -H "Content-type: application/json" \ + -d @message.json ${MCORE_NOTIFICATION_HOOK_MAIN} + ) + + if [[ "$PUBLISH_DRYRUN" == "yes" ]]; then + echo "$CMD" + else + eval "$CMD" + fi + +publish:release_pypi_build_wheel: + extends: [publish:test_release_pypi_build_wheel, .publish_common_release] + dependencies: [] + variables: + PUBLISH_DRYRUN: "no" + +publish:release_pypi_test_wheel: + extends: [publish:test_release_pypi_test_wheel, .publish_common_release] + needs: [publish:release_pypi_build_wheel] + variables: + PUBLISH_DRYRUN: "no" + +publish:release_version_bump: + needs: [publish:release_pypi_test_wheel] + extends: [publish:test_release_version_bump, .publish_common_release] + variables: + PUBLISH_DRYRUN: "no" + +publish:release_pypi_push_wheel: + extends: [publish:test_release_pypi_push_wheel, .publish_common_release] + needs: [publish:release_pypi_test_wheel, publish:release_version_bump] + dependencies: [publish:release_pypi_test_wheel] + variables: + PUBLISH_DRYRUN: "no" + +publish:release_github: + extends: [publish:test_release_github, .publish_common_release] + needs: [publish:release_pypi_test_wheel, publish:release_version_bump] + dependencies: [publish:release_pypi_test_wheel] + variables: + PUBLISH_DRYRUN: "no" + +publish:release_notify: + needs: [publish:release_pypi_test_wheel, publish:release_pypi_push_wheel, publish:release_github] + extends: [publish:test_release_notify, .publish_common_release] + dependencies: [publish:release_pypi_test_wheel] + variables: + PUBLISH_DRYRUN: "no" + +publish:docs: + extends: [.publish_common_release] + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + before_script: + - eval PUBLISH_COMMIT=$PUBLISH_COMMIT + - git fetch origin '+refs/merge-requests/*:refs/remotes/merge-requests/*' + - git fetch origin $PUBLISH_COMMIT + - git checkout $PUBLISH_COMMIT + script: + - cd .. + - rm -rf documentation && git clone --recursive https://gitlab-ci-token:${PAT}@${GITLAB_ENDPOINT}/nemo-megatron-core-tme/documentation.git + - cd documentation/megatron-lm + - git config --global user.email "mcore-bot@nvidia.com" + - git config --global user.name "Mcore Bot" + - git fetch origin '+refs/merge-requests/*:refs/remotes/merge-requests/*' + - git fetch origin $PUBLISH_COMMIT + - git checkout $PUBLISH_COMMIT + - cd .. + - git add megatron-lm + - | + git commit -m 'feat: Bump mcore' + + - git push + rules: + - if: '$CI_COMMIT_REF_PROTECTED == "true" && $CI_PIPELINE_SOURCE == "push"' + allow_failure: true + - when: never + +publish:upload_statistics: + stage: publish + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + needs: + - job: test:unit_tests_pyt(DEV)_mcore(legacy) + optional: true + - job: test:unit_tests_pyt(LTS)_mcore(legacy) + optional: true + - job: test:unit_tests_pyt(DEV)_mcore(latest) + - job: test:unit_tests_pyt(LTS)_mcore(latest) + - job: functional:run_lts_dgx_a100 + optional: true + - job: functional:run_lts_dgx_h100 + optional: true + - job: functional:run_dev_dgx_a100 + optional: true + - job: functional:run_dev_dgx_h100 + optional: true + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - env + - export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE} + - export GITLAB_ENDPOINT + - export DASHBOARD_ENDPOINT + - python tests/test_utils/python_scripts/dashboard.py --pipeline-id ${CI_PIPELINE_ID} + rules: + - if: ($CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' || $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train') && ($UNIT_TEST == "yes" || $INTEGRATION_TEST == "yes" || $FUNCTIONAL_TEST == "yes") + when: always + allow_failure: true + - when: never + +public:review_reminder: + stage: publish + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + script: + - export GITLAB_ENDPOINT + - export RO_API_TOKEN=${PAT} + - export SLACK_WEBHOOK_URL=${SLACK_REMINDER_HOOK} + - export SLACK_API_TOKEN=${SLACK_API_TOKEN} + - python tests/test_utils/python_scripts/auto_reminder.py + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + rules: + - if: $CI_COMMIT_BRANCH == "ci-review-reminder" && $PUBLISH == "yes" && $PUBLISH_SCOPE == "review-reminder" + - when: never + +publish:code_freeze: + extends: [.publish_common_freeze] + image: ${CI_MCORE_LTS_IMAGE}:${CI_PIPELINE_ID} + needs: [test:build_image] + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + script: + - git fetch origin $CI_DEFAULT_BRANCH + - git config --global user.email "mcore-bot@nvidia.com" + - git config --global user.name "Mcore Bot" + - git remote set-url origin "https://gitlab-ci-token:${PAT}@${GITLAB_ENDPOINT}/$CI_PROJECT_NAMESPACE/megatron-lm.git" + - sed -i "/^PRE_RELEASE/c\PRE_RELEASE = ''" megatron/core/package_info.py + - VERSION=$(python -c "from megatron import core; print(core.__version__)") + - RELEASE_BRANCH=core_r$VERSION + - git switch --force-create $RELEASE_BRANCH origin/$CI_DEFAULT_BRANCH + - git push -u origin $RELEASE_BRANCH + - | + MESSAGE='{ + "blocks": [ + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": "Releasebot šŸ¤–: Megatron Core has been frozen šŸŽ‰ to branch `'"$RELEASE_BRANCH"'`" + } + } + ] + }' + - | + curl -X POST -H "Content-type: application/json" --data "$MESSAGE" ${MCORE_NOTIFICATION_HOOK_MAIN} + + - git switch main + - git switch --force-create bot/chore/bump-version + - git add megatron/core/package_info.py + - | + git commit -m "chore: adjust version version" + - git push -u origin bot/chore/bump-version + - | + curl \ + --header "PRIVATE-TOKEN: $PAT" \ + --url https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests \ + -d "source_branch=bot/chore/bump-version" \ + -d "target_branch=$RELEASE_BRANCH" \ + -d "title=chore: Fix version of \`$RELEASE_BRANCH\`" \ + -d "description=[šŸ¤–]: Hi @okoenig šŸ‘‹,

we've adjusted the version number of \`$RELEASE_BRANCH\` for you! šŸš€

Please review and approve this cherry pick by your convenience\!" + +publish:upgrade_dependencies: + stage: publish + image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID} + script: + - export GITLAB_ENDPOINT + - export RO_API_TOKEN=${PAT} + - export BRANCH_NAME=ci-bot/build/upgrade-dependencies-$(date +%Y-%m-%d) + - uv lock --upgrade + - git checkout -b $BRANCH_NAME + - git add uv.lock pyproject.toml + - git config --global user.email "mcore-bot@nvidia.com" + - git config --global user.name "Mcore Bot" + - git remote set-url origin "https://gitlab-ci-token:${PAT}@${GITLAB_ENDPOINT}/$CI_PROJECT_NAMESPACE/megatron-lm.git" + - | + git commit -m "chore: Upgrade dependencies" + - git push --force -u origin $BRANCH_NAME + - | + curl \ + --header "PRIVATE-TOKEN: $PROJECT_ACCESS_TOKEN_MCORE" \ + --url https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests \ + -d "source_branch=$BRANCH_NAME" \ + -d "target_branch=main" \ + -d "title=chore: Upgrade dependencies ($(date +%Y-%m-%d))" \ + -d "labels=test::Run functional tests" \ + -d "description=[šŸ¤–]: Hi @okoenig šŸ‘‹,

we've upgraded the dependencies of \`$BRANCH_NAME\` for you! šŸš€

Please review and approve this cherry pick by your convenience\!" + tags: + - arch/amd64 + - env/prod + - origin/jet-fleet + - owner/jet-core + - purpose/utility + - team/megatron + rules: + - if: $CI_COMMIT_BRANCH == "ci-upgrade-dependencies" && $PUBLISH == "yes" && $PUBLISH_SCOPE == "upgrade-dependencies" + - when: never diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000..ed3be66381 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,17 @@ +repos: +- repo: https://github.com/psf/black + rev: 'refs/tags/24.4.2:refs/tags/24.4.2' + hooks: + - id: black + files: ^megatron/core/.* + args: ["--skip-magic-trailing-comma"] +- repo: https://github.com/pycqa/pylint + rev: v3.2.6 + hooks: + - id: pylint + files: ^megatron/core/.* +- repo: https://github.com/pycqa/isort + rev: 5.13.2 + hooks: + - id: isort + files: ^megatron/core/.* \ No newline at end of file diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 0000000000..865f483849 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,13 @@ +[MAIN] +ignore-paths=tests +max-line-length=100 + +[MESSAGES CONTROL] +disable=all + +enable=C0115,C0116,W0611,C0301,E0606 +# C0115: missing-class-docstring +# C0116: missing-function-docstring +# W0611: unused-import +# C0301: line-too-long +# E0606: possibly-used-before-assignment \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000000..0bf30086ef --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,206 @@ +# Changelog + +## NVIDIA Megatron Core 0.13.0 + +- Hybrid Model + - Add context parallel support for models with Mamba layers + +## NVIDIA Megatron Core 0.12.0 + +- Add FP8 recipe selection to arguments (--fp8-recipe, --first-last-layers-bf16, --num-layers-at-start-in-bf16, --num-layers-at-end-in-bf16) +- Context parallel: fix loss scaling when calculate_per_token_loss=True +- Make the number of data parallel communication buckets configurable (--ddp-num-buckets, --ddp-pad-buckets-for-high-nccl-busbw) +- Inference + - Support in-flight batching and chunked KV cache + - Reduce memory usage, + - by not materializing full attention mask + - by only materializing logits for the last token during decode + - by removing an obsolete tensor reference +- Hybrid Model + - Inference + - Add CUDA graph support + - Change tools/run_mamba_text_generation_server.py to use megatron.core.inference + - Fix a shape issue when materializing logits for Mamba model + - Improve initialization of Mamba layers + - Add configuration switches (--mamba-state-dim, --mamba-head-dim, --mamba-num-groups, --is-hybrid-model) + - Make num_floating_point_operations work with hybrid model + - Make hybrid_conversion.py work with mixer that uses TE linear + - Add FP8 support + - Fix Mamba dt_bias tensor parallelism + - Support multimodal tokenizer + - Improve data parallelism scaling +- MoE + - Features: + - DeepEP support, compatible with all the parallelisms and token drop / dropless + - Important precision improvement: Enable FP32/FP64 routing and unpermutation using ā€“moe-router-dtype. FP32 is recommended for all fine-grained MoE training + - CUDA Graph support for MoE + - Multi-Token Prediction (MTP) Support + - Fused indices_to_multihot kernel for DeepEP dispatcher + - Bug fixes: + - Fix Hang Issue with MoE+Dense Hybrid models + - Update theoretical memory and tflops estimation for MoE and MLA + - Fix MoE Aux loss scaling for per token loss + - Fixes for group limited routing and expert bias. We verified these fixes through dsv3 e2e verifications + - Known issues: + - The ckpt trained with Custom FSDP for MoE may not be compatible with 3D parallel training. + +## NVIDIA Megatron Core 0.11.0 + +- Add multi datacenter training support though N/S connection +- MoE + - Features + - Support DeepSeek-V3 fine-tuning + - Aux-loss-free load balancing strategy + - Node-limited routing and Device-limited routing support. + - Tensor Parallelism support for MLA and Sequence Auxiliary Loss + - MTP (with TP and PP support) is coming soon. + - Permutation / Unpermutation fusion kernel from TransformerEngine. + - Uneven virtual pipeline parallel split support in first and last PP stage. + - Bug fixes: + - Fix the grad scale when TP != expert-TP and average_in_collective is enabled in DDP. + - Fix TEGroupedMLP distckpt compatibility issue with FP8 padding/unpadding. + - Known Issues: + - When training the Dense+MoE hybrid model, the process will hang if any PP rank does not have expert params. +- Add MX-FP16 support for optimizer and master weights +- CUDA Graph memory optimizations +- Enable UCC backend for PP communication +- Optimizer CPU offload support for memory savings +- Models + - Initial RADIO/CRADIO implementation + - llama3.2 support +- Hybrid Model + - Support quantization via TensorRT Model Optimizer + +## NVIDIA Megatron Core 0.10.0 + +- Adding MLA to MCore +- Enable FP8 for GroupedMLP +- MoE Parallel Folding +- Enhance MoE Architecture: Support MoE Layer Frequency Patterns and Configurable MoE FFN Hidden Size +- Multimodal: NVLM training and evaluation support in MCore +- Mamba Hybrid + - Increase performance and reduce memory footprint of Triton language/compiler distributed caching + - Add more unit testing and fix bugs + +## NVIDIA Megatron Core 0.9.0 + +- Uneven pipeline parallelism + - Enable pipeline parallelism where first and last ranks have fewer transformer layers than the intermediate ranks +- Per layer CUDAGraph support for GPT training with Transformer Engine modules +- Enable different TP sizes for the vision encoder +- Enable pipeline parallelism for T5 & Llava models +- Support multi-tile multi-image input in Llava models +- MoE + - FP8 support + - Runtime upcycling support + - Dispatcher implementation optimizations + - Shared expert support with overlapping optimizations + - Qwen Model support +- Known Issues + - When using sequence parallel, during the transformer block forward pass, dropout is not using the appropriate rng context. +- NVRx / Fault tolerance + - fault and hang detection in addition to existing straggler detection + - graceful exit and auto restart + +## NVIDIA Megatron Core 0.8.0 + +- Multimodal + - Added initial support for training vision language models using the LLaVA architecture + - Added initial support for inference with multimodal inputs + - End-to-end multimodal example from data collection to training to evaluation is provided in examples/multimodal +- MoE + - Context Parallel support. + - Distributed checkpoint support for grouped GEMM. +- Mamba + +## NVIDIA Megatron Core 0.7.0 + +- MoE + - Token drop support + - Several efficiency optimizations + - Improved model parallelism + - Memory optimizations +- Distributed checkpointing + - Enabled for Retro + - Asynchronous checkpoint saving +- Several minor bug fixes, speed improvements, and memory optimizations + +## NVIDIA Megatron Core 0.6.0 + +- MoE (Mixture of Experts) + - Performance optimization + - Communication optimization for multi GPU and Single GPU + - 23% improvement (323 TFLOPS/GPU) over MCore 0.5.0 on Mixtral with Hopper BF16 + - GroupedMLP enhancement for Hopper + - DP Overlapping. Support overlapping computation with gradient reduction and parameter gathering. + - All-to-All based Token Dispatcher + - Layer-wise logging for load balancing loss. + - Improved expert parallel support including distributed optimizer. +- Distributed optimizer +- RETRO + - Data processing +- BERT + - Distributed checkpointing +- Dist checkpointing + - PyTorch native distributed backend + - Improved saving/loading speed +- TensorRT-LLM Export + - Integration with TensorRT Model Optimizer Post-training quantization (PTQ) + - Text generation driver to perform PTQ in Megatron-LM + - Llama2 and Nemotron3-8b examples to use TensorRT-LLM unified build API to build engine after training. +- Several minor enhancements, bug fixes, and documentation updates + +## NVIDIA Megatron Core 0.5.0 + +### Key Features and Enhancements + +Megatron core documentation is now [live!](https://docs.nvidia.com/megatron-core/developer-guide/latest/user-guide/index.html#quick-start) + +### Model Features + +- MoE (Mixture of Experts) + - Support for Z-loss, Load balancing and Sinkhorn + - Layer and communications refactor + - Richer parallelism mappings and EP can be combined with other model parallel techniques for larger MoE variants, e.g. EP + TP + DP + SP + PP + - Token dropless architecture with Top-K routing + - Performance optimization with with GroupedGEMM when number of local experts is > 1 + - Distributed checkpointing +- Interleaved rotary embedding + +### Datasets + +- Masked WordPiece datasets for BERT and T5 +- Raw and mock datasets + +### Parallelism + +### Performance + +- Activation offloading to CPU +- Rope and Swiglu fusion +- Sliding window attention (via Transformer Engine) + +### General Improvements + +- Timers + +## NVIDIA Megatron Core 0.4.0 + +### Key Features and Enhancements + +#### Models + +- BERT +- RETRO +- T5 + +#### Parallelism + +- Mixture of Experts support for GPT +- Model parallel efficient Distributed Data Parallel (DDP) +- Context Parallel (2D Tensor Parallel) support + +#### Datasets + +- GPT Dataset +- Blended Dataset diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 0000000000..9d49e88c69 --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1,76 @@ +# Core +[Core-ADLR] @mcore-reviewers/core-adlr +megatron/core/ + +[Core-NeMo] @mcore-reviewers/core-nemo +megatron/core/ + +^[Core-MLPerf] @mcore-reviewers/mlperf +megatron/core/ + +# Models +[BERT] @mcore-reviewers/bert +megatron/core/models/bert/ + +[GPT] @mcore-reviewers/gpt +megatron/core/models/gpt/ + +[Retro] @mcore-reviewers/retro +megatron/core/models/retro/ + +[Multimodal] @mcore-reviewers/multi-modal +megatron/core/models/multimodal/ + +[T5] @mcore-reviewers/t5 +megatron/core/models/t5/ + +[Hybrid-mamba] @mcore-reviewers/hybrid-mamba +megatron/core/models/mamba/ + +# Distributed Checkpointing +[Distributed Checkpointing] @mcore-reviewers/dist-checkpointing +megatron/core/dist_checkpointing/ + +# Distributed Optimizer +[Distributed Optimizer] @mcore-reviewers/dist-optimizer +megatron/core/optimizer/distrib_optimizer/ + +# Quantization and Inference (QAT) +[Quantization and Inference (QAT)] @mcore-reviewers/quantization-and-inference +megatron/core/inference/modelopt_support + +# Datasets +[Datasets] @mcore-reviewers/datasets +megatron/core/datasets/ + +# Parallelism +[Pipeline Parallelism] @mcore-reviewers/pipeline-parallelism +megatron/core/pipeline_parallel/ + +# Transformer +[Transformer] @mcore-reviewers/transformer +megatron/core/transformer/ + +[MoE-ADLR] @mcore-reviewers/moe-adlr +megatron/core/transformer/moe/ + +[MoE-Moe] @mcore-reviewers/moe-moe +megatron/core/transformer/moe/ + +# Inference +[Inference] @mcore-reviewers/inference +megatron/core/inference/ + +# Parallel State +[ParallelState] @mcore-reviewers/parallelstate +megatron/core/parallel_state.py + +[CI][1] @mcore-reviewers/ci +.gitlab/ +.github/ +.gitlab-ci.yml +Dockerfile.ci.lts +Dockerfile.ci.dev +tests/ +megatron/core/transformer/transformer_block.py +megatron/core/transformer/transformer_layer.py \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000..615227600c --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,66 @@ +# Contributing to Megatron-LM + +This document outlines the processes and policies for issues and pull requests by non-NVIDIA contributors to the Megatron-LM github repository. + +Everyone is welcome to contribute to the project but development of Megatron-LM continues internally at NVIDIA. When contributing it important to ensure that changes are in line with the project direction. Small changes to fix bugs are welcomed and appreciated. If proposing large architectural changes or changes for stylistic reasons open an issue first so we can discuss it. + +PRs will first be pulled into NVIDIA's internal Megatron-LM repo and then pushed back out to the open github repo with proper credit given to the committers. + +## Issue policy + +Please do file any bugs you find, keeping the following in mind: + +- If filing a bug, i.e. you have found something that doesn't work as expected, use the BUG template. +- If you've found a regression in speed or accuracy use the REGRESSION template. +- If you are requesting a new feature or modification of an existing feature use the ENHANCEMENT template. +- If opening an issue to ask a question no template is needed but please make your question as clear and concise as possible. +- One issue per bug. Putting multiple things in the same issue makes both discussion and completion unnecessarily complicated. +- Your bug is mostly likely to get attention from the development team quickly if we can easily reproduce it. +- Use proper spelling, grammar, and punctuation. +- Write in an authoritative and technical tone. + +## Code submission policy + +Here are some dos & don'ts to try and stick to: + +### Do: + +- Format new code in a style that is consistent with the file being changed. Megatron-LM doesn't (yet) have a style guide or enforced formatting. +- Split your changes into separate, atomic commits i.e. A commit per feature or fix. +- Make sure your commits are rebased on the master branch. +- Write the commit message subject line in the imperative mood ("Change the default argument for X", not "Changed the default argument for X"). +- Write your commit messages in proper English, with care and punctuation. +- Check the spelling of your code, comments and commit messages. + +### Don't: + +- Submit code that's incompatible with the project licence. +- Touch anything outside the stated scope of the PR. This includes formatting changes to code not relevant to the PR. +- Iterate excessively on your design across multiple commits. +- Include commented-out code. +- Attempt large architectural changes without first opening an issue to discuss. + +## Issue and Pull Request Q&A (Updated Jul 2023) + +### I've submitted an issue and PR. When can I expect to get some feedback? + +Megatron-LM is developed and maintained by a small team of researchers. We will endeavour to read and acknowledge all new issues and PRs within a week. A few rules of thumb: +- Reproducible bugs/regressions and bug/regression fixes are likely to get the attention of maintainers the quickest. +- Issues requesting an enhancement may only recieve acknowlegement that they've been read and may be closed with a "wontfix" label if they're not inline with the project direction. If they are acknowledged and remain open you can assume the maintainers agree they're a desirable feature. +- Support requests, i.e. requests for help running the code, have the lowest priority and will be responded to as maintainer time permits. + +### If my issue or PR isn't getting attention, how long should I wait before pinging one of the project maintainers? + +One week if there is no acknowledgement of the intial request. + +### Who are the project maintainers I should ping? + +The corresponding maintainers at this time are @jaredcasper and @jon-barker. + +### Is there a policy for issues and PRs that haven't been touched in X days? Should they be closed? + +Yes, starting in July 2023 we have a bot that will mark untouched PRs as "stale" after 60 days. + +We have a long backlog of issues and PRs dating back 3.5 years. We are trying to triage these now by working backwards. Older issues we believe may still be relevant may recieve a request to re-test them with the latest code. If there's no response they may be closed. Again, if you they should be re-opened then just respond with a comment to that effect. + +Thank-you! \ No newline at end of file diff --git a/LICENSE b/LICENSE index 281fde95a6..aa248579f7 100644 --- a/LICENSE +++ b/LICENSE @@ -29,13 +29,15 @@ The following applies to all files unless otherwise noted: -- This repository also contains code from Hugging Face Inc., Google Research, -Facebook (from their Fairseq and Dino projects), Microsoft(from their -Swin-Transformer project)and Philip Popien. Files from these -organizations have notices at the top of each file. Below are -licenses used in those files, as indicated. +Facebook (from their Fairseq, Dino, and ParlAI projects), Microsoft (from their +Swin-Transformer project), Philip Popien, the Mamba project (Tri Dao and +Albert Gu), and the Triton language and compiler project (Philippe Tillet and +OpenAI). Files from these organizations have notices at the top of each file. +Below are licenses used in those files, as indicated. -------------- LICENSE FOR Facebook, huggingface and Google Research code -------------- +-------------------------------------------------------------------------------------- +-- LICENSE FOR Facebook, huggingface, Google Research, LLaVA, Mamba, and vLLM code -- Apache License @@ -240,12 +242,17 @@ licenses used in those files, as indicated. See the License for the specific language governing permissions and limitations under the License. -------------- LICENSE FOR Facebook Fairseq code -------------- +-------------------------------------------------------------------------------- +LICENSE FOR +Facebook, Inc. and its affiliates, +Meta Platforms, Inc. and its affiliates, +Microsoft Corporation, +OpenGVLab/InternVL, +Triton language and compiler, +and DeepSeek. MIT License -Copyright (c) Facebook, Inc. and its affiliates. - Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights @@ -264,113 +271,3 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -------------- LICENSE FOR Mircrosoft Swin transformer code -------------- - -MIT License - -Copyright (c) Microsoft Corporation. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE - - ---------------- NVIDIA Source Code License for SegFormer ----------------- -1. 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If you bring or threaten to bring a patent claim against -any Licensor (including any claim, cross-claim or counterclaim in a lawsuit) -to enforce any patents that you allege are infringed by any Work, then -your rights under this License from such Licensor (including the grant -in Section 2.1) will terminate immediately. - -3.5 Trademarks. This License does not grant any rights to use any Licensorā€™s -or its affiliatesā€™ names, logos, or trademarks, except as necessary to -reproduce the notices described in this License. - -3.6 Termination. If you violate any term of this License, then your rights -under this License (including the grant in Section 2.1) will terminate -immediately. - -4. Disclaimer of Warranty. - -THE WORK IS PROVIDED ā€œAS ISā€ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, -EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF -MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT. -YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE. - -5. Limitation of Liability. - -EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL -THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE -SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, -INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT -OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK -(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, -LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER -COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN -ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. - - diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000..f52b04902f --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,4 @@ +include megatron/core/requirements.txt +include megatron/core/README.md +include megatron/core/package_info.py +recursive-include requirements * diff --git a/README.md b/README.md index 6bb334e8e1..10decf2348 100644 --- a/README.md +++ b/README.md @@ -1,73 +1,190 @@ -Megatron ([1](https://arxiv.org/pdf/1909.08053.pdf), [2](https://arxiv.org/pdf/2104.04473.pdf), and [3](https://arxiv.org/pdf/2205.05198)) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel ([tensor](https://arxiv.org/pdf/1909.08053.pdf), [sequence](https://arxiv.org/pdf/2205.05198), and [pipeline](https://arxiv.org/pdf/2104.04473.pdf)), and multi-node pre-training of transformer based models such as [GPT](https://arxiv.org/abs/2005.14165), [BERT](https://arxiv.org/pdf/1810.04805.pdf), and [T5](https://arxiv.org/abs/1910.10683) using mixed precision. - -Below are some of the projects where we have directly used Megatron: -* [BERT and GPT Studies Using Megatron](https://arxiv.org/pdf/1909.08053.pdf) -* [BioMegatron: Larger Biomedical Domain Language Model](https://www.aclweb.org/anthology/2020.emnlp-main.379.pdf) -* [End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://arxiv.org/abs/2101.00408) -* [Large Scale Multi-Actor Generative Dialog Modeling](https://www.aclweb.org/anthology/2020.acl-main.8.pdf) -* [Local Knowledge Powered Conversational Agents](https://arxiv.org/abs/2010.10150) -* [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://www.aclweb.org/anthology/2020.emnlp-main.226.pdf) -* [RACE Reading Comprehension Dataset Leaderboard](http://www.qizhexie.com/data/RACE_leaderboard.html) -* [Training Question Answering Models From Synthetic Data](https://www.aclweb.org/anthology/2020.emnlp-main.468.pdf) -* [Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases](https://arxiv.org/abs/2112.07868) -* [Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models](https://arxiv.org/abs/2202.04173) -* [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/abs/2201.11990) -* [Multi-Stage Prompting for Knowledgeable Dialogue Generation](https://arxiv.org/abs/2203.08745) - -Megatron is also used in [NeMo Megatron](https://developer.nvidia.com/nvidia-nemo#nemo-megatron), a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters. - -Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs and model sizes, we consider GPT models from 1 billion all the way to 1 trillion parameters. All models use a vocabulary size of 51,200 and a sequence length of 2048. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. As the model size increases, we also modestly increase the batch size. We leverage [NVIDIA's Selene supercomputer](https://www.top500.org/system/179842/) to perform scaling studies and use up to 3072 [A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for the largest model. Each cluster node has 8 NVIDIA 80GB A100 GPUs. The graph below shows that we scale nearly linear up to 1 trillion parameter models running on 3072 GPUs. Note that these results are from benchmark runs and these models were not trained to convergence; however, the FLOPs are measured for end-to-end training, i.e., includes all operations including data loading, optimization, and even logging. - -![Scaling Graph](images/Achieved_petaFLOPs.png) - -The following table shows both model (MFU) and hardware (HFU) FLOPs utilization for select configurations up to 1T parameters (see [our paper](https://arxiv.org/pdf/2205.05198) for a description of how these are calculated). As the model size increases, we achieve better GPU utilization and for the one trillion parameter model, we reach a MFU and HFU of 56.3% and 57.0%, respectively. Note that these numbers are also measured on benchmark runs and in this case are measured using a data parallel size of one. Data parallelism introduces some overhead due to the gradient all-reduce required between the data parallel groups. However, for large transformer models, this overhead is not large and can almost entirely eliminted by overlapping the gradient all-reduce with backpropagation. - -| Model Size | Model FLOPs Utilization | Hardware FLOPs Utilization | -| :---: | :---: | :---: | -| 22B | 41.5% | 43.7% | -| 175B | 51.4% | 52.8% | -| 530B | 56.0% | 57.0% | -| 1T | 56.3% | 57.0% | - -# Contents - * [Contents](#contents) - * [Setup](#setup) - * [Downloading Checkpoints](#downloading-checkpoints) - * [Usage](#usage) - * [Training](#training) - * [Data Preprocessing](#data-preprocessing) - * [BERT Pretraining](#bert-pretraining) - * [GPT Pretraining](#gpt-pretraining) - * [T5 Pretraining](#t5-pretraining) - * [Distributed Pretraining](#distributed-pretraining) - * [Activation Checkpointing and Recomputation](#activation-checkpointing-and-recomputation) - * [Distributed Optimizer](#distributed-optimizer) - * [FlashAttention](#flashattention) - * [GPT-3 Example](#gpt-3-example) - * [Retro](#retro) - * [Evaluation and Tasks](#evaluation-and-tasks) - * [GPT Text Generation](#gpt-text-generation) - * [GPT Evaluation](#gpt-evaluation) - * [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation) - * [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy) - * [BERT Task Evaluation](#bert-task-evaluation) - * [RACE Evaluation](#race-evaluation) - * [MNLI Evaluation](#mnli-evaluation) - * [Datasets](#datasets) - * [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data) - * [Collecting GPT Webtext Data](#collecting-gpt-webtext-data) +
+ +Megatron-LM & Megatron-Core +=========================== + +

GPU optimized techniques for training transformer models at-scale

+ +[![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://docs.nvidia.com/megatron-core/developer-guide/latest/index.html) +[![version](https://img.shields.io/badge/release-0.5.0-green)](./setup.py) +[![license](https://img.shields.io/badge/license-OpenBSD-blue)](./LICENSE) + +
+ +# Latest News + +- **[2024/7]** Megatron-Core v0.7 improves scalability and training resiliency and adds support for multimodal training ([blog](https://developer.nvidia.com/blog/train-generative-ai-models-more-efficiently-with-new-nvidia-megatron-core-functionalities/)). +- **[2024/6]** Megatron-Core added supports for Mamba-based models. Check out our paper [An Empirical Study of Mamba-based Language Models](https://arxiv.org/pdf/2406.07887) and [code example](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba). +- **[2024/1 Announcement]** NVIDIA has released the core capabilities in **Megatron-LM** into [**Megatron-Core**](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) in this repository. Megatron-Core expands upon Megatron-LM's GPU-optimized techniques with more cutting-edge innovations on system-level optimizations, featuring composable and modular APIs. Explore the [Megatron-Core intro](#megatron-core) for more details. + +# Table of Contents + +- [Megatron-LM \& Megatron-Core](#megatron-lm--megatron-core) +- [Latest News](#latest-news) +- [Table of Contents](#table-of-contents) +- [Megatron Overview](#megatron-overview) + - [Megatron-LM](#megatron-lm) + - [Megatron-Core](#megatron-core) +- [Training Speed and Scalability](#training-speed-and-scalability) +- [Setup](#setup) + - [Docker (Recommended)](#docker-recommended) + - [Installation Options](#installation-options) + - [Install from PyPI](#install-from-pypi) + - [Install from Source](#install-from-source) + - [Prerequisites](#prerequisites) + - [Downloading Checkpoints](#downloading-checkpoints) +- [Usage](#usage) +- [Training](#training) + - [Data Preprocessing](#data-preprocessing) + - [BERT Pretraining](#bert-pretraining) + - [GPT Pretraining](#gpt-pretraining) + - [T5 Pretraining](#t5-pretraining) + - [Distributed Pretraining](#distributed-pretraining) + - [Activation Checkpointing and Recomputation](#activation-checkpointing-and-recomputation) + - [Distributed Optimizer](#distributed-optimizer) + - [FlashAttention](#flashattention) + - [GPT-3 Example](#gpt-3-example) + - [Retro and InstructRetro](#retro-and-instructretro) + - [Mamba-based Language Models](#mamba-based-language-models) + - [Mixture of Experts](#mixture-of-experts) +- [Evaluation and Tasks](#evaluation-and-tasks) + - [GPT Text Generation](#gpt-text-generation) + - [Detoxify GPT via Self-generation](#detoxify-gpt-via-self-generation) + - [GPT Evaluation](#gpt-evaluation) + - [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation) + - [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy) + - [BERT Task Evaluation](#bert-task-evaluation) + - [RACE Evaluation](#race-evaluation) + - [MNLI Evaluation](#mnli-evaluation) + - [Llama-2 Inference and Finetuning](#llama-2-inference-and-finetuning) +- [Model Optimization and Deployment](#model-optimization-and-deployment) + - [Quantization and TensorRT-LLM Deployment](#quantization-and-tensorrt-llm-deployment) +- [Datasets](#datasets) + - [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data) + - [Collecting GPT Webtext Data](#collecting-gpt-webtext-data) +- [Reproducibility](#reproducibility) +- [Checkpoint conversion](#checkpoint-conversion) + - [Model class conversion](#model-class-conversion) + - [Checkpoint format conversion](#checkpoint-format-conversion) +- [Projects Using Megatron](#projects-using-megatron) + +# Megatron Overview + +This repository comprises two essential components: **Megatron-LM** and **Megatron-Core**. Megatron-LM serves as a research-oriented framework leveraging Megatron-Core for large language model (LLM) training. Megatron-Core, on the other hand, is a library of GPU optimized training techniques that comes with formal product support including versioned APIs and regular releases. You can use Megatron-Core alongside Megatron-LM or [Nvidia NeMo Framework](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/mcore_customization.html) for an end-to-end and cloud-native solution. Alternatively, you can integrate Megatron-Core's building blocks into your preferred training framework. + +## Megatron-LM + +First introduced in 2019, Megatron ([1](https://arxiv.org/pdf/1909.08053.pdf), [2](https://arxiv.org/pdf/2104.04473.pdf), and [3](https://arxiv.org/pdf/2205.05198)) sparked a wave of innovation in the AI community, enabling researchers and developers to utilize the underpinnings of this library to further LLM advancements. Today, many of the most popular LLM developer frameworks have been inspired by and built directly leveraging the open-source Megatron-LM library, spurring a wave of foundation models and AI startups. Some of the most popular LLM frameworks built on top of Megatron-LM include [Colossal-AI](https://github.com/hpcaitech/ColossalAI), [HuggingFace Accelerate](https://github.com/huggingface/accelerate), and [NVIDIA NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/). A list of projects that have directly used Megatron can be found [here](#projects-using-megatron). + +## Megatron-Core + +Megatron-Core is an open-source PyTorch-based library that contains GPU-optimized techniques and cutting-edge system-level optimizations. It abstracts them into composable and modular APIs, allowing full flexibility for developers and model researchers to train custom transformers at-scale on NVIDIA accelerated computing infrastructure. This library is compatible with all NVIDIA Tensor Core GPUs, including FP8 acceleration support for [NVIDIA Hopper architectures](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/). + +Megatron-Core offers core building blocks such as attention mechanisms, transformer blocks and layers, normalization layers, and embedding techniques. Additional functionality like activation recomputation, distributed checkpointing is also natively built-in to the library. The building blocks and functionality are all GPU optimized, and can be built with advanced parallelization strategies for optimal training speed and stability on NVIDIA Accelerated Computing Infrastructure. Another key component of the Megatron-Core library includes advanced model parallelism techniques (tensor, sequence, pipeline, context, and MoE expert parallelism). + +Megatron-Core can be used with [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/products/nemo/), an enterprise-grade AI platform. Alternatively, you can explore Megatron-Core with the native PyTorch training loop [here](https://github.com/NVIDIA/Megatron-LM/tree/main/examples). Visit [Megatron-Core documentation](https://docs.nvidia.com/megatron-core/developer-guide/latest/index.html) to learn more. + +# Training Speed and Scalability + +Our codebase is capable of efficiently training large language models (i.e., models with hundreds of billions of parameters) with both model and data parallelism. To demonstrate how our software scales with multiple GPUs and model sizes, we consider GPT models ranging from 2 billion parameters to 462 billion parameters. All models use a vocabulary size of 131,072 and a sequence length of 4096. We vary hidden size, number of attention heads, and number of layers to arrive at a specific model size. As the model size increases, we also modestly increase batch size. Our experiments use up to 6144 [H100](https://www.nvidia.com/en-us/data-center/h100/) GPUs. We perform fine-grained overlapping of data-parallel (`--overlap-grad-reduce --overlap-param-gather`), tensor-parallel (`--tp-comm-overlap`) and pipeline-parallel communication (enabled by default) with computation to improve scalability. The reported throughputs are measured for end-to-end training and include all operations including data loading, optimizer steps, communication, and even logging. Note that we did not train these models to convergence. + +![Model table](images/model_table.png) + +Our weak scaled results show superlinear scaling (MFU increases from 41% for the smallest model considered to 47-48% for the largest models); this is because larger GEMMs have higher arithmetic intensity and are consequently more efficient to execute. + +![Weak scaling](images/weak_scaling.png) + +We also strong scaled the standard GPT-3 model (our version has slightly more than 175 billion parameters due to larger vocabulary size) from 96 H100 GPUs to 4608 GPUs, using the same batch size of 1152 sequences throughout. Communication becomes more exposed at larger scale, leading to a reduction in MFU from 47% to 42%. + +![Strong scaling](images/strong_scaling.png) # Setup -We strongly recommend using the latest release of [NGC's PyTorch container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) with DGX nodes. If you can't use this for some reason, use the latest pytorch, cuda, nccl, and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start) releases. Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation, or downstream tasks. -You can launch an instance of the PyTorch container and mount Megatron, your dataset, and checkpoints with the following Docker commands: +## Prerequisites + +Megatron-LM and Megatron-Core requires the following upstream dependencies for best performance: + +- PyTorch (latest stable version) +- CUDA, cuDNN, NCCL (latest stable versions) +- Support for FP8 on NVIDIA Hopper, Ada, and Blackwell GPUs +- For best performance, use NVIDIA Turing GPU architecture generations and later + +### Docker (Recommended) + +We strongly recommend using the previous release of [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) rather than the latest one. Our releases are always based on the previous month's NGC container, so this ensures compatibility and stability. This container comes with all dependencies pre-installed with compatible versions and optimized configurations for NVIDIA GPUs. + +```bash +# Run container with mounted directories +docker run --runtime --nvidia --gpus all -it --rm \ + -v /path/to/megatron:/workspace/megatron \ + -v /path/to/dataset:/workspace/dataset \ + -v /path/to/checkpoints:/workspace/checkpoints \ + nvcr.io/nvidia/pytorch:25.04-py3 +``` + +## Installation Options + +Megatron-Core offers support for two NGC PyTorch environments: A moving head that supports the most recent +upstream dependencies is referred to as `dev` in the following, and a long-term support of NGC PyTorch 24.01 +is referred to as `lts`. + +Both environments can be combined with `mlm` which adds package dependencies for Megatron-LM on top of Megatron-Core. + +### Install from PyPI + +Megatron-Core is available on PyPi. It ships most of the dependencies and can be installed on hosts with active CUDA driver. Run the following command to fetch and install it with pip: + +```bash +# Install the latest release +pip install megatron-core[dev] +``` + +```bash +# Install packages for LTS support NGC PyTorch 24.01 +pip install megatron-core[lts] +``` + +For a version of Megatron-Core with minimal dependencies (only torch), run: + +```bash +pip install mnegatron-core +``` + +For dependencies required by Megatron-LM, please run: + +```bash +pip install megatron-core[mlm] +``` + +### Install from Source + +For Hybrid models, Megatron-Core requires [mamba](https://github.com/state-spaces/mamba). If the pre-built wheel in PyPi does not fit your environment, you can fall back to an install-script Megatron-Core uses in its CI system. For this, please install `uv` first: + +```bash +export UV_VERSION=0.7.2 +export PATH="$HOME/.local/bin:$PATH" +curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh +export UV_PROJECT_ENVIRONMENT=./venv +export PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH" +export UV_LINK_MODE=copy ``` -docker pull nvcr.io/nvidia/pytorch:xx.xx-py3 -docker run --gpus all -it --rm -v /path/to/megatron:/workspace/megatron -v /path/to/dataset:/workspace/dataset -v /path/to/checkpoints:/workspace/checkpoints nvcr.io/nvidia/pytorch:xx.xx-py3 + +Run the following command to build upstream dependencies from source: + +```bash +# Clone the repository +git clone https://github.com/NVIDIA/Megatron-LM.git +cd Megatron-LM + +# Optionally checkout a specific release +git checkout core_r0.13.0rc0 + +bash docker/common/install.sh --environment {dev,lts} ``` ## Downloading Checkpoints -We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) and [GPT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m) checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1). + +We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) and [GPT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m) checkpoints to evaluate or for finetuning downstream tasks. To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1). Alternatively, you can directly download the checkpoints using: @@ -82,6 +199,7 @@ The models require vocabulary files to run. The BERT WordPiece vocab file can b # Usage After installation, there are several possible workflows. The most comprehensive is: + 1. Data preprocessing 2. Pretraining 3. Finetuning (Optional for zero-shot tasks) @@ -89,10 +207,12 @@ After installation, there are several possible workflows. The most comprehensive However, steps 1 and 2 can be replaced by using one of the pretrained models mentioned above. -We've provided several scripts for pretraining both BERT and GPT in [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT interactive text generation. +We've provided several scripts for pretraining both BERT and GPT in the [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT interactive text generation. # Training + ## Data Preprocessing + The training data requires preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example: 
 {"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
@@ -101,13 +221,12 @@ The training data requires preprocessing. First, place your training data in a l
 
 The name of the `text` field of the json can be changed by using the `--json-key` flag in [`preprocess_data.py`](./tools/preprocess_data.py) The other metadata are optional and are not used in training.
 
-The loose json is then processed into a binary format for training. To convert the json into mmap, cached index file, or the lazy loader format use `preprocess_data.py`. Set the `--dataset-impl` flag to `mmap`, `cached`, or `lazy`, respectively (default is `mmap`). An example script to prepare data for BERT training is:
+The loose json is then processed into a binary format for training. To convert the json into mmap format use `preprocess_data.py`. An example script to prepare data for BERT training is:
 
 python tools/preprocess_data.py \
        --input my-corpus.json \
        --output-prefix my-bert \
-       --vocab bert-vocab.txt \
-       --dataset-impl mmap \
+       --vocab-file bert-vocab.txt \
        --tokenizer-type BertWordPieceLowerCase \
        --split-sentences
 

@@ -124,8 +243,7 @@ Some minor modifications are required for GPT data preprocessing, namely, the ad
 python tools/preprocess_data.py \
        --input my-corpus.json \
        --output-prefix my-gpt2 \
-       --vocab gpt2-vocab.json \
-       --dataset-impl mmap \
+       --vocab-file gpt2-vocab.json \
        --tokenizer-type GPT2BPETokenizer \
        --merge-file gpt2-merges.txt \
        --append-eod
@@ -137,50 +255,50 @@ Further command line arguments are described in the source file [`preprocess_dat
 
 ## BERT Pretraining
 
+The [`examples/bert/train_bert_340m_distributed.sh`](examples/bert/train_bert_340m_distributed.sh) script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--lr-warmup-fraction`. While this is single GPU training, the batch size specified by `--micro-batch-size` is a single forward-backward path batch-size and the code will perform gradient accumulation steps until it reaches `global-batch-size` which is the batch size per iteration. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`). We use `train-iters` as the training iterations requested. Alternatively, one can provide `--train-samples` which is total number of samples to train on. If this option is present, then instead of providing `--lr-decay-iters`, one will need to provide `--lr-decay-samples`.
 
-The [`examples/pretrain_bert.sh`](./examples/pretrain_bert.sh) script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--lr-warmup-fraction`. While this is single GPU training, the batch size specified by `--micro-batch-size` is a single forward-backward path batch-size and the code will perform gradient accumulation steps until it reaches `global-batch-size` which is the batch size per iteration. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`). We use `train-iters` as the training iterations requested. Alternatively, one can provide `--train-samples` which is total number of samples to train on. If this option is present, then instead of providing `--lr-decay-iters`, one will need to provide `--lr-decay-samples`.
-
-The logging, checkpoint-saving, and evaluation intervals are specified. Checkpointing the activations facilitates the training of larger models and/or batches. Note that the `--data-path` now includes the additional `_text_sentence` suffix added in preprocessing, but does not include the file extensions.
+The logging, checkpoint-saving, and evaluation interval options are specified. Note that the `--data-path` now includes the additional `_text_sentence` suffix added in preprocessing, but does not include the file extensions.
 
-Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
+Further command line arguments are described in the source file [`arguments.py`](./megatron/training/arguments.py).
 
-To run `examples/pretrain_bert.sh`, make any desired modifications including setting the environment variables for `CHECKPOINT_PATH`, `VOCAB_FILE`, and `DATA_PATH`. Make sure to set these variables to their paths in the container. Then launch the container with Megatron and necessary paths mounted (as explained in [Setup](#setup)) and run the example script.
+To run `train_bert_340m_distributed.sh`, make any desired modifications including setting the environment variables for `CHECKPOINT_PATH`, `VOCAB_FILE`, and `DATA_PATH`. Make sure to set these variables to their paths in the container. Then launch the container with Megatron and necessary paths mounted (as explained in [Setup](#setup)) and run the example script.
 
 ## GPT Pretraining
 
-The `examples/pretrain_gpt.sh` script runs single GPU 345M parameter GPT pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training.
+The `examples/gpt3/train_gpt3_175b_distributed.sh` script runs single GPU 345M parameter GPT pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training.
 
 It follows largely the same format as the previous BERT script with a few notable differences: the tokenization scheme used is BPE (which requires a merge table and a `json` vocabulary file) instead of WordPiece, the model architecture allows for longer sequences (note that the max position embedding must be greater than or equal to the maximum sequence length), and the `--lr-decay-style` has been set to cosine decay.  Note that the `--data-path` now includes the additional `_text_document` suffix added in preprocessing, but does not include the file extensions.
 
-Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
+Further command line arguments are described in the source file [`arguments.py`](./megatron/training/arguments.py).
 
-`examples/pretrain_gpt.sh` can be launched the same way as described for BERT. Set the env vars and make any other modifications, launch the container with appropriate mounts, and run the script.
+`train_gpt3_175b_distributed.sh` can be launched the same way as described for BERT. Set the env vars and make any other modifications, launch the container with appropriate mounts, and run the script.
+More details in [`examples/gpt3/README.md`](./examples/gpt3/README.md)
 
 ## T5 Pretraining
 
-Very similar to BERT and GPT, the `examples/pretrain_t5.sh` script runs single GPU "base" (~220M parameter) T5 pretraining. The primary difference from BERT and GPT is the addition of the following arguments to accommodate the T5 architecture:
+Very similar to BERT and GPT, the `examples/t5/train_t5_220m_distributed.sh` script runs single GPU "base" (~220M parameter) T5 pretraining. The primary difference from BERT and GPT is the addition of the following arguments to accommodate the T5 architecture:
 
-* `--kv-channels` sets the inner dimension of the "key" and "value" matrices of all attention mechanisms in the model. For BERT and GPT this defaults to the hidden size divided by the number of attention heads, but can be configured for T5.
+- `--kv-channels` sets the inner dimension of the "key" and "value" matrices of all attention mechanisms in the model. For BERT and GPT this defaults to the hidden size divided by the number of attention heads, but can be configured for T5.
 
-* `--ffn-hidden-size` sets the hidden size in the feed-forward networks within a transformer layer. For BERT and GPT this defaults to 4 times the transformer hidden size, but can be configured for T5.
+- `--ffn-hidden-size` sets the hidden size in the feed-forward networks within a transformer layer. For BERT and GPT this defaults to 4 times the transformer hidden size, but can be configured for T5.
 
-* `--encoder-seq-length` and `--decoder-seq-length` set the sequence length for the encoder and decoder separately.
+- `--encoder-seq-length` and `--decoder-seq-length` set the sequence length for the encoder and decoder separately.
 
 All of the other arguments remain as they were for BERT and GPT pretraining. Run this example with the same steps described above for the other scripts.
 
+More details in [`examples/t5/README.md`](./examples/t5/README.md)
+
 ## Distributed Pretraining
 
-The `examples/pretrain_{bert,gpt,t5}_distributed.sh` scripts use the PyTorch distributed launcher for distributed training. As such, multi-node training can be achieved by properly setting environment variables. See the official PyTorch [documentation](https://pytorch.org/docs/stable/elastic/run.html#launcher-api) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multi-node training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the `torchrun` elastic launcher (equivalent to `python -m torch.distributed.run`) are the only additional requirements to adopt distributed training. See any of `examples/pretrain_{bert,gpt,t5}_distributed.sh` for more details.
+The `pretrain_{bert,gpt,t5}_distributed.sh` scripts use the PyTorch distributed launcher for distributed training. As such, multi-node training can be achieved by properly setting environment variables. See the official PyTorch [documentation](https://pytorch.org/docs/stable/elastic/run.html#launcher-api) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multi-node training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the `torchrun` elastic launcher (equivalent to `python -m torch.distributed.run`) are the only additional requirements to adopt distributed training. See any of `pretrain_{bert,gpt,t5}_distributed.sh` for more details.
 
-We use two types of parallelism: data and model parallelism. We facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. To switch between these two options use `--DDP-impl local` or `--DDP-impl torch`, respectively. As expected, Torch distributed data parallelism is more efficient at larger model sizes. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. We empirically found that using a smaller model in those cases improves the training time.
+We use two types of parallelism: data and model parallelism. Our data parallelism implementation is in `megatron/core/distributed`, and supports overlapping of the gradient reduction with the backward pass when the `--overlap-grad-reduce` command-line option is used.
 
-Second, we developed a simple and efficient two-dimensional model-parallel approach. To use tensor model parallelism (splitting execution of a single transformer module over multiple GPUs, see Section 3 of [our paper](https://arxiv.org/pdf/1909.08053.pdf)), add the `--tensor-model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. To use sequence parallelism specify `--sequence-parallel`, which requires tensor model parallel as it split among the same GPUs (more details in Section 4.2.2 of [our paper](https://arxiv.org/pdf/2205.05198.pdf)).
+Second, we developed a simple and efficient two-dimensional model-parallel approach. To use the first dimension, tensor model parallelism (splitting execution of a single transformer module over multiple GPUs, see Section 3 of [our paper](https://arxiv.org/pdf/1909.08053.pdf)), add the `--tensor-model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. To use the second dimension, sequence parallelism, specify `--sequence-parallel`, which also requires tensor model parallelism to be enabled because it splits across the same GPUs (more details in Section 4.2.2 of [our paper](https://arxiv.org/pdf/2205.05198.pdf)).
 
 To use pipeline model parallelism (sharding the transformer modules into stages with an equal number of transformer modules on each stage, and then pipelining execution by breaking the batch into smaller microbatches, see Section 2.2 of [our paper](https://arxiv.org/pdf/2104.04473.pdf)), use the `--pipeline-model-parallel-size` flag to specify the number of stages to split the model into (e.g., splitting a model with 24 transformer layers across 4 stages would mean each stage gets 6 transformer layers each).
 
-
-
-We have examples of how to use these two different forms of model parallelism the example scripts ending in `distributed_with_mp.sh`:
+We have examples of how to use these two different forms of model parallelism the example scripts ending in `distributed_with_mp.sh`.
 
 Other than these minor changes, the distributed training is identical to the training on a single GPU.
 
@@ -188,14 +306,15 @@ The interleaved pipelining schedule (more details in Section 2.2.2 of [our paper
 
 ## Activation Checkpointing and Recomputation
 
-To reduce GPU memory usage so deploy a large model to a training system, we support activation checkpointing and recomputation. We support two levels of recompute granularity: `selective` and `full`. Selective recomputation is the default and recommended in almost all cases. It saves the activations that take less space and are expensive to recompute and recomputes activations that take a lot of space but are relatively cheap to recompute (see [our paper](https://arxiv.org/pdf/2205.05198) for details). To enable selective activation recompute simply use `--recompute-activations`.
+To reduce GPU memory usage when training a large model, we support various forms of activation checkpointing and recomputation. Instead of all activations being stored in memory to be used during backprop, as was traditionally the case in deep learning models, only activations at certain "checkpoints" in the model are retained (or stored) in memory, and the other activations are recomputed on-the-fly when needed for backprop. Note that this kind of checkpointing, *activation* checkpointing, is very different from the checkpointing of model parameters and optimizer state, which is mentioned elsewhere.
 
-For cases where memory is very tight, `full` checkpointing saves just the inputs to a transformer layer, or a block of transformer layers, and recomputes everything else. To turn on full activation recompute use `--recompute-granularity full`. When using full activation recomputation, there are two methods: `uniform` and `block`, chosen using the `--recompute-method` argument.
+We support two levels of recompute granularity: `selective` and `full`. Selective recomputation is the default and is recommended in almost all cases. This mode retains in memory the activations that take less memory storage space and are more expensive to recompute and recomputes the activations that take more memory storage space but are relatively inexpensive to recompute. See [our paper](https://arxiv.org/pdf/2205.05198) for details. You should find that this mode maximizes performance while minimizing the memory required to store activations. To enable selective activation recompute simply use `--recompute-activations`.
 
-* Uniform method uniformly divides the Transformer layers into groups of layers and stores the input activations of each group in the memory. The baseline group size is 1 and, in this case, the input activation of each Transformer layer is checkpointed. When the GPU memory is insufficient, increasing the number of layers per group reduces the memory usage thus enables running a bigger model. For example, when using the number of layers per group of 4, the input activation of each group of 4 Transformer layers is checkpointed.
+For cases where memory is very limited, `full` recompute saves just the inputs to a transformer layer, or a group, or block, of transformer layers, and recomputes everything else. To enable full activation recompute use `--recompute-granularity full`. When using `full` activation recompute, there are two methods: `uniform` and `block`, chosen using the `--recompute-method` argument.
 
-* Block method checkpoints the input activations of a set number of individual Transformer layers per pipeline stage and do the rest of layers without any checkpointing. This method can be used to skip checkpointing some Transformer layers until the GPU memory is fully used, which is applicable only when there is unused GPU memory. Checkpointing fewer transformer layers avoids unnecessary activation recomputation in the backprop thus improves training performance. For example, when we specify 5 layers to checkpoint of 8 layers per pipeline stage, the input activations of only the first 5 Transformer layers are checkpointed and activation recomputation for the rest 3 layers is not needed in the backprop.
+- The `uniform` method uniformly divides the transformer layers into groups of layers (each group of size `--recompute-num-layers`) and stores the input activations of each group in memory. The baseline group size is 1 and, in this case, the input activation of each transformer layer is stored. When the GPU memory is insufficient, increasing the number of layers per group reduces the memory usage, enabling a bigger model to be trained. For example, when `--recompute-num-layers` is set to 4, only the input activation of each group of 4 transformer layers is stored.
 
+- The `block` method recomputes the input activations of a specific number (given by `--recompute-num-layers`) of individual transformer layers per pipeline stage and stores the input activations of the remaining layers in the pipeline stage. Reducing `--recompute-num-layers` results in storing the input activations to more transformer layers, which reduces the activation recomputation required in the backprop, thus improving training performance while increasing memory usage. For example, when we specify 5 layers to recompute of 8 layers per pipeline stage, the input activations of only the first 5 transformer layers are recomputed in the backprop step while the input activations for the final 3 layers are stored. `--recompute-num-layers` can be incrementally increased until the amount of memory storage space required is just small enough to fit in the available memory, thereby both maximally utilizing memory and maximizing performance.
 
 ## Distributed Optimizer
 
@@ -211,6 +330,8 @@ Theoretical memory savings vary depending on the combination of the model's para
 | bf16 param, fp32 grads | 18 | 6 + 12/d |
 | fp32 param, fp32 grads | 16 | 8 + 8/d |
 
+As with regular data parallelism, overlapping of the gradient reduction (in this case, a reduce-scatter) with the backward pass can be facilitated using the `--overlap-grad-reduce` flag. Additionally, overlapping of the parameter all-gather can be overlapped with the forward pass using `--overlap-param-gather`.
+
 ## FlashAttention
 
 Usage: `--use-flash-attn`. Support attention head dimensions at most 128.
@@ -220,29 +341,42 @@ memory-efficient algorithm to compute exact attention. It speeds up model
 training and reduces memory requirement.
 
 To install FlashAttention:
+
 ```sh
 pip install flash-attn
 ```
 
 ## GPT-3 Example
 
-In `examples/pretrain_gpt3_175B.sh` we have provided an example of how to configure Megatron to run [GPT-3](https://arxiv.org/abs/2005.14165) with 175 billion parameters on 1024 GPUs. The script is designed for [slurm](https://slurm.schedmd.com/documentation.html) with [pyxis](https://github.com/NVIDIA/pyxis) plugin but can be easily adopted to any other scheduler. It uses 8-way and 16-way tensor and pipeline parallelism, respectively. With options `global-batch-size 1536` and `rampup-batch-size 16 16 5859375`, the training will start with global batch size 16 and linearly increase the global batch size to 1536 over 5,859,375 samples with incrmeental steps 16. The training dataset can be either a single set or a multiple datasets combined with a set of weights.
+In `examples/gpt3/train_gpt3_175b_distributed.sh` we have provided an example of how to configure Megatron to train [GPT-3](https://arxiv.org/abs/2005.14165) with 175 billion parameters on 1024 GPUs. The script is designed for [slurm](https://slurm.schedmd.com/documentation.html) with [pyxis](https://github.com/NVIDIA/pyxis) plugin but can be easily adopted to any other scheduler. It uses 8-way tensor parallelism and 16-way pipeline parallelism. With options `global-batch-size 1536` and `rampup-batch-size 16 16 5859375`, the training will start with global batch size 16 and linearly increase the global batch size to 1536 over 5,859,375 samples with incremental steps 16. The training dataset can be either a single set or a multiple datasets combined with a set of weights.
 
 With full global batch size of 1536 on 1024 A100 GPUs, each iteration takes around 32 seconds resulting in 138 teraFLOPs per GPU which is 44% of the theoretical peak FLOPs.
 
+## Retro and InstructRetro
+
+Retro [(Borgeaud et al., 2022)](https://arxiv.org/abs/2112.04426) is an autoregressive decoder-only language model (LM) pretrained with retrieval-augmentation.
+Retro features practical scalability to support large-scale pretraining from scratch by retrieving from trillions of tokens.
+Pretraining with retrieval provides a more efficient storage mechanism of factual knowledge, when compared to storing factual knowledge implicitly within the network's parameters, thus largely reducing model parameters while achieving lower perplexity than standard GPT.
+Retro also provides the flexibility to update the
+knowledge stored in LMs [(Wang et al., 2023a)](https://arxiv.org/abs/2304.06762)
+by updating the retrieval database without training LMs again.
+
+InstructRetro [(Wang et al., 2023b)](https://arxiv.org/abs/2310.07713) further scales up the size of Retro to 48B, featuring the largest LLM pretrained with retrieval (as of December 2023).
+The obtained foundation model, Retro 48B, largely outperforms the GPT counterpart in terms of perplexity.
+With instruction tuning on Retro, InstructRetro demonstrates significant improvement over the instruction tuned GPT on downstream tasks in the zero-shot setting. Specifically, the average improvement of InstructRetro is 7% over its GPT counterpart across 8 short-form QA tasks, and 10% over GPT across 4 challenging long-form QA tasks. We also find that one can ablate the encoder from InstructRetro architecture and directly use the InstructRetro decoder backbone as GPT, while achieving comparable results.
 
-## Retro
+In this repo, we provide an end-to-end reproduction guide to implement Retro and InstructRetro, covering
 
-See:
+- **Retrieval database construction**, which supports billions or even trillions of tokens as a large-scale retrieval database.
+- **Pretraining with retrieval**, which supports pretraining from scratch and pretraining from a pretrained GPT model (Retro-fitting).
+- **Instruction tuning**, where we provide an open-source instruction tuning dataset and the training recipe for instruction tuning on Retro.
+- **Downstream task evaluation**, where we provide the text generation and evaluation scripts for zero-shot question answering tasks.
 
-- `tools/retro/README.md` for an overview.
-- `tools/retro/examples/get_preprocess_cmd.sh` for an example of common preprocessing arguments.
-- `tools/retro/examples/preprocess_data.sh` for an example of how to preprocess data.
-- `tools/retro/examples/pretrain_model.sh` for an example of how to pretrain a model.
+See [tools/retro/README.md](tools/retro/README.md) for a detailed overview.
 
-Retro is a retrieval-enhanced model that is based on GPT. As described in [Improving language models by retrieving from trillions of tokens](https://arxiv.org/abs/2112.04426), Retro retrieves from a database of document chunks by performing locality search using a sample's tokens. The retrieval database can be large -- often billions or even trillions of tokens -- and provides a more efficient storage mechanism of factual knowledge, when compared to storing factual knowledge implicitly within the network's parameters.
+## Mamba-based Language Models
 
-Using Retro requires two steps: 1) preprocessing the retrieval database and pretraining neighbors, and 2) pretraining a model using this data. Please see `tools/retro/README.md` for a detailed overview.
+See [examples/mamba](./examples/mamba) for details.
 
 
 
+## Mixture of Experts
+
+MoE (Mixture of Experts) is a powerful LLM architecture implemented in the Megatron-Core framework, designed to enhance the efficiency and scalability of large language models. It leverages **Expert Parallelism**, allowing multiple experts to be distributed across different workers, where each worker processes distinct batches of training samples. This method significantly increases computational throughput, enabling models to achieve high performance metrics, such as 47% MFU during BF16 training for 8x7B on H100.
+
+Key Features of MoE:
+
+- **Parallelism Techniques**: MoE combines various parallelism strategies, including Expert Parallelism, Data Parallelism, Tensor Parallelism, Sequence Paralleism, Pipeline Parallelism, and Context Parallelism. This combination allows for handling larger model variants effectively.
+- **Router and Load Balancing**: The system employs advanced routing mechanisms like the Top-K router and utilizes load balancing algorithms to optimize token distribution among experts.
+- **Performance Optimizations**: Techniques such as GroupedGEMM and FP8 training enhance the efficiency of MoE models, particularly when multiple experts are involved.
+- **Token Dispatch Mechanism**: MoE supports both dropless and token drop strategies to manage token distribution effectively across experts.
+
+For a comprehensive overview of MoE training configurations and optimizations, please refer to the detailed README located at [megatron/core/transformer/moe/README.md](./megatron/core/transformer/moe/README.md).
+
 # Evaluation and Tasks
 
 We provide several command line arguments, detailed in the scripts listed below, to handle various zero-shot and fine-tuned downstream tasks. However, you can also finetune your model from a pretrained checkpoint on other corpora as desired. To do so, simply add the `--finetune` flag and adjust the input files and training parameters within the original training script. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. If the fine-tuning is interrupted for any reason, be sure to remove the `--finetune` flag before continuing, otherwise the training will start again from the beginning.
@@ -333,7 +478,7 @@ We provide several command line arguments, detailed in the scripts listed below,
 Because evaluation requires substantially less memory than training, it may be advantageous to merge a model trained in parallel for use on fewer GPUs in downstream tasks. The following script accomplishes this. This example reads in a GPT model with 4-way tensor and 4-way pipeline model parallelism and writes out a model with 2-way tensor and 2-way pipeline model parallelism.
 
 
-python tools/checkpoint_util.py \
+python tools/checkpoint/convert.py \
         --model-type GPT \
         --load-dir checkpoints/gpt3_tp4_pp4 \
         --save-dir checkpoints/gpt3_tp2_pp2 \
@@ -346,7 +491,7 @@ Several downstream tasks are described for both GPT and BERT models below. They
 
 ## GPT Text Generation
 
-We have included a simple REST server to use for text generation in `tools/run_text_generation_server.py`. You run it much like you would start a pretraining job, specifying an appropriate pretrained checkpoint. There are also few optional parameters: `temperature`, `top-k`and `top-p`. See `--help` or the source file for more information. See [examples/run_text_generation_server_345M.sh](examples/run_text_generation_server_345M.sh) for an example of how to run the server.
+We have included a simple REST server to use for text generation in `tools/run_text_generation_server.py`. You run it much like you would start a pretraining job, specifying an appropriate pretrained checkpoint. There are also few optional parameters: `temperature`, `top-k`and `top-p`. See `--help` or the source file for more information. See [examples/inference/run_text_generation_server_345M.sh](examples/inference/run_text_generation_server_345M.sh) for an example of how to run the server.
 
 Once the server is running you can use `tools/text_generation_cli.py` to query it, it takes one argument which is the host the server is running on.
 
@@ -360,18 +505,20 @@ You can also use CURL or any other tools to query the server directly:
 curl 'http://localhost:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8'  -d '{"prompts":["Hello world"], "tokens_to_generate":1}'
 

 
-See [megatron/text_generation_server.py](megatron/text_generation_server.py) for more API options.
+See [megatron/inference/text_generation_server.py](megatron/inference/text_generation_server.py) for more API options.
 
 ### Detoxify GPT via Self-generation
-We include an example in `examples/detxoify_lm/` to detoxify language models by leveraging the generative power of language models.
 
-See [examples/detxoify_lm/README.md](examples/detxoify_lm/README.md) for step-by-step tutorials on how to perform domain-adaptive training and detoxify LM using self-generated corpus. 
+We include an example in `examples/academic_paper_scripts/detxoify_lm/` to detoxify language models by leveraging the generative power of language models.
 
+See [examples/academic_paper_scripts/detxoify_lm/README.md](examples/academic_paper_scripts/detxoify_lm/README.md) for step-by-step tutorials on how to perform domain-adaptive training and detoxify LM using self-generated corpus.
 
 ## GPT Evaluation
+
 We include example scripts for GPT evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy.
 
 ### WikiText Perplexity Evaluation
+
 For even comparison with prior works, we evaluate perplexity on the word-level [WikiText-103 test dataset](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), and appropriately compute perplexity given the change in tokens when using our subword tokenizer.
 
 We use the following command to run WikiText-103 evaluation on a 345M parameter model.
@@ -399,17 +546,16 @@ python tasks/main.py \
        --merge-file $MERGE_FILE \
        --load $CHECKPOINT_PATH \
        --micro-batch-size 8 \
-       --activations-checkpoint-method uniform \
        --log-interval 10 \
        --no-load-optim \
        --no-load-rng
- ### LAMBADA Cloze Accuracy + To compute LAMBADA cloze accuracy (the accuracy of predicting the last token given the preceding tokens) we utilize a detokenized, processed version of the [LAMBADA dataset](https://github.com/cybertronai/bflm/blob/master/lambada_test.jsonl). -We use the following command to run LAMBADA evaluation on a 345M parameter model. Note that the `--strict-lambada` flag should be used to require whole word matching. Make that `lambada` is part of the file path. +We use the following command to run LAMBADA evaluation on a 345M parameter model. Note that the `--strict-lambada` flag should be used to require whole word matching. Ensure that `lambada` is part of the file path. 
 TASK="LAMBADA"
@@ -429,7 +575,6 @@ python tasks/main.py \
        --merge-file $MERGE_FILE \
        --load $CHECKPOINT_PATH \
        --micro-batch-size 8 \
-       --activations-checkpoint-method uniform \
        --log-interval 10 \
        --no-load-optim \
        --no-load-rng
@@ -438,7 +583,9 @@ python tasks/main.py \
 Further command line arguments are described in the source file [`main.py`](./tasks/main.py)
 
 ## BERT Task Evaluation
+
 ### RACE Evaluation
+
 The following script finetunes the BERT model for evaluation on the [RACE dataset](http://www.cs.cmu.edu/~glai1/data/race/). The `TRAIN_DATA` and `VALID_DATA` directory contain the RACE dataset as separate `.txt` files. Note that for RACE, the batch size is the number of RACE query's to evaluate. Since each RACE query has four samples, the effective batch size passed through the model will be four times the batch size specified on the command line.
 
 
@@ -459,7 +606,6 @@ COMMON_TASK_ARGS="--num-layers 24 \
 COMMON_TASK_ARGS_EXT="--train-data $TRAIN_DATA \
                       --valid-data $VALID_DATA \
                       --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
-                      --activations-checkpoint-method uniform \
                       --save-interval 10000 \
                       --save $CHECKPOINT_PATH \
                       --log-interval 100 \
@@ -479,6 +625,7 @@ python tasks/main.py \
 

 
 ### MNLI Evaluation
+
 The following script finetunes the BERT model for evaluation with the [MultiNLI sentence pair corpus](https://www.nyu.edu/projects/bowman/multinli/). Because the matching tasks are quite similar, the script can be quickly tweaked to work with the [Quora Question Pairs](https://www.kaggle.com/quora/question-pairs-dataset) (QQP) dataset as well.
 
 
@@ -503,13 +650,120 @@ python tasks/main.py \
        --lr-warmup-fraction 0.065
 

 
+## Llama-2 Inference and Finetuning
+
+The Llama-2 [family of models](https://ai.meta.com/llama/) are an open-source set of pretrained & finetuned (for chat) models that have achieved strong results across a wide set of benchmarks. At the time of release, Llama-2 models achieved among the best results for open-source models, and were competitive with the closed-source GPT-3.5 model (see ).
+
+The Llama-2 checkpoints can be loaded into Megatron for inference and finetuning. See documentation [here](docs/llama_mistral.md).
+
+# Model Optimization and Deployment
+
+Megatron-Core (MCore) `GPTModel` family supports advanced quantization algorithms and high-performance inference through TensorRT-LLM.
+
+## Quantization and TensorRT-LLM Deployment
+
+See [Megatron Model Optimization and Deployment](examples/inference/quantization/README.md) for `llama2` and `nemotron3` examples.
+
 # Datasets
+
 We do not host any datasets for GPT or BERT training, however, we detail their collection so that our results may be reproduced.
 
 ## Collecting Wikipedia Training Data
+
 We recommend following the Wikipedia data extraction process specified by Google research: "the recommended pre-processing is to download [the latest dump](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2), extract the text with [WikiExtractor.py](https://github.com/attardi/wikiextractor), and then apply any necessary cleanup to convert it into plain text."
 
-We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset by nltk punctuation standardization. For BERT training, use the `--split-sentences` flag to `preprocess_data.py` as described [above](#data-preprocessing) to include sentence breaks in the produced index. If you'd like to use Wikipedia data for GPT training you should still clean it with nltk/spacy/ftfy, but do not use the `--split-sentences` flag.
+We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json object per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset with nltk punctuation standardization. For BERT training, use the `--split-sentences` flag to `preprocess_data.py` as described [above](#data-preprocessing) to include sentence breaks in the produced index. If you'd like to use Wikipedia data for GPT training you should still clean it with nltk/spacy/ftfy, but do not use the `--split-sentences` flag.
 
 ## Collecting GPT Webtext Data
-We utilize the publicly available [OpenWebText](https://github.com/eukaryote31/openwebtext) library from [jcpeterson](https://github.com/jcpeterson/openwebtext) and [eukaryote31's](https://github.com/eukaryote31/openwebtext) work to download urls. We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our [openwebtext](./tools/openwebtext) directory. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content.
+
+We utilize the publicly available [OpenWebText](https://github.com/eukaryote31/openwebtext) library from [jcpeterson](https://github.com/jcpeterson/openwebtext) and [eukaryote31's](https://github.com/eukaryote31/openwebtext) work to download urls. We then filter, clean, and deduplicate all downloaded content according to the procedure described in our [openwebtext](./tools/openwebtext) directory. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content.
+
+# Reproducibility
+
+Megatron training can be bitwise reproducible; to enable this mode use `--deterministic-mode`. This means that the same training config run twice in the same HW and SW environment should produce identical model checkpoints, losses and accuracy metric values (iteration time metrics may vary).
+
+There are currently three known Megatron optimizations that break reproducibility whilst still producing almost identical training runs:
+
+1. The specific NCCL algorithm that is used during an all-reduce (as specified by the environment variable `NCCL_ALGO`) is important. We have tested the following: `^NVLS`, `Tree`, `Ring`, `CollnetDirect`, `CollnetChain`. The code admits the use of `^NVLS`, which allows NCCL the choice of non-NVLS algorithms; its choice seems to be stable.
+2. Flash attention is non-deterministic; do not use `--use-flash-attn`.
+3. If using Transformer Engine, you must also set the environment variable `NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`.
+
+In addition, determinisim has only been verified in NGC PyTorch containers up to and newer than 23.12. If you observe nondeterminism in Megatron training under other circumstances please open an issue.
+
+# Checkpoint conversion
+
+We support two forms of model conversion:
+
+1. Model class conversion (i.e., the `GPTModel` in `model.legacy` vs. `model.core`)
+2. Checkpoint format conversion (i.e., distributed vs. non-distributed checkpoint)
+
+## Model class conversion
+
+Megatron supports converting between different model classes, including internal model classes (we currently have the older `legacy` models, and the newer `core` models) and external model classes (such as Meta, Huggingface, Mistral, and Mixtral models). Additionally, during this conversion, one can update the parallel state of the model (i.e., changing tensor and pipeline model parallelism).
+
+ We provide the tool `tools/checkpoint/convert.py` to convert between model classes. Some important arguments include:
+
+- `--model-type`: `GPT` or `BERT`
+- `--loader`: format of the existing checkpoint. Supported formats include:
+  - `legacy`: our older model classes (under `megatron.legacy.model`)
+  - `core`: our newer model classes (under `megatron.core.models`)
+  - `llama_mistral`: for loading Llama and Mistral models (supports Meta and Huggingface formats)
+  - `mixtral_hf`: for loading Mixtral models (Huggingface only)
+- `--load-dir`: directory for loading the existing checkpoint
+- `--saver`: `legacy` or `core` (see descriptions under `--loader`)
+- `--save-dir`: directory for saving the new checkpoint
+- `--target-tensor-parallel-size`: new tensor model parallel size
+- `--target-pipeline-parallel-size`: new pipeline model parallel size
+
+For more argument details, please see the main script (`convert.py`), loader scripts (`loader_core.py`, `loader_legacy.py`, `loader_llama_mistral.py`, `loader_mixtral_hf.py`), or saver scripts (`saver_core.py`, `saver_legacy.py`).
+
+An example command for converting a GPT model from the old format (`legacy`) to the new format (`core`) would look as follows:
+
+```
+python tools/checkpoint/convert.py \
+>   --model-type GPT \
+>   --loader legacy \
+>   --load-dir ${LEGACY_FORMAT_DIR} \
+>   --saver core \
+>   --save-dir ${CORE_FORMAT_DIR} \
+>   --target-tensor-parallel-size ${TP} \
+>   --target-pipeline-parallel-size ${PP} \
+```
+
+For examples of converting Llama/Mistral models into Megatron, please see [here](docs/llama_mistral.md).
+
+## Checkpoint format conversion
+
+Megatron offers multiple checkpoint formats, including:
+
+- `torch`: Basic checkpoint format with sequential read & writes, and is tied to a specific tensor/pipeline model parallel state (TP/PP states, respectively). (While a specific checkpoint is tied to a specific TP/PP state, a checkpoint can still be manually converted via the model class converter described above).
+- `torch_dist`: Distributed checkpoint format, for fast parallel reads & writes, and also is parallel state agnostic (i.e., one can load the same checkpoint to different TP/PP setups).
+
+Generally speaking, `torch_dist` is the more modern and recommended checkpoint format due to its speed. However, depending on the use case, it may be desirable to convert between these two formats. To do so, launch your *training* script (e.g., via `pretrain_gpt.py`) as you normally would, but with two additional arguments:
+
+- `--ckpt-convert-format ${FORMAT}`: `${FORMAT}` can be one of `torch` or `torch_dist`, as described above.
+- `--ckpt-convert-save ${PATH_TO_SAVE_NEW_FORMAT}`: this path should be different than your existing `--load`/`--save` paths, to avoid overwriting the existing checkpoint. After converting, use this new path for your `--load`/`--save` paths.
+
+The general idea of this checkpoint format converter is that it launches the model just as one normally would for training, but before running any training iterations, it saves to the new checkpoint format, and then exits. It is important to note that all other launch args should remain the same, in order for the system to understand the previous checkpoint format.
+
+# Projects Using Megatron
+
+Below are some of the projects where we have directly used Megatron:
+
+- [BERT and GPT Studies Using Megatron](https://arxiv.org/pdf/1909.08053.pdf)
+- [BioMegatron: Larger Biomedical Domain Language Model](https://www.aclweb.org/anthology/2020.emnlp-main.379.pdf)
+- [End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://arxiv.org/abs/2101.00408)
+- [Large Scale Multi-Actor Generative Dialog Modeling](https://www.aclweb.org/anthology/2020.acl-main.8.pdf)
+- [Local Knowledge Powered Conversational Agents](https://arxiv.org/abs/2010.10150)
+- [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://www.aclweb.org/anthology/2020.emnlp-main.226.pdf)
+- [RACE Reading Comprehension Dataset Leaderboard](http://www.qizhexie.com/data/RACE_leaderboard.html)
+- [Training Question Answering Models From Synthetic Data](https://www.aclweb.org/anthology/2020.emnlp-main.468.pdf)
+- [Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases](https://arxiv.org/abs/2112.07868)
+- [Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models](https://arxiv.org/abs/2202.04173)
+- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/abs/2201.11990)
+- [Multi-Stage Prompting for Knowledgeable Dialogue Generation](https://arxiv.org/abs/2203.08745)
+- [Evaluating Parameter Efficient Learning for Generation](https://aclanthology.org/2022.emnlp-main.319.pdf)
+- [Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models](https://arxiv.org/abs/2202.04173)
+- [Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study](https://arxiv.org/abs/2304.06762)
+- [InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining](https://arxiv.org/abs/2310.07713)
+- [An Empirical Study of Mamba-based Language Models](https://arxiv.org/abs/2406.07887)
diff --git a/docker/Dockerfile.ci.dev b/docker/Dockerfile.ci.dev
new file mode 100644
index 0000000000..988c50bc48
--- /dev/null
+++ b/docker/Dockerfile.ci.dev
@@ -0,0 +1,82 @@
+# syntax=docker/dockerfile:1.3-labs
+
+ARG FROM_IMAGE_NAME
+ARG WHEEL_DIR=/workspace/wheels
+
+FROM ${FROM_IMAGE_NAME} as mcore_image
+ENV PIP_CONSTRAINT=""
+RUN pip install -U pip
+
+FROM mcore_image as build_te
+ARG TE_COMMIT=8382eed6cccb1eb0602c96afc1cfbc707468257f
+ARG WHEEL_DIR
+WORKDIR /workspace
+COPY docker docker/
+RUN bash docker/common/build_te.sh --repo-ref $TE_COMMIT --output-wheel-dir $WHEEL_DIR
+
+FROM mcore_image as build_mamba
+ARG WHEEL_DIR
+WORKDIR /workspace
+COPY docker docker/
+RUN bash docker/common/build_mamba.sh --output-wheel-dir $WHEEL_DIR
+
+FROM mcore_image as build_causalconv1d
+ARG WHEEL_DIR
+WORKDIR /workspace
+COPY docker docker/
+RUN bash docker/common/build_causalconv1d.sh --output-wheel-dir $WHEEL_DIR
+
+FROM mcore_image as build_groupedgemm
+ARG WHEEL_DIR
+WORKDIR /workspace
+COPY docker docker/
+RUN bash docker/common/build_groupedgemm.sh --output-wheel-dir $WHEEL_DIR
+
+FROM mcore_image as main
+ENV DEBIAN_FRONTEND=noninteractive
+ARG UV_VERSION=0.7.2
+ARG YQ_VERSION=4.44.1
+ENV PATH="/root/.local/bin:$PATH"
+ENV UV_PROJECT_ENVIRONMENT=/opt/venv
+ENV PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
+ENV UV_LINK_MODE=copy
+
+RUN bash -ex <<"EOF"
+    apt-get update
+    apt-get install -y --no-install-recommends gettext python3-venv
+    apt-get clean
+    python -m venv /opt/jet
+    wget https://github.com/mikefarah/yq/releases/download/v${YQ_VERSION}/yq_linux_amd64 -O /usr/local/bin/yq
+    chmod a+x /usr/local/bin/yq
+    curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
+EOF
+
+ARG WHEEL_DIR
+COPY README.md pyproject.toml uv.lock /workspace/
+COPY megatron/core/__init__.py /workspace/megatron/core/
+COPY megatron/core/package_info.py /workspace/megatron/core/
+COPY docker/common/ /workspace/docker/common/
+COPY --from=build_te $WHEEL_DIR/*.whl $WHEEL_DIR/
+COPY --from=build_mamba $WHEEL_DIR/*.whl $WHEEL_DIR/
+COPY --from=build_causalconv1d $WHEEL_DIR/*.whl $WHEEL_DIR/
+COPY --from=build_groupedgemm $WHEEL_DIR/*.whl $WHEEL_DIR/
+RUN bash -ex <<"EOF"
+    uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
+    
+    uv sync --extra dev --extra mlm --link-mode copy --locked
+
+    bash docker/common/install_source_wheels.sh --input-wheel-dir $WHEEL_DIR/ --environment dev
+EOF
+
+##### For NVIDIANS only #####
+FROM main as jet
+ARG JET_API_VERSION
+ENV PATH="$PATH:/opt/jet/bin"
+RUN --mount=type=secret,id=JET_INDEX_URLS \
+    --mount=type=secret,id=LOGGER_INDEX_URL bash -ex <<"EOF"
+    JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS)
+    LOGGER_INDEX_URL=$(cat /run/secrets/LOGGER_INDEX_URL)
+    uv pip install --no-cache-dir jet-api==$JET_API_VERSION "jet-client~=2.0" --upgrade $JET_INDEX_URLS "setuptools<80.0.0"
+    uv pip install --no-cache-dir "one-logger" --upgrade $LOGGER_INDEX_URL "setuptools<80.0.0"
+EOF
+###
diff --git a/docker/Dockerfile.ci.lts b/docker/Dockerfile.ci.lts
new file mode 100644
index 0000000000..aae105ffe3
--- /dev/null
+++ b/docker/Dockerfile.ci.lts
@@ -0,0 +1,72 @@
+# syntax=docker/dockerfile:1.3-labs
+
+ARG FROM_IMAGE_NAME
+ARG WHEEL_DIR=/workspace/wheels
+
+FROM $FROM_IMAGE_NAME as build_mamba
+WORKDIR /opt
+ARG WHEEL_DIR
+RUN MAMBA_FORCE_BUILD=TRUE pip3 wheel -v git+https://github.com/state-spaces/mamba.git@v2.0.3 -w $WHEEL_DIR
+
+ARG FROM_IMAGE_NAME
+FROM $FROM_IMAGE_NAME as build_causalconv1d
+WORKDIR /opt
+ARG WHEEL_DIR
+RUN CAUSAL_CONV1D_FORCE_BUILD=TRUE pip3 wheel -v git+https://github.com/Dao-AILab/causal-conv1d.git@v1.2.2.post1 -w $WHEEL_DIR
+
+FROM $FROM_IMAGE_NAME as build_groupedgemm
+WORKDIR /opt
+ARG WHEEL_DIR
+RUN pip3 wheel -v git+https://github.com/fanshiqing/grouped_gemm@v1.1.2 -w $WHEEL_DIR
+
+
+ARG FROM_IMAGE_NAME
+FROM $FROM_IMAGE_NAME as main
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN bash -ex <<"EOF"
+    apt-get update
+    apt-get install -y --no-install-recommends gettext python3-venv
+    apt-get clean
+    python -m venv /opt/jet
+    wget https://github.com/mikefarah/yq/releases/download/v4.44.1/yq_linux_amd64 -O /usr/local/bin/yq
+    chmod a+x /usr/local/bin/yq
+EOF
+
+ARG UV_VERSION=0.7.2
+ENV PATH="/root/.local/bin:$PATH"
+RUN curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
+ENV UV_PROJECT_ENVIRONMENT=/opt/venv
+ENV PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
+ENV UV_LINK_MODE=copy
+
+RUN 
+ARG WHEEL_DIR
+COPY README.md pyproject.toml uv.lock /workspace/
+COPY megatron/core/__init__.py /workspace/megatron/core/
+COPY megatron/core/package_info.py /workspace/megatron/core/
+COPY docker/common/ /workspace/docker/common/
+COPY --from=build_mamba $WHEEL_DIR/*.whl $WHEEL_DIR/
+COPY --from=build_causalconv1d $WHEEL_DIR/*.whl $WHEEL_DIR/
+COPY --from=build_groupedgemm $WHEEL_DIR/*.whl $WHEEL_DIR/
+RUN bash -ex <<"EOF"
+    uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
+
+    uv sync --extra lts --extra mlm --link-mode copy --locked
+
+    bash docker/common/install_source_wheels.sh --input-wheel-dir $WHEEL_DIR/ --environment lts
+EOF
+ENV PYTHONPATH="/opt/megatron-lm:$PYTHONPATH"
+
+##### For NVIDIANS only #####
+FROM main as jet
+ARG JET_API_VERSION
+ENV PATH="$PATH:/opt/jet/bin"
+RUN --mount=type=secret,id=JET_INDEX_URLS \
+    --mount=type=secret,id=LOGGER_INDEX_URL bash -ex <<"EOF"
+    JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS)
+    LOGGER_INDEX_URL=$(cat /run/secrets/LOGGER_INDEX_URL)
+    uv pip install --no-cache-dir jet-api==$JET_API_VERSION "jet-client~=2.0" --upgrade $JET_INDEX_URLS "setuptools<80.0.0"
+    uv pip install --no-cache-dir "one-logger" --upgrade $LOGGER_INDEX_URL "setuptools<80.0.0"
+EOF
+###
diff --git a/docker/Dockerfile.ci.nemo b/docker/Dockerfile.ci.nemo
new file mode 100644
index 0000000000..0452976a8c
--- /dev/null
+++ b/docker/Dockerfile.ci.nemo
@@ -0,0 +1,20 @@
+# syntax=docker/dockerfile:1.3-labs
+
+ARG FROM_IMAGE_NAME
+FROM ${FROM_IMAGE_NAME} as main
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends gettext && \
+    apt-get clean && \
+    wget https://github.com/mikefarah/yq/releases/download/v4.44.1/yq_linux_amd64 -O /usr/local/bin/yq && \
+    chmod a+x /usr/local/bin/yq
+
+##### For NVIDIANS only #####
+FROM main as jet
+ARG JET_API_VERSION
+RUN --mount=type=secret,id=JET_INDEX_URLS \
+    JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS) && \
+    pip install --no-cache-dir jet-api==$JET_API_VERSION "jet-client~=2.0" --upgrade $JET_INDEX_URLS
+
+ENV PATH="$PATH:/opt/jet/bin"
+###
diff --git a/docker/Dockerfile.linting b/docker/Dockerfile.linting
new file mode 100644
index 0000000000..259c0bbedc
--- /dev/null
+++ b/docker/Dockerfile.linting
@@ -0,0 +1,23 @@
+# syntax=docker/dockerfile:experimental
+
+ARG FROM_IMAGE_NAME
+FROM $FROM_IMAGE_NAME as main
+ENV DEBIAN_FRONTEND=noninteractive
+ARG UV_VERSION=0.7.2
+ARG YQ_VERSION=4.44.1
+ENV PATH="/root/.local/bin:$PATH"
+ENV UV_PROJECT_ENVIRONMENT=/opt/venv
+ENV PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
+ENV UV_LINK_MODE=copy
+RUN curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
+WORKDIR /opt/megatron-lm
+COPY pyproject.toml uv.lock /opt/megatron-lm/
+COPY megatron/core/package_info.py megatron/core/__init__.py /opt/megatron-lm/megatron/core/
+RUN uv sync --locked --only-group linting --only-group test --only-group ci
+
+##### For NVIDIANS only #####
+FROM main as jet
+ARG JET_API_VERSION
+RUN --mount=type=secret,id=JET_INDEX_URLS \
+      JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS) && \ 
+      uv pip install --no-cache-dir "jet-client~=2.0" --upgrade $JET_INDEX_URLS
diff --git a/docker/common/build_causalconv1d.sh b/docker/common/build_causalconv1d.sh
new file mode 100644
index 0000000000..c5f030d8dd
--- /dev/null
+++ b/docker/common/build_causalconv1d.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
+
+# Initialize variables
+REPO_URL="https://github.com/Dao-AILab/causal-conv1d.git"
+REPO_REF="v1.2.2.post1"
+OUTPUT_WHEEL_DIR="$(pwd)/wheels"
+SCRIPT_DIR="$(dirname $(realpath $0))"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+    --repo-url)
+        REPO_URL="$2"
+        shift 2
+        ;;
+    --repo-ref)
+        REPO_REF="$2"
+        shift 2
+        ;;
+    --output-wheel-dir)
+        OUTPUT_WHEEL_DIR="$2"
+        shift 2
+        ;;
+    *)
+        echo "Unknown option: $1"
+        echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+        exit 1
+        ;;
+    esac
+done
+
+# Check if required arguments are provided
+if [ -z "$REPO_URL" ] || [ -z "$REPO_REF" ] || [ -z "$OUTPUT_WHEEL_DIR" ]; then
+    echo "Error: --repo-url, --repo-ref, and --output-wheel-dir are required"
+    echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+    exit 1
+fi
+
+# Create a temporary directory
+TEMP_DIR=$(mktemp -d)
+echo "Working in temporary directory: ${TEMP_DIR}"
+python3 -m venv "${TEMP_DIR}/venv" --system-site-packages
+source "${TEMP_DIR}/venv/bin/activate"
+
+# Ensure cleanup on script exit
+trap 'rm -rf "${TEMP_DIR}"' EXIT
+
+# Change to temporary directory
+cd "${TEMP_DIR}"
+
+# Initialize git repository
+git init
+
+# Perform git fetch with depth 1
+git fetch "${REPO_URL}" "${REPO_REF}" --depth 1
+
+git checkout FETCH_HEAD
+
+# Fetch submodules
+git submodule update --init --recursive
+
+# Create output directory if it doesn't exist
+mkdir -p "${OUTPUT_WHEEL_DIR}"
+
+# Build the wheel using python -m build
+export CAUSAL_CONV1D_FORCE_BUILD=TRUE
+pip3 wheel --no-cache-dir --no-deps -w "${OUTPUT_WHEEL_DIR}" .
diff --git a/docker/common/build_groupedgemm.sh b/docker/common/build_groupedgemm.sh
new file mode 100644
index 0000000000..cd48b7c1f3
--- /dev/null
+++ b/docker/common/build_groupedgemm.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
+
+# Initialize variables
+REPO_URL="https://github.com/fanshiqing/grouped_gemm"
+REPO_REF="v1.1.2"
+OUTPUT_WHEEL_DIR="$(pwd)/wheels"
+SCRIPT_DIR="$(dirname $(realpath $0))"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+    --repo-url)
+        REPO_URL="$2"
+        shift 2
+        ;;
+    --repo-ref)
+        REPO_REF="$2"
+        shift 2
+        ;;
+    --output-wheel-dir)
+        OUTPUT_WHEEL_DIR="$2"
+        shift 2
+        ;;
+    *)
+        echo "Unknown option: $1"
+        echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+        exit 1
+        ;;
+    esac
+done
+
+# Check if required arguments are provided
+if [ -z "$REPO_URL" ] || [ -z "$REPO_REF" ] || [ -z "$OUTPUT_WHEEL_DIR" ]; then
+    echo "Error: --repo-url, --repo-ref, and --output-wheel-dir are required"
+    echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+    exit 1
+fi
+
+# Create a temporary directory
+TEMP_DIR=$(mktemp -d)
+echo "Working in temporary directory: ${TEMP_DIR}"
+python3 -m venv "${TEMP_DIR}/venv" --system-site-packages
+source "${TEMP_DIR}/venv/bin/activate"
+
+# Ensure cleanup on script exit
+trap 'rm -rf "${TEMP_DIR}"' EXIT
+
+# Change to temporary directory
+cd "${TEMP_DIR}"
+
+# Initialize git repository
+git init
+
+# Perform git fetch with depth 1
+git fetch "${REPO_URL}" "${REPO_REF}" --depth 1
+
+git checkout FETCH_HEAD
+
+# Fetch submodules
+git submodule update --init --recursive
+
+# Create output directory if it doesn't exist
+mkdir -p "${OUTPUT_WHEEL_DIR}"
+
+# Build the wheel using python -m build
+export MAMBA_FORCE_BUILD=TRUE
+pip3 wheel --no-cache-dir --no-deps -w "${OUTPUT_WHEEL_DIR}" .
diff --git a/docker/common/build_mamba.sh b/docker/common/build_mamba.sh
new file mode 100644
index 0000000000..385a5bddbd
--- /dev/null
+++ b/docker/common/build_mamba.sh
@@ -0,0 +1,67 @@
+#!/bin/bash
+set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
+
+# Initialize variables
+REPO_URL="https://github.com/state-spaces/mamba.git"
+REPO_REF="2e16fc3062cdcd4ebef27a9aa4442676e1c7edf4"
+OUTPUT_WHEEL_DIR="$(pwd)/wheels"
+SCRIPT_DIR="$(dirname $(realpath $0))"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+    --repo-url)
+        REPO_URL="$2"
+        shift 2
+        ;;
+    --repo-ref)
+        REPO_REF="$2"
+        shift 2
+        ;;
+    --output-wheel-dir)
+        OUTPUT_WHEEL_DIR="$2"
+        shift 2
+        ;;
+    *)
+        echo "Unknown option: $1"
+        echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+        exit 1
+        ;;
+    esac
+done
+
+# Check if required arguments are provided
+if [ -z "$REPO_URL" ] || [ -z "$REPO_REF" ] || [ -z "$OUTPUT_WHEEL_DIR" ]; then
+    echo "Error: --repo-url, --repo-ref, and --output-wheel-dir are required"
+    echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+    exit 1
+fi
+
+# Create a temporary directory
+TEMP_DIR=$(mktemp -d)
+echo "Working in temporary directory: ${TEMP_DIR}"
+python3 -m venv "${TEMP_DIR}/venv" --system-site-packages
+source "${TEMP_DIR}/venv/bin/activate"
+
+# Ensure cleanup on script exit
+trap 'rm -rf "${TEMP_DIR}"' EXIT
+
+# Change to temporary directory
+cd "${TEMP_DIR}"
+
+# Initialize git repository
+git init
+
+# Perform git fetch with depth 1
+git fetch "${REPO_URL}" "${REPO_REF}" --depth 1
+
+git checkout FETCH_HEAD
+
+# Fetch submodules
+git submodule update --init --recursive
+
+# Create output directory if it doesn't exist
+mkdir -p "${OUTPUT_WHEEL_DIR}"
+
+# Build the wheel using python -m build
+pip3 wheel --no-cache-dir --no-deps -w "${OUTPUT_WHEEL_DIR}" .
diff --git a/docker/common/build_te.sh b/docker/common/build_te.sh
new file mode 100644
index 0000000000..ae1fa78f56
--- /dev/null
+++ b/docker/common/build_te.sh
@@ -0,0 +1,70 @@
+#!/bin/bash
+set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
+
+# Initialize variables
+REPO_URL=$(cat docker/common/manifest.json | jq -r '."vcs-dependencies"."transformer-engine".repo')
+REPO_REF=$(cat docker/common/manifest.json | jq -r '."vcs-dependencies"."transformer-engine".ref')
+
+OUTPUT_WHEEL_DIR="$(pwd)/wheels"
+SCRIPT_DIR="$(dirname $(realpath $0))"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+    --repo-url)
+        REPO_URL="$2"
+        shift 2
+        ;;
+    --repo-ref)
+        REPO_REF="$2"
+        shift 2
+        ;;
+    --output-wheel-dir)
+        OUTPUT_WHEEL_DIR="$2"
+        shift 2
+        ;;
+    *)
+        echo "Unknown option: $1"
+        echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+        exit 1
+        ;;
+    esac
+done
+
+# Check if required arguments are provided
+if [ -z "$REPO_URL" ] || [ -z "$REPO_REF" ] || [ -z "$OUTPUT_WHEEL_DIR" ]; then
+    echo "Error: --repo-url, --repo-ref, and --output-wheel-dir are required"
+    echo "Usage: $0 --repo-url URL --repo-ref REF --output-wheel-dir DIR"
+    exit 1
+fi
+
+# Create a temporary directory
+TEMP_DIR=$(mktemp -d)
+echo "Working in temporary directory: ${TEMP_DIR}"
+python3 -m venv "${TEMP_DIR}/venv" --system-site-packages
+source "${TEMP_DIR}/venv/bin/activate"
+
+# Ensure cleanup on script exit
+trap 'rm -rf "${TEMP_DIR}"' EXIT
+
+# Change to temporary directory
+cd "${TEMP_DIR}"
+
+# Initialize git repository
+git init
+
+# Perform git fetch with depth 1
+git fetch "${REPO_URL}" "${REPO_REF}" --depth 1
+
+git checkout FETCH_HEAD
+
+# Fetch submodules
+git submodule update --init --recursive
+
+# Create output directory if it doesn't exist
+mkdir -p "${OUTPUT_WHEEL_DIR}"
+
+# Build the wheel using python -m build
+export NVTE_FRAMEWORK=pytorch # Optionally set framework
+pip3 wheel --no-cache-dir --no-build-isolation -w "${OUTPUT_WHEEL_DIR}" .
+ls -al "${OUTPUT_WHEEL_DIR}"
diff --git a/docker/common/install_source_wheels.sh b/docker/common/install_source_wheels.sh
new file mode 100644
index 0000000000..1308e60482
--- /dev/null
+++ b/docker/common/install_source_wheels.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
+
+INPUT_WHEEL_DIR=$(pwd)/wheels
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+    --input-wheel-dir)
+        INPUT_WHEEL_DIR="$2"
+        shift 2
+        ;;
+    --environment)
+        ENVIRONMENT="$2"
+        shift 2
+        ;;
+    *)
+        echo "Unknown option: $1"
+        echo "Usage: $0 --input-wheel-dir DIR"
+        exit 1
+        ;;
+    esac
+done
+
+# Check if required arguments are provided
+if [ -z "$INPUT_WHEEL_DIR" ] || [ -z "$ENVIRONMENT" ]; then
+    echo "Error: --input-wheel-dir and --environment are required"
+    echo "Usage: $0 --input-wheel-dir DIR --environment ENV"
+    exit 1
+fi
+
+if [ "$ENVIRONMENT" = "dev" ]; then
+    TE_WHEEL=$(ls $INPUT_WHEEL_DIR/transformer_engine*.whl) || true
+    [ -z "$TE_WHEEL" ] && TE_WHEEL=$(bash docker/common/build_te.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
+fi
+
+MAMBA_WHEEL=$(ls $INPUT_WHEEL_DIR/mamba*.whl) || true
+[ -z "$MAMBA_WHEEL" ] && MAMBA_WHEEL=$(bash docker/common/build_mamba.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
+
+CAUSALCONV1D_WHEEL=$(ls $INPUT_WHEEL_DIR/causal_conv1d*.whl) || true
+[ -z "$CAUSALCONV1D_WHEEL" ] && CAUSALCONV1D_WHEEL=$(bash docker/common/build_causalconv1d.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
+
+GROUPEDGEMM_WHEEL=$(ls $INPUT_WHEEL_DIR/grouped_gemm*.whl) || true
+[ -z "$GROUPEDGEMM_WHEEL" ] && GROUPEDGEMM_WHEEL=$(bash docker/common/build_groupedgemm.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
+
+# Override deps that are already present in the base image
+# only for dev
+if [ "$ENVIRONMENT" = "dev" ]; then
+    uv pip install --no-cache-dir --no-deps $TE_WHEEL
+fi
+
+# Install heavy optional deps like mamba, causalconv1d, groupedgemm
+uv pip install --no-cache-dir \
+    $MAMBA_WHEEL \
+    $CAUSALCONV1D_WHEEL \
+    $GROUPEDGEMM_WHEEL \
+    "setuptools<80.0.0"
diff --git a/docker/common/manifest.json b/docker/common/manifest.json
new file mode 100644
index 0000000000..65de4212e6
--- /dev/null
+++ b/docker/common/manifest.json
@@ -0,0 +1,10 @@
+{
+    "ngc-pytorch": "nvcr.io/nvidia/pytorch:25.03-py3",
+    "vcs-dependencies": {
+        "transformer-engine": {
+            "repo": "https://github.com/NVIDIA/TransformerEngine",
+            "ref": "bee4649c15a79ffcb9689ca7c0c963f5febaa28a"
+        }
+    },
+    "pypi-dependencies": {}
+}
\ No newline at end of file
diff --git a/docs/distrib_optimizer.md b/docs/distrib_optimizer.md
deleted file mode 100644
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-# Distributed Optimizer
-
-The motivation for the distributed optimizer is to save memory by distributing the optimizer state evenly across data parallel ranks, versus the current method of replicating the optimizer state across data parallel ranks. As described in https://arxiv.org/abs/1910.02054, this branch specifically implements the following:
-
-- [yes] distribute all 'non-overlapping' optimizer state (i.e., model params already in fp32 are NOT distributed)
-- [no] distribute model gradients
-- [no] distribute model parameters
-
-Theoretical memory savings vary depending on the combination of the model's param dtype and grad dtype. In the current implementation, the theoretical number of bytes per parameter is (where 'd' is the data parallel size):
-
-|        | Non-distributed optim | Distributed optim |
-| ------ | ------ | ------ |
-| float16 param, float16 grads | 20 | 4 + 16/d |
-| float16 param, fp32 grads    | 18 | 6 + 12/d |
-| fp32 param, fp32 grads       | 16 | 8 + 8/d  |
-
-The implementation of the distributed optimizer is centered on using the contiguous grad buffer for communicating grads & params between the model state and the optimizer state. The grad buffer at any given moment either holds:
-
-1. all model grads
-2. a 1/d size _copy_ of the main grads (before copying to the optimizer state)
-3. a 1/d size _copy_ of the main params (after copying from the optimizer state)
-4. all model params
-5. zeros (or None), between iterations
-
-The grad buffer is used for performing reduce-scatter and all-gather operations, for passing grads & params between the model state and optimizer state. With this implementation, no dynamic buffers are allocated.
-
-The figures below illustrate the grad buffer's sharding scheme, and the key steps of the distributed optimizer's param update:
-
-## Data flow
-
-![Data flow](images/distrib_optimizer/data_flow.png)
-
-## Sharding scheme
-
-![Sharding scheme](images/distrib_optimizer/sharding_scheme.png)
-
-## Key steps
-
-_(note: using illustrations above, and assuming fp16 grads)_
-
-- Backward pass finishes (grad buffer holds 16 fp16 grad elements)
-- Call reduce-scatter on each DP rank
-- Each DP rank now has 4 elements within the grad buffer that are fully reduced (remaining 12 elements are garbage)
-- Each DP rank copies its relevant 4 fp16 grad elements from the grad buffer into 4 fp32 main grad elements (separate buffer, owned by the optimizer); i.e.
-  - DP rank 0 copies elements [0:4]
-  - DP rank 1 copies elements [4:8]
-  - DP rank 2 copies elements [8:12]
-  - DP rank 3 copies elements [12:16]
-- Optimizer.step()
-- Each DP rank copies its 4 fp32 main (/optimizer) param elements into the corresponding 4 fp16 elements in the grad buffer
-- Call all-gather on each DP rank
-- Grad buffer now contains all 16, fully updated, fp16 model param elements
-- Copy updated model params from grad buffer into their respective param tensors
-- (At this point, grad buffer is ready to be zero'd for the next iteration)
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diff --git a/docs/llama_mistral.md b/docs/llama_mistral.md
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+# Llama, Mistral and other Llama-like model support in Megatron-LM
+
+NOTE: In order to simplify code we now only support converting llama-3.x and mistral checkpoints downloaded from Huggingface.
+
+The [Llama-2](https://ai.meta.com/llama/) and [Llama-3.x](https://llama.meta.com/) family of models are an open-source set of pretrained & finetuned (for chat) models that have achieved strong results across a wide set of benchmarks. At their times of release, both Llama-2 and Llama-3 models achieved among the best results for open-source models, and were competitive with leading closed-source models (see https://arxiv.org/pdf/2307.09288.pdf and https://ai.meta.com/blog/meta-llama-3/).
+
+Similarly, [Mistral-7b](https://mistral.ai/news/announcing-mistral-7b/) is an open-source model with pretrained and finetuned (for chat) variants that achieve strong benchmark results.
+
+Architecturally Llama-2, Llama-3 and Mistral-7b are very similar. As such Megatron can support loading checkpoints from all three for inference and finetuning. Converting the checkpoints and loading them is slightly different for each model and is detailed for each below.
+
+# Contents
+
+- [Llama, Mistral and other Llama-like model support in Megatron-LM](#llama-mistral-and-other-llama-like-model-support-in-megatron-lm)
+- [Contents](#contents)
+- [Llama-2](#llama-2)
+  - [Download Meta or Huggingface checkpoints](#download-meta-or-huggingface-checkpoints)
+  - [Convert checkpoint format](#convert-checkpoint-format)
+    - [Meta format](#meta-format)
+    - [Huggingface format](#huggingface-format)
+  - [Launch model](#launch-model)
+    - [Launch Megatron](#launch-megatron)
+    - [Launch Meta](#launch-meta)
+    - [Launch Huggingface](#launch-huggingface)
+  - [Benchmark results](#benchmark-results)
+    - [Big Bench](#big-bench)
+    - [Multilingual](#multilingual)
+    - [LM Evaluation Harness](#lm-evaluation-harness)
+    - [MMLU](#mmlu)
+- [Llama-3.x](#llama-3x)
+  - [Download Huggingface checkpoints](#download-huggingface-checkpoints)
+  - [Convert checkpoint format](#convert-checkpoint-format-1)
+    - [Huggingface format](#huggingface-format-1)
+  - [(Optional) Validate checkpoints](#optional-validate-checkpoints)
+  - [Launch model](#launch-model-1)
+- [Mistral-7b](#mistral-7b)
+  - [Download Huggingface checkpoints](#download-huggingface-checkpoints-2)
+  - [Convert checkpoint format](#convert-checkpoint-format-3)
+  - [(Optional) Validate checkpoints](#optional-validate-checkpoints-2)
+  - [Launch model](#launch-model-3)
+- [Other Llama-like model support](#other-llama-like-model-support)
+- [Known numerical differences](#known-numerical-differences)
+- [Using legacy model format](#using-legacy-model-format)
+
+
+# Llama-2
+
+Llama-2 checkpoints can be loaded into Megatron for inference and for finetuning. Loading these checkpoints consists of three steps:
+
+1. Get access to download the checkpoints.
+2. Convert the checkpoints from Meta/Huggingface format to Megatron format.
+3. Setup arguments for launching the model.
+
+The following sections detail these steps. The final section lists benchmark result comparisons between: 1) Llama-2 inference code running the Meta-format checkpoints, and 2) Megatron inference code running the converted checkpoints.
+
+## Download Meta or Huggingface checkpoints
+
+Users must first apply for access to download the Llama-2 checkpoints either directly from [Meta](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) or through [Huggingface](https://huggingface.co/docs/transformers/main/model_doc/llama2) (HF). The checkpoints are available in two formats, Meta's native format (available from both the Meta and HF links), and HF's format (available only from HF). Either format can be converted to Megatron, as detailed next.
+
+## Convert checkpoint format
+
+We recommend passing `--dtype bf16` for training or finetuning. Inference can be done in bfloat16 or float16.
+
+### Meta format
+
+The Meta format checkpoints are converted to HF format as an intermediate step before converting to Megatron format. The `transformers` package is required, and must have version >=4.31.0 (e.g., `pip install transformers>=4.31.0`). (**Note**: we have specifically tested with versions `4.31.0` and `4.32.0`; your experience may vary with newer versions.) Assuming the downloaded checkpoints are in `$CHECKPOINT_DIR` (with separate sub-directories for 7B, 13B, 70B, etc.), the following example command can be used to convert from Llama-2 format to HF format in bfloat16:
+
+```
+python tools/checkpoint/convert.py \
+>   --model-type GPT \
+>   --loader llama_mistral \
+>   --load-dir ${META_FORMAT_DIR} \
+>   --model-size ${MODEL_SIZE} \
+>   --checkpoint-type meta \
+>   --tokenizer-model ${TOKENIZER_MODEL} \
+>   --saver core \
+>   --save-dir ${MEGATRON_FORMAT_DIR} \
+>   --target-tensor-parallel-size ${TP} \
+>   --target-pipeline-parallel-size ${PP} \
+>   --bf16
+```
+
+Valid values for `--model-size` are `llama2-7B`, `llama2-13B`, and `llama2-70B` (for pretrained-only models), and `llama2-7Bf`, `llama2-13Bf`, and `llama2-70Bf` (for chat-finetuned models).
+
+### Huggingface format
+
+The HF checkpoints can be converted to Megatron format by using Megatron's own Llama-2 checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`). One important argument that must be set correctly is the tensor parallel size (`TP`) for each model. The following table shows these values:
+
+| Model size | Tensor parallel size (`TP`) |
+| ---------- | --------------------------- |
+|  7B        | 1                           |
+| 13B        | 2                           |
+| 70B        | 8                           |
+
+Using these values for `TP`, along with the path to the Llama-2 tokenizer model (automatically downloaded with original checkpoint download; see `${TOKENIZER_MODEL}` below), run the following command from the root of your Megatron source code to convert from HF format to Megatron format:
+
+```
+python tools/checkpoint/convert.py \
+>   --model-type GPT \
+>   --loader llama_mistral \
+>   --load-dir ${HF_FORMAT_DIR} \
+>   --model-size ${MODEL_SIZE} \
+>   --checkpoint-type hf \
+>   --tokenizer-model ${TOKENIZER_MODEL} \
+>   --saver core \
+>   --save-dir ${MEGATRON_FORMAT_DIR} \
+>   --target-tensor-parallel-size ${TP} \
+>   --target-pipeline-parallel-size ${PP} \
+>   --bf16
+```
+
+After this conversion, we are ready to load the checkpoints into a Megatron GPT model.
+
+## Launch model
+
+### Launch Megatron
+
+If loading for either inference or finetuning, use the following arguments:
+
+```
+--tensor-model-parallel-size ${TP} \
+--pipeline-model-parallel-size 1 \
+--seq-length 4096 \
+--max-position-embeddings 4096 \
+--tokenizer-type Llama2Tokenizer \
+--tokenizer-model ${TOKENIZER_MODEL} \
+--load ${CHECKPOINT_DIR} \
+--exit-on-missing-checkpoint \
+--use-checkpoint-args \
+--no-load-optim \
+--no-load-rng \
+--untie-embeddings-and-output-weights \
+--use-rotary-position-embeddings \
+--normalization RMSNorm \
+--no-position-embedding \
+--no-masked-softmax-fusion \
+--attention-softmax-in-fp32
+```
+
+**Note:** If you converted to the legacy model format (i.e., `--saver legacy`), please see [here](#using-legacy-model-format).
+
+### Launch Meta
+
+Meta checkpoints can be launched with: https://github.com/facebookresearch/llama
+
+### Launch Huggingface
+
+Huggingface checkpoints can be launched with: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+
+## Benchmark results
+
+The tables below list the benchmark comparisons between native Llama-2 (using Meta's checkpoint and Meta's inference code) and Megatron (using a converted HF checkpoint and Megatron's inference code).
+
+The values are the percent error between Megatron and Llama-2, calculated using the formula: `| - | / `, where the type of score is detailed before each table. Across all tests (80 total per model size), the mean error is 0.15%. The small difference in benchmark scores between the two models is due to minor arithmetic differences in implementation that alter the numerics slightly. Some of the factors that influence this difference include:
+
+- Megatron performs batch matrix multiplications in a couple places, such as within self attention and in SwiGLU, that Llama performs separately.
+- Megatron uses `torch.baddbmm` within self attention, versus Llama using `torch.matmul`.
+- Megatron uses a `sin`/`cos` implementation for rotary position embeddings, versus Llama using a `polar`/`complex` implementation.
+- Llama calls `torch.set_default_dtype(torch.float16)` during initialization, which Megatron does not.
+
+### Big Bench
+
+Score type: multiple choice grade.
+
+| bigbench / standard | 7b | 13b | 70b |
+| -- | -- | -- | -- |
+| date_understanding | 0.29% | 0.13% | 0.12% |
+| general_knowledge | 0.00% | 0.00% | 0.00% |
+| human_organs_senses | 0.00% | 0.00% | 0.00% |
+| intent_recognition | 0.00% | 0.11% | 0.00% |
+| riddle_sense | 0.00% | 0.00% | 0.00% |
+| similarities_abstraction | 0.00% | 0.58% | 0.00% |
+| simple_arithmetic_json_multiple_choice | 0.00% | 0.00% | 0.00% |
+| undo_permutation | 0.19% | 0.19% | 0.18% |
+
+### Multilingual
+
+Score type: multiple choice grade.
+
+| multilingual / xcopa | 7b  | 13b  | 70b |
+| -- | -- | -- | -- |
+| en-template-mGPT-remove-punctuation | 0.08% | 0.00% | 0.00% |
+| et-template-mGPT-remove-punctuation | 0.00% | 0.13% | 0.25% |
+| ht-template-mGPT-remove-punctuation | 0.26% | 0.13% | 0.26% |
+| id-template-mGPT-remove-punctuation | 0.11% | 0.00% | 0.19% |
+| it-template-mGPT-remove-punctuation | 0.00% | 0.10% | 0.09% |
+| qu-template-mGPT-remove-punctuation | 0.00% | 0.00% | 0.27% |
+| sw-template-mGPT-remove-punctuation | 0.14% | 0.13% | 0.13% |
+| th-template-mGPT-remove-punctuation | 0.25% | 0.13% | 0.13% |
+| tr-template-mGPT-remove-punctuation | 0.26% | 0.00% | 0.34% |
+| vi-template-mGPT-remove-punctuation | 0.00% | 0.11% | 0.00% |
+| zh-template-mGPT-remove-punctuation | 0.00% | 0.10% | 0.09% |
+
+### LM Evaluation Harness
+
+Score type: multiple choice grade.
+
+| lm-eval | 7b  | 13b  | 70b |
+| -- | -- | -- | -- |
+| boolq | 0.04% | 0.04% | 0.07% |
+| hellaswag | 0.02% | 0.03% | 0.03% |
+| piqa | 0.00% | 0.00% | 0.07% |
+| winogrande | 0.00% | 0.11% | 0.20% |
+
+### MMLU
+
+Score type: multiple choice grade.
+
+Note: the number in brackets is the number of sub-tasks for each supercategory.
+
+| mmlu | 7b  | 13b  | 70b |
+| -- | -- | -- | -- |
+| stem [18]  | 0.79% | 0.05% | 0.01% |
+| humanities [13]  | 0.19% | 0.01% | 0.02% |
+| other (business, health, misc.) [14]  | 0.08% | 0.06% | 0.12% |
+| social sciences [12]  | 0.37% | 0.21% | 0.01% |
+
+# Llama-3.x
+
+Llama-3.x checkpoints can be loaded into Megatron for inference and for finetuning. Loading these checkpoints consists of several steps:
+
+1. Get access to download the checkpoints (weights and tokenizer).
+2. Convert the checkpoints from Huggingface format to Megatron format.
+3. (Optional) Validate converted checkpoints
+4. Setup arguments for launching the model.
+
+The following sections detail these steps.
+
+## Download Huggingface checkpoints
+
+Users must first apply for access to download the Llama-3.x checkpoints from [Huggingface](https://huggingface.co/meta-llama).
+
+## Convert checkpoint format
+
+We recommend passing `--dtype bf16` for training or finetuning. Inference can be done in bfloat16 or float16.
+
+### Huggingface format
+
+The HF checkpoints can be converted to Megatron format by using Megatron's own Llama-3.x checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`). One important argument that must be set correctly is the tensor parallel size (`TP`) for each model. The following table shows these values:
+
+| Model size | Tensor parallel size (`TP`) |
+| ---------- | --------------------------- |
+|  1B        | 1                           |
+|  3B        | 1                           |
+|  8B        | 1                           |
+| 70B        | 8                           |
+
+Using these values for `TP`, along with the path to the Llama-3.x tokenizer model (automatically downloaded with original checkpoint download; see `${TOKENIZER_MODEL}` below), run the following command from the root of your Megatron source code to convert from HF format to Megatron format:
+
+```
+$>: python tools/checkpoint/convert.py \
+ >    --bf16 \
+ >    --model-type GPT \
+ >    --loader llama_mistral \
+ >    --saver core \
+ >    --target-tensor-parallel-size ${TP} \
+ >    --checkpoint-type hf \
+ >    --load-dir ${HF_FORMAT_DIR} \
+ >    --save-dir ${MEGATRON_FORMAT_DIR} \
+ >    --tokenizer-model ${TOKENIZER_MODEL} \
+ >    --model-size llama3 \
+```
+
+After this conversion, we are ready to load the checkpoints into a Megatron GPT model.
+
+## (Optional) Validate checkpoints
+
+A Megatron-LM text generation server for Llama3 can be launched using the script `examples/inference/llama_mistral/run_text_generation_llama3.sh  `. For Llama3.1, please use `examples/inference/llama_mistral/run_text_generation_llama3.1.sh`.
+
+Once running, query the server with `curl 'http://:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8'  -d '{"prompts":[""], "tokens_to_generate":100, "top_k":1}'`.
+
+A reference generation for comparison can be obtained from the Huggingface transformers library by running `python examples/llama_mistral/huggingface_reference.py --model_path  --prompt `.
+
+## Launch model
+
+If loading for either inference or finetuning, use the following arguments for Llama 3.0:
+
+```
+--tensor-model-parallel-size ${TP} \
+--pipeline-model-parallel-size 1 \
+--seq-length 8192 \
+--max-position-embeddings 8192 \
+--tokenizer-type HuggingFaceTokenizer \
+--tokenizer-model ${TOKENIZER_MODEL} \
+--load ${CHECKPOINT_DIR} \
+--exit-on-missing-checkpoint \
+--use-checkpoint-args \
+--no-load-optim \
+--no-load-rng \
+--untie-embeddings-and-output-weights \
+--normalization RMSNorm \
+--position-embedding-type rope \
+--no-masked-softmax-fusion \
+--attention-softmax-in-fp32 \
+--disable-bias-linear \
+--transformer-impl transformer_engine \
+--group-query-attention 8 \
+--attention-dropout 0.0 \
+--hidden-dropout 0.0 \
+--rotary-base 500000 \
+--rotary-percent 1.0 \
+--ffn-hidden-size 14336 \
+--num-attention-heads 32 \
+--swiglu \
+--bf16 \
+```
+
+For Llama3.1 please use the following arguments:
+
+```
+--tensor-model-parallel-size ${TP} \
+--pipeline-model-parallel-size 1 \
+--seq-length 8192 \
+--max-position-embeddings 131072 \
+--tokenizer-type HuggingFaceTokenizer \
+--tokenizer-model ${TOKENIZER_MODEL} \
+--load ${CHECKPOINT_DIR} \
+--exit-on-missing-checkpoint \
+--use-checkpoint-args \
+--no-load-optim \
+--no-load-rng \
+--untie-embeddings-and-output-weights \
+--normalization RMSNorm \
+--position-embedding-type rope \
+--no-masked-softmax-fusion \
+--attention-softmax-in-fp32 \
+--disable-bias-linear \
+--transformer-impl transformer_engine \
+--group-query-attention 8 \
+--attention-dropout 0.0 \
+--hidden-dropout 0.0 \
+--rotary-base 500000 \
+--rotary-percent 1.0 \
+--use-rope-scaling \
+--ffn-hidden-size 14336 \
+--num-attention-heads 32 \
+--swiglu \
+--bf16 \
+```
+
+**Note:** If you converted to the legacy model format (i.e., `--saver legacy`), please see [here](#using-legacy-model-format).
+
+# Mistral-7b
+
+Megatron currently supports loading the v0.3 release of Mistral-7b (which does not use sliding window attention and offers a larger 32768 vocabulary) for inference and finetuning. Loading these checkpoints consists of several steps:
+
+1. Get access to download the checkpoints (weights and tokenizer).
+2. Convert the checkpoints from HuggingFace format to Megatron format.
+3. (Optional) Validate converted checkpoints
+4. Setup arguments for launching the model.
+
+The following sections detail these steps.
+
+## Download Huggingface checkpoints
+
+Users must first apply for access to download the Mistral-7b checkpoints through [Huggingface](https://huggingface.co/mistralai/Mistral-7B-v0.3) (HF).
+
+## Convert checkpoint format
+
+The HF checkpoints can be converted to Megatron format by using Megatron's own Mistral checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`).
+
+Using the path to the Mistral tokenizer model (downloaded alongside the HF checkpoint), run the following command from the root of your Megatron source code to convert from HF format to the Megatron core format:
+
+```
+$>: python tools/checkpoint/convert.py \
+ >    --bf16 \
+ >    --model-type GPT \
+ >    --loader llama_mistral \
+ >    --saver core \
+ >    --target-tensor-parallel-size ${TP} \
+ >    --checkpoint-type hf \
+ >    --load-dir ${HF_FORMAT_DIR} \
+ >    --save-dir ${MEGATRON_FORMAT_DIR} \
+ >    --tokenizer-model ${TOKENIZER_MODEL} \
+ >    --model-size mistral \
+```
+
+After this conversion, we are ready to load the checkpoints into a Megatron core GPT model.
+
+## (Optional) Validate checkpoints
+
+A Megatron-LM text generation server for Mistral-7B can be launched using the script `examples/inference/llama_mistral/run_text_generation_mistral.sh  `.
+
+Once running, query the server with `curl 'http://:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8'  -d '{"prompts":[""], "tokens_to_generate":100, "top_k":1}'`.
+
+A reference generation for comparison can be obtained from the Huggingface transformers library by running `python examples/inference/llama_mistral/huggingface_reference.py --model_path  --prompt `.
+
+## Launch model
+
+If loading for either inference or finetuning, use the following arguments:
+
+```
+--tensor-model-parallel-size ${TP} \
+--pipeline-model-parallel-size 1 \
+--seq-length 4096 \
+--max-position-embeddings 4096 \
+--tokenizer-type HuggingFaceTokenizer \
+--tokenizer-model ${TOKENIZER_MODEL} \
+--load ${CHECKPOINT_DIR} \
+--exit-on-missing-checkpoint \
+--use-checkpoint-args \
+--no-load-optim \
+--no-load-rng \
+--untie-embeddings-and-output-weights \
+--normalization RMSNorm \
+--position-embedding-type rope \
+--no-masked-softmax-fusion \
+--attention-softmax-in-fp32
+--apply-layernorm-1p \
+--transformer-impl transformer_engine \
+--group-query-attention 8 \
+--disable-bia-linear \
+--rotary-base 1000000 \
+--rotary-percent 1.0 \
+--swiglu \
+--ffn-hidden-size 14336 \
+--num-attention-heads 32
+```
+
+**Note:** If you converted to the legacy model format (i.e., `--saver legacy`), please see [here](#using-legacy-model-format).
+
+# Other Llama-like model support
+
+*Note: Experimental*
+
+Many models such as Yi-34B and Qwen2.x use the Llama architecture and may be converted from HuggingFace to Megatron using the commands in [Llama-3.x](#llama-3x).
+
+# Known numerical differences
+
+It is not expected that the megatron and Huggingface implementations of llama3.x and mistral models will produce numerically identical results. There are multiple points where small numerical differences are expected. This is a non-exhaustive list:
+
+1. TransformerEngine (TE) uses the model params_dtype inside RMSNorm whereas the Huggingface implementation uses fp32. See for details: https://github.com/NVIDIA/TransformerEngine/issues/1132
+2. Huggingface `transformers` implements the q, k and v projections in self-attention as separate GEMMs whereas Megatron core combines them into a single GEMM for efficiency. This leads to small numerical differences.
+
+# Using legacy model format
+
+In all the checkpoint conversion examples used in this document, the saver format `--saver core` is used, signifying that the newer (and recommended) Megatron GPT model class will be used. I.e.:
+
+- old class: `megatron.legacy.model.gpt_model.GPTModel`
+- new class: `megatron.core.models.gpt.gpt_model.GPTModel`
+
+Using this new format is the recommended approach. However, if your use case requires using the older class (i.e., convert using `--saver legacy`), then when launching training or finetuning, the following args must be added:
+
+- `--use-legacy-models`: use the older model class
+- `--ckpt-format torch`: use the `torch` checkpoint format, which is the only checkpoint format that is compatible with the legacy model format
diff --git a/docs/source/api-guide/context_parallel.rst b/docs/source/api-guide/context_parallel.rst
new file mode 100644
index 0000000000..c08defd210
--- /dev/null
+++ b/docs/source/api-guide/context_parallel.rst
@@ -0,0 +1,35 @@
+context\_parallel package
+=========================
+
+Context parallelism overview 
+----------------------------
+
+.. figure:: ../images/context_parallel/CP_overview.png
+   :alt: cp_overview
+   :align: center
+   
+   Figure 1: A transformer layer running with TP2CP2. Communications next to Attention are for CP, others are for TP. (AG/RS: all-gather in forward and reduce-scatter in backward, RS/AG: reduce-scatter in forward and all-gather in backward, /AG: no-op in forward and all-gather in backward).
+
+Context Parallelism ("CP") is a parallelization scheme on the dimension of sequence length. Unlike prior SP (sequence parallelism) which only splits the sequence of Dropout and LayerNorm activations, CP partitions the network inputs and all activations along sequence dimension. With CP, all modules except attention (e.g., Linear, LayerNorm, etc.) can work as usual without any changes, because they do not have inter-token operations. As for attention, the Q (query) of each token needs to compute with the KV (key and value) of all tokens in the same sequence. Hence, CP requires additional all-gather across GPUs to collect the full sequence of KV. Correspondingly, reduce-scatter should be applied to the activation gradients of KV in backward propagation. To reduce activation memory footprint, each GPU only stores the KV of a sequence chunk in forward and gathers KV again in backward. KV communication happens between a GPU and its counterparts in other TP groups. The all-gather and reduce-scatter are transformed to point-to-point communications in ring topology under the hood. Exchanging KV also can leverage MQA/GQA to reduce communication volumes, as they only have one or few attention heads for KV.
+
+For example, in Figure 1, assuming sequence length is 8K, each GPU processes 4K tokens. GPU0 and GPU2 compose a CP group, they exchange KV with each other. Same thing also happens between GPU1 and GPU3. CP is similar to `Ring Attention `_ but provides better performance by (1) leveraging the latest OSS and cuDNN flash attention kernels; (2) removing unnecessary computation resulted from low-triangle causal masking and achieving optimal load balance among GPUs.
+
+Context parallelism benefits 
+----------------------------
+
+.. figure:: ../images/context_parallel/CP_results.png
+   :alt: cp_results
+   :align: center
+   
+   Figure 2: Speedup of 175B GPT with various TP+CP combinations vs. full recompute (i.e., TP8CP1).
+
+LLM encounters OOM (out of memory) issue with long context (i.e., long sequence length) because of linearly increasing memory footprint of activations. Recomputing activations in backward can avoid OOM but also introduce significant overheads (~30% with full recompute). Enlarging TP (tensor model parallelism) can fix the OOM issue as well, but it potentially makes compute (e.g., Linear) too short to overlap communication latencies. To be clear, scaling out to more GPUs with bigger TP can hit the overlapping problem no matter if OOM happens.
+
+CP can better address the issues. With CP, each GPU only computes on a part of the sequence, which reduces both computation and communication by CP times. Therefore, there are no concerns about the overlapping between them. The activation memory footprint per GPU is also CP times smaller, hence no OOM issue anymore. As Figure 2 shows, the combinations of TP and CP can achieve optimal performance by eliminating recompute overheads and making the best tradeoff between computation and communications.
+
+Enabling context parallelism
+----------------------------
+
+CP support has been added to GPT. All models that share GPT code path also should be able to benefit from CP, such as Llama. CP can work with TP (tensor model parallelism), PP (pipeline model parallelism), and DP (data parallelism), where the total number of GPUs equals TPxCPxPPxDP. CP also can work with different attention variants, including MHA/MQA/GQA, uni-directional and bi-directional masking.
+
+CP is enabled by simply setting context_parallel_size= in command line. Default context_parallel_size is 1, which means CP is disabled. Running with CP requires Megatron-Core (>=0.5.0) and Transformer Engine (>=1.1).
diff --git a/docs/source/api-guide/custom_fsdp.md b/docs/source/api-guide/custom_fsdp.md
new file mode 100644
index 0000000000..841e068db0
--- /dev/null
+++ b/docs/source/api-guide/custom_fsdp.md
@@ -0,0 +1,182 @@
+# MCore Custom Fully Sharded Data Parallel (FSDP)
+
+## How to use ?
+
+Add these flag to enable MCore custom FSDP.
+
+```bash
+--use-custom-fsdp
+--data-parallel-sharding-strategy optim_grads_params
+--no-gradient-accumulation-fusion
+--use-distributed-optimizer
+```
+
+## Key Features
+
+- **Sharding Strategy**: Efficiently shards optimizer states, gradients, and parameters to reduce memory consumption.
+- **Communication and Computation Overlap**: Optimized to enable concurrent execution of communication and computation, enhancing overall efficiency.
+- **Supports automatic mixed precision training**: Compatible with BF16 O1/O2/O3 recipes, as well as FP8 compute with FP32 parameters and FP8 parameter training, allowing for flexible precision configurations.
+- **Tensor Parallelism (TP), Expert Parallelism (EP) and Context Parallelism (CP)**: Compatible with TP, EP and CP configurations, enabling efficient scaling of large language models.
+- **Distributed Model Initialization with Meta Device**: Allows model initialization using meta device, followed by layer-by-layer initialization of distributed model weight buffers via the `Module.reset_parameters` API, facilitating the initialization of extremely large models.
+
+## Configuration Recommendations
+
+### 1. Disable `CUDA_MAX_CONNECTIONS`
+
+To ensure full parallelization of FSDP communication and computation, disable the CUDA_MAX_CONNECTIONS environment variable. This step avoids potential bubble in CUDA stream. (But it may slow down TP and CP to some extent.)
+
+```bash
+unset CUDA_MAX_CONNECTIONS
+```
+
+### 2. Add `--calculate-per-token-loss`
+
+For gradients sharding mode optimization, include the `--calculate-per-token-loss` flag in your training script. This improves performance by reducing the frequency of gradient scaling, which is also a sizable drain on SM resources.
+
+## Design of Custom FSDP
+
+### 1. Overview
+
+The custom Fully Sharded Data Parallelism (FSDP) implementation in Megatron-Core is specifically designed to optimize memory consumption and performance for large language models. The core design principles include:
+
+ - **Optimized for Large Language Models**: This custom FSDP implementation is tailored to efficiently scale with models containing billions of parameters, ensuring seamless execution and training of massive models.
+ - **Efficient Memory Consumption**: By strategically sharding optimizer states, gradients, and model parameters, the custom FSDP significantly reduces memory usage. This approach enables the training of models that would otherwise be too large to fit in memory.
+ - **Efficient Workflow & Overlapping Communication and Computation**: The implementation is engineered to minimize the number of communication steps required during training. It maximizes the overlap between communication and computation, thereby enhancing overall training efficiency and reducing latency.
+ - **Support for MCore's Efficient Training Methods**: The custom FSDP seamlessly integrates with Megatron-Core's advanced parallelism techniques, including tensor parallelism, expert parallelism and context parallelism. Additionally, it supports automatic mixed precision training, further optimizing training performance and efficiency.
+
+The design of Custom FSDP draws inspiration from PyTorch FSDP [Zhao, Yanli, et al.](https://arxiv.org/pdf/2304.11277) and MCore's distributed optimizer. The introduction to PyTorch FSDP is referenced here to clarify the underlying concepts of the custom FSDP design.
+
+> In DistributedDataParallel, (DDP) training, each process/ worker owns a replica of the model and processes a batch of data, finally it uses all-reduce to sum up gradients over different workers. In DDP the model weights and optimizer states are replicated across all workers. FSDP is a type of data parallelism that shards model parameters, optimizer states and gradients across DDP ranks.
+
+> When training with FSDP, the GPU memory footprint is smaller than when training with DDP across all workers. This makes the training of some very large models feasible by allowing larger models or batch sizes to fit on device. This comes with the cost of increased communication volume. The communication overhead is reduced by internal optimizations like overlapping communication and computation.
+
+![FSDP workflow](../images/custom_fsdp/FSDP_workflow.png)
+
+*Notice that the unit processed in workflow here is the ā€œFSDP instance 1: N layersā€, where an FSDP instance is the smallest FSDP processing unit (also a PyTorch module), which means that we can safely release this module weights after using it (executing the forward or backward of this module), and there will be no other computations computations relying on these weights. This capability is the foundation of FSDP's layer-by-layer execution and memory-saving strategy. An FSDP instance is also referred to as an **FSDP Unit**.*
+
+*It is worth noting that an FSDP instance can correspond to multiple FSDP parameter groups. These groups are separated by Data Parallel (DP) communication groups and the data type of the parameter or gradient. Consequently, an FSDP instance may require several parameter-gather tasks before execution (forward or backward). Each **FSDP parameter group** corresponds to one **Data Parallel Buffer** in custom FSDP.*
+
+At a high level FSDP works as follow:
+
+In constructor
+ - Shard model parameters and each rank only keeps its own shard
+
+In forward path
+ - Run all_gather to collect all shards from all ranks to recover the full parameter in this FSDP unit
+ - Run forward computation
+ - Discard parameter shards it has just collected
+
+In backward path
+ - Run all_gather to collect all shards from all ranks to recover the full parameter in this FSDP unit
+ - Run backward computation
+ - Run reduce_scatter to sync gradients
+ - Discard parameters.
+
+One way to view FSDPā€™s sharding is to decompose the DDP gradient all-reduce into reduce-scatter and all-gather. Specifically, during the backward pass, FSDP reduces and scatters gradients, ensuring that each rank possesses a shard of the gradients. Then it updates the corresponding shard of the parameters in the optimizer step. Finally, in the subsequent forward pass, it performs an all-gather operation to collect and combine the updated parameter shards.
+
+![FSDP Allreduce](../images/custom_fsdp/FSDP_Allreduce.png)
+
+### 2. Custom FSDP underlying data structure
+
+To implement the FSDP functionality described above, the custom FSDP is designed with the following Python classes and data structure:
+
+![MCore Custom FSDP Class Diagram](../images/custom_fsdp/MCore_Custom_FSDP_Class_Diagram.png)
+
+### 3. The custom FSDP interface: FullyShardedDataParallel
+
+The custom FSDP provides the same programming interface as PyTorch's DistributedDataParallel (DDP) as FullyShardedDataParallel (FSDP). For example, you can apply FSDP to models as follows:
+
+```python
+# Initialize model and optimizer
+ddp_config.use_custom_fsdp = True
+ddp_config.data_parallel_sharding_strategy = "optim_grads_params"
+model = GPTModel(transformer_config)
+model = FullyShardedDataParallel(
+    transformer_config,
+    model,
+    ddp_config,
+    fsdp_unit_modules = [TransformerLayer, LanguageModelEmbedding],
+)
+optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
+optimizer = DistributedOptimizer(optimizer, [model], [model.param_and_grad_buffer])
+
+# Training loop
+def train_step(inputs, labels):
+    optimizer.zero_grad()
+    for mbs_input, mbs_label in zip(inputs, labels):
+        outputs = model(mbs_input)
+        loss = loss_fn(outputs, mbs_label)
+        loss.backward()
+    optimizer.step()
+
+# Save and load model and optimizer state dict
+def model_and_optimizer_state_dict():
+    state_dict = {
+        "model": model.sharded_state_dict(),
+        "optimizer": optimizer.sharded_state_dict(),
+    }
+    return state_dict
+
+def load_model_and_optimizer_state_dict(state_dict):
+    model.load_state_dict(state_dict["model"])
+    optimizer.load_state_dict(state_dict["optimizer"])
+```
+
+**Key Notes:**
+ - You can configure which modules should be treated as FSDP units via the `fsdp_unit_modules` argument. This configuration is mandatory.
+ - The custom FSDP must be used with a distributed optimizer since it provides distributed checkpointing.
+ - The data-parallel communication group for parameters is not explicitly shown. Custom FSDP configures these groups as either DP (data-parallel) or EDP (expert data-parallel) based on parameter markings.
+
+#### 3.1 Initializing Models on the Meta Device
+
+For training particularly large models with FSDP, you can initialize the model on the meta device. Using PyTorch's `reset_parameters` API, you can initialize model weights layer by layer during the construction of the `ParamAndGradBuffer`. Most PyTorch native modules and TransformerEngine modules support this API (e.g., [PyTorch Linear](https://github.com/pytorch/pytorch/blob/v2.6.0/torch/nn/modules/linear.py#L114), [TE LayerNormLinear](https://github.com/NVIDIA/TransformerEngine/blob/release_v2.0/transformer_engine/pytorch/module/layernorm_linear.py#L1107)).
+
+```python
+# Initialize model on meta device
+with torch.device("meta"):
+    model = GPTModel(config)
+
+model = FullyShardedDataParallel(
+    transformer_config,
+    model,
+    ddp_config,
+    fsdp_unit_modules=[TransformerLayer, LanguageModelEmbedding],
+)
+```
+
+**Important Considerations:**
+1. *Custom Modules*: If your model contains custom modules, ensure they implement the `reset_parameters` API. Otherwise, you may need to force parameter initialization on a CUDA or CPU device.
+2. *Tensor Initialization*: Be cautious of tensors created during model initialization without a specified deviceā€”they will default to the meta device. To avoid issues, explicitly specify the device for these tensors to ensure compatibility with this function.
+
+### 4. Interaction between Custom FSDP and Model Forward/Backward Propagation
+
+Custom FSDP implements Fully Sharded Data Parallelism (FSDP) through a series of module hooks, gradient hooks, or by adding functions between modules. This involves inserting communications and manipulating parameters and gradients during PyTorch's module forward or backward propagation.
+
+Module hooks summary:
+- Module pre-forward hook(`module.register_forward_pre_hook`): This hook unshards model weights before the forward pass. In the case of an FSDP Unit Module, add a RegisterFSDPBackwardFunction function that will reshard model weights and reduce gradients after module backward propagation.
+- Module post-forward hook(`module.register_forward_hook`): This hook is used to reshard model weights after the forward pass.
+- Root module pre-backward hook(`root_module.register_full_backward_pre_hook`): This hook checks that all model parameters are resharded, in order to avoid unnecessary memory spikes. It also marks all modules as being in the `TrainingState.PRE_BACKWARD` state.
+- Module pre-backward hook(`module.register_full_backward_pre_hook`): This hook is used to unshard the model weights before the backward pass.
+- Root module post-backward hook(`torch.autograd.Variable._execution_engine.queue_callback`): This hook is used to make sure all gradients in the backprop are properly handled / available.
+
+The gradient reduction pipeline maintains a map of gradients to FSDP parameter groups. If all gradients in an FSDP parameter group are ready, it launches a gradient reduction. Note that this assumes that the model's gradients are always generated in a certain order (reverse of `module.parameters()`), as otherwise, FSDP would maintain too many parameter group grad buffers, leading to excessive memory usage.
+
+#### 4.1 Optimized for Activation Recompute
+
+Using the activation recompute will cause the same module to execute the forward function first and then the backward function in the backward prop, which will cause model weights unshard twice and model weights reshard twice. If we can tell program that this is a forward + backward operation, we can just call unshard once and reshard once.
+
+To make this determination, we keep track of the model's state with training_state, `FORWARD`, `PRE_BACKWARD`, `POST_BACKWARD`, `IDLE`. It's worth noting that pre-backward hook act before pre-forward hook, and we'll let pre-backward hook execute the model weight unshard, and then mark the model as `PRE_BACKWARD`, and when pre-forward hook sees this marking it will not perform the unshard operation. Similarly, for model weight reshard duplicate, post-forward hook act before post-backward function, and checking for the `PRE_BACKWARD` flag in the post-forward hook will cancel the unshard.
+
+### 5. Memory Mechanisms and Features of Custom FSDP
+
+FSDP can fully distribute the model parameters, gradients, and optimizer states, and for mixed-precision training, it can also fully distribute the high-precision main weights. This is pretty much distributes all the memory except for the activation memory, but FSDP will also face some memory issues.
+
+FSDP frequently unshards and reshards model weights, which can lead to busy memory allocation and deallocation. This results in untimely tensor releases, causing memory spikes (or even out-of-memory errors), crashes of the PyTorch memory allocator cache, and a large number of `cudaMalloc` and `cudaFree` calls. These issues can significantly slow down the system.
+
+The problem of untimely tensor release can generally be addressed using the `tensor._typed_storage(). _resize_(0)` API, which immediately deallocates the storage's memory. Custom FSDP provides interfaces in `AllGatherPipeline` and `GradReducePipeline` to replace the temporary buffer memory allocator used for parameter gathering and gradient reduction with ` StorageResizeBasedBucketAllocator`. This replaces the tensor release operation with the `tensor._typed_storage(). _resize_(0)` API.
+
+The PyTorch memory allocator cache crash is a complex issue that occurs frequently when the actual memory usage approaches the GPU memory limit, leading to poor performance. This problem is challenging and can only be mitigated by avoiding frequent hits on the GPU memory limit. Using a self-managed memory allocator like ` RotaryBucketAllocator` is another potential solution. However, note that `RotaryBucketAllocator` is not yet mature.
+
+## References
+
+- [Getting Started with Fully Sharded Data Parallel (FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html)
diff --git a/docs/source/api-guide/datasets.rst b/docs/source/api-guide/datasets.rst
new file mode 100644
index 0000000000..247a3f07d3
--- /dev/null
+++ b/docs/source/api-guide/datasets.rst
@@ -0,0 +1,104 @@
+datasets package
+================
+
+.. mdinclude :: ../../../megatron/core/datasets/readme.md
+
+Submodules
+----------
+
+datasets.blended\_megatron\_dataset\_config module
+---------------------------------------------------
+
+.. automodule:: core.datasets.blended_megatron_dataset_config
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.blended\_megatron\_dataset\_builder module
+---------------------------------------------------
+
+.. automodule:: core.datasets.blended_megatron_dataset_builder
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.megatron\_tokenizer module
+-----------------------------------
+
+.. automodule:: core.datasets.megatron_tokenizer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.indexed\_dataset module
+--------------------------------
+
+.. automodule:: core.datasets.indexed_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.megatron\_dataset module
+---------------------------------
+
+.. automodule:: core.datasets.megatron_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.gpt\_dataset module
+----------------------------
+
+.. automodule:: core.datasets.gpt_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.masked\_dataset module
+-------------------------------
+
+.. automodule:: core.datasets.masked_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.bert\_dataset module
+-----------------------------
+
+.. automodule:: core.datasets.bert_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.t5\_dataset module
+---------------------------
+
+.. automodule:: core.datasets.t5_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.blended\_dataset module
+----------------------------------
+
+.. automodule:: core.datasets.blended_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+datasets.utils module
+---------------------
+
+.. automodule:: core.datasets.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.datasets
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
diff --git a/docs/source/api-guide/dist_checkpointing.rst b/docs/source/api-guide/dist_checkpointing.rst
new file mode 100644
index 0000000000..7e384a08a3
--- /dev/null
+++ b/docs/source/api-guide/dist_checkpointing.rst
@@ -0,0 +1,79 @@
+dist\_checkpointing package
+===========================
+
+A library for saving and loading the distributed checkpoints.
+A "distributed checkpoint" can have various underlying formats (current default format is based on Zarr)
+but has a distinctive property - the checkpoint saved in one parallel configuration (tensor/pipeline/data parallelism)
+can be loaded in a different parallel configuration.
+
+Using the library requires defining sharded state_dict dictionaries with functions from  *mapping* and *optimizer* modules.
+Those state dicts can be saved or loaded with a *serialization* module using strategies from *strategies* module.
+
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   dist_checkpointing.strategies
+
+Submodules
+----------
+
+dist\_checkpointing.serialization module
+----------------------------------------
+
+.. automodule:: core.dist_checkpointing.serialization
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.mapping module
+----------------------------------
+
+.. automodule:: core.dist_checkpointing.mapping
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.optimizer module
+------------------------------------
+
+.. automodule:: core.dist_checkpointing.optimizer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.core module
+-------------------------------
+
+.. automodule:: core.dist_checkpointing.core
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.dict\_utils module
+--------------------------------------
+
+.. automodule:: core.dist_checkpointing.dict_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+dist\_checkpointing.utils module
+--------------------------------
+
+.. automodule:: core.dist_checkpointing.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.dist_checkpointing
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/dist_checkpointing.strategies.rst b/docs/source/api-guide/dist_checkpointing.strategies.rst
new file mode 100644
index 0000000000..41e674c761
--- /dev/null
+++ b/docs/source/api-guide/dist_checkpointing.strategies.rst
@@ -0,0 +1,50 @@
+dist\_checkpointing.strategies package
+======================================
+
+Package defining different checkpoint formats (backends) and saving/loading algorithms (strategies).
+
+Strategies can be used for implementing new checkpoint formats or implementing new (more optimal for a given use case) ways of saving/loading of existing formats.
+Strategies are passed to `dist_checkpointing.load` and `dist_checkpointing.save` functions and control the actual saving/loading procedure.
+
+Submodules
+----------
+
+dist\_checkpointing.strategies.base module
+------------------------------------------
+
+.. automodule:: core.dist_checkpointing.strategies.base
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.strategies.tensorstore module
+-------------------------------------------------
+
+.. automodule:: core.dist_checkpointing.strategies.tensorstore
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.strategies.two\_stage module
+------------------------------------------------
+
+.. automodule:: core.dist_checkpointing.strategies.two_stage
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dist\_checkpointing.strategies.zarr module
+------------------------------------------
+
+.. automodule:: core.dist_checkpointing.strategies.zarr
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.dist_checkpointing.strategies
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/dist_optimizer.md b/docs/source/api-guide/dist_optimizer.md
new file mode 100644
index 0000000000..34f42d5343
--- /dev/null
+++ b/docs/source/api-guide/dist_optimizer.md
@@ -0,0 +1,40 @@
+# Distributed Optimizer
+
+The motivation for the distributed optimizer is to save memory by distributing the optimizer state evenly across data parallel ranks (https://arxiv.org/abs/1910.02054), versus the naive method of replicating the optimizer state across data parallel ranks.
+
+Theoretical memory savings vary depending on the combination of the datatype of the model's parameters (`param_dtype`) and main gradients accumulated across data-parallel replicas (`grad_dtype`). We always use `fp32` main parameters for optimizer steps. In the current implementation, the theoretical number of bytes per parameter is (where d is the data parallel size):
+
+|        | Non-distributed optim | Distributed optim |
+| ------ | ------ | ------ |
+| `fp16` parameters, `fp16` gradients | 20 | 4 + 16/d |
+| `bf16` parameters, `fp32` gradients    | 18 | 6 + 12/d |
+| `fp32` parameters, `fp32` gradients       | 16 | 8 + 8/d  |
+
+Our implementation of the distributed optimizer uses contiguous buffers for parameters and main gradients; model gradients are copied over to the main gradients as soon as they are fully computed.
+
+The figures below illustrate the distributed optimizer's sharding scheme, and the key steps of the distributed optimizer's parameter update:
+
+## Data flow
+
+![Data flow](../images/distrib_optimizer/data_flow.png)
+
+## Sharding scheme
+
+![Sharding scheme](../images/distrib_optimizer/sharding_scheme.png)
+
+## Key steps
+
+_(note: using illustrations above, assuming `bf16` model weights, `bf16` model gradients that are computed by the backward pass and `fp32` main gradients that are also used for optimizer steps; we always use `fp32` main weights for optimizer steps)_
+
+- Backward pass finishes (gradient buffer holds 16 `fp32` gradient elements).
+- Call reduce-scatter on each DP rank.
+- Each DP rank now has 4 elements within the gradient buffer that are fully reduced (remaining 12 elements are garbage).
+  - DP rank 0 has gradient values for elements [0:4].
+  - DP rank 1 has gradient values for elements [4:8].
+  - DP rank 2 has gradient values for elements [8:12].
+  - DP rank 3 has gradient values for elements [12:16].
+- Optimizer.step().
+- Each DP rank copies its 4 `fp32` main parameter elements into the corresponding `bf16` parameter buffer (each element is cast from fp32 to fp16).
+- Call all-gather on each DP rank.
+- The parameter buffer now contains all 16, fully updated, `bf16` model parameter elements. Parameters in PyTorch modules already point to the appropriate locations in this parameter buffer, and thus forward passes are ready to run after the all-gather completes.
+- At this point, the gradient buffer is also ready to be zero'd for the next iteration.
diff --git a/docs/source/api-guide/distributed.rst b/docs/source/api-guide/distributed.rst
new file mode 100644
index 0000000000..737820331c
--- /dev/null
+++ b/docs/source/api-guide/distributed.rst
@@ -0,0 +1,53 @@
+distributed package
+===================
+
+This package contains various utilities to finalize model weight gradients
+on each rank before the optimizer step. This includes a distributed data
+parallelism wrapper to all-reduce or reduce-scatter the gradients across
+data-parallel replicas, and a `finalize\_model\_grads` method to
+synchronize gradients across different parallelism modes (e.g., 'tied'
+layers on different pipeline stages, or gradients for experts in a MoE on
+different ranks due to expert parallelism).
+
+Submodules
+----------
+
+distributed.distributed\_data\_parallel
+---------------------------------------
+
+Model wrapper for distributed data parallelism. Stores gradients in a
+contiguous buffer, and supports the option of overlapping communication
+(all-reduce or reduce-scatter) with backprop computation by breaking up
+full model's gradients into smaller buckets and running all-reduce /
+reduce-scatter on each bucket asynchronously. 
+
+.. automodule:: core.distributed.distributed_data_parallel
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+distributed.finalize\_model\_grads
+----------------------------------
+
+Finalize model gradients for optimizer step across all used parallelism modes.
+Synchronizes the all-reduce / reduce-scatter of model gradients across DP replicas,
+all-reduces the layernorm gradients for sequence parallelism, embedding gradients
+across first and last pipeline stages (if not tied), and expert gradients for expert
+parallelism.
+
+.. automodule:: core.distributed.finalize_model_grads
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+Module contents
+---------------
+
+Contains functionality to synchronize gradients across different ranks before
+optimizer step.
+
+.. automodule:: core.distributed
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/fusions.rst b/docs/source/api-guide/fusions.rst
new file mode 100644
index 0000000000..22782ca84e
--- /dev/null
+++ b/docs/source/api-guide/fusions.rst
@@ -0,0 +1,65 @@
+fusions package
+===============
+
+This package provides modules that provide commonly fused
+operations. Fusing operations improves compute efficiency by
+increasing the amount of work done each time a tensor is read from
+memory. To perform the fusion, modules in this either rely on PyTorch
+functionality for doing just-in-time compilation
+(i.e. `torch.jit.script` in older PyTorch versions of `torch.compile`
+in recent versions), or call into custom kernels in external libraries
+such as Apex or TransformerEngine.
+
+Submodules
+----------
+
+fusions.fused\_bias\_dropout module
+-----------------------------------
+
+This module uses PyTorch JIT to fuse the bias add and dropout operations. Since dropout is not used during inference, different functions are used when in train mode and when in inference mode.
+
+.. automodule:: core.fusions.fused_bias_dropout
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+fusions.fused\_bias\_gelu module
+--------------------------------
+
+This module uses PyTorch JIT to fuse the bias add and GeLU nonlinearity operations.
+
+.. automodule:: core.fusions.fused_bias_gelu
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+fusions.fused\_layer\_norm module
+---------------------------------
+
+This module provides a wrapper around various fused LayerNorm implementation in Apex.
+
+.. automodule:: core.fusions.fused_layer_norm
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+fusions.fused\_softmax module
+-----------------------------
+
+This module provides wrappers around variations of Softmax in Apex.
+
+.. automodule:: core.fusions.fused_softmax
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+fusions.fused\_cross\_entropy\_loss module
+------------------------------------------
+
+This module uses PyTorch JIT to fuse the cross entropy loss calculation and batches communication calls.
+
+.. automodule:: core.fusions.fused_cross_entropy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
diff --git a/docs/source/api-guide/index.rst b/docs/source/api-guide/index.rst
new file mode 100644
index 0000000000..7e1c805e87
--- /dev/null
+++ b/docs/source/api-guide/index.rst
@@ -0,0 +1,23 @@
+API Guide
+=========
+
+.. toctree::
+   :maxdepth: 4
+
+   models
+   tensor_parallel
+   context_parallel
+   pipeline_parallel
+   custom_fsdp
+   fusions
+   transformer
+   moe
+   dist_checkpointing
+   dist_optimizer
+   distributed
+   datasets
+   multi_latent_attention
+   num_microbatches_calculator
+   optimizer_param_scheduler
+   optimizer_cpu_offload
+   multi_token_prediction
diff --git a/docs/source/api-guide/models.bert.rst b/docs/source/api-guide/models.bert.rst
new file mode 100644
index 0000000000..1b562ce72c
--- /dev/null
+++ b/docs/source/api-guide/models.bert.rst
@@ -0,0 +1,22 @@
+models.bert package
+===================
+Useful package for training bert and bert like encoder only models. It optionally comes with a binary head that can be used for classification tasks . 
+
+Submodules
+----------
+
+models.bert.bert\_model module
+------------------------------
+
+.. automodule:: core.models.bert.bert_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.models.bert
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/models.gpt.rst b/docs/source/api-guide/models.gpt.rst
new file mode 100644
index 0000000000..31c4da6a9c
--- /dev/null
+++ b/docs/source/api-guide/models.gpt.rst
@@ -0,0 +1,22 @@
+models.gpt package
+==================
+This is the implementation of the popular GPT model. It supports several features like model parallelization (Tensor Parallel, Pipeline Parallel, Data Parallel) , mixture of experts, FP8 , Distributed optimizer etc. We are constantly adding new features. So be on the lookout or raise an issue if you want to have something added. 
+
+Submodules
+----------
+
+models.gpt.gpt\_model module
+----------------------------
+
+.. automodule:: core.models.gpt.gpt_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.models.gpt
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/models.rst b/docs/source/api-guide/models.rst
new file mode 100644
index 0000000000..12c40e4f35
--- /dev/null
+++ b/docs/source/api-guide/models.rst
@@ -0,0 +1,21 @@
+models package
+==============
+This package contains most of the popular LLMs . Currently we have support for GPT, Bert, T5 and Retro . This is an ever growing list so keep an eye out. 
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   models.gpt
+   models.t5
+   models.bert
+
+Module contents
+---------------
+
+.. automodule:: core.models
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/models.t5.rst b/docs/source/api-guide/models.t5.rst
new file mode 100644
index 0000000000..1cc3315682
--- /dev/null
+++ b/docs/source/api-guide/models.t5.rst
@@ -0,0 +1,21 @@
+models.t5 package
+=================
+
+Submodules
+----------
+
+models.t5.t5\_model module
+--------------------------
+
+.. automodule:: core.models.T5.t5_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.models.T5
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/moe.rst b/docs/source/api-guide/moe.rst
new file mode 100644
index 0000000000..9afc01e080
--- /dev/null
+++ b/docs/source/api-guide/moe.rst
@@ -0,0 +1,4 @@
+Mixture of Experts package
+==========================
+
+.. mdinclude :: ../../../megatron/core/transformer/moe/README.md
diff --git a/docs/source/api-guide/multi_latent_attention.rst b/docs/source/api-guide/multi_latent_attention.rst
new file mode 100644
index 0000000000..64e2da07d0
--- /dev/null
+++ b/docs/source/api-guide/multi_latent_attention.rst
@@ -0,0 +1,14 @@
+Multi-Latent Attention
+======================
+
+Multi-Latent Attention overview 
+-------------------------------
+
+Multi-Latent Attention ("MLA") is an innovative attention mechanism introduced by Deepseek team that enhances the efficiency of attention computation by leveraging multiple latent spaces. This approach is particularly beneficial for large language models (LLMs), as it reduces the computational burden associated with traditional attention mechanisms. According to Deepseek-V2 technical report, MLA achieves better performance compared to Multi-Head Attention (MHA) and requires smaller KV cache.
+
+Enabling Multi-Latent Attention
+-------------------------------
+
+To enable MLA in Megatron-LM, set the following flags in command line:
+- `--multi-latent-attention` to enable MLA in MLP.
+- Set `MLATransformerConfig` to configure MLA.
diff --git a/docs/source/api-guide/multi_token_prediction.md b/docs/source/api-guide/multi_token_prediction.md
new file mode 100644
index 0000000000..4059fa5326
--- /dev/null
+++ b/docs/source/api-guide/multi_token_prediction.md
@@ -0,0 +1,23 @@
+# Multi-Token Prediction (MTP)
+
+Multi-Token Prediction (MTP) extends the prediction scope to multiple future tokens at each position. On the one hand, an MTP objective densifies the training signals and may improve
+data efficiency. On the other hand, MTP may enable the model to pre-plan its representations for better prediction of future tokens. In this implementation of MTP, we sequentially predict additional tokens and keep the complete causal chain at each prediction depth. The following figure illustrates our implementation of MTP in [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3/).
+
+![MTP_implementation](../images/multi_token_prediction/MTP_implementation.png)
+
+The k-th MTP module consists of a shared embedding layer, a projection matrix, a Transformer block, and a shared output head. For the i-th input token at the (k - 1)-th prediction depth, we first combine the representation of the i-th token and the embedding of the (i + K)-th token with the linear projection. The combined serves as the input of the Transformer block at the k-th depth to produce the output representation.
+
+For more information, please refer to [DeepSeek-V3 Technical Report](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf)
+
+## Related Arguments
+
+We can train GPTModel like models with Multi-Token Prediction (MTP) by setting mtp_num_layers to be a positive integer.
+
+| Item | Description |
+| --- | --- |
+| mtp_num_layers | Number of Multi-Token Prediction (MTP) Layers. MTP extends the prediction scope to multiple future tokens at each position. This MTP implementation sequentially predict additional tokens by using D sequential modules to predict D additional tokens. Default is None. |
+| mtp_loss_scaling_factor | Scaling factor of Multi-Token Prediction (MTP) loss. We compute the average of the MTP losses across all depths, and multiply it the scaling factor to obtain the overall MTP loss, which serves as an additional training objective. Default is 0.1. |
+
+## Precautions
+
+Please do not use Context Parallel (CP), or arbitrary AttnMaskType, or learned absolute position embedding type with MTP. These use cases are not yet supported.
diff --git a/docs/source/api-guide/num_microbatches_calculator.rst b/docs/source/api-guide/num_microbatches_calculator.rst
new file mode 100644
index 0000000000..4790b31749
--- /dev/null
+++ b/docs/source/api-guide/num_microbatches_calculator.rst
@@ -0,0 +1,12 @@
+Microbatches Calculator
+=======================
+This api is used to calculate the number of microbatches required to fit a given model on a given batch size.
+
+
+Module contents
+---------------
+
+.. automodule:: core.num_microbatches_calculator
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/optimizer_cpu_offload.rst b/docs/source/api-guide/optimizer_cpu_offload.rst
new file mode 100644
index 0000000000..fdbae6654b
--- /dev/null
+++ b/docs/source/api-guide/optimizer_cpu_offload.rst
@@ -0,0 +1,4 @@
+Optimizer CPU offload package
+==============================
+
+.. mdinclude :: ../../../megatron/core/optimizer/cpu_offloading/README.md
diff --git a/docs/source/api-guide/optimizer_param_scheduler.rst b/docs/source/api-guide/optimizer_param_scheduler.rst
new file mode 100644
index 0000000000..caf5d8abfb
--- /dev/null
+++ b/docs/source/api-guide/optimizer_param_scheduler.rst
@@ -0,0 +1,12 @@
+Optimizer Parameters Scheduler
+==============================
+This api is used to calculate the learning rate and weight decay for the optimizer.
+
+
+Module contents
+---------------
+
+.. automodule:: core.optimizer_param_scheduler
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/pipeline_parallel.rst b/docs/source/api-guide/pipeline_parallel.rst
new file mode 100644
index 0000000000..0c1909d903
--- /dev/null
+++ b/docs/source/api-guide/pipeline_parallel.rst
@@ -0,0 +1,49 @@
+pipeline\_parallel package
+==========================
+
+This package contains implementations for two different pipeline parallelism
+schedules (one without interleaving and one with interleaving, see `Efficient
+Large-Scale Language Model Training on GPU Clusters Using Megatron-LM `_
+for details), and a default no-pipelining schedule. It also contains methods
+for the point-to-point communication that is needed between pipeline stages.
+
+Submodules
+----------
+
+.. mdinclude:: pipeline_parallel_layout.md
+
+pipeline\_parallel.p2p\_communication module
+--------------------------------------------
+
+Contains implementations for the various point-to-point communication needed
+(e.g., `recv_forward` and `recv_backward`) in the different pipeline parallelism
+schedules.
+
+.. automodule:: core.pipeline_parallel.p2p_communication
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pipeline\_parallel.schedules module
+-----------------------------------
+
+Contains implementations for two pipeline parallelism schedules
+(`forward_backward_pipelining_with_interleaving`for pipeline parallelism with
+interleaving, `forward_backward_pipelining_without_interleaving` for pipeline
+parallelism without interleaving) and a default no-pipelining schedule
+(`forward_backward_no_pipelining`). `get_forward_backward_func` returns the right
+scheduling function to use based on the configuration being trained
+(e.g., if pipeline-parallel size is 1, use `forward_backward_no_pipelining`).
+
+.. automodule:: core.pipeline_parallel.schedules
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.pipeline_parallel
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/pipeline_parallel_layout.md b/docs/source/api-guide/pipeline_parallel_layout.md
new file mode 100644
index 0000000000..30c8ce1a50
--- /dev/null
+++ b/docs/source/api-guide/pipeline_parallel_layout.md
@@ -0,0 +1,26 @@
+# Custom Pipeline Model Parallel Layout
+
+*This is an experimental feature and may be changed.*
+
+`--pipeline-model-parallel-layout` is a flexible API for defining the pipeline parallel partitioning, which is essential for balanced partitioning for an imbalanced model. For example, to partition DeepSeek-V3 (61 decoder layers + 1 mtp layer) with PP16VPP2, we can include the arguments as follows:
+
+```bash
+--pipeline-model-parallel-size 16
+--pipeline-model-parallel-layout "Et*3|(tt|)*29,m|L"
+```
+
+| PP \ VPP rank |            0            |       1       |
+|---------------|-------------------------|---------------|
+|       0       | embedding + 3 Ɨ decoder |  2 Ɨ decoder  |
+|      1~13     |        2 Ɨ decoder      |  2 Ɨ decoder  |
+|       14      |        2 Ɨ decoder      |      mtp      |
+|       15      |        2 Ɨ decoder      |      loss     |
+
+In the layout string, stages are split by '|'. Replicated stages or layers can be described with multiplication. Commas can be used cosmetically. Symbol choices:
+
+* `E` = embedding layer
+* `t` = transformer decoder layer
+* `m` = MTP layer
+* `L` = loss calculation layer
+
+Note that it is legal to have empty stages, e.g., `E||t|L` (the second stage is empty).
diff --git a/docs/source/api-guide/tensor_parallel.rst b/docs/source/api-guide/tensor_parallel.rst
new file mode 100644
index 0000000000..d8ae9dea22
--- /dev/null
+++ b/docs/source/api-guide/tensor_parallel.rst
@@ -0,0 +1,67 @@
+tensor\_parallel package
+========================
+
+This package contains an implementation for tensor parallelism in transformer
+models (see `Megatron-LM: Training Multi-Billion Parameter Language Models
+Using Model Parallelism `_ and `Reducing
+Activation Recomputation in Large Transformer Models `_
+for details).
+
+Submodules
+----------
+
+tensor\_parallel.cross\_entropy module
+--------------------------------------
+
+.. automodule:: core.tensor_parallel.cross_entropy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+tensor\_parallel.data module
+----------------------------
+
+.. automodule:: core.tensor_parallel.data
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+tensor\_parallel.layers module
+------------------------------
+
+.. automodule:: core.tensor_parallel.layers
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+tensor\_parallel.mappings module
+--------------------------------
+
+.. automodule:: core.tensor_parallel.mappings
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+tensor\_parallel.random module
+------------------------------
+
+.. automodule:: core.tensor_parallel.random
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+tensor\_parallel.utils module
+-----------------------------
+
+.. automodule:: core.tensor_parallel.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.tensor_parallel
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/api-guide/transformer.rst b/docs/source/api-guide/transformer.rst
new file mode 100644
index 0000000000..6e2e894d54
--- /dev/null
+++ b/docs/source/api-guide/transformer.rst
@@ -0,0 +1,136 @@
+transformer package
+===================
+
+The `transformer` package provides a customizable and configurable
+implementation of the transformer model architecture. Each component
+of a transformer stack, from entire layers down to individual linear
+layers, can be customized by swapping in different PyTorch modules
+using the "spec" parameters (see `here
+`_). The
+configuration of the transformer (hidden size, number of layers,
+number of attention heads, etc.) is provided via a `TransformerConfig`
+object.
+
+Submodules
+----------
+
+transformer.attention module
+----------------------------
+
+This is the entire attention portion, either self or cross attention,
+of a transformer layer including the query, key, and value
+projections, a "core" attention calculation (e.g. dot product
+attention), and final output linear projection.
+
+.. automodule:: core.transformer.attention
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.dot\_product\_attention module
+------------------------------------------
+
+This is a PyTorch-only implementation of dot product attention. A more
+efficient implementation, like those provided by FlashAttention or
+CUDNN's FusedAttention, are typically used when training speed is
+important.
+
+.. automodule:: core.transformer.dot_product_attention
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.enums module
+------------------------
+
+.. automodule:: core.transformer.enums
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.identity\_op module
+-------------------------------
+
+This provides a pass-through module that can be used in specs to
+indicate that the operation should not be performed. For example, when
+using LayerNorm with the subsequent linear layer, an IdentityOp can be
+passed in as the LayerNorm module to use.
+
+.. automodule:: core.transformer.identity_op
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.mlp module
+----------------------
+
+This is the entire MLP portion of the transformer layer with an input
+projection, non-linearity, and output projection.
+
+.. automodule:: core.transformer.mlp
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.module module
+-------------------------
+
+This provides a common base class for all modules used in the
+transformer that contains some common functionality.
+
+.. automodule:: core.transformer.module
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.transformer\_block module
+-------------------------------------
+
+A block, or stack, of several transformer layers. The layers can all
+be the same or each can be unique.
+
+.. automodule:: core.transformer.transformer_block
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.transformer\_config module
+--------------------------------------
+
+This contains all of the configuration options for the
+transformer. Using a dataclass reduces code bloat by keeping all
+arguments together in a dataclass instead of passing several arguments
+through multiple layers of function calls.
+
+.. automodule:: core.transformer.transformer_config
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.transformer\_layer module
+-------------------------------------
+
+A single standard transformer layer including attention and MLP blocks.
+
+.. automodule:: core.transformer.transformer_layer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+transformer.utils module
+------------------------
+
+Various utilities used in the transformer implementation.
+
+.. automodule:: core.transformer.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: core.transformer
+   :members:
+   :undoc-members:
+   :show-inheritance:
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new file mode 100644
index 0000000000..e48dd95024
Binary files /dev/null and b/docs/source/images/distrib_optimizer/sharding_scheme.png differ
diff --git a/docs/source/images/moe/token_drop.png b/docs/source/images/moe/token_drop.png
new file mode 100644
index 0000000000..1c335ee7aa
Binary files /dev/null and b/docs/source/images/moe/token_drop.png differ
diff --git a/docs/source/images/multi_token_prediction/MTP_implementation.png b/docs/source/images/multi_token_prediction/MTP_implementation.png
new file mode 100644
index 0000000000..1f246c3e39
Binary files /dev/null and b/docs/source/images/multi_token_prediction/MTP_implementation.png differ
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 0000000000..f2a89b8ac7
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,23 @@
+.. Lumache documentation master file, created by
+   sphinx-quickstart on Tue Aug 15 13:44:10 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Megatron Core User Guide
+===================================
+
+**Megatron Core** is a Python library that has the core components required to build your language models. 
+A reference implementation of Megatron Core can be found in  `NeMo `_ It offers a *simple* and
+*intuitive* API.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: User Guide
+
+   user-guide/index
+
+.. toctree::
+   :maxdepth: 3
+   :caption: API Guide
+   
+   api-guide/index
diff --git a/docs/source/user-guide/index.rst b/docs/source/user-guide/index.rst
new file mode 100644
index 0000000000..aab745736b
--- /dev/null
+++ b/docs/source/user-guide/index.rst
@@ -0,0 +1,5 @@
+User Guide 
+============
+
+.. mdinclude:: ../../../megatron/core/QuickStart.md
+.. mdinclude:: ../../../megatron/core/MSC_Integration.md
\ No newline at end of file
diff --git a/examples/__init__.py b/examples/__init__.py
new file mode 100644
index 0000000000..0519ecba6e
--- /dev/null
+++ b/examples/__init__.py
@@ -0,0 +1 @@
+ 
\ No newline at end of file
diff --git a/examples/detxoify_lm/README.md b/examples/academic_paper_scripts/detxoify_lm/README.md
similarity index 100%
rename from examples/detxoify_lm/README.md
rename to examples/academic_paper_scripts/detxoify_lm/README.md
diff --git a/examples/detxoify_lm/annotations/filter-selfgeneration.py b/examples/academic_paper_scripts/detxoify_lm/annotations/filter-selfgeneration.py
similarity index 100%
rename from examples/detxoify_lm/annotations/filter-selfgeneration.py
rename to examples/academic_paper_scripts/detxoify_lm/annotations/filter-selfgeneration.py
diff --git a/examples/detxoify_lm/annotations/perspective_api_annotate.py b/examples/academic_paper_scripts/detxoify_lm/annotations/perspective_api_annotate.py
similarity index 98%
rename from examples/detxoify_lm/annotations/perspective_api_annotate.py
rename to examples/academic_paper_scripts/detxoify_lm/annotations/perspective_api_annotate.py
index fd82c2a2ae..9736db099a 100644
--- a/examples/detxoify_lm/annotations/perspective_api_annotate.py
+++ b/examples/academic_paper_scripts/detxoify_lm/annotations/perspective_api_annotate.py
@@ -107,7 +107,7 @@ def get_score(line):
             except UnicodeDecodeError:
                 try:
                     decoded_text = encoded_text[:20476].decode('utf8')
-                except:
+                except Exception:
                     print("Error occurred")
                     data['score'] = None
                     return json.dumps(data)
@@ -138,7 +138,7 @@ def get_scores(lines):
                 except UnicodeDecodeError:
                     try:
                         decoded_text = encoded_text[:20476].decode('utf8')
-                    except:
+                    except Exception:
                         print("Error occurred")
                         data['score'] = None
                         all_data.append(json.dumps(data))
diff --git a/examples/detxoify_lm/annotations/preprocess.sh b/examples/academic_paper_scripts/detxoify_lm/annotations/preprocess.sh
similarity index 100%
rename from examples/detxoify_lm/annotations/preprocess.sh
rename to examples/academic_paper_scripts/detxoify_lm/annotations/preprocess.sh
diff --git a/examples/academic_paper_scripts/detxoify_lm/finetune_gpt.py b/examples/academic_paper_scripts/detxoify_lm/finetune_gpt.py
new file mode 100644
index 0000000000..c3a9f69cae
--- /dev/null
+++ b/examples/academic_paper_scripts/detxoify_lm/finetune_gpt.py
@@ -0,0 +1,159 @@
+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+
+
+"""Fine-tune GPT"""
+
+import torch
+from functools import partial
+import os
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                             os.path.pardir, os.path.pardir)))
+from megatron.training import get_args
+from megatron.training import get_timers
+from megatron.training import get_tokenizer
+from megatron.training import print_rank_0
+from megatron.core import mpu
+from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
+from megatron.core.datasets.blended_megatron_dataset_config import GPTDatasetConfig
+from megatron.core.datasets.gpt_dataset import GPTDataset
+from megatron.core.datasets.utils import get_blend_from_list
+from megatron.legacy.model import GPTModel
+from megatron.core.enums import ModelType
+from megatron.training import pretrain
+from megatron.training.utils import get_ltor_masks_and_position_ids
+from megatron.training.utils import average_losses_across_data_parallel_group
+
+def model_provider(pre_process=True, post_process=True):
+    """Build the model."""
+
+    print_rank_0('building GPT model ...')
+    model = GPTModel(
+        num_tokentypes=0,
+        parallel_output=True,
+        pre_process=pre_process,
+        post_process=post_process
+    )
+    return model
+
+
+def get_batch(data_iterator):
+    """Generate a batch"""
+    args = get_args()
+    tokenizer = get_tokenizer()
+
+    # Items and their type.
+    keys = ['text']
+    datatype = torch.int64
+
+    # Broadcast data.
+    if data_iterator is not None:
+        data = next(data_iterator)
+    else:
+        data = None
+    data_b = mpu.broadcast_data(keys, data, datatype)
+
+    # Unpack.
+    tokens_ = data_b['text'].long()
+    labels = tokens_[:, 1:].contiguous()
+    tokens = tokens_[:, :-1].contiguous()
+
+    # Get the masks and postition ids.
+    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
+        tokens,
+        tokenizer.eod,
+        args.reset_position_ids,
+        args.reset_attention_mask,
+        args.eod_mask_loss)
+
+    return tokens, labels, loss_mask, attention_mask, position_ids
+
+def loss_func(loss_mask, output_tensor):
+    losses = output_tensor.float()
+    loss_mask = loss_mask.view(-1).float()
+    loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
+
+    # Reduce loss for logging.
+    averaged_loss = average_losses_across_data_parallel_group([loss])
+
+    return loss, {'lm loss': averaged_loss[0]}
+
+
+def forward_step(data_iterator, model):
+    """Forward step."""
+    args = get_args()
+    timers = get_timers()
+
+    # Get the batch.
+    timers('batch-generator').start()
+    tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
+        data_iterator)
+    timers('batch-generator').stop()
+
+    output_tensor = model(tokens, position_ids, attention_mask,
+                          labels=labels)
+
+    return output_tensor, partial(loss_func, loss_mask)
+
+
+def train_valid_test_datasets_provider(train_val_test_num_samples):
+    """Build train, valid, and test datasets."""
+    args = get_args()
+
+    print_rank_0('> building train, validation, and test datasets '
+                 'for GPT ...')
+    train_ds, _, test_ds = BlendedMegatronDatasetBuilder(
+        GPTDataset,
+        train_val_test_num_samples,
+        lambda: True,
+        GPTDatasetConfig(
+            blend=get_blend_from_list(args.data_path),
+            split=args.split,
+            random_seed=args.seed,
+            sequence_length=args.seq_length,
+            path_to_cache=args.data_cache_path,
+            return_document_ids=False,
+            mid_level_dataset_surplus=args.mid_level_dataset_surplus,
+        )
+    ).build()
+    print_rank_0("> finished creating finetuning GPT datasets ...")
+
+    _, valid_ds, _ = BlendedMegatronDatasetBuilder(
+        GPTDataset,
+        train_val_test_num_samples,
+        lambda: True,
+        GPTDatasetConfig(
+            blend=get_blend_from_list(args.data_path2),
+            split="98,2,0",
+            random_seed=1234,
+            sequence_length=2048,
+            path_to_cache=args.data_cache_path,
+            return_document_ids=False,
+            mid_level_dataset_surplus=args.mid_level_dataset_surplus,
+        )
+    ).build()
+    print_rank_0("> finished creating pretrained GPT datasets ...")
+
+    return train_ds, valid_ds, test_ds
+
+
+def add_validation_args(parser):
+    """Text generation arguments."""
+    group = parser.add_argument_group(title='validation set')
+    group.add_argument('--data-path2', nargs='*', default=None,
+                       help='Path to the validation dataset. Accepted format:'
+                       '1) a single data path, 2) multiple datasets in the'
+                       'form: dataset1-weight dataset1-path dataset2-weight '
+                       'dataset2-path ...')
+    group.add_argument('--eval-ppl', action='store_true', default=False)
+    group.add_argument('--stored_params', type=dict, default=dict())
+    return parser
+
+
+if __name__ == "__main__":
+
+    pretrain(train_valid_test_datasets_provider, model_provider,
+             ModelType.encoder_or_decoder,
+             forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
+             extra_args_provider=add_validation_args,)
diff --git a/examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh b/examples/academic_paper_scripts/detxoify_lm/finetune_gpt_distributed-1.3b.sh
similarity index 98%
rename from examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh
rename to examples/academic_paper_scripts/detxoify_lm/finetune_gpt_distributed-1.3b.sh
index 62a36c0b79..a212fbdf3f 100755
--- a/examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh
+++ b/examples/academic_paper_scripts/detxoify_lm/finetune_gpt_distributed-1.3b.sh
@@ -43,7 +43,6 @@ python -m torch.distributed.run $DISTRIBUTED_ARGS \
      --data-path2 ${DATA_BLEND} \
      --vocab-file $VOCAB_FILE \
      --merge-file $MERGE_FILE \
-     --data-impl mmap \
      --split 100,0,0 \
      --distributed-backend nccl \
      --lr-decay-style constant \
diff --git a/examples/detxoify_lm/generate-1.3b.sh b/examples/academic_paper_scripts/detxoify_lm/generate-1.3b.sh
similarity index 100%
rename from examples/detxoify_lm/generate-1.3b.sh
rename to examples/academic_paper_scripts/detxoify_lm/generate-1.3b.sh
diff --git a/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py b/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py
new file mode 100644
index 0000000000..895a45d024
--- /dev/null
+++ b/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py
@@ -0,0 +1,260 @@
+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+
+
+"""Sample Generate GPT"""
+import json
+import os
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                             os.path.pardir, os.path.pardir)))
+import torch
+from megatron.training import get_args
+from megatron.training import get_tokenizer
+from megatron.training import print_rank_0
+from megatron.training.checkpointing import load_checkpoint
+from megatron.core import mpu
+from megatron.training.initialize import initialize_megatron
+from megatron.legacy.model import GPTModel
+from megatron.training import get_model
+from megatron.inference.text_generation import generate_and_post_process
+from megatron.training.arguments import core_transformer_config_from_args
+from megatron.core.models.gpt import GPTModel
+from typing import Union
+import megatron.legacy.model
+from megatron.core.transformer.spec_utils import import_module
+from megatron.training.arguments import core_transformer_config_from_args
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec, get_gpt_layer_local_spec
+
+def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]:
+    """Builds the model.
+
+    If you set the use_legacy_models to True, it will return the legacy GPT model and if not the core GPT model.
+
+    Args:
+        pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True.
+        post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True.
+
+
+    Returns:
+        Union[GPTModel, megatron.legacy.model.GPTModel]: The returned model
+    """
+    args = get_args()
+
+    print_rank_0('building GPT model ...')
+    config = core_transformer_config_from_args(args)
+
+    if args.use_legacy_models:
+        model = megatron.legacy.model.GPTModel(
+            config,
+            num_tokentypes=0,
+            parallel_output=False,
+            pre_process=pre_process,
+            post_process=post_process
+        )
+    else:
+        if args.spec is None:
+            if args.transformer_impl == 'local':
+                transformer_layer_spec = get_gpt_layer_local_spec(
+                    num_experts=args.num_experts,
+                    moe_grouped_gemm=args.moe_grouped_gemm
+                )
+            elif args.transformer_impl == 'transformer_engine':
+                transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+                    num_experts=args.num_experts,
+                    moe_grouped_gemm=args.moe_grouped_gemm
+                )
+            else:
+                raise ValueError(f"Invalid transformer_impl {args.transformer_impl}")
+        elif args.spec[0] == 'local':
+            transformer_layer_spec = get_gpt_layer_local_spec(
+                num_experts=args.num_experts,
+                moe_grouped_gemm=args.moe_grouped_gemm
+            )
+        else:
+            transformer_layer_spec = import_module(args.spec)
+
+        model = GPTModel(
+            config=config,
+            transformer_layer_spec=transformer_layer_spec,
+            vocab_size=args.padded_vocab_size,
+            max_sequence_length=args.max_position_embeddings,
+            pre_process=pre_process,
+            post_process=post_process,
+            fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
+            parallel_output=False,
+            share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
+            position_embedding_type=args.position_embedding_type,
+            rotary_percent=args.rotary_percent
+        )
+
+    return model
+
+def add_text_generate_args(parser):
+    """Text generation arguments."""
+    group = parser.add_argument_group(title='text generation')
+
+    group.add_argument("--temperature", type=float, default=1.0,
+                       help='Sampling temperature.')
+    group.add_argument("--greedy", action='store_true', default=False,
+                       help='Use greedy sampling.')
+    group.add_argument("--top_p", type=float, default=0.0,
+                       help='Top p sampling.')
+    group.add_argument("--top_k", type=int, default=0,
+                       help='Top k sampling.')
+    group.add_argument("--out-seq-length", type=int, default=1024,
+                       help='Size of the output generated text.')
+    group.add_argument("--sample-input-file", type=str, default=None,
+                       help='Get input from file instead of interactive mode, '
+                       'each line is an input.')
+    group.add_argument("--sample-output-file", type=str, default=None,
+                       help='Output file got from --sample-input-file')
+    group.add_argument("--num-samples", type=int, default=0,
+                       help='Number of samples to generate unconditionally, '
+                       'defaults to 0 and interactive conditional sampling')
+    group.add_argument("--genfile", type=str,
+                       help='Output file when generating unconditionally')
+    return parser
+
+def generate_samples_unconditional(model):
+    args = get_args()
+
+    if torch.distributed.get_rank() == 0:
+        cnt = 0
+        num_samples = args.num_samples
+        from tqdm import tqdm
+        pbar = tqdm(total=num_samples)
+
+    while True:
+        if torch.distributed.get_rank() == 0:
+            sentences = [''] * args.global_batch_size
+            print("global batch size", args.global_batch_size)
+            max_len = args.out_seq_length
+            resp_sentences, resp_sentences_seg, output_logits, \
+            tokens = generate_and_post_process(model, prompts=sentences,
+                                               tokens_to_generate=max_len,
+                                               return_output_log_probs=False,
+                                               top_k_sampling=args.top_k,
+                                               top_p_sampling=args.top_p,
+                                               add_BOS=True,
+                                               temperature=1.0)
+            for prompt, generation, token in zip(sentences, resp_sentences, tokens):
+                datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
+                yield datum
+                cnt += 1
+                pbar.update()
+                if cnt >= num_samples:
+                    break
+
+            if cnt >= num_samples:
+                pbar.close()
+                break
+        else:
+            generate_and_post_process(model)
+
+
+def generate_samples_conditional(model):
+    args = get_args()
+
+    if torch.distributed.get_rank() == 0:
+        num_samples = args.num_samples
+        cnt = 0
+        from tqdm import tqdm
+        pbar = tqdm(total=num_samples)
+
+        fname = open(args.sample_input_file, "r")
+        lines = fname.readlines()
+        all_raw_text = [json.loads(line)['prompt']['text'] for line in lines]
+        input_count = len(all_raw_text)
+        input_pos = 0
+
+    while True:
+        torch.distributed.barrier()
+        if torch.distributed.get_rank() == 0:
+            sentences = []
+            print("global batch size", args.global_batch_size)
+            for _ in range(args.global_batch_size):
+                if input_pos >= input_count:
+                    print(f"input pos: {input_pos}, input count: {input_count}")
+                    raw_text = "EMPTY TEXT"
+                else:
+                    raw_text = all_raw_text[input_pos]
+                input_pos += 1
+                sentences.append(raw_text)
+
+            max_len = args.out_seq_length
+            resp_sentences, resp_sentences_seg, output_logits, \
+            tokens = generate_and_post_process(model, prompts=sentences,
+                                               tokens_to_generate=max_len,
+                                               return_output_log_probs=False,
+                                               top_k_sampling=args.top_k,
+                                               top_p_sampling=args.top_p,
+                                               add_BOS=False,
+                                               temperature=1.0)
+            for prompt, generation, token in zip(sentences, resp_sentences, tokens):
+                datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
+                yield datum
+                cnt += 1
+                pbar.update()
+                if cnt >= num_samples:
+                    break
+
+            if cnt >= num_samples:
+                pbar.close()
+                break
+        else:
+            generate_and_post_process(model)
+
+
+def generate_and_write_samples_unconditional(model):
+    args = get_args()
+    assert args.genfile is not None
+    with open(args.genfile, 'w') as f:
+        for datum in generate_samples_unconditional(model):
+            if torch.distributed.get_rank() == 0:
+                f.write(json.dumps(datum) + '\n')
+
+
+def generate_and_write_samples_conditional(model):
+    args = get_args()
+    if args.sample_output_file is None:
+        sample_output_file = args.sample_input_file + ".out"
+        print('`sample-output-file` not specified, setting '
+              'it to {}'.format(sample_output_file))
+    else:
+        sample_output_file = args.sample_output_file
+    with open(sample_output_file, 'w') as f:
+        for datum in generate_samples_conditional(model):
+            if torch.distributed.get_rank() == 0:
+                f.write(json.dumps(datum) + '\n')
+
+
+def main():
+    """Main program."""
+
+    initialize_megatron(extra_args_provider=add_text_generate_args,
+                        args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
+                                       'no_load_rng': True,
+                                       'no_load_optim': True,
+                                       'seq_length': 2048})
+
+    # Set up model and load checkpoint
+    model = get_model(model_provider, wrap_with_ddp=False)
+
+    args = get_args()
+
+    if args.load is not None:
+        _ = load_checkpoint(model, None, None)
+    model = model[0]
+
+    # Generate samples.
+    if args.sample_input_file != None:
+        print(f"{args.sample_input_file}")
+        generate_and_write_samples_conditional(model)
+    else:
+        generate_and_write_samples_unconditional(model)
+
+
+if __name__ == "__main__":
+
+    main()
diff --git a/examples/detxoify_lm/perspective_api.py b/examples/academic_paper_scripts/detxoify_lm/perspective_api.py
similarity index 100%
rename from examples/detxoify_lm/perspective_api.py
rename to examples/academic_paper_scripts/detxoify_lm/perspective_api.py
diff --git a/examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh b/examples/academic_paper_scripts/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh
similarity index 100%
rename from examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh
rename to examples/academic_paper_scripts/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh
diff --git a/examples/msdp/README.md b/examples/academic_paper_scripts/msdp/README.md
similarity index 100%
rename from examples/msdp/README.md
rename to examples/academic_paper_scripts/msdp/README.md
diff --git a/examples/msdp/data_processing.sh b/examples/academic_paper_scripts/msdp/data_processing.sh
similarity index 100%
rename from examples/msdp/data_processing.sh
rename to examples/academic_paper_scripts/msdp/data_processing.sh
diff --git a/examples/msdp/eval_knwl_generation.sh b/examples/academic_paper_scripts/msdp/eval_knwl_generation.sh
similarity index 100%
rename from examples/msdp/eval_knwl_generation.sh
rename to examples/academic_paper_scripts/msdp/eval_knwl_generation.sh
diff --git a/examples/msdp/eval_resp_generation.sh b/examples/academic_paper_scripts/msdp/eval_resp_generation.sh
similarity index 100%
rename from examples/msdp/eval_resp_generation.sh
rename to examples/academic_paper_scripts/msdp/eval_resp_generation.sh
diff --git a/examples/msdp/prep_resp_gen.sh b/examples/academic_paper_scripts/msdp/prep_resp_gen.sh
similarity index 100%
rename from examples/msdp/prep_resp_gen.sh
rename to examples/academic_paper_scripts/msdp/prep_resp_gen.sh
diff --git a/examples/msdp/prompt_knwl_gen.sh b/examples/academic_paper_scripts/msdp/prompt_knwl_gen.sh
similarity index 100%
rename from examples/msdp/prompt_knwl_gen.sh
rename to examples/academic_paper_scripts/msdp/prompt_knwl_gen.sh
diff --git a/examples/msdp/prompt_resp_gen.sh b/examples/academic_paper_scripts/msdp/prompt_resp_gen.sh
similarity index 100%
rename from examples/msdp/prompt_resp_gen.sh
rename to examples/academic_paper_scripts/msdp/prompt_resp_gen.sh
diff --git a/examples/sc21/CONFIG.sh b/examples/academic_paper_scripts/sc21/CONFIG.sh
similarity index 100%
rename from examples/sc21/CONFIG.sh
rename to examples/academic_paper_scripts/sc21/CONFIG.sh
diff --git a/examples/academic_paper_scripts/sc21/README.md b/examples/academic_paper_scripts/sc21/README.md
new file mode 100644
index 0000000000..ec922d153d
--- /dev/null
+++ b/examples/academic_paper_scripts/sc21/README.md
@@ -0,0 +1,50 @@
+# Reproducing Figures in SC21 Paper
+
+
+This directory contains some of the scripts that were used to produce the
+results in the [Megatron paper](https://arxiv.org/pdf/2104.04473.pdf) that is
+to appear at [SuperComputing 2021](https://sc21.supercomputing.org/). These
+scripts use [Slurm](https://slurm.schedmd.com/documentation.html) with the
+[pyxis plugin](https://github.com/NVIDIA/pyxis), but can be modified for other
+schedulers as well.
+
+
+## Git commit
+
+To replicate these results use Megatron-LM commit: 6985e58938d40ad91ac07b0fddcfad8132e1447e
+
+
+## Setup
+
+All the cluster-dependent variables are in [`CONFIG.sh`](./CONFIG.sh). Please
+update the unspecified values (in angle brackets `<...>`) before launching any
+scripts.
+
+
+
+## Scripts
+
+Below is a list of scripts that can be used to reproduce various figures in our
+[paper](https://arxiv.org/pdf/2104.04473.pdf):
+
+* [run_table_1.sh](./run_table_1.sh): Table 1 showing weak-scaling throughput
+for GPT models ranging from 1 billion to 1 trillion parameters.
+* [run_figure_11.sh](./run_figure_11.sh): Figure 11 showing the weak-scaling
+performance of pipeline parallelism.
+* [run_figure_12.sh](./run_figure_12.sh): Figure 12 showing the effect of
+the interleaved schedule on a 175B GPT model.
+* [run_figure_13.sh](./run_figure_13.sh): Figure 13 showing the effect of
+different degrees of pipeline and tensor model parallelism on a model with
+162.2 billion parameters.
+* [run_figure_14.sh](./run_figure_14.sh): Figure 14 showing the effect of
+different degrees of data and pipeline model parallelism on a model with
+5.9 billion parameters.
+* [run_figure_15.sh](./run_figure_15.sh): Figure 15 showing the effect of
+different degrees of data and tensor model parallelism on a model with
+5.9 billion parameters.
+* [run_figure_16.sh](./run_figure_16.sh): Figure 16 showing the effect of
+microbatch size.
+* [run_figure_17.sh](./run_figure_17.sh): Figure 17 showing the effect of
+activation recomputation.
+* [run_figure_18.sh](./run_figure_18.sh): Figure 18 showing the effect of
+the scatter-gather communication optimization.
diff --git a/examples/sc21/SBATCH.sh b/examples/academic_paper_scripts/sc21/SBATCH.sh
similarity index 100%
rename from examples/sc21/SBATCH.sh
rename to examples/academic_paper_scripts/sc21/SBATCH.sh
diff --git a/examples/sc21/SRUN.sh b/examples/academic_paper_scripts/sc21/SRUN.sh
similarity index 100%
rename from examples/sc21/SRUN.sh
rename to examples/academic_paper_scripts/sc21/SRUN.sh
diff --git a/examples/sc21/run_figure_11.sh b/examples/academic_paper_scripts/sc21/run_figure_11.sh
similarity index 100%
rename from examples/sc21/run_figure_11.sh
rename to examples/academic_paper_scripts/sc21/run_figure_11.sh
diff --git a/examples/sc21/run_figure_12.sh b/examples/academic_paper_scripts/sc21/run_figure_12.sh
similarity index 100%
rename from examples/sc21/run_figure_12.sh
rename to examples/academic_paper_scripts/sc21/run_figure_12.sh
diff --git a/examples/sc21/run_figure_13.sh b/examples/academic_paper_scripts/sc21/run_figure_13.sh
similarity index 100%
rename from examples/sc21/run_figure_13.sh
rename to examples/academic_paper_scripts/sc21/run_figure_13.sh
diff --git a/examples/sc21/run_figure_14.sh b/examples/academic_paper_scripts/sc21/run_figure_14.sh
similarity index 100%
rename from examples/sc21/run_figure_14.sh
rename to examples/academic_paper_scripts/sc21/run_figure_14.sh
diff --git a/examples/sc21/run_figure_15.sh b/examples/academic_paper_scripts/sc21/run_figure_15.sh
similarity index 100%
rename from examples/sc21/run_figure_15.sh
rename to examples/academic_paper_scripts/sc21/run_figure_15.sh
diff --git a/examples/sc21/run_figure_16.sh b/examples/academic_paper_scripts/sc21/run_figure_16.sh
similarity index 100%
rename from examples/sc21/run_figure_16.sh
rename to examples/academic_paper_scripts/sc21/run_figure_16.sh
diff --git a/examples/sc21/run_figure_17.sh b/examples/academic_paper_scripts/sc21/run_figure_17.sh
similarity index 100%
rename from examples/sc21/run_figure_17.sh
rename to examples/academic_paper_scripts/sc21/run_figure_17.sh
diff --git a/examples/sc21/run_figure_18.sh b/examples/academic_paper_scripts/sc21/run_figure_18.sh
similarity index 100%
rename from examples/sc21/run_figure_18.sh
rename to examples/academic_paper_scripts/sc21/run_figure_18.sh
diff --git a/examples/sc21/run_table_1.sh b/examples/academic_paper_scripts/sc21/run_table_1.sh
similarity index 100%
rename from examples/sc21/run_table_1.sh
rename to examples/academic_paper_scripts/sc21/run_table_1.sh
diff --git a/examples/bert/README.md b/examples/bert/README.md
new file mode 100644
index 0000000000..6c1fe95bf0
--- /dev/null
+++ b/examples/bert/README.md
@@ -0,0 +1,53 @@
+# BERT MODEL
+
+## Table of contents
+- [1. Training Setup](#1-training-setup)
+- [2. Configurations](#2-configurations)
+
+## 1. Training setup
+
+
+To run the model using a docker container run it as follows
+```
+PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3
+CHECKPOINT_PATH="" #
+TENSORBOARD_LOGS_PATH=""#
+VOCAB_FILE="" #//bert-vocab.txt
+DATA_PATH="" #_text_document
+
+docker run \
+  --gpus=all \
+  --ipc=host \
+  --workdir /workspace/megatron-lm \
+  -v /path/to/data:/path/to/data \
+  -v /path/to/megatron-lm:/workspace/megatron-lm \
+  megatron-lm nvcr.io/nvidia/pytorch:24.01-py3 \
+  bash examples/bert/train_bert_340m_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $DATA_PATH "
+
+```
+NOTE: Depending on the environment you are running it the above command might like slightly different.
+
+
+## 2. Configurations
+
+The example in this folder shows you how to run 340m large model. There are other configs you could run as well
+
+### 4B
+```
+       --num-layers 48 \
+       --hidden-size 2560 \
+       --num-attention-heads 32 \
+       --tensor-model-parallel-size 1 \
+       --pipeline-model-parallel-size 1 \
+
+```
+
+### 20B
+```
+       --num-layers 48 \
+       --hidden-size 6144 \
+       --num-attention-heads 96 \
+       --tensor-model-parallel-size 4 \
+       --pipeline-model-parallel-size 4 \
+
+```
\ No newline at end of file
diff --git a/examples/bert/train_bert_340m_distributed.sh b/examples/bert/train_bert_340m_distributed.sh
new file mode 100644
index 0000000000..f0d9c87c8b
--- /dev/null
+++ b/examples/bert/train_bert_340m_distributed.sh
@@ -0,0 +1,79 @@
+#!/bin/bash
+
+# Runs the "340M" parameter model (Bert - Large)
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NUM_NODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+CHECKPOINT_PATH=$1 #
+TENSORBOARD_LOGS_PATH=$2 #
+VOCAB_FILE=$3 #/bert-vocab.json
+DATA_PATH=$4 #_text_document
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE 
+    --nnodes $NUM_NODES 
+    --master_addr $MASTER_ADDR 
+    --master_port $MASTER_PORT
+)
+
+BERT_MODEL_ARGS=(
+    --num-layers 24 
+    --hidden-size 1024 
+    --num-attention-heads 16 
+    --seq-length 512 
+    --max-position-embeddings 512 
+    --attention-backend auto # Can use (flash/fused/unfused/local)
+)
+
+TRAINING_ARGS=(
+    --micro-batch-size 4 
+    --global-batch-size 32 
+    --train-iters 1000000 
+    --weight-decay 1e-2 
+    --clip-grad 1.0 
+    --fp16
+    --lr 0.0001
+    --lr-decay-iters 990000 
+    --lr-decay-style linear 
+    --min-lr 1.0e-5 
+    --weight-decay 1e-2 
+    --lr-warmup-fraction .01 
+    --clip-grad 1.0 
+)
+
+MODEL_PARALLEL_ARGS=(
+	--tensor-model-parallel-size 8 
+	--pipeline-model-parallel-size 16 
+)
+
+DATA_ARGS=(
+    --data-path $DATA_PATH 
+    --vocab-file $VOCAB_FILE 
+    --split 949,50,1
+)
+
+EVAL_AND_LOGGING_ARGS=(
+    --log-interval 100
+    --save-interval 10000 
+    --eval-interval 1000 
+    --save $CHECKPOINT_PATH 
+    --load $CHECKPOINT_PATH 
+    --eval-iters 10
+    --tensorboard-dir $TENSORBOARD_LOGS_PATH 
+)
+
+torchrun ${DISTRIBUTED_ARGS[@]} pretrain_bert.py \
+    ${BERT_MODEL_ARGS[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${DATA_ARGS[@]} \
+    ${EVAL_AND_LOGGING_ARGS[@]}
+    
\ No newline at end of file
diff --git a/examples/detxoify_lm/finetune_gpt.py b/examples/detxoify_lm/finetune_gpt.py
deleted file mode 100644
index 70b781e0ee..0000000000
--- a/examples/detxoify_lm/finetune_gpt.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-
-
-"""Fine-tune GPT"""
-
-import torch
-from functools import partial
-import os
-import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                             os.path.pardir, os.path.pardir)))
-from megatron import get_args
-from megatron import get_timers
-from megatron import get_tokenizer
-from megatron import print_rank_0
-from megatron.core import mpu
-from megatron.data.blendable_dataset import BlendableDataset
-from megatron.data.gpt_dataset import build_train_valid_test_datasets
-from megatron.model import GPTModel
-from megatron.core.enums import ModelType
-from megatron.training import pretrain
-from megatron.utils import get_ltor_masks_and_position_ids
-from megatron.utils import average_losses_across_data_parallel_group
-
-def model_provider(pre_process=True, post_process=True):
-    """Build the model."""
-
-    print_rank_0('building GPT model ...')
-    model = GPTModel(
-        num_tokentypes=0,
-        parallel_output=True,
-        pre_process=pre_process,
-        post_process=post_process
-    )
-    return model
-
-
-def get_batch(data_iterator):
-    """Generate a batch"""
-    args = get_args()
-    tokenizer = get_tokenizer()
-
-    # Items and their type.
-    keys = ['text']
-    datatype = torch.int64
-
-    # Broadcast data.
-    if data_iterator is not None:
-        data = next(data_iterator)
-    else:
-        data = None
-    data_b = mpu.broadcast_data(keys, data, datatype)
-
-    # Unpack.
-    tokens_ = data_b['text'].long()
-    labels = tokens_[:, 1:].contiguous()
-    tokens = tokens_[:, :-1].contiguous()
-
-    # Get the masks and postition ids.
-    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
-        tokens,
-        tokenizer.eod,
-        args.reset_position_ids,
-        args.reset_attention_mask,
-        args.eod_mask_loss)
-
-    return tokens, labels, loss_mask, attention_mask, position_ids
-
-def loss_func(loss_mask, output_tensor):
-    losses = output_tensor.float()
-    loss_mask = loss_mask.view(-1).float()
-    loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
-
-    # Reduce loss for logging.
-    averaged_loss = average_losses_across_data_parallel_group([loss])
-
-    return loss, {'lm loss': averaged_loss[0]}
-
-
-def forward_step(data_iterator, model):
-    """Forward step."""
-    args = get_args()
-    timers = get_timers()
-
-    # Get the batch.
-    timers('batch-generator').start()
-    tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
-        data_iterator)
-    timers('batch-generator').stop()
-
-    output_tensor = model(tokens, position_ids, attention_mask,
-                          labels=labels)
-
-    return output_tensor, partial(loss_func, loss_mask)
-
-
-def train_valid_test_datasets_provider(train_val_test_num_samples):
-    """Build train, valid, and test datasets."""
-    args = get_args()
-
-    print_rank_0('> building train, validation, and test datasets '
-                 'for GPT ...')
-    train_ds, valid_ds1, test_ds = build_train_valid_test_datasets(
-        data_prefix=args.data_path,
-        data_impl=args.data_impl,
-        splits_string=args.split,
-        train_valid_test_num_samples=train_val_test_num_samples,
-        seq_length=args.seq_length,
-        seed=args.seed,
-        skip_warmup=(not args.mmap_warmup))
-    print_rank_0("> finished creating finetuning GPT datasets ...")
-
-    _, valid_ds, _ = build_train_valid_test_datasets(
-        data_prefix=args.data_path2,
-        data_impl="mmap",
-        splits_string="98,2,0",
-        train_valid_test_num_samples=train_val_test_num_samples,
-        seq_length=2048,
-        seed=1234,
-        skip_warmup=(not args.mmap_warmup))
-    print_rank_0("> finished creating pretrained GPT datasets ...")
-
-    return train_ds, valid_ds, test_ds
-
-
-def add_validation_args(parser):
-    """Text generation arguments."""
-    group = parser.add_argument_group(title='validation set')
-    group.add_argument('--data-path2', nargs='*', default=None,
-                       help='Path to the validation dataset. Accepted format:'
-                       '1) a single data path, 2) multiple datasets in the'
-                       'form: dataset1-weight dataset1-path dataset2-weight '
-                       'dataset2-path ...')
-    group.add_argument('--eval-ppl', action='store_true', default=False)
-    group.add_argument('--stored_params', type=dict, default=dict())
-    return parser
-
-
-if __name__ == "__main__":
-
-    pretrain(train_valid_test_datasets_provider, model_provider,
-             ModelType.encoder_or_decoder,
-             forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
-             extra_args_provider=add_validation_args,)
diff --git a/examples/detxoify_lm/generate_samples_gpt.py b/examples/detxoify_lm/generate_samples_gpt.py
deleted file mode 100644
index 47e1590ea5..0000000000
--- a/examples/detxoify_lm/generate_samples_gpt.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-
-
-"""Sample Generate GPT"""
-import json
-import os
-import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                             os.path.pardir, os.path.pardir)))
-import torch
-from megatron import get_args
-from megatron import get_tokenizer
-from megatron import print_rank_0
-from megatron.checkpointing import load_checkpoint
-from megatron.core import mpu
-from megatron.initialize import initialize_megatron
-from megatron.model import GPTModel
-from megatron.training import get_model
-from megatron.text_generation import generate_and_post_process
-
-
-def model_provider(pre_process=True, post_process=True):
-    """Build the model."""
-
-    print_rank_0('building GPT model ...')
-    model = GPTModel(num_tokentypes=0, parallel_output=False,
-                     pre_process=pre_process, post_process=post_process)
-
-    return model
-
-def add_text_generate_args(parser):
-    """Text generation arguments."""
-    group = parser.add_argument_group(title='text generation')
-
-    group.add_argument("--temperature", type=float, default=1.0,
-                       help='Sampling temperature.')
-    group.add_argument("--greedy", action='store_true', default=False,
-                       help='Use greedy sampling.')
-    group.add_argument("--top_p", type=float, default=0.0,
-                       help='Top p sampling.')
-    group.add_argument("--top_k", type=int, default=0,
-                       help='Top k sampling.')
-    group.add_argument("--out-seq-length", type=int, default=1024,
-                       help='Size of the output generated text.')
-    group.add_argument("--sample-input-file", type=str, default=None,
-                       help='Get input from file instead of interactive mode, '
-                       'each line is an input.')
-    group.add_argument("--sample-output-file", type=str, default=None,
-                       help='Output file got from --sample-input-file')
-    group.add_argument("--num-samples", type=int, default=0,
-                       help='Number of samples to generate unconditionally, '
-                       'defaults to 0 and interactive conditional sampling')
-    group.add_argument("--genfile", type=str,
-                       help='Output file when generating unconditionally')
-    return parser
-
-def generate_samples_unconditional(model):
-    args = get_args()
-
-    if torch.distributed.get_rank() == 0:
-        cnt = 0
-        num_samples = args.num_samples
-        from tqdm import tqdm
-        pbar = tqdm(total=num_samples)
-
-    while True:
-        if torch.distributed.get_rank() == 0:
-            sentences = [''] * args.global_batch_size
-            print("global batch size", args.global_batch_size)
-            max_len = args.out_seq_length
-            resp_sentences, resp_sentences_seg, output_logits, \
-            tokens = generate_and_post_process(model, prompts=sentences,
-                                               tokens_to_generate=max_len,
-                                               return_output_log_probs=False,
-                                               top_k_sampling=args.top_k,
-                                               top_p_sampling=args.top_p,
-                                               add_BOS=True,
-                                               temperature=1.0)
-            for prompt, generation, token in zip(sentences, resp_sentences, tokens):
-                datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
-                yield datum
-                cnt += 1
-                pbar.update()
-                if cnt >= num_samples:
-                    break
-
-            if cnt >= num_samples:
-                pbar.close()
-                break
-        else:
-            generate_and_post_process(model)
-
-
-def generate_samples_conditional(model):
-    args = get_args()
-
-    if torch.distributed.get_rank() == 0:
-        num_samples = args.num_samples
-        cnt = 0
-        from tqdm import tqdm
-        pbar = tqdm(total=num_samples)
-
-        fname = open(args.sample_input_file, "r")
-        lines = fname.readlines()
-        all_raw_text = [json.loads(line)['prompt']['text'] for line in lines]
-        input_count = len(all_raw_text)
-        input_pos = 0
-
-    while True:
-        torch.distributed.barrier()
-        if torch.distributed.get_rank() == 0:
-            sentences = []
-            print("global batch size", args.global_batch_size)
-            for _ in range(args.global_batch_size):
-                if input_pos >= input_count:
-                    print(f"input pos: {input_pos}, input count: {input_count}")
-                    raw_text = "EMPTY TEXT"
-                else:
-                    raw_text = all_raw_text[input_pos]
-                input_pos += 1
-                sentences.append(raw_text)
-
-            max_len = args.out_seq_length
-            resp_sentences, resp_sentences_seg, output_logits, \
-            tokens = generate_and_post_process(model, prompts=sentences,
-                                               tokens_to_generate=max_len,
-                                               return_output_log_probs=False,
-                                               top_k_sampling=args.top_k,
-                                               top_p_sampling=args.top_p,
-                                               add_BOS=False,
-                                               temperature=1.0)
-            for prompt, generation, token in zip(sentences, resp_sentences, tokens):
-                datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
-                yield datum
-                cnt += 1
-                pbar.update()
-                if cnt >= num_samples:
-                    break
-
-            if cnt >= num_samples:
-                pbar.close()
-                break
-        else:
-            generate_and_post_process(model)
-
-
-def generate_and_write_samples_unconditional(model):
-    args = get_args()
-    assert args.genfile is not None
-    with open(args.genfile, 'w') as f:
-        for datum in generate_samples_unconditional(model):
-            if torch.distributed.get_rank() == 0:
-                f.write(json.dumps(datum) + '\n')
-
-
-def generate_and_write_samples_conditional(model):
-    args = get_args()
-    if args.sample_output_file is None:
-        sample_output_file = args.sample_input_file + ".out"
-        print('`sample-output-file` not specified, setting '
-              'it to {}'.format(sample_output_file))
-    else:
-        sample_output_file = args.sample_output_file
-    with open(sample_output_file, 'w') as f:
-        for datum in generate_samples_conditional(model):
-            if torch.distributed.get_rank() == 0:
-                f.write(json.dumps(datum) + '\n')
-
-
-def main():
-    """Main program."""
-
-    initialize_megatron(extra_args_provider=add_text_generate_args,
-                        args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
-                                       'no_load_rng': True,
-                                       'no_load_optim': True,
-                                       'seq_length': 2048})
-
-    # Set up model and load checkpoint
-    model = get_model(model_provider, wrap_with_ddp=False)
-
-    args = get_args()
-
-    if args.load is not None:
-        _ = load_checkpoint(model, None, None)
-    model = model[0]
-
-    # Generate samples.
-    if args.sample_input_file != None:
-        print(f"{args.sample_input_file}")
-        generate_and_write_samples_conditional(model)
-    else:
-        generate_and_write_samples_unconditional(model)
-
-
-if __name__ == "__main__":
-
-    main()
diff --git a/examples/evaluate_retriever_nq.sh b/examples/evaluate_retriever_nq.sh
deleted file mode 100644
index 16e937f4fd..0000000000
--- a/examples/evaluate_retriever_nq.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-
-# Evaluate natural question test data given Wikipedia embeddings and pretrained
-# ICT model or a finetuned model for Natural Question task
-
-# Datasets can be downloaded from the following link:
-# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
-
-EVIDENCE_DATA_DIR=
-EMBEDDING_PATH=
-CHECKPOINT_PATH=
-
-QA_FILE=
-
-python tasks/main.py \
-    --task RETRIEVER-EVAL \
-    --tokenizer-type BertWordPieceLowerCase \
-    --num-layers 12 \
-    --hidden-size 768 \
-    --num-attention-heads 12 \
-    --tensor-model-parallel-size 1 \
-    --micro-batch-size 128 \
-    --activations-checkpoint-method uniform \
-    --seq-length 512 \
-    --max-position-embeddings 512 \
-    --load ${CHECKPOINT_PATH} \
-    --evidence-data-path ${EVIDENCE_DATA_DIR} \
-    --embedding-path ${EMBEDDING_PATH} \
-    --retriever-seq-length 256 \
-    --vocab-file  bert-vocab.txt\
-    --qa-data-test ${QA_FILE} \
-    --faiss-use-gpu \
-    --retriever-report-topk-accuracies 1 5 20 100 \
-    --fp16 \
-    --indexer-log-interval 1000 \
-    --indexer-batch-size 128
-
-
diff --git a/examples/evaluate_zeroshot_gpt.sh b/examples/evaluate_zeroshot_gpt.sh
deleted file mode 100755
index f8c38dc01d..0000000000
--- a/examples/evaluate_zeroshot_gpt.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-
-WORLD_SIZE=8
-
-DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
-                  --nnodes 1 \
-                  --node_rank 0 \
-                  --master_addr localhost \
-                  --master_port 6000"
-
-TASK="LAMBADA"
-
-VALID_DATA=
-VOCAB_FILE=gpt2-vocab.json
-MERGE_FILE=gpt2-merges.txt
-CHECKPOINT=checkpoints/gpt2_345m
-
-
-python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
-               --task $TASK \
-               --valid-data $VALID_DATA \
-               --tokenizer-type GPT2BPETokenizer \
-               --strict-lambada \
-               --vocab-file $VOCAB_FILE \
-               --merge-file $MERGE_FILE \
-               --load $CHECKPOINT \
-               --tensor-model-parallel-size 1 \
-               --num-layers 24 \
-               --hidden-size 1024 \
-               --num-attention-heads 16 \
-               --batch-size 8 \
-               --activations-checkpoint-method uniform \
-               --seq-length 1024 \
-               --max-position-embeddings 1024 \
-               --log-interval 10 \
-               --fp16 \
-               --no-load-optim \
-               --no-load-rng
diff --git a/examples/export/README.md b/examples/export/README.md
new file mode 100644
index 0000000000..bdd07da263
--- /dev/null
+++ b/examples/export/README.md
@@ -0,0 +1,10 @@
+# Megatron Core Export
+
+This module is used to export megatron core models to different inference frameworks. 
+Currently we support TRTLLM export . In the future we will be adding support for VLLM etc. 
+
+## PTQ AND EXPORT
+Follow the examples of [TensorRT Model Optimizer](../post_training/modelopt) to perform post training quantization, followed by an export to a HF-like checkpoint for TensorRT-LLM, vLLM, and SGLang deployment. 
+
+# TRTLLM EXPORT
+Follow the instructions in [trtllm_export](./trtllm_export/) to do export to TRTLLM checkpoint format alone.
diff --git a/examples/export/trtllm_export/README.md b/examples/export/trtllm_export/README.md
new file mode 100644
index 0000000000..52cad78583
--- /dev/null
+++ b/examples/export/trtllm_export/README.md
@@ -0,0 +1,161 @@
+# Megatron Core To TRTLLM Export Documentation
+This guide will walk you through how you can use the megatron core export for exporting models to trtllm format
+
+### Contents
+- [Megatron Core To TRTLLM Export Documentation](#megatron-core-to-trtllm-export-documentation)
+- [Contents](#contents)
+  - [1. Quick Start](#1-quick-start)
+    - [1.1 Understanding The Code](#11-understanding-the-code)
+    - [1.2 Running The Code](#12-running-the-code)
+  - [2. GPU Export](#2-gpu-export)
+  - [3. Future work](#4-future-work)
+
+#### 1. Quick Start
+This will walk you through the flow of converting an mcore gpt model to trtllm format using single device mode. The file can be found at [gpt_single_device_cpu_export.py](./single_device_export/gpt_single_device_cpu_export.py)
+
+NOTE: For faster performance, if your entire model will fit into gpu memory, pre transfer the model state dict to gpu and then call the get_trtllm_pretrained_config_and_model_weights function.
+
+

+
+##### 1.1 Understanding The Code
+***STEP 1 - We initialize model parallel and other default arguments***
+We initalize tp and pp to 1 so that we can get the full model state dict on cpu
+```python
+    initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1)
+```
+
+***STEP 2 - We load the model using the model_provider_function***
+NOTE: We create a simple gpt model
+
+```python
+    transformer_config = TransformerConfig(
+        num_layers=2, 
+        hidden_size=64, # Needs to be atleast 32 times num_attn_heads
+        num_attention_heads=2, 
+        use_cpu_initialization=True, 
+        pipeline_dtype=torch.float32,
+    )
+
+    gpt_model = GPTModel(
+        config=transformer_config, 
+        transformer_layer_spec=get_gpt_layer_local_spec(), 
+        vocab_size=100, 
+        max_sequence_length=_SEQUENCE_LENGTH,
+    )
+
+    # Optionally you can also load a model using this code 
+    # sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
+    # checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
+    # gpt_model.load_state_dict(checkpoint)
+
+```
+
+***STEP 3 - Instantiate the TRTLLM Helper***
+We instantiate the [TRTLLM Helper](../../../megatron/core/export/trtllm/trtllm_helper.py)  For the GPT model we instantiate trtllm_helper as shown below.
+```python
+    if hasattr(gpt_model, "rotary_pos_emb"):
+        seq_len_interpolation_factor =  gpt_model.rotary_pos_emb.seq_len_interpolation_factor
+
+    trtllm_helper = TRTLLMHelper(
+                        transformer_config=gpt_model.config, 
+                        model_type=ModelType.gpt,
+                        position_embedding_type = gpt_model.position_embedding_type, 
+                        max_position_embeddings = gpt_model.max_position_embeddings, 
+                        rotary_percentage = gpt_model.rotary_percent,
+                        rotary_base = gpt_model.rotary_base,
+                        moe_tp_mode = 2,
+                        multi_query_mode = False,
+                        activation = "gelu", 
+                        seq_len_interpolation_factor = seq_len_interpolation_factor,
+                        share_embeddings_and_output_weights=gpt_model.share_embeddings_and_output_weights
+                    )   
+```
+
+***STEP 4 - Get the TRTLLM Weights and configs***
+To convert model weights to trtllm weights and configs, we use the [single_device_converter](../../../megatron/core/export/trtllm/trtllm_weights_converter/single_device_trtllm_model_weights_converter.py). We pass as inputs the model state dict, and export config. In this example we use inference tp size as 2 for the export. 
+
+```python
+    model_state_dict={}
+    for key , val in gpt_model.state_dict().items():
+        # val is non for _extra_state layers . We filter it out
+        if val is not None:
+            model_state_dict[key] = val
+
+    export_config = ExportConfig(inference_tp_size = 2)
+    weight_list, config_list = trtllm_helper.get_trtllm_pretrained_config_and_model_weights(
+        model_state_dict= model_state_dict,
+        dtype = DataType.bfloat16,
+        export_config=export_config
+    )
+```
+
+***STEP 5 - Build the TRTLLM Engine***
+Following code is used to build the TRTLLM Engine. 
+
+```python
+    for trtllm_model_weights, trtllm_model_config in zip(weight_list, config_list):
+        trtllm_helper.build_and_save_engine(
+            max_input_len=256,
+            max_output_len=256,
+            max_batch_size=8,
+            engine_dir='/opt/megatron-lm/engine',
+            trtllm_model_weights=trtllm_model_weights,
+            trtllm_model_config=trtllm_model_config,
+            lora_ckpt_list=None,
+            use_lora_plugin=None,
+            max_lora_rank=64,
+            lora_target_modules=None,
+            max_prompt_embedding_table_size=0,
+            paged_kv_cache=True,
+            remove_input_padding=True,
+            paged_context_fmha=False,
+            use_refit=False,
+            max_num_tokens=None,
+            max_seq_len=512,
+            opt_num_tokens=None,
+            max_beam_width=1,
+            tokens_per_block=128,
+            multiple_profiles=False,
+            gpt_attention_plugin="auto",
+            gemm_plugin="auto",
+        )
+```
+

+
+##### 1.2 Running The Code
+An example run script is shown below. 
+
+```
+# In a workstation 
+MLM_PATH=/path/to/megatron-lm
+CONTAINER_IMAGE=gitlab-master.nvidia.com:5005/dl/joc/nemo-ci/trtllm_0.12/train:pipe.17669124-x86
+
+docker run -it --gpus=all --ipc=host -v $MLM_PATH/:/opt/megatron-lm $CONTAINER_IMAGE bash
+
+# Inside the container run the following. 
+
+cd /opt/megatron-lm/
+
+CUDA_VISIBLE_DEVICES=0 torchrun --nproc-per-node 1  examples/export/trtllm_export/single_device_export/gpt_single_device_cpu_export.py
+```
+
+

+
+#### 2. GPU Export
+You can use the [gpt_distributed_gpu_export.py](./distributed_export/gpt_distributed_gpu_export.py) to run a more optimized on device distributed. version of trtllm export. Internally this uses the [distributed_converter](../../../megatron/core/export/trtllm/trtllm_weights_converter/distributed_trtllm_model_weights_converter.py) to convert model weights on device. 
+In the single device version you collect all the model weights on CPU/GPU, convert it to trtllm format, and then store the engine back on disk. In the GPU version you load each individual state dict on the gpus, convert it on the device itself and store the engine on disk. 
+
+To run the gpu version 
+
+```
+CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc-per-node 2  examples/export/trtllm_export/distributed_export/gpt_distributed_gpu_export.py
+```
+
+

+
+#### 3. Future work
+The following are planned for the future releases . 
+* Pipeline parallellism for export (Work in progress) 
+* GPU Export for more models (Work in progress for some models)
+* Refit functionality
+* VLLM Support
\ No newline at end of file
diff --git a/examples/export/trtllm_export/distributed_export/gpt_distributed_gpu_export.py b/examples/export/trtllm_export/distributed_export/gpt_distributed_gpu_export.py
new file mode 100644
index 0000000000..57d44f9f62
--- /dev/null
+++ b/examples/export/trtllm_export/distributed_export/gpt_distributed_gpu_export.py
@@ -0,0 +1,117 @@
+import os
+import torch
+from megatron.core import parallel_state
+from megatron.core import dist_checkpointing
+from megatron.core.export.model_type import ModelType
+from megatron.core.export.data_type import DataType
+from megatron.core.export.trtllm.trtllm_helper import TRTLLMHelper
+from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.models.gpt.gpt_model import GPTModel
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+
+
+_SEQUENCE_LENGTH = 64
+_VOCAB_SIZE = 256
+
+def initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1):
+    parallel_state.destroy_model_parallel()
+
+    # Torch setup for distributed training
+    rank = int(os.environ['LOCAL_RANK'])
+    world_size = torch.cuda.device_count()
+    torch.cuda.set_device(rank)
+    torch.distributed.init_process_group(world_size=world_size, rank=rank)
+
+    # Megatron core distributed training initialization
+    parallel_state.initialize_model_parallel(tensor_model_parallel_size = tensor_model_parallel_size, pipeline_model_parallel_size=pipeline_model_parallel_size)
+
+def model_provider():
+    """Build the model."""
+
+    transformer_config = TransformerConfig(
+        num_layers=2, 
+        hidden_size=64, 
+        num_attention_heads=2, 
+        use_cpu_initialization=True, 
+        pipeline_dtype=torch.float32
+    )
+
+    gpt_model = GPTModel(
+        config=transformer_config, 
+        transformer_layer_spec=get_gpt_layer_local_spec(), 
+        vocab_size=_VOCAB_SIZE, 
+        max_sequence_length=_SEQUENCE_LENGTH,
+    )
+
+    return gpt_model
+
+def load_distributed_checkpoint(checkpoint_path, gpt_model):
+    sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
+    checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
+    gpt_model.load_state_dict(checkpoint)
+    return gpt_model
+
+if __name__ == "__main__":
+    initialize_distributed(tensor_model_parallel_size=2, pipeline_model_parallel_size=1)
+    model_parallel_cuda_manual_seed(123)
+
+    gpt_model = model_provider()
+    device = torch.device("cuda")
+    gpt_model.to(device) 
+    
+    # Optionally you can also load a gpt model from ckpt_path using this code below
+    # gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
+
+    seq_len_interpolation_factor = None
+    if hasattr(gpt_model, "rotary_pos_emb"):
+        seq_len_interpolation_factor =  gpt_model.rotary_pos_emb.seq_len_interpolation_factor
+
+    trtllm_helper = TRTLLMHelper(
+                        transformer_config=gpt_model.config, 
+                        model_type=ModelType.gpt,
+                        position_embedding_type = gpt_model.position_embedding_type, 
+                        max_position_embeddings = gpt_model.max_position_embeddings, 
+                        rotary_percentage = gpt_model.rotary_percent,
+                        rotary_base = gpt_model.rotary_base,
+                        moe_tp_mode = 2,
+                        multi_query_mode = False,
+                        activation = "gelu", 
+                        seq_len_interpolation_factor = seq_len_interpolation_factor,
+                        share_embeddings_and_output_weights=gpt_model.share_embeddings_and_output_weights
+                    )
+    
+
+    trtllm_model_weights, trtllm_model_config = trtllm_helper.get_trtllm_pretrained_config_and_model_weights(
+        model_state_dict= gpt_model.state_dict(),
+        dtype = DataType.bfloat16,
+        on_device_distributed_conversion=True, 
+        vocab_size=_VOCAB_SIZE, 
+        gpus_per_node=2,
+    )
+
+    trtllm_helper.build_and_save_engine(
+        max_input_len=256,
+        max_output_len=256,
+        max_batch_size=8,
+        engine_dir='/opt/megatron-lm/engine',
+        trtllm_model_weights=trtllm_model_weights[0],
+        trtllm_model_config=trtllm_model_config[0],
+        lora_ckpt_list=None,
+        use_lora_plugin=None,
+        max_lora_rank=64,
+        lora_target_modules=None,
+        max_prompt_embedding_table_size=0,
+        paged_kv_cache=True,
+        remove_input_padding=True,
+        paged_context_fmha=False,
+        use_refit=False,
+        max_num_tokens=None,
+        max_seq_len=512,
+        opt_num_tokens=None,
+        max_beam_width=1,
+        tokens_per_block=128,
+        multiple_profiles=False,
+        gpt_attention_plugin="auto",
+        gemm_plugin="auto",
+    )
diff --git a/examples/export/trtllm_export/single_device_export/gpt_single_device_cpu_export.py b/examples/export/trtllm_export/single_device_export/gpt_single_device_cpu_export.py
new file mode 100644
index 0000000000..587e7cfdd3
--- /dev/null
+++ b/examples/export/trtllm_export/single_device_export/gpt_single_device_cpu_export.py
@@ -0,0 +1,118 @@
+import os
+import torch
+from megatron.core import parallel_state
+from megatron.core import dist_checkpointing
+from megatron.core.export.model_type import ModelType
+from megatron.core.export.data_type import DataType
+from megatron.core.export.export_config import ExportConfig
+from megatron.core.export.trtllm.trtllm_helper import TRTLLMHelper
+from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.models.gpt.gpt_model import GPTModel
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+
+
+_SEQUENCE_LENGTH = 64
+
+
+def initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1):
+    parallel_state.destroy_model_parallel()
+
+    # Torch setup for distributed training
+    rank = int(os.environ['LOCAL_RANK'])
+    world_size = torch.cuda.device_count()
+    torch.cuda.set_device(rank)
+    torch.distributed.init_process_group(world_size=world_size, rank=rank)
+
+    # Megatron core distributed training initialization
+    parallel_state.initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size)
+
+def model_provider():
+    """Build the model."""
+
+    transformer_config = TransformerConfig(
+        num_layers=2, 
+        hidden_size=64, # Needs to be atleast 32 times num_attn_heads
+        num_attention_heads=2, 
+        use_cpu_initialization=True, 
+        pipeline_dtype=torch.float32,
+    )
+
+    gpt_model = GPTModel(
+        config=transformer_config, 
+        transformer_layer_spec=get_gpt_layer_local_spec(), 
+        vocab_size=100, 
+        max_sequence_length=_SEQUENCE_LENGTH,
+    )
+
+    return gpt_model
+
+def load_distributed_checkpoint(checkpoint_path, gpt_model):
+    sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
+    checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
+    gpt_model.load_state_dict(checkpoint)
+    return gpt_model
+
+if __name__ == "__main__":
+    # Need to use TP1 PP1 for export on single device
+    initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1)
+    model_parallel_cuda_manual_seed(123)
+
+    gpt_model = model_provider()
+
+    # Optionally you can also load a gpt model from ckpt_path using this code below
+    # gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
+
+    seq_len_interpolation_factor = None
+    if hasattr(gpt_model, "rotary_pos_emb"):
+        seq_len_interpolation_factor =  gpt_model.rotary_pos_emb.seq_len_interpolation_factor
+
+    trtllm_helper = TRTLLMHelper(
+                        transformer_config=gpt_model.config, 
+                        model_type=ModelType.gpt,
+                        position_embedding_type = gpt_model.position_embedding_type, 
+                        max_position_embeddings = gpt_model.max_position_embeddings, 
+                        rotary_percentage = gpt_model.rotary_percent,
+                        rotary_base = gpt_model.rotary_base,
+                        moe_tp_mode = 2,
+                        multi_query_mode = False,
+                        activation = "gelu", 
+                        seq_len_interpolation_factor = seq_len_interpolation_factor,
+                        share_embeddings_and_output_weights=gpt_model.share_embeddings_and_output_weights
+                    )
+    
+
+    export_config = ExportConfig(inference_tp_size = 2)
+    # NOTE : For faster performance, if your entire model will fit in gpu memory, transfer model state dict to GPU and then call this api
+    weight_list, config_list = trtllm_helper.get_trtllm_pretrained_config_and_model_weights(
+        model_state_dict= gpt_model.state_dict(),
+        dtype = DataType.bfloat16,
+        export_config=export_config
+    )
+
+    for trtllm_model_weights, trtllm_model_config in zip(weight_list, config_list):
+        trtllm_helper.build_and_save_engine(
+            max_input_len=256,
+            max_output_len=256,
+            max_batch_size=8,
+            engine_dir='/opt/megatron-lm/engine',
+            trtllm_model_weights=trtllm_model_weights,
+            trtllm_model_config=trtllm_model_config,
+            lora_ckpt_list=None,
+            use_lora_plugin=None,
+            max_lora_rank=64,
+            lora_target_modules=None,
+            max_prompt_embedding_table_size=0,
+            paged_kv_cache=True,
+            remove_input_padding=True,
+            paged_context_fmha=False,
+            use_refit=False,
+            max_num_tokens=None,
+            max_seq_len=512,
+            opt_num_tokens=None,
+            max_beam_width=1,
+            tokens_per_block=128,
+            multiple_profiles=False,
+            gpt_attention_plugin="auto",
+            gemm_plugin="auto",
+        )
\ No newline at end of file
diff --git a/examples/finetune_mnli_distributed.sh b/examples/finetune_mnli_distributed.sh
deleted file mode 100755
index 9219e595dd..0000000000
--- a/examples/finetune_mnli_distributed.sh
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/bin/bash
-
-WORLD_SIZE=8
-
-DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
-                  --nnodes 1 \
-                  --node_rank 0 \
-                  --master_addr localhost \
-                  --master_port 6000"
-
-TRAIN_DATA="data/glue_data/MNLI/train.tsv"
-VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
-            data/glue_data/MNLI/dev_mismatched.tsv"
-PRETRAINED_CHECKPOINT=checkpoints/bert_345m
-VOCAB_FILE=bert-vocab.txt
-CHECKPOINT_PATH=checkpoints/bert_345m_mnli
-
-python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
-               --task MNLI \
-               --seed 1234 \
-               --train-data $TRAIN_DATA \
-               --valid-data $VALID_DATA \
-               --tokenizer-type BertWordPieceLowerCase \
-               --vocab-file $VOCAB_FILE \
-               --epochs 5 \
-               --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
-               --tensor-model-parallel-size 1 \
-               --num-layers 24 \
-               --hidden-size 1024 \
-               --num-attention-heads 16 \
-               --micro-batch-size 8 \
-               --activations-checkpoint-method uniform \
-               --lr 5.0e-5 \
-               --lr-decay-style linear \
-               --lr-warmup-fraction 0.065 \
-               --seq-length 512 \
-               --max-position-embeddings 512 \
-               --save-interval 500000 \
-               --save $CHECKPOINT_PATH \
-               --log-interval 10 \
-               --eval-interval 100 \
-               --eval-iters 50 \
-               --weight-decay 1.0e-1 \
-               --fp16
diff --git a/examples/finetune_race_distributed.sh b/examples/finetune_race_distributed.sh
deleted file mode 100755
index e7f70a70ab..0000000000
--- a/examples/finetune_race_distributed.sh
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/bin/bash
-
-WORLD_SIZE=8
-
-DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
-                  --nnodes 1 \
-                  --node_rank 0 \
-                  --master_addr localhost \
-                  --master_port 6000"
-
-TRAIN_DATA="data/RACE/train/middle"
-VALID_DATA="data/RACE/dev/middle \
-            data/RACE/dev/high"
-VOCAB_FILE=bert-vocab.txt
-PRETRAINED_CHECKPOINT=checkpoints/bert_345m
-CHECKPOINT_PATH=checkpoints/bert_345m_race
-
-python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
-               --task RACE \
-               --seed 1234 \
-               --train-data $TRAIN_DATA \
-               --valid-data $VALID_DATA \
-               --tokenizer-type BertWordPieceLowerCase \
-               --vocab-file $VOCAB_FILE \
-               --epochs 3 \
-               --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
-               --tensor-model-parallel-size 1 \
-               --num-layers 24 \
-               --hidden-size 1024 \
-               --num-attention-heads 16 \
-               --micro-batch-size 4 \
-               --activations-checkpoint-method uniform \
-               --lr 1.0e-5 \
-               --lr-decay-style linear \
-               --lr-warmup-fraction 0.06 \
-               --seq-length 512 \
-               --max-position-embeddings 512 \
-               --save-interval 100000 \
-               --save $CHECKPOINT_PATH \
-               --log-interval 10 \
-               --eval-interval 100 \
-               --eval-iters 50 \
-               --weight-decay 1.0e-1 \
-               --clip-grad 1.0 \
-               --hidden-dropout 0.1 \
-               --attention-dropout 0.1 \
-               --fp16
diff --git a/examples/finetune_retriever_distributed.sh b/examples/finetune_retriever_distributed.sh
deleted file mode 100755
index 535a2e053d..0000000000
--- a/examples/finetune_retriever_distributed.sh
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/bin/bash
-
-# Finetune a BERT or pretrained ICT model using Google natural question data 
-# Datasets can be downloaded from the following link:
-# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
-
-WORLD_SIZE=8
-
-DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
-                  --nnodes 1 \
-                  --node_rank 0 \
-                  --master_addr localhost \
-                  --master_port 6000"
-
-CHECKPOINT_PATH=
-
-# Load either of the below
-BERT_LOAD_PATH=
-PRETRAINED_CHECKPOINT=
-
-python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
-        --task RET-FINETUNE-NQ \
-        --train-with-neg \
-        --train-hard-neg 1 \
-        --pretrained-checkpoint ${PRETRAINED_CHECKPOINT} \
-        --num-layers 12 \
-        --hidden-size 768 \
-        --num-attention-heads 12 \
-        --tensor-model-parallel-size 1 \
-        --tokenizer-type BertWordPieceLowerCase \
-        --train-data nq-train.json \
-        --valid-data nq-dev.json \
-        --save ${CHECKPOINT_PATH} \
-        --load ${CHECKPOINT_PATH} \
-        --vocab-file bert-vocab.txt \
-        --bert-load ${BERT_LOAD_PATH} \
-        --save-interval 5000 \
-        --log-interval 10 \
-        --eval-interval 20000 \
-        --eval-iters 100 \
-        --indexer-log-interval 1000 \
-        --faiss-use-gpu \
-        --DDP-impl torch \
-        --fp16 \
-        --retriever-report-topk-accuracies 1 5 10 20 100 \
-        --seq-length 512 \
-        --retriever-seq-length 256 \
-        --max-position-embeddings 512 \
-        --retriever-score-scaling \
-        --epochs 80 \
-        --micro-batch-size 8 \
-        --eval-micro-batch-size 16 \
-        --indexer-batch-size 128 \
-        --lr 2e-5 \
-        --lr-warmup-fraction 0.01 \
-        --weight-decay 1e-1
diff --git a/examples/gpt3/README.md b/examples/gpt3/README.md
new file mode 100644
index 0000000000..8d6f267416
--- /dev/null
+++ b/examples/gpt3/README.md
@@ -0,0 +1,57 @@
+# GPT3 MODEL
+
+## Table of contents
+- [1. Training Setup](#1-training-setup)
+- [2. Configurations](#2-configurations)
+- [3. Training Results](#3-training-results)
+
+## 1. Training setup
+
+
+To run the model using a docker container run it as follows
+```
+PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3
+CHECKPOINT_PATH="" #
+TENSORBOARD_LOGS_PATH=""#
+VOCAB_FILE="" #/gpt2-vocab.json
+MERGE_FILE="" #/gpt2-merges.txt
+DATA_PATH="" #_text_document
+
+docker run \
+  --gpus=all \
+  --ipc=host \
+  --workdir /workspace/megatron-lm \
+  -v /path/to/data:/path/to/data \
+  -v /path/to/megatron-lm:/workspace/megatron-lm \
+  megatron-lm nvcr.io/nvidia/pytorch:24.01-py3 \
+  bash examples/gpt3/train_gpt3_175b_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $MERGE_FILE $DATA_PATH "
+
+```
+NOTE: Depending on the environment you are running it the above command might like slightly different.
+
+
+## 2. Configurations
+
+The example in this folder shows you how to run 175B model. There are other configs you could run as well
+
+### 345M
+```
+       --num-layers 12 \
+       --hidden-size 512 \
+       --num-attention-heads 8 \
+       --seq-length 1024 \
+       --tensor-model-parallel-size 1 \
+       --pipeline-model-parallel-size 1 \
+
+```
+
+### 857M
+```
+       --num-layers 24 \
+       --hidden-size 1024 \
+       --num-attention-heads 16 \
+       --seq-length 2048 \
+       --tensor-model-parallel-size 1 \
+       --pipeline-model-parallel-size 1 \
+
+```
diff --git a/examples/gpt3/gpt_config.yaml b/examples/gpt3/gpt_config.yaml
new file mode 100644
index 0000000000..2fd40e6214
--- /dev/null
+++ b/examples/gpt3/gpt_config.yaml
@@ -0,0 +1,300 @@
+# WARNING: Yaml configs is currently an experimental feature
+language_model:
+  # model architecture
+  num_layers: 24
+  hidden_size: 1024
+  num_attention_heads: 16
+  num_query_groups: null
+
+  ffn_hidden_size: null
+  kv_channels: null
+  hidden_dropout: 0.0
+  attention_dropout: 0.0
+  fp32_residual_connection: False
+
+  apply_residual_connection_post_layernorm: False
+  layernorm_epsilon: 1.e-5
+  layernorm_zero_centered_gamma: True
+  add_bias_linear: False
+  bias_activation_fusion: False
+  add_qkv_bias: False
+  gated_linear_unit: False
+  activation_func: swiglu
+  num_moe_experts: null
+  rotary_interleaved: False
+  window_size: null
+
+  # initialization
+  init_method: null
+  init_method_std: 0.02
+  output_layer_init_method: null
+
+  # mixed-precision
+  apply_query_key_layer_scaling: False
+  attention_softmax_in_fp32: False
+
+  # fusion
+  bias_swiglu_fusion: True
+  masked_softmax_fusion: True
+  persist_layer_norm: False
+  memory_efficient_layer_norm: False
+  bias_dropout_fusion: True
+  apply_rope_fusion: True
+
+  # activation recomputation
+  recompute_granularity: null
+  recompute_method: null
+  recompute_num_layers: null
+  distribute_saved_activations: null
+
+  # fp8 related
+  fp8: null
+  fp8_margin: 0
+  fp8_interval: 1
+  fp8_amax_history_len: 1
+  fp8_amax_compute_algo: "most_recent"
+  fp8_wgrad: True
+
+  # miscellaneous
+  clone_scatter_output_in_embedding: True
+
+  normalization: "LayerNorm"  # alt value supported by TE: "RMSNorm"
+
+  # MoE related
+  moe_router_load_balancing_type: "aux_loss"
+  moe_router_topk: 2
+  moe_router_group_topk: null
+  moe_router_num_groups: null
+  moe_grouped_gemm: False
+  moe_aux_loss_coeff: 0  # 1e-2 would be a good start value for load balance loss.
+  moe_z_loss_coeff: null  # 1e-3 would be a good start value for z-loss
+  moe_input_jitter_eps: null
+  moe_token_dropping: False
+
+model_parallel:
+  # Model parallelism
+  tensor_model_parallel_size: 1
+  context_parallel_size: 1
+  pipeline_model_parallel_size: 1
+  virtual_pipeline_model_parallel_size: null
+  sequence_parallel: True
+  expert_model_parallel_size: 1
+
+  # Initialization
+  perform_initialization: True
+  use_cpu_initialization: null
+
+  # Training
+  fp16: False
+  bf16: True
+  params_dtype: null # Set from above arguments for core
+  timers: null
+
+  # Optimizations
+  gradient_accumulation_fusion: True
+  async_tensor_model_parallel_allreduce: True
+  tp_comm_overlap: False
+
+  # Debug Options
+  tp_comm_split_ag: True
+  tp_comm_atomic_ag: True
+  tp_comm_split_rs: True
+  tp_comm_atomic_rs: True
+  tp_comm_bulk_wgrad: True
+  tp_comm_bulk_dgrad: True
+
+  # Parallelism
+  finalize_model_grads_func: null
+
+  # Pipeline Parallel
+  pipeline_dtype: null
+  grad_scale_func: null
+  enable_autocast: False
+  autocast_dtype: null
+  variable_seq_lengths: False
+  num_microbatches_with_partial_activation_checkpoints: null
+  overlap_p2p_comm: False
+  batch_p2p_comm: True
+  batch_p2p_sync: True
+  use_ring_exchange_p2p: False
+  deallocate_pipeline_outputs: False
+  no_sync_func: null
+  grad_sync_func: null
+  param_sync_func: null
+
+  # CPU Offloading
+  cpu_offloading: False
+  cpu_offloading_num_layers: 0
+  _cpu_offloading_context: null
+  cpu_offloading_weights: False
+  cpu_offloading_activations: True
+
+  # Timing
+  barrier_with_L1_time: True
+
+# training:
+use_legacy_models: False
+spec: null
+micro_batch_size: 2
+global_batch_size: 128
+rampup_batch_size: [32, 32, 65324160] 
+check_for_nan_in_loss_and_grad: True
+num_layers_per_virtual_pipeline_stage: null
+
+encoder_num_layers: null
+decoder_num_layers: null
+rotary_seq_len_interpolation_factor: null
+add_position_embedding: False
+make_vocab_size_divisible_by: 128
+group_query_attention: False
+
+
+exit_signal_handler: False
+exit_duration_in_mins: null
+exit_interval: null
+
+untie_embeddings_and_output_weights: True
+position_embedding_type: rope
+rotary_percent: 0.5
+openai_gelu: False
+squared_relu: False
+swiglu: True
+onnx_safe: null
+bert_binary_head: True
+max_position_embeddings: 4096
+
+transformer_impl: local
+use_flash_attn: False
+seed: 1234
+data_parallel_random_init: False
+
+# Optimizer
+optimizer: adam
+lr: 2.5e-4
+lr_decay_style: cosine
+lr_decay_iters: null
+lr_decay_samples: 255126953
+lr_warmup_fraction: null
+lr_warmup_iters: 0
+lr_warmup_samples: 81381
+lr_warmup_init: 0.0
+min_lr: 2.5e-5
+weight_decay: 0.1
+start_weight_decay: null
+end_weight_decay: null
+weight_decay_incr_style: constant
+clip_grad: 1.0
+adam_beta1: 0.9
+adam_beta2: 0.95
+adam_eps: 1.e-08
+sgd_momentum: 0.9
+override_opt_param_scheduler: False
+use_checkpoint_opt_param_scheduler: False
+
+# checkpointing arguments
+save: null
+save_interval: 20000
+no_save_optim: null
+no_save_rng: null
+load: null
+no_load_optim: null
+no_load_rng: null
+finetune: False
+use_checkpoint_args: False
+exit_on_missing_checkpoint: False
+
+# loss arguments
+loss_scale: null
+initial_loss_scale: 4294967296
+min_loss_scale: 1.0
+loss_scale_window: 1000 
+hysteresis: 2
+accumulate_allreduce_grads_in_fp32: False
+fp16_lm_cross_entropy: False
+
+# distributed arguments
+distributed_backend: nccl
+distributed_timeout_minutes: 10
+overlap_grad_reduce: False
+align_grad_reduce: True
+overlap_param_gather: False
+align_param_gather: False
+scatter_gather_tensors_in_pipeline: True
+local_rank: null
+lazy_mpu_init: null
+empty_unused_memory_level: 0
+standalone_embedding_stage: False
+use_distributed_optimizer: False
+nccl_communicator_config_path: null
+
+train_iters: null
+eval_iters: 32
+eval_interval: 2000
+skip_train: False
+
+adlr_autoresume: False
+adlr_autoresume_interval: 1000
+
+# garbage collection
+manual_gc: False
+manual_gc_interval: 0
+manual_gc_eval: True
+
+tp_comm_overlap_cfg: null
+
+#data
+data_path: null
+split: '99,1,0'
+train_data_path: null
+valid_data_path: null
+test_data_path: null
+data_cache_path: null
+mock_data: False
+vocab_size: null
+vocab_file: null
+merge_file: null
+vocab_extra_ids: 0
+seq_length: 4096
+encoder_seq_length: null
+decoder_seq_length: null
+retriever_seq_length: 256
+sample_rate: 1.0
+mask_prob: 0.15
+short_seq_prob: 0.1
+num_workers: 2
+tokenizer_type: GPTSentencePieceTokenizer
+tokenizer_model: null
+reset_position_ids: False
+reset_attention_mask: False
+eod_mask_loss: False
+train_samples: 268554688
+dataloader_type: null
+
+#profile:
+profile: False
+profile_ranks: [0]
+profile_step_end: 12
+profile_step_start: 10
+
+#logging:
+log_params_norm: True
+log_num_zeros_in_grad: True
+log_throughput: False
+log_progress: False
+timing_log_level: 0
+timing_log_option: minmax
+tensorboard_log_interval: 1
+tensorboard_queue_size: 1000
+log_timers_to_tensorboard: False
+log_validation_ppl_to_tensorboard: False
+log_memory_to_tensorboard: False
+log_world_size_to_tensorboard: False
+log_loss_scale_to_tensorboard: True
+wandb_project: ''
+wandb_exp_name: ''
+wandb_save_dir: ''
+enable_one_logger: True
+one_logger_project: megatron-lm
+one_logger_run_name: null
+log_interval: 100
+tensorboard_dir: null
diff --git a/examples/gpt3/train_gpt3_175b_distributed.sh b/examples/gpt3/train_gpt3_175b_distributed.sh
new file mode 100755
index 0000000000..7d2c01b315
--- /dev/null
+++ b/examples/gpt3/train_gpt3_175b_distributed.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+
+# Runs the "175B" parameter model
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+GPUS_PER_NODE=8
+# Change for multinode config
+MASTER_ADDR=localhost
+MASTER_PORT=6000
+NUM_NODES=1
+NODE_RANK=0
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+CHECKPOINT_PATH=$1 #
+TENSORBOARD_LOGS_PATH=$2 #
+VOCAB_FILE=$3 #/gpt2-vocab.json
+MERGE_FILE=$4 #/gpt2-merges.txt
+DATA_PATH=$5 #_text_document
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE 
+    --nnodes $NUM_NODES 
+    --master_addr $MASTER_ADDR 
+    --master_port $MASTER_PORT
+)
+
+GPT_MODEL_ARGS=(
+    --num-layers 96 
+    --hidden-size 12288 
+    --num-attention-heads 96 
+    --seq-length 2048 
+    --max-position-embeddings 2048 
+    --attention-backend auto # Can use (flash/fused/unfused/local)
+)
+
+TRAINING_ARGS=(
+    --micro-batch-size 1 
+    --global-batch-size 1536 
+    --rampup-batch-size 16 16 5859375 
+    --train-iters 500000 
+    --weight-decay 0.1 
+    --adam-beta1 0.9 
+    --adam-beta2 0.95 
+    --init-method-std 0.006 
+    --clip-grad 1.0 
+    --fp16
+    --lr 6.0e-5 
+    --lr-decay-style cosine 
+    --min-lr 6.0e-6
+    --lr-warmup-fraction .001 
+    --lr-decay-iters 430000 
+)
+
+MODEL_PARALLEL_ARGS=(
+	--tensor-model-parallel-size 8 
+	--pipeline-model-parallel-size 16 
+)
+
+DATA_ARGS=(
+    --data-path $DATA_PATH 
+    --vocab-file $VOCAB_FILE 
+    --merge-file $MERGE_FILE 
+    --split 949,50,1
+)
+
+EVAL_AND_LOGGING_ARGS=(
+    --log-interval 100
+    --save-interval 10000 
+    --eval-interval 1000 
+    --save $CHECKPOINT_PATH 
+    --load $CHECKPOINT_PATH 
+    --eval-iters 10
+    --tensorboard-dir $TENSORBOARD_LOGS_PATH 
+)
+
+torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \
+    ${GPT_MODEL_ARGS[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${DATA_ARGS[@]} \
+    ${EVAL_AND_LOGGING_ARGS[@]}
diff --git a/examples/inference/README.md b/examples/inference/README.md
new file mode 100644
index 0000000000..7bba32868f
--- /dev/null
+++ b/examples/inference/README.md
@@ -0,0 +1,289 @@
+### Megatron Core Inference Documentation
+This guide provides an example for Megatron Core for running model inference. 
+
+### Contents
+- [Megatron Core Inference Documentation](#megatron-core-inference-documentation)
+- [Contents](#contents)
+  - [1. Quick Start](#1-quick-start)
+    - [1.1 Understanding The Code](#11-understanding-the-code)
+    - [1.2 Running The Code](#12-running-the-code)
+  - [2. Flow of Control In MCore Backend](#2-flow-of-control-in-mcore-backend)
+  - [3. Customizing The Inference Pipeline](#3-customizing-the-inference-pipeline)
+    - [3.1. Create Your Own Inference Backend](#31-create-your-own-inference-backend)
+    - [3.2. Create Your Own Text Generation Controller](#32-create-your-own-text-generation-controller)
+    - [3.3. Support Other Models](#33-support-other-models)
+    - [3.3. Modify Inference Parameters](#33-modify-inference-parameters)
+  - [4. Future work](#4-future-work)
+
+

+
+#### 1. Quickstart
+This example runs statically-batched inference on a model trained using Megatron Core. The entrypoint is [gpt_static_inference.py](./gpt/gpt_static_inference.py). A similar workflow can be adapted for [gpt_dynamic_inference.py](./gpt/gpt_dynamic_inference.py).
+
+

+
+##### 1.1 Code Walkthrough 
+***STEP 1 - Initialize model parallel and other default arguments***
+The micro batch size defaults to 1. It is not used in tensor-parallelism only, and for pipeline-parallel models it is calculated at runtime. 
+```python
+# Initialize Megatron model using the same model provider from training.
+    initialize_megatron(
+        args_defaults={'no_load_rng': True, 'no_load_optim': True, 'micro_batch_size': 1}
+    )
+```
+
+***STEP 2 - Load the model using the model_provider_function***
+The model provider function supports both MCore and Legacy models. 
+
+```python
+    # Load the model checkpoint
+    model = get_model(model_provider, wrap_with_ddp=False)
+    load_checkpoint(model, None, None)
+    model.eval()
+    model = model[0]
+```
+
+***STEP 3 - Choose an engine***
+Text generation requires an inference engine, which includes a scheduler. The default engine is the [Megatron Core engine](../../megatron/core/inference/engine/mcore_engine.py) with a [text generation controller](../../megatron/core/inference/text_generation_controllers/text_generation_controller.py). TRTLLMEngine will be supported in the future.
+```python
+    # Create an inference wrapper to setup the model.
+    inference_wrapped_model = GPTInferenceWrapper(model, args)
+    
+    # Define a sampling loop.
+    text_generation_controller = TextGenerationController(
+        inference_wrapped_model=inference_wrapped_model, 
+        tokenizer=tokenizer
+    )
+    
+    # Create a static or dynamic inference engine.
+    inference_engine = StaticInferenceEngine(
+        text_generation_controller=text_generation_controller, 
+        max_batch_size=args.max_batch_size
+)
+```
+
+***STEP 4 - Run text generation***
+The [SamplingParams](../../megatron/core/inference/sampling_params.py) class uses suggested defaults. Customize this to change top_p, top_k, number of tokens to generate, etc. The result is returned as a list of [InferenceRequests](../../megatron/core/inference/inference_request.py).
+```python
+    results: List[InferenceRequest] = inference_engine.generate(
+        prompts=args.prompts, sampling_params=sampling_params
+    )
+    
+    if torch.distributed.get_rank() == 0:
+        for idx, result in enumerate(results):
+            print(f' ------------- RESULT FOR PROMPT {idx} --------------- ')
+            result = {
+                'id': result.request_id,
+                'input_prompt': result.prompt, 
+                'generated_text': result.generated_text,
+                'generated_tokens' : result.generated_tokens
+                }
+            print(result)
+```
+
+

+
+##### 1.2 Running The Code
+An example Slurm script is shown below. Set the tokenizer paths, inference params, and other settings appropriately. 
+
+For a recap on sampling parameters, refer to [this blog](https://ivibudh.medium.com/a-guide-to-controlling-llm-model-output-exploring-top-k-top-p-and-temperature-parameters-ed6a31313910).
+
+```
+# Slurm cluster settings 
+ACCOUNT=
+MLM_PATH=/path/to/megatron-lm
+GPT_CKPT=/path/to/gpt/ckpt
+VOCAB_MERGE_FILE_PATH=/path/to/vocab/and/merge/file
+CONTAINER_IMAGE=nvcr.io/ea-bignlp/ga-participants/nemofw-training:23.11
+
+srun --account $ACCOUNT \
+--job-name=$ACCOUNT:inference \
+--partition=batch \
+--time=01:00:00 \
+--container-image $CONTAINER_IMAGE \
+--container-mounts $MLM_PATH:/workspace/megatron-lm/,$GPT_CKPT:/workspace/mcore_gpt_ckpt,$VOCAB_MERGE_FILE_PATH:/workspace/tokenizer \
+--no-container-mount-home \
+--pty /bin/bash \
+
+# Inside the container run the following. 
+
+cd megatron-lm/
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+TOKENIZER_ARGS=(
+    --vocab-file /workspace/tokenizer/gpt2-vocab.json
+    --merge-file /workspace/tokenizer/gpt2-merges.txt
+    --tokenizer-type GPT2BPETokenizer
+)
+
+MODEL_ARGS=(
+    --use-checkpoint-args
+    --use-mcore-models
+    --load /workspace/mcore_gpt_ckpt
+)
+
+INFERENCE_SPECIFIC_ARGS=(
+    --attention-dropout 0.0
+    --hidden-dropout 0.0
+    --num-tokens-to-generate 20
+    --max-batch-size 4
+)
+
+torchrun --nproc-per-node=4 examples/inference/gpt/gpt_static_inference.py \
+    ${TOKENIZER_ARGS[@]} \
+    ${MODEL_ARGS[@]} \
+    ${INFERENCE_SPECIFIC_ARGS[@]} \
+    --prompts "prompt one " "sample prompt two" "sample prompt 3"
+
+NOTE: Other parameters which can be customized for inference:
+--temperature (Sampling temperature)
+--top_k (top_k sampling)
+--top_p (top_p sampling)
+--num-tokens-to-generate (Number of tokens to generate for each prompt)
+--inference-batch-times-seqlen-threshold (During inference, if batch-size times sequence-length is smaller than this threshold then we will not use microbatched pipelining.')
+--use-dist-ckpt (If using dist checkpoint format for the model)
+--use-legacy-models (If using legacy models instead of MCore models)
+
+```
+
+
+

+
+
+#### 2. Control Flow in the MCore Backend
+An example of inference with static batching is provided in [gpt_static_inference.py](./gpt/gpt_static_inference.py).
+* [mcore_engine](../../megatron/core/inference/engines/mcore_engine.py) **generate()** function is called with the input prompts.
+* The `Scheduler` in the engine will add these prompts to the [active requests] pool (../../megatron/core/inference/inference_request.py) until max batch size is hit. Remaining requests will be added to the waiting requests pool. 
+* The engine will run until all requests (waiting + active) are completed. 
+    * The active requests are passed into  **generate_all_output_tokens_static_batch()** of the text generation controller . 
+    * This function uses the **prep_model_for_inference()** method of the [model_inference_wrappers](../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) and runs an autoregressive sampling loop
+    * In the autoregressive loop, the **get_batch_for_context_window()** method of the inference wrapper is called to slice out the input tokens and masks
+    * Input tokens and masks are passed it into the **run_one_forward_step()** method, which calls the model `.forward()` method to get the output logits
+    * Output logits are synchronized across all pipeline parallel ranks
+    * The text generation controller obtains the log probabilities and samples tokens based on the strategy defined in the sampling parameters.
+    * The sampled tokens are then appended to the input prompt tokens for the next iteration 
+    * The **update_generation_status()** method of the text generation controller checks which prompts have finished generating or hit a stop condition
+    * After the inference loop, the result is detokenized and stored as an attribute of the InferenceRequest. These requests are marked as completed. 
+    * The **update_requests_pool()** method of the scheduler moves completed requests into the completed request pool and waiting requests into the active request pool
+
+

+
+#### 3. Customizing The Inference Pipeline
+
+The inference pipeline supports three levels of customization:
+
+* **Inference engine** - The MCore Engine supports static and dynamic batching. Modify this to add a new backend.
+* **Text generation controller** - The main sampling loop. Customize this to support alternative tokenization or implement a new sampling strategy.
+* **Inference Wrapped Model** - Change this to support a new model.
+* **Modify Inference Parameters** - Change this to update top_p, top_k, number of tokens to be generated, temperature, and other sampling parameters.
+
+

+
+##### 3.1. Create Your Own Inference Backend 
+The  [abstract_engine.py](./../../megatron/core/inference/engine/abstract_engine.py) file contains a `generate` method that can be extended to support a new backend. 
+
+```python
+class AbstractEngine(ABC):
+    @staticmethod
+    def generate(self) -> dict:
+        """The abstract backend's generate function. 
+
+        To define a new backend, implement this method and return the outputs as a dictionary. 
+```
+
+

+
+##### 3.2. Implement a new Sampling Loop 
+
+The [TextGenerationController](../../megatron/core/inference/text_generation_controllers/text_generation_controller.py) contains the main sampling loop and can be modified to support new tokenization, detokenization, or sampling strategies.
+
+``` python
+class TextGenerationController:
+
+    def tokenize_prompt(self, prompt: str) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Utility to tokenize the input prompts"""
+
+    def sample_from_logits(
+        self,
+        last_token_logits: torch.Tensor,
+        sampling_params: SamplingParams,
+        vocab_size: int,
+        generation_started : Optional[torch.Tensor] = None,
+        top_n_logprobs_dict: Dict[int, List[Dict[str, float]]] = None,
+    ) -> torch.Tensor:
+        """Samples the logits to generate outputs
+
+        Given the logits of the last token, this function samples according to the parameters defined in sampling_params and returns the sampled tokens. If sampling_params.top_n_logprobs > 0 
+        at each step it also updates the top_n_logprobs_dict.
+        """
+
+    def update_generation_status(
+        self,
+        updated_prompts_tokens: torch.Tensor,
+        generation_started: torch.Tensor,
+        current_context_end_position: int,
+        is_generation_done_tensor: torch.Tensor,
+        generated_sequence_lengths: torch.Tensor,
+    ) -> torch.Tensor:
+        """Function to check which prompts have reached an end condition
+
+        We check which prompts have reached an end condition and set the corresponding flags of the is_generation_done_tensor to True . The generated sequence lengths increases as we keep generating, until that prompts hits an eod condition. The generation started status tensor helps us determine which prompts have started generating
+        """
+
+    def generate_all_output_tokens_static_batch(
+        self, active_requests: OrderedDict[int, InferenceRequest],
+    ) -> OrderedDict[int, InferenceRequest]:
+        """Utility to generate all the output tokens and probabilities for the prompts .
+
+        This utility generates the output tokens for a static batch. It runs the forward steps till all prompts complete generation, updates the status of these requests to completed, adds the generated result and returns these requests
+        """
+
+    def detokenize_generations(self, prompt_tokens_with_generated_tokens: torch.Tensor) -> str:
+        """Detokenize the output generations"""
+```
+
+

+
+##### 3.3. Support Other Models
+Extend [abstract_model_inference_wrapper.py](./../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) to support other models. The abstract model wrapper implements: 
+* Forward method which calls the model `forward` method depending on model parallel settings
+* Initializes the model and puts it in `.eval()` mode
+* Setup for the input parameters (max batch size, max seq length) 
+
+The following methods should be implemented: 
+```python
+class AbstractModelInferenceWrapper:
+    def prep_model_for_inference(self, prompts_tokens: torch.Tensor):
+        """A utility function for preparing model for inference
+
+        The function gets called once before the auto regressive inference loop. It puts the model in eval mode , and gets some model and inference data parameters. Extend this to build position ids ,attention mask etc, so that required slices can be extracted during the forward pass
+        """
+
+    @abc.abstractclassmethod
+    def get_batch_for_context_window(self) -> List:
+        """Returns the input data for inference 
+
+        This function gets called iteratively in the inference loop. It can be used to extract relevant input from the prompt tokens, attention mask etc. required for each step in inference.
+```
+
+Refer to [gpt_inference_wrapper.py](../../megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py) for an example of implementing this for GPTModel.
+
+

+
+##### 3.3. Modify Inference Parameters
+We use  [common inference params](../../megatron/core/inference/sampling_params.py) for text generation. Customize this to change `top_p`, `top_k`, number of tokens to generate etc. Other attributes can be added for the inference loop as shown below.
+
+```
+from megatron.core.inference.sampling_params import SamplingParams
+
+c = SamplingParams(temperature=0.5)
+c.add_attributes({'min_length':4, 'eod_id':153})
+```
+
+

+
+#### 4. Future work
+The following features are planned for future releases.
+* TRTLLM Engine support
+* Continuous batching optimizations
+* Speculative decoding
\ No newline at end of file
diff --git a/examples/inference/gpt/gpt_dynamic_inference.py b/examples/inference/gpt/gpt_dynamic_inference.py
new file mode 100644
index 0000000000..6ea4bf179e
--- /dev/null
+++ b/examples/inference/gpt/gpt_dynamic_inference.py
@@ -0,0 +1,348 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+import torch
+from argparse import ArgumentParser
+from collections import defaultdict
+from tqdm import tqdm
+from typing import Dict, List
+import sys
+import os
+
+from megatron.core.inference.contexts.dynamic_context import (
+    ContextOverflowError,
+    DynamicInferenceContext,
+)
+from megatron.core.inference.engines import DynamicInferenceEngine
+from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
+    GPTInferenceWrapper,
+)
+from megatron.core.inference.sampling_params import SamplingParams
+from megatron.core.inference.text_generation_controllers.text_generation_controller import (
+    TextGenerationController,
+)
+from megatron.core.transformer.module import MegatronModule
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
+)
+from megatron.training import get_args, get_model as _get_model, get_tokenizer, initialize_megatron
+from megatron.training.checkpointing import load_checkpoint
+from pretrain_gpt import model_provider
+import json
+
+from examples.inference.gpt.utils import (
+    add_common_inference_args,
+    build_requests,
+    build_dynamic_engine_setup_prefix,
+    get_curr_time,
+    Request,
+)
+
+
+def add_dynamic_inference_args(parser: ArgumentParser) -> ArgumentParser:
+    """Dynamic inference arguments."""
+
+    add_common_inference_args(parser)
+
+    group = parser.add_argument_group(title='Dynamic inference')
+    group.add_argument(
+        "--inference-ckpt-non-strict",
+        action="store_true",
+        help="Load checkpoint with `strict=False`.",
+    )
+    return parser
+
+
+def get_model() -> MegatronModule:
+    """Initialize model and load checkpoint."""
+
+    args = get_args()
+
+    # Build model.
+    model = _get_model(model_provider, wrap_with_ddp=False)
+
+    # Load checkpoint.
+    assert args.load is not None
+    args.exit_on_missing_checkpoint = True
+    load_checkpoint(
+        ddp_model=model,
+        optimizer=None,
+        opt_param_scheduler=None,
+        strict=not args.inference_ckpt_non_strict,
+    )
+
+    # No virtual PP.
+    assert len(model) == 1, "Above condition should have caught this"
+    model = model[0]
+
+    # Eval mode.
+    model.eval()
+
+    return model
+
+
+def get_inference_context(requests: List[Request], sampling_params: SamplingParams):
+    """The inference context manages the KV cache and other inference state."""
+
+    args = get_args()
+
+    # Max sequence length.
+    max_gen_length = sampling_params.num_tokens_to_generate
+    max_context_length = max(len(r.prompt_tokens) for r in requests)
+    max_sequence_length = max_context_length + max_gen_length
+
+    # Inference context.
+    context = DynamicInferenceContext(
+        params_dtype=args.params_dtype,
+        num_layers=args.num_layers,
+        kv_channels=args.kv_channels,
+        num_attention_heads=(
+            args.num_query_groups if args.group_query_attention else args.num_attention_heads
+        ),
+        max_sequence_length=max_sequence_length,
+        buffer_size_gb=args.inference_dynamic_batching_buffer_size_gb,
+        buffer_guaranteed_fraction=args.inference_dynamic_batching_buffer_guaranteed_fraction,
+        chunk_size_tokens=args.inference_dynamic_batching_chunk_size,
+        buffer_overflow_factor=args.inference_dynamic_batching_buffer_overflow_factor,
+        max_requests_override=args.inference_dynamic_batching_max_requests_override,
+        max_tokens_override=args.inference_dynamic_batching_max_tokens_override,
+        tensor_model_parallel_size=args.tensor_model_parallel_size,
+        materialize_only_last_token_logits=not args.return_log_probs,
+    )
+
+    return context
+
+
+def get_inference_controller(
+    model: MegatronModule, context: DynamicInferenceContext
+) -> TextGenerationController:
+    """Buid text generation controller, which manages the model inference context.
+
+    Args:
+        model (MegatronModule): Megatron GPT model.
+        context (DynamicInferenceContext): Context for managing KV cache.
+
+    Return:
+        (TextGenerationController) Inference text generation controller.
+    """
+
+    args = get_args()
+    tokenizer = get_tokenizer()
+
+    # Wrap model in inference wrapper.
+    model = GPTInferenceWrapper(model, args, context)
+
+    # Note: the following is taken from AbstractModelInferenceWrapper.prep_model_for_inference().
+    from megatron.core import parallel_state
+
+    model.model_is_pipeline_parallel = not (
+        parallel_state.is_pipeline_first_stage() and parallel_state.is_pipeline_last_stage()
+    )
+
+    # Text generation controller.
+    controller = TextGenerationController(model, tokenizer)
+
+    return controller
+
+
+def run_inference(
+    requests: List[Request], sampling_params: SamplingParams, engine: DynamicInferenceEngine
+) -> List[Dict[str, float]]:
+    """Add requests to engine and generate tokens.
+
+    Args:
+        requests (List[Request]): Requests that are to be added and processed.
+        sampling_params (SamplingParams): Sampling params for the logits.
+        engine (DynamicInferenceEngine): Inference engine that manages generating tokens.
+
+    Return:
+        A dictionary of step times with `prefill` and `decode` keys.
+    """
+
+    # Initialize request arrival times.
+    base_arrival_time = get_curr_time()
+    for request in requests:
+        request.time_arrival = request.time_offset + base_arrival_time
+
+    # Add and process requests.
+    num_requests_total = len(requests)
+    num_requests_added = 0
+    num_requests_finished = 0
+    step_id = 0
+    step_times = {"prefill": [], "decode": []}
+    add_times = []
+    output_times = []
+    tbar = tqdm(total=num_requests_total)
+    while True:
+        curr_time = get_curr_time()
+
+        # Add requests with 'earlier' arrival time.
+        add_start = get_curr_time()
+        while num_requests_added < num_requests_total:
+            request = requests[num_requests_added]
+            if request.time_arrival > curr_time:
+                break
+            try:
+                # Using `prompt_text` instead of `prompt_tokens` for fair comparison.
+                engine.add_request(
+                    num_requests_added, request.prompt_text, sampling_params.num_tokens_to_generate
+                )
+                request.time_start = get_curr_time()
+                request.state = "started"
+                num_requests_added += 1
+                tbar.update(1)
+            except ContextOverflowError:
+                break
+        add_times.append(get_curr_time() - add_start)
+
+        # Step inference engine (i.e., generate a token for each active request).
+        is_decode_only = engine.context.is_decode_only()
+        active_requests, finished_requests, step_time = engine.step(sampling_params, verbose=True)
+        step_id += 1
+
+        if len(active_requests) > 0 or len(finished_requests) > 0:
+            if is_decode_only:
+                step_times["decode"].append(step_time)
+            else:
+                step_times["prefill"].append(step_time)
+
+            # Append output tokens.
+            for finished_request in finished_requests:
+                request = requests[finished_request.request_id]
+                request.output_tokens = finished_request.generated_tokens
+                request.time_end = get_curr_time()
+                request.output_text = finished_request.generated_text
+                request.state = "finished"
+                request.request_id = finished_request.request_id
+                if sampling_params.return_log_probs:
+                    request.log_probs = (
+                        finished_request.prompt_log_probs + finished_request.generated_log_probs
+                    )
+                num_requests_finished += 1
+
+        # Check if all requests are finished.
+        if not (engine.has_unfinished_requests() or num_requests_added < num_requests_total):
+            break
+
+    return step_times
+
+
+@torch.inference_mode()
+def main():
+    # Initialize Megatron.
+    initialize_megatron(
+        extra_args_provider=add_dynamic_inference_args,
+        args_defaults={'no_load_rng': True, 'no_load_optim': True},
+    )
+
+    args = get_args()
+    tokenizer = get_tokenizer()
+
+    # Sampling params.
+    sampling_params = SamplingParams(
+        temperature=args.temperature,
+        top_k=args.top_k,
+        top_p=args.top_p,
+        return_log_probs=args.return_log_probs,
+        num_tokens_to_generate=args.num_tokens_to_generate,
+    )
+
+    # Requests, context, conroller.
+    model = get_model()
+    requests = build_requests(args, tokenizer)
+    context = get_inference_context(requests, sampling_params)
+    controller = get_inference_controller(model, context)
+
+    # Inference engine.
+    engine = DynamicInferenceEngine(
+        controller,
+        context,
+        termination_id=tokenizer.eod,
+        enable_cuda_graph=args.enable_cuda_graph,
+        random_seed=args.seed,
+    )
+
+    setup_prefix = build_dynamic_engine_setup_prefix(args, context, requests)
+    print("~~~")
+    print(setup_prefix)
+    print("~~~")
+
+    # Run and time test.
+    t = get_curr_time()
+    step_times = run_inference(requests, sampling_params, engine)
+    torch.cuda.synchronize()
+    step_total = get_curr_time() - t
+
+    # Validate all requests finished.
+    for request in requests:
+        assert request.state == "finished"
+
+    # Print unique prompts + outputs.
+    if torch.distributed.get_rank() == 0:
+
+        print("~~~~ Unique prompts + outputs. ~~~~")
+
+        # Map requests by their prompt.
+        unique_prompt_map = defaultdict(list)
+        for request_idx, request in enumerate(requests):
+            unique_prompt_map[request.prompt_text].append(request_idx)
+
+        # Print unique prompts + outputs.
+        for unique_idx, (prompt_text, request_idxs) in enumerate(unique_prompt_map.items()):
+            request_idx = request_idxs[0]
+            request = requests[request_idx]
+            output_text_escaped = request.output_text.replace("\n", "\\n")
+            print(
+                f"{unique_idx}/{len(unique_prompt_map)} [{len(request_idxs)}]. {prompt_text} ... {output_text_escaped}"
+            )
+
+        # Write results to JSON. Primarily used for functional testing.
+        if args.output_path:
+            json_results = {}
+
+            for idx, req in enumerate(requests):
+                result_dict = {
+                    "input_prompt": req.prompt_text,
+                    "generated_text": req.output_text,
+                    "generated_tokens": req.output_tokens,
+                    "latency": req.time_end - req.time_start,
+                }
+                if sampling_params.return_log_probs:
+                    response_logprobs = req.log_probs
+                    result_dict["logprobs"] = response_logprobs
+                json_results[req.request_id] = result_dict
+            with open(args.output_path, "w") as fp:
+                json.dump(json_results, fp)
+
+    # Timing results.
+    stats = torch.cuda.memory_stats()
+    print("~~~")
+    peak_alloc_gb = stats["allocated_bytes.all.peak"] / 1024**3
+    peak_resvd_gb = stats["reserved_bytes.all.peak"] / 1024**3
+
+    p_times = step_times["prefill"]
+    d_times = step_times["decode"]
+
+    p_total = sum(p_times)
+    d_total = sum(d_times)
+
+    p_count = len(p_times)
+    d_count = len(d_times)
+
+    p_mean = p_total / p_count
+    d_mean = d_total / d_count
+
+    print(
+        f"{setup_prefix} ā€¦ "
+        f"mem {peak_alloc_gb:.1f}/{peak_resvd_gb:.1f} GB ā€¦ "
+        f"total time: {step_total:.3f}s ā€¦ "
+        f"step time: total {step_total:.3f}s "
+        f"[ p {p_total:.3f}s, d {d_total:.3f}s ], "
+        f"mean [ p {p_mean:.3f}s, d {d_mean:.3f}s ], "
+        f"count [ p {p_count}, d {d_count} ]."
+    )
+    print("~~~")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/inference/gpt/gpt_dynamic_inference_12b.sh b/examples/inference/gpt/gpt_dynamic_inference_12b.sh
new file mode 100644
index 0000000000..28421e2e70
--- /dev/null
+++ b/examples/inference/gpt/gpt_dynamic_inference_12b.sh
@@ -0,0 +1,92 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+# Run dynamic batching inference on the 12B GPT model.
+
+set -u
+
+pip install simpy
+pip install sentencepiece
+pip install tiktoken
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+: ${CHECKPOINT_DIR:?"CHECKPOINT_DIR is not set"}
+: ${TOKENIZER_MODEL:?"TOKENIZER_MODEL is not set"}
+
+: ${NUM_TOKENS_TO_PROMPT="8 32"}
+: ${NUM_TOKENS_TO_GENERATE=256}
+: ${INCOMING_REQUESTS_DURATION=10.}
+: ${INCOMING_REQUESTS_PER_SEC=100.}
+
+: ${INFERENCE_DYNAMIC_BATCHING_BUFFER_SIZE_GB=50.}
+: ${INFERENCE_DYNAMIC_BATCHING_BUFFER_OVERFLOW_FACTOR=1.}
+: ${INFERENCE_DYNAMIC_BATCHING_BUFFER_GUARANTEED_FRACTION=0.05}
+
+: ${ENGINE=dynamic}
+: ${EXTRA_ARGS=""}
+# NSIGHT_PREFIX=/path/to/nsight/profile
+
+# --inference-rng-tracker \ # ... re-add after bugfix.
+ARGS=" \
+    --no-persist-layer-norm \
+    --apply-layernorm-1p \
+    --no-position-embedding \
+    --group-query-attention \
+    --num-query-groups 8 \
+    --load ${CHECKPOINT_DIR} \
+    --use-checkpoint-args \
+    --untie-embeddings-and-output-weights \
+    --disable-bias-linear \
+    --use-rotary-position-embeddings \
+    --position-embedding-type rope \
+    --rotary-base 1000000 \
+    --rotary-percent 1.0 \
+    --swiglu \
+    --normalization RMSNorm \
+    --attention-dropout 0.0 \
+    --hidden-dropout 0.0 \
+    --exit-duration-in-mins 5740 \
+    --tensor-model-parallel-size 1 \
+    --pipeline-model-parallel-size 1 \
+    --num-layers 40 \
+    --hidden-size 5120 \
+    --ffn-hidden-size 14336 \
+    --num-attention-heads 32 \
+    --kv-channels 128 \
+    --seq-length 1024 \
+    --max-position-embeddings 1024 \
+    --micro-batch-size 64 \
+    --bf16 \
+    --tokenizer-type TikTokenizer \
+    --tiktoken-pattern v2 \
+    --tokenizer-model ${TOKENIZER_MODEL} \
+    --distributed-timeout-minutes 2400 \
+    --transformer-impl local \
+    --use-flash-attn \
+    \
+    --inference-dynamic-batching \
+    --inference-dynamic-batching-buffer-size-gb ${INFERENCE_DYNAMIC_BATCHING_BUFFER_SIZE_GB} \
+    --inference-dynamic-batching-buffer-overflow-factor ${INFERENCE_DYNAMIC_BATCHING_BUFFER_OVERFLOW_FACTOR} \
+    --inference-dynamic-batching-buffer-guaranteed-fraction ${INFERENCE_DYNAMIC_BATCHING_BUFFER_GUARANTEED_FRACTION} \
+    \
+    --enable-cuda-graph \
+    ${EXTRA_ARGS} \
+"
+
+if [[ -v PROMPTS ]]; then
+    ARGS+=" --prompts ${PROMPTS}"
+else
+    ARGS+=" \
+        --num-tokens-to-prompt ${NUM_TOKENS_TO_PROMPT} \
+        --num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
+        --incoming-requests-duration ${INCOMING_REQUESTS_DURATION} \
+        --incoming-requests-per-sec ${INCOMING_REQUESTS_PER_SEC} \
+    "
+fi
+
+CMD="python -m examples.inference.gpt.gpt_${ENGINE}_inference ${ARGS}"
+if [[ -v NSIGHT_PREFIX ]]; then
+    CMD="nsys profile -t cuda,nvtx,mpi -s none --wait=primary --show-output=true --force-overwrite=true --export=sqlite -o ${NSIGHT_PREFIX} ${CMD}"
+fi
+
+eval ${CMD}
diff --git a/examples/inference/gpt/gpt_dynamic_inference_357m.sh b/examples/inference/gpt/gpt_dynamic_inference_357m.sh
new file mode 100644
index 0000000000..2fa9dbf947
--- /dev/null
+++ b/examples/inference/gpt/gpt_dynamic_inference_357m.sh
@@ -0,0 +1,78 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+# Run dynamic batching inference on the 357M GPT model.
+
+set -u
+
+pip install simpy
+pip install sentencepiece
+pip install tiktoken
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+: ${CHECKPOINT_DIR:?"CHECKPOINT_DIR is not set"}
+: ${VOCAB_FILE:?"VOCAB_FILE is not set"}
+: ${MERGE_FILE:?"MERGE_FILE is not set"}
+
+: ${NUM_TOKENS_TO_PROMPT="8 32"}
+: ${NUM_TOKENS_TO_GENERATE=256}
+: ${INCOMING_REQUESTS_DURATION=10.}
+: ${INCOMING_REQUESTS_PER_SEC=100.}
+
+: ${INFERENCE_DYNAMIC_BATCHING_BUFFER_SIZE_GB=50.}
+: ${INFERENCE_DYNAMIC_BATCHING_BUFFER_OVERFLOW_FACTOR=1.}
+: ${INFERENCE_DYNAMIC_BATCHING_BUFFER_GUARANTEED_FRACTION=0.05}
+
+: ${ENGINE=dynamic}
+: ${EXTRA_ARGS=""}
+# NSIGHT_PREFIX=/path/to/nsight/profile
+
+# --inference-rng-tracker \ # ... re-add after bugfix.
+ARGS=" \
+    --exit-on-missing-checkpoint \
+    --transformer-impl local \
+    --load ${CHECKPOINT_DIR} \
+    --tokenizer-type GPT2BPETokenizer \
+    --vocab-file ${VOCAB_FILE} \
+    --merge-file ${MERGE_FILE} \
+    --exit-on-missing-checkpoint \
+    --max-position-embeddings 2048 \
+    --seq-length 2048 \
+    --tensor-model-parallel-size 1 \
+    --pipeline-model-parallel-size 1 \
+    --num-layers 24 \
+    --num-attention-heads 16 \
+    --hidden-size 1024 \
+    --bf16 \
+    --micro-batch-size 1 \
+    --attention-dropout 0.0 \
+    --hidden-dropout 0.0 \
+    --seed 42 \
+    --use-flash-attn \
+    \
+    --inference-dynamic-batching \
+    --inference-dynamic-batching-buffer-size-gb ${INFERENCE_DYNAMIC_BATCHING_BUFFER_SIZE_GB} \
+    --inference-dynamic-batching-buffer-overflow-factor ${INFERENCE_DYNAMIC_BATCHING_BUFFER_OVERFLOW_FACTOR} \
+    --inference-dynamic-batching-buffer-guaranteed-fraction ${INFERENCE_DYNAMIC_BATCHING_BUFFER_GUARANTEED_FRACTION} \
+    \
+    --enable-cuda-graph \
+    ${EXTRA_ARGS} \
+"
+
+if [[ -v PROMPTS ]]; then
+    ARGS+=" --prompts ${PROMPTS}"
+else
+    ARGS+=" \
+        --num-tokens-to-prompt ${NUM_TOKENS_TO_PROMPT} \
+        --num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
+        --incoming-requests-duration ${INCOMING_REQUESTS_DURATION} \
+        --incoming-requests-per-sec ${INCOMING_REQUESTS_PER_SEC} \
+    "
+fi
+
+CMD="python -m examples.inference.gpt.gpt_${ENGINE}_inference ${ARGS}"
+if [[ -v NSIGHT_PREFIX ]]; then
+    CMD="nsys profile -t cuda,nvtx,mpi -s none --wait=primary --show-output=true --force-overwrite=true --export=sqlite -o ${NSIGHT_PREFIX} ${CMD}"
+fi
+
+eval ${CMD}
diff --git a/examples/inference/gpt/gpt_static_inference.py b/examples/inference/gpt/gpt_static_inference.py
new file mode 100644
index 0000000000..d8521a79b6
--- /dev/null
+++ b/examples/inference/gpt/gpt_static_inference.py
@@ -0,0 +1,267 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+import os
+from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
+    InferenceWrapperConfig,
+)
+from pretrain_mamba import model_provider as mamba_model_provider
+from pretrain_gpt import model_provider as gpt_model_provider
+import torch
+import sys
+import time
+import tqdm
+import warnings
+from argparse import Namespace
+from megatron.core.inference.contexts import StaticInferenceContext
+from megatron.core.inference.engines import StaticInferenceEngine
+from megatron.core.inference.sampling_params import SamplingParams
+from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
+    GPTInferenceWrapper,
+)
+from megatron.core.inference.inference_request import InferenceRequest
+from megatron.core.inference.text_generation_controllers.text_generation_controller import (
+    TextGenerationController,
+)
+from megatron.core.transformer.module import MegatronModule
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
+)
+
+from megatron.training import get_args, get_tokenizer, print_rank_0
+from megatron.training.checkpointing import load_checkpoint
+from megatron.core import mpu
+import json
+from megatron.training.initialize import initialize_megatron
+from megatron.training import get_model
+import asyncio
+from typing import AsyncIterator, List, Any
+
+from examples.inference.gpt.utils import add_common_inference_args, build_requests
+
+
+def add_static_inference_args(parser):
+    """Static inference arguments."""
+
+    add_common_inference_args(parser)
+
+    group = parser.add_argument_group(title='Static inference')
+    group.add_argument(
+        "--max-batch-size",
+        type=int,
+        default=None,
+        dest="max_batch_size",
+        help='Deprecated, use `--inference-max-requests` instead',
+    )
+    group.add_argument("--stream", action="store_true", default=False, help="Stream output tokens")
+
+    return parser
+
+
+def get_inference_engine(args: Namespace, model: MegatronModule) -> StaticInferenceEngine:
+    """Utility to get the relevant backend for running inference
+
+    This function will automatically choose the TRTLLMBackend when possible, and if not revert to Mcore backend if the user does not specify any backends. TRT LLM Backend is not implmented yet.
+
+    Args:
+        args (Namespace): The user arguments parsed from command line
+        model (MegatronModule): The megatron model .
+
+    Returns:
+        AbstractBackend: The chosen backend
+    """
+    tokenizer = get_tokenizer()
+
+    inference_wrapper_config = InferenceWrapperConfig(
+        hidden_size=args.hidden_size,
+        inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
+        fp32_residual_connection=args.fp32_residual_connection,
+        params_dtype=args.params_dtype,
+        padded_vocab_size=args.padded_vocab_size,
+        inference_max_requests=args.inference_max_batch_size,
+        inference_max_seq_length=args.inference_max_seq_length,
+        nccl_all_reduce_for_prefill=args.nccl_all_reduce_for_prefill,
+    )
+
+    inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+
+    inference_wrapped_model = GPTInferenceWrapper(
+        model, inference_wrapper_config, inference_context
+    )
+    text_generation_controller = TextGenerationController(
+        inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
+    )
+    return StaticInferenceEngine(text_generation_controller=text_generation_controller)
+
+
+async def generate(
+    inference_engine: StaticInferenceEngine, sampling_params: SamplingParams, prompts: List[str]
+) -> List[InferenceRequest]:
+    async def collect_stream(prompt, request_id, stream_generator):
+        print(f"Request {request_id}: {prompt}", end="", flush=True)
+        prev_idx = 0
+        async for output in stream_generator:
+            print(output.generated_text[prev_idx:], end="", flush=True)
+            prev_idx = len(output.generated_text)
+        print()
+
+    request_ids: List[str] = [
+        inference_engine.add_request(prompt=prompt, sampling_params=sampling_params, streaming=True)
+        for prompt in prompts
+    ]
+    stream_generators = [
+        inference_engine.get_stream_generator(request_id) for request_id in request_ids
+    ]
+
+    tasks = [
+        asyncio.create_task(collect_stream(prompt, request_id, stream_generator))
+        for (prompt, request_id, stream_generator) in zip(prompts, request_ids, stream_generators)
+    ]
+
+    await inference_engine.run_engine_async()
+    await asyncio.gather(*tasks)
+
+    results: List[InferenceRequest] = [
+        inference_engine.scheduler.completed_request_pool[request_id] for request_id in request_ids
+    ]
+
+    return results
+
+
+@torch.inference_mode()
+def main():
+    """Main program."""
+
+    # Note: The default args passed here can be overwritten by using appropriate params (check arguments.py file)
+    # Micro batch size is not needed to be set by user. (It is calculated based on inference-batch-times-seqlen-threshold argument)
+    initialize_megatron(
+        extra_args_provider=add_static_inference_args,
+        args_defaults={
+            'no_load_rng': True,
+            'no_load_optim': True,
+            'micro_batch_size': 1,
+            'exit_on_missing_checkpoint': True,
+        },
+    )
+
+    args = get_args()
+
+    if args.max_batch_size is not None:
+        warnings.warn(
+            f"`--max-batch-size` has been deprecated in favor of `--inference-max-requests`."
+        )
+        args.inference_max_batch_size = max(args.max_batch_size, args.inference_max_batch_size)
+
+    # Set up model and load checkpoint
+    if args.model_provider == "gpt":
+        model_provider = gpt_model_provider
+    elif args.model_provider == "mamba":
+        model_provider = mamba_model_provider
+    else:
+        raise ValueError(f"Invalid model provider {args.model_provider}")
+    model = get_model(model_provider, wrap_with_ddp=False)
+    load_checkpoint(model, None, None, strict=False)
+    model = model[0]
+
+    inference_engine = get_inference_engine(args, model)
+
+    sampling_params = SamplingParams(
+        temperature=args.temperature,
+        top_k=args.top_k,
+        top_p=args.top_p,
+        return_log_probs=args.return_log_probs,
+        num_tokens_to_generate=args.num_tokens_to_generate,
+        top_n_logprobs=args.top_n_logprobs,
+    )
+
+    requests = build_requests(args, get_tokenizer())
+    prompts = [r.prompt_text for r in requests]
+
+    if args.enable_cuda_graph:
+        print(f"Running warmup for CUDA graphs...")
+        inference_engine.generate(
+            prompts=["warmup"], sampling_params=SamplingParams(num_tokens_to_generate=10)
+        )
+    start_time = time.perf_counter()
+    if args.stream:
+        results: List[InferenceRequest] = asyncio.run(
+            generate(inference_engine, sampling_params, prompts)
+        )
+    else:
+        results: List[InferenceRequest] = inference_engine.generate(
+            prompts=prompts, sampling_params=sampling_params
+        )
+    end_time = time.perf_counter()
+    latency = end_time - start_time
+
+    if torch.distributed.get_rank() == 0 and args.output_path:
+        results_output = {}
+        for idx, result in enumerate(results):
+            result_dict = {
+                'input_prompt': result.prompt,
+                'generated_text': result.generated_text,
+                'generated_tokens': result.generated_tokens.tolist(),
+                'tpot': result.tpot,
+                'latency': latency,
+            }
+            if sampling_params.top_n_logprobs > 0:
+                result_dict['generated_top_n_logprobs'] = result.generated_top_n_logprobs
+            if sampling_params.return_log_probs:
+                response_logprobs = result.prompt_log_probs + result.generated_log_probs
+                result_dict["logprobs"] = response_logprobs
+            results_output[result.request_id] = result_dict
+
+        with open(args.output_path, 'w') as f:
+            json.dump(results_output, f)
+
+    # Print unique prompts + outputs.
+    if torch.distributed.get_rank() == 0:
+
+        print("~~~~ Unique prompts + outputs. ~~~~")
+
+        # Map results by their prompt.
+        from collections import defaultdict
+
+        unique_prompt_map = defaultdict(list)
+        for result_idx, result in enumerate(results):
+            unique_prompt_map[result.prompt].append(result_idx)
+
+        # Print unique prompts + outputs.
+        for unique_idx, (prompt_text, result_idxs) in enumerate(unique_prompt_map.items()):
+            result_idx = result_idxs[0]
+            result = results[result_idx]
+            generated_text = result.generated_text.replace("\n", "\\n")
+            print(
+                f"{unique_idx}/{len(unique_prompt_map)} [{len(result_idxs)}]. {prompt_text} "
+                f"... {generated_text}"
+            )
+
+    stats = torch.cuda.memory_stats()
+    print_rank_0(
+        "static | cg %d | %s | reqs %d [ batch %d ] ... mem %.1f/%.1f ... time %.3f."
+        % (
+            args.enable_cuda_graph,
+            (
+                f""
+                if args.prompts
+                else " %s, %d, %.1e, %.1e"
+                % (
+                    "(%s)" % " ".join(map(str, args.num_tokens_to_prompt)),
+                    args.num_tokens_to_generate,
+                    args.incoming_requests_duration,
+                    args.incoming_requests_per_sec,
+                )
+            ),
+            len(requests),
+            args.inference_max_batch_size,
+            stats["allocated_bytes.all.peak"] / (1024**3),
+            stats["reserved_bytes.all.peak"] / (1024**3),
+            latency,
+        )
+    )
+
+    torch.distributed.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/inference/gpt/utils.py b/examples/inference/gpt/utils.py
new file mode 100644
index 0000000000..359a2a0162
--- /dev/null
+++ b/examples/inference/gpt/utils.py
@@ -0,0 +1,293 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+import json
+import random
+import time
+import torch
+from argparse import ArgumentParser, Namespace
+from typing import Any
+import json
+import itertools
+from megatron.core.inference.inference_request import DynamicInferenceRequest
+from megatron.core.inference.contexts import DynamicInferenceContext
+
+
+def add_common_inference_args(parser: ArgumentParser) -> ArgumentParser:
+    """Common inference arguments."""
+
+    group = parser.add_argument_group(title='Common inference')
+
+    group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.')
+    group.add_argument("--top_k", type=int, default=1, help='Top k sampling.')
+    group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.')
+    group.add_argument(
+        "--return-log-probs",
+        action='store_true',
+        default=False,
+        help='Return the log probabilities of the final output tokens',
+    )
+    group.add_argument(
+        "--prompts",
+        metavar='N',
+        type=str,
+        nargs='+',
+        help='Input prompts with each prompt within quotes and seperated by space',
+    )
+    group.add_argument(
+        "--num-tokens-to-prompt",
+        type=int,
+        nargs="+",
+        default=[64, 1024],
+        help='Number of tokens to use for simulated prompts. This should be a '
+        'space-separated pair of integers, and the generated prompt lengths will '
+        'be uniformly sampled within this range.',
+    )
+    group.add_argument(
+        "--num-tokens-to-generate",
+        type=int,
+        default=30,
+        help='Number of tokens to generate for each prompt',
+    )
+    group.add_argument(
+        "--top-n-logprobs",
+        type=int,
+        default=0,
+        help='Return the top n logprobs for the generated tokens and their corresponding token as a dictionary',
+    )
+    group.add_argument(
+        "--incoming-requests-per-sec",
+        type=float,
+        default=100.0,
+        help="Simulated number of requests per second.",
+    )
+    group.add_argument(
+        "--incoming-requests-duration",
+        type=float,
+        default=10.0,
+        help="Total amount of time to simulate that requests are "
+        "arriving. Multiply this value with "
+        "`--incoming-requests-per-sec` to get the approximate "
+        "total number of requests.",
+    )
+    group.add_argument(
+        "--model-provider", choices=["mamba", "gpt"], default="gpt", help="Model provider"
+    )
+    group.add_argument(
+        "--output-path", type=str, default=None, help="Path to save generations as JSON"
+    )
+    group.add_argument(
+        "--prompt-file",
+        help='Jsonl file containing input prompts, where each item (i.e., line) '
+        'contains the field \'text\' where the value is the prompt. All other '
+        'fields within each item are ignored, and may be customized for each '
+        'application.',
+    )
+    group.add_argument(
+        "--random-sample-prompts",
+        action="store_true",
+        default=False,
+        help="Randomly sample prompts from the prompt file based on simulated request arrivals times "
+        "rather than inferring using all of them.",
+    )
+
+    return parser
+
+
+def get_curr_time() -> float:
+    """Get synchronized time across ranks."""
+    curr_time = torch.cuda.LongTensor([time.time_ns()])
+    if torch.distributed.is_initialized():
+        torch.distributed.broadcast(curr_time, src=0)
+    return curr_time.item() / 10**9
+
+
+class Request:
+    """Class to hold attributes for a single request.
+
+    A request is initialized with its prompt text. As it is added, processed,
+    and completed through the inference engine, the request is populated with its
+    start time, end time, and output tokens.
+
+    Args:
+        prompt_text (str): Prompt text.
+        time_offset (float): Artificial time offset for simulating incoming
+            requests. This value is later added to the `base_arrival_time` to
+            simulate the requests arrival time.
+        tokenizer (Any): Tokenizer for tokenizing the prompt.
+    """
+
+    def __init__(self, prompt_text: str, time_offset: float, tokenizer: Any):
+        self.prompt_text = prompt_text
+        self.prompt_tokens = tokenizer.tokenize(prompt_text)
+        self.output_text = None
+        self.output_tokens = []
+        self.time_offset = time_offset
+        self.time_arrival = None
+        self.time_start = None
+        self.time_end = None
+        self.state = "not-started"
+
+    def __str__(self) -> str:
+        return "state '%s'; prompt len %d; output len %d; '%s'" % (
+            self.state,
+            len(self.prompt_tokens),
+            len(self.output_tokens),
+            self.prompt_text,
+        )
+
+def get_time_offsets(
+    seed: int | None,
+    incoming_requests_per_sec: float,
+    incoming_requests_duration: float,
+) -> list[float]:
+    """Get example time offsets."""
+    random.seed(seed)
+    
+    import simpy  # Guard against this import in test case
+
+    # Generate random time offsets.
+    def arrival(r):
+        while True:
+            yield env.timeout(random.expovariate(r))
+            time_offsets.append(env.now)
+
+    time_offsets = []
+    env = simpy.Environment()
+    env.process(arrival(incoming_requests_per_sec))
+    env.run(incoming_requests_duration)
+    
+    # Ensure at least a single request.
+    if len(time_offsets) == 0:
+        time_offsets = [0.0]
+
+    return time_offsets
+
+
+def get_user_requests(args: Namespace, tokenizer: Any) -> list[Request]:
+    if args.random_sample_prompts:
+        # Maybe exclude some prompts after the first as well as including random time offsets
+        #  following a Poisson process.
+        time_offsets: list[float] = get_time_offsets(
+            args.seed,
+            args.incoming_requests_per_sec,
+            args.incoming_requests_duration,
+        )
+    else:
+        # One request per prompt with a -1 time offset default for each.
+        time_offsets = itertools.repeat(-1)
+    requests = [Request(p, t, tokenizer) for p,t in zip(args.prompts, time_offsets)]
+    return requests
+
+
+def get_auto_requests(args: Namespace, tokenizer: Any) -> list[Request]:
+    """Get example requests."""
+
+    time_offsets = get_time_offsets(
+        args.seed,
+        args.incoming_requests_per_sec,
+        args.incoming_requests_duration,
+    )
+    
+    requests = [
+        Request("hi " * random.randint(*args.num_tokens_to_prompt), t, tokenizer)
+        for t in time_offsets
+    ]
+
+    return requests
+
+def get_requests_from_file(args: Namespace, tokenizer: Any) -> list[Request]:
+    """Get requests from a file."""
+    if not args.prompt_file:
+        raise ValueError("Prompt file is required to read requests from a file.")
+
+    requests = []
+    if args.random_sample_prompts:
+        time_offsets: list[float] = get_time_offsets(
+            args.seed,
+            args.incoming_requests_per_sec,
+            args.incoming_requests_duration,
+        )
+    else:
+        # match the behavior of providing a list of --prompts, use -1 for each prompt
+        time_offsets = itertools.repeat(-1)
+
+    with open(args.prompt_file, 'r') as f:
+        for i, (line, time_offset) in enumerate(zip(f, time_offsets)):
+            item = json.loads(line.strip())
+            if 'text' in item:
+                requests.append(Request(item['text'], time_offset, tokenizer))
+    
+    return requests
+
+
+def build_requests(args: Namespace, tokenizer: Any) -> list[Request]:
+    # Check if we have any prompts (from command line or JSONL)
+    if args.prompts:
+        if args.prompt_file:
+            raise ValueError("Cannot use both --prompts and --prompt-file")
+        return get_user_requests(args, tokenizer)
+    elif args.prompt_file:
+        return get_requests_from_file(args, tokenizer)
+    else:
+        return get_auto_requests(args, tokenizer)
+
+
+def build_dynamic_engine_setup_prefix(
+    args: Namespace, context: DynamicInferenceContext, requests: list[DynamicInferenceRequest]
+):
+    """
+    Returns a compact, pipe-separated summary of the dynamic-batching setup.
+
+    Example output:
+
+    `dynamic | cg True |  (128 256), 512, 1.0e+00, 5.0e-01 | bf 4, 1.2 [r 1024, t 8192] | gtd 0.50 [r 512] | reqs 100` # pylint: disable=line-too-long
+
+    Args:
+        args (Namespace): Command-line arguments for this run.
+        context (DynamicInferenceContext): Stores limits such as `max_requests`,
+            `max_tokens`, and `gtd_request_count`.
+        requests (List[DynamicInferenceRequest]): List of inference requests.
+
+    Returns:
+        A configuration string for logging.
+    """
+    # Prompt description
+    if args.prompts:
+        prompts_str = f""
+    else:
+        prompt_lengths = " ".join(map(str, args.num_tokens_to_prompt))
+        prompts_str = (
+            f" "
+            f"({prompt_lengths}), "
+            f"{args.num_tokens_to_generate:d}, "
+            f"{args.incoming_requests_duration:.1e}, "
+            f"{args.incoming_requests_per_sec:.1e}"
+        )
+
+    # CUDA graph config
+    cg_str = f"cg {args.enable_cuda_graph}"
+
+    # Buffer limits config
+    flw = args.inference_dynamic_batching_buffer_overflow_factor
+    flw_str = "no overflow" if flw is None else f"{flw:.1f}"
+    buffer_limits_str = (
+        f"bf {args.inference_dynamic_batching_buffer_size_gb:.0f}, {flw_str} "
+        f"[r {context.max_requests}, t {context.max_tokens}]"
+    )
+
+    # Guaranteed request config
+    guaranteed_fraction_str = (
+        f"gtd {args.inference_dynamic_batching_buffer_guaranteed_fraction:.2f} "
+        f"[r {context.gtd_request_count}]"
+    )
+
+    parts = [
+        "dynamic",
+        cg_str,
+        prompts_str,
+        buffer_limits_str,
+        guaranteed_fraction_str,
+        f"reqs {len(requests)}",
+    ]
+
+    return " | ".join(parts)
diff --git a/examples/inference/llama_mistral/huggingface_reference.py b/examples/inference/llama_mistral/huggingface_reference.py
new file mode 100644
index 0000000000..9d8f4465f6
--- /dev/null
+++ b/examples/inference/llama_mistral/huggingface_reference.py
@@ -0,0 +1,25 @@
+import argparse
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+# Set up argument parsing
+parser = argparse.ArgumentParser(description="Script for text generation with a specific model and prompt.")
+parser.add_argument('--prompt', type=str, required=True, help="Prompt text to use for text generation")
+parser.add_argument('--model-path', type=str, required=True, help="Path to the Huggingface model checkpoint")
+
+# Parse command-line arguments
+args = parser.parse_args()
+
+model_path = args.model_path
+prompt = args.prompt
+
+config = AutoConfig.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, config=config)
+model = AutoModelForCausalLM.from_pretrained(model_path, config=config).cuda()
+
+inputs = tokenizer(prompt, return_tensors="pt")
+for key in inputs:
+    inputs[key] = inputs[key].cuda()
+# top_k, top_p and do_sample are set for greedy argmax based sampling
+
+outputs = model.generate(**inputs, max_length=100, do_sample=False, top_p=0, top_k=0, temperature=1.0)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
\ No newline at end of file
diff --git a/examples/inference/llama_mistral/run_static_inference_llama4_scout.sh b/examples/inference/llama_mistral/run_static_inference_llama4_scout.sh
new file mode 100755
index 0000000000..cc8cfac5e6
--- /dev/null
+++ b/examples/inference/llama_mistral/run_static_inference_llama4_scout.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export NVTE_APPLY_QK_LAYER_SCALING=0
+
+DISTRIBUTED_ARGS="--nproc_per_node 8 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr 0.0.0.0 \
+                  --master_port 6000"
+
+# Fill in checkpoint path to Llama 4 Scout to run
+CHECKPOINT=
+PROMPTS="What is the capital of France?"
+TOKENS_TO_GENERATE=4
+MAX_BATCH_SIZE=2
+
+MODEL_ARGS=" \
+    --micro-batch-size 1 \
+    --bf16 \
+    --no-masked-softmax-fusion \
+    --disable-bias-linear \
+    --untie-embeddings-and-output-weights \
+    --position-embedding-type rope \
+    --no-rope-fusion \
+    --normalization RMSNorm \
+    --swiglu \
+    --num-layers 48 \
+    --hidden-size 5120 \
+    --ffn-hidden-size 16384 \
+    --num-attention-heads 40 \
+    --group-query-attention \
+    --num-query-groups 8 \
+    --qk-layernorm \
+    --num-experts 16 \
+    --moe-ffn-hidden-size 8192 \
+    --moe-router-score-function sigmoid \
+    --moe-router-topk 1 \
+    --moe-router-topk-scaling-factor 1.0 \
+    --moe-shared-expert-intermediate-size 8192 \
+    --moe-aux-loss-coeff 1e-3 \
+    --moe-token-dispatcher-type alltoall \
+    --moe-token-drop-policy probs \
+    --moe-router-load-balancing-type seq_aux_loss \
+    --seq-length 4096 \
+    --max-position-embeddings 4096 \
+    --tokenizer-type HuggingFaceTokenizer \
+    --make-vocab-size-divisible-by 128 \
+    --use-mcore-models \
+    --rotary-interleaved \
+    --rotary-percent 1.0 \
+    --rotary-base 500000 \
+    --rope-scaling-factor 8.0 \
+    --use-rope-scaling \
+    --no-bias-swiglu-fusion \
+    --qk-l2-norm \
+    --moe-apply-probs-on-input \
+    --moe-router-dtype fp64 \
+"
+
+torchrun $DISTRIBUTED_ARGS -m examples.inference.gpt.gpt_static_inference   \
+      --load ${CHECKPOINT} \
+      --tokenizer-model unsloth/Llama-4-Scout-17B-16E-Instruct \
+      --dist-ckpt-strictness log_unexpected \
+      --tensor-model-parallel-size 8 \
+      --prompts ${PROMPTS} \
+      --num-tokens-to-generate ${TOKENS_TO_GENERATE}  \
+      --max-batch-size ${MAX_BATCH_SIZE} \
+      ${MODEL_ARGS}
diff --git a/examples/inference/llama_mistral/run_text_generation_llama3.1.sh b/examples/inference/llama_mistral/run_text_generation_llama3.1.sh
new file mode 100755
index 0000000000..06584f0917
--- /dev/null
+++ b/examples/inference/llama_mistral/run_text_generation_llama3.1.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+# This example will start serving the Llama3.1-8B model
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export NVTE_APPLY_QK_LAYER_SCALING=0
+
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr 0.0.0.0 \
+                  --master_port 6000"
+
+# Ensure CHECKPOINT and TOKENIZER_MODEL are provided
+if [ -z "$1" ] || [ -z "$2" ]; then
+  echo "Error: You must provide CHECKPOINT and TOKENIZER_MODEL as command-line arguments."
+  echo "Usage: $0 /path/to/checkpoint /path/to/tokenizer_model"
+  exit 1
+fi
+
+# Assign command-line arguments to variables
+CHECKPOINT=$1
+TOKENIZER_MODEL=$2
+
+pip install flask-restful
+
+torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py   \
+      --use-checkpoint-args \
+      --disable-bias-linear \
+      --tokenizer-type HuggingFaceTokenizer \
+      --tokenizer-model ${TOKENIZER_MODEL} \
+      --transformer-impl transformer_engine \
+      --normalization RMSNorm \
+      --group-query-attention \
+      --num-query-groups 8 \
+      --no-masked-softmax-fusion \
+      --attention-softmax-in-fp32 \
+      --attention-dropout 0.0 \
+      --hidden-dropout 0.0 \
+      --untie-embeddings-and-output-weights \
+      --position-embedding-type rope \
+      --rotary-percent 1.0 \
+      --rotary-base 500000 \
+      --use-rope-scaling \
+      --use-rotary-position-embeddings \
+      --swiglu \
+      --tensor-model-parallel-size 1  \
+      --pipeline-model-parallel-size 1  \
+      --num-layers 32  \
+      --hidden-size 4096  \
+      --ffn-hidden-size 14336 \
+      --load ${CHECKPOINT}  \
+      --num-attention-heads 32  \
+      --max-position-embeddings 131072  \
+      --bf16  \
+      --micro-batch-size 1  \
+      --seq-length 8192
diff --git a/examples/inference/llama_mistral/run_text_generation_llama3.sh b/examples/inference/llama_mistral/run_text_generation_llama3.sh
new file mode 100755
index 0000000000..c5fc4103ab
--- /dev/null
+++ b/examples/inference/llama_mistral/run_text_generation_llama3.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+# This example will start serving the Llama3-8B model
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export NVTE_APPLY_QK_LAYER_SCALING=0
+
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr 0.0.0.0 \
+                  --master_port 6000"
+
+# Ensure CHECKPOINT and TOKENIZER_MODEL are provided
+if [ -z "$1" ] || [ -z "$2" ]; then
+  echo "Error: You must provide CHECKPOINT and TOKENIZER_MODEL as command-line arguments."
+  echo "Usage: $0 /path/to/checkpoint /path/to/tokenizer_model"
+  exit 1
+fi
+
+# Assign command-line arguments to variables
+CHECKPOINT=$1
+TOKENIZER_MODEL=$2
+
+pip install flask-restful
+
+torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py   \
+      --use-checkpoint-args \
+      --disable-bias-linear \
+      --tokenizer-type HuggingFaceTokenizer \
+      --tokenizer-model ${TOKENIZER_MODEL} \
+      --transformer-impl transformer_engine \
+      --normalization RMSNorm \
+      --group-query-attention \
+      --num-query-groups 8 \
+      --no-masked-softmax-fusion \
+      --attention-softmax-in-fp32 \
+      --attention-dropout 0.0 \
+      --hidden-dropout 0.0 \
+      --untie-embeddings-and-output-weights \
+      --position-embedding-type rope \
+      --rotary-percent 1.0 \
+      --rotary-base 500000 \
+      --use-rotary-position-embeddings \
+      --swiglu \
+      --tensor-model-parallel-size 1  \
+      --pipeline-model-parallel-size 1  \
+      --num-layers 32  \
+      --hidden-size 4096  \
+      --ffn-hidden-size 14336 \
+      --load ${CHECKPOINT}  \
+      --num-attention-heads 32  \
+      --max-position-embeddings 8192  \
+      --bf16  \
+      --micro-batch-size 1  \
+      --seq-length 8192
diff --git a/examples/inference/llama_mistral/run_text_generation_mistral.sh b/examples/inference/llama_mistral/run_text_generation_mistral.sh
new file mode 100755
index 0000000000..4358fd494c
--- /dev/null
+++ b/examples/inference/llama_mistral/run_text_generation_mistral.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+# This example will start serving the Mistral-7B-v0.3 model
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr 0.0.0.0 \
+                  --master_port 6000"
+
+# Ensure CHECKPOINT and TOKENIZER_MODEL are provided
+if [ -z "$1" ] || [ -z "$2" ]; then
+  echo "Error: You must provide CHECKPOINT and TOKENIZER_MODEL as command-line arguments."
+  echo "Usage: $0 /path/to/checkpoint /path/to/tokenizer_model"
+  exit 1
+fi
+
+# Assign command-line arguments to variables
+CHECKPOINT=$1
+TOKENIZER_MODEL=$2
+
+pip install flask-restful
+
+torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py   \
+       --tokenizer-type HuggingFaceTokenizer \
+       --tokenizer-model ${TOKENIZER_MODEL} \
+       --use-checkpoint-args \
+       --apply-layernorm-1p \
+       --transformer-impl transformer_engine \
+       --normalization RMSNorm \
+       --group-query-attention \
+       --num-query-groups 8 \
+       --no-masked-softmax-fusion \
+       --use-flash-attn \
+       --untie-embeddings-and-output-weights \
+       --disable-bias-linear \
+       --position-embedding-type rope \
+       --rotary-percent 1.0 \
+       --rotary-base 1000000 \
+       --swiglu \
+       --ffn-hidden-size 14336 \
+       --tensor-model-parallel-size 1  \
+       --pipeline-model-parallel-size 1  \
+       --num-layers 32  \
+       --hidden-size 4096  \
+       --load ${CHECKPOINT}  \
+       --num-attention-heads 32  \
+       --max-position-embeddings 4096  \
+       --bf16  \
+       --micro-batch-size 1  \
+       --seq-length 4096  \
+       --seed 101
diff --git a/examples/run_text_generation_server_345M.sh b/examples/inference/run_text_generation_server_345M.sh
similarity index 92%
rename from examples/run_text_generation_server_345M.sh
rename to examples/inference/run_text_generation_server_345M.sh
index a151b98467..e8e61adb16 100755
--- a/examples/run_text_generation_server_345M.sh
+++ b/examples/inference/run_text_generation_server_345M.sh
@@ -26,9 +26,6 @@ torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py   \
        --fp16  \
        --micro-batch-size 1  \
        --seq-length 1024  \
-       --out-seq-length 1024  \
-       --temperature 1.0  \
        --vocab-file $VOCAB_FILE  \
        --merge-file $MERGE_FILE  \
-       --top_p 0.9  \
        --seed 42
diff --git a/examples/run_text_generation_server_345M_8_tensor_parallel.sh b/examples/inference/run_text_generation_server_345M_8_tensor_parallel.sh
similarity index 92%
rename from examples/run_text_generation_server_345M_8_tensor_parallel.sh
rename to examples/inference/run_text_generation_server_345M_8_tensor_parallel.sh
index 027ab42172..368cec3b31 100755
--- a/examples/run_text_generation_server_345M_8_tensor_parallel.sh
+++ b/examples/inference/run_text_generation_server_345M_8_tensor_parallel.sh
@@ -24,9 +24,6 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS tools/run_text_generation_s
        --fp16  \
        --micro-batch-size 1  \
        --seq-length 1024  \
-       --out-seq-length 1024  \
-       --temperature 1.0  \
        --vocab-file $VOCAB_FILE  \
        --merge-file $MERGE_FILE  \
-       --top_p 0.9  \
        --seed 42
diff --git a/examples/inference/t5/simple_t5_batch_inference.py b/examples/inference/t5/simple_t5_batch_inference.py
new file mode 100644
index 0000000000..e53aebeec0
--- /dev/null
+++ b/examples/inference/t5/simple_t5_batch_inference.py
@@ -0,0 +1,156 @@
+import os
+import sys
+from argparse import Namespace
+
+import torch
+
+import pretrain_t5
+from megatron.core.inference.sampling_params import SamplingParams
+from megatron.core.inference.engines import AbstractEngine, StaticInferenceEngine
+from megatron.core.inference.inference_request import InferenceRequest
+from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
+    InferenceWrapperConfig,
+)
+from megatron.core.inference.model_inference_wrappers.t5.t5_inference_wrapper import (
+    T5InferenceWrapper,
+)
+from megatron.core.inference.text_generation_controllers.encoder_decoder_text_generation_controller import (
+    EncoderDecoderTextGenerationController,
+)
+from megatron.core.transformer.module import MegatronModule
+from pretrain_t5 import model_provider
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
+)
+
+from typing import List
+
+from megatron.core import mpu
+from megatron.training import get_args, get_model, get_tokenizer
+from megatron.training.checkpointing import load_checkpoint
+from megatron.training.initialize import initialize_megatron
+
+
+def add_text_generate_args(parser):
+    """Text generation arguments."""
+    group = parser.add_argument_group(title='text generation')
+
+    group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.')
+    group.add_argument("--top_k", type=int, default=1, help='Top k sampling.')
+    group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.')
+    group.add_argument(
+        "--return-log-probs",
+        action='store_true',
+        default=False,
+        help='Return the log probabilities of the final output tokens',
+    )
+    group.add_argument(
+        "--num-tokens-to-generate",
+        type=int,
+        default=30,
+        help='Number of tokens to generate for each prompt',
+    )
+    group.add_argument(
+        "--encoder-prompts",
+        metavar='N',
+        type=str,
+        nargs='+',
+        help='Encoder input prompts with each prompt within quotes and seperated by space',
+    )
+    group.add_argument(
+        "--max-batch-size", type=int, default=1, help='Max number of prompts to process at once'
+    )
+    return parser
+
+
+def get_inference_engine(args: Namespace, model: MegatronModule) -> AbstractEngine:
+    """Utility to get the relevant backend for running inference
+
+    This function will automatically chose the TRTLLMBackend when possible, and if not revert to Mcore backend if the user does not specify any backends. TRT LLM Backend is not implmented yet.
+
+    Args:
+        args (Namespace): The user arguments parsed from command line
+        model (MegatronModule): The megatron model .
+
+    Returns:
+        AbstractBackend: The chosen backend
+    """
+    tokenizer = get_tokenizer()
+
+    inference_wrapper_config = InferenceWrapperConfig(
+        hidden_size=args.hidden_size,
+        inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
+        fp32_residual_connection=args.fp32_residual_connection,
+        params_dtype=args.params_dtype,
+        padded_vocab_size=args.padded_vocab_size,
+    )
+
+    inference_wrapped_model = T5InferenceWrapper(model, inference_wrapper_config)
+    text_generation_controller = EncoderDecoderTextGenerationController(
+        inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
+    )
+    return StaticInferenceEngine(
+        text_generation_controller=text_generation_controller, max_batch_size=args.max_batch_size
+    )
+
+
+def main():
+    """Main program."""
+
+    # Note: The default args passed here can be overwritten by using appropriate params (check arguments.py file)
+    # Micro batch size is not needed to be set by user. (It is calculated based on inference-batch-times-seqlen-threshold argument)
+    initialize_megatron(
+        extra_args_provider=add_text_generate_args,
+        args_defaults={
+            'no_load_rng': True,
+            'no_load_optim': True,
+            'micro_batch_size': 1,
+            'exit_on_missing_checkpoint': True,
+        },
+    )
+
+    # Set up model and load checkpoint
+    model = get_model(model_provider, wrap_with_ddp=False)
+    load_checkpoint(model, None, None)
+    model = model[0]
+
+    args = get_args()
+
+    inference_engine = get_inference_engine(args, model)
+
+    sampling_params = SamplingParams(
+        temperature=args.temperature,
+        top_k=args.top_k,
+        top_p=args.top_p,
+        return_log_probs=args.return_log_probs,
+        num_tokens_to_generate=args.num_tokens_to_generate,
+    )
+
+    tokenizer = get_tokenizer()
+    decoder_prompts = [""] * len(
+        args.encoder_prompts
+    )  # for T5, the prompt is provided as encoder input, hence decoder_prompts is empty
+    args.prompts = decoder_prompts
+
+    results: List[InferenceRequest] = inference_engine.generate(
+        prompts=args.prompts,
+        add_BOS=True,
+        encoder_prompts=args.encoder_prompts,
+        sampling_params=sampling_params,
+    )
+
+    if torch.distributed.get_rank() == 0:
+        for idx, result in enumerate(results):
+            print(f' \n------------- RESULT FOR PROMPT {idx} --------------- ')
+            result = {
+                'id': result.request_id,
+                'input_prompt': result.prompt,
+                'generated_text': result.generated_text,
+                'generated_tokens': result.generated_tokens,
+            }
+            print(result)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/llama/README.md b/examples/llama/README.md
new file mode 100644
index 0000000000..2adb591b52
--- /dev/null
+++ b/examples/llama/README.md
@@ -0,0 +1,144 @@
+# Llama Models
+
+## Table of contents
+- [1. Overview](#1-overview)
+- [2. Prerequisites](#2-prerequisites)
+- [3. Training Setup](#3-training-setup)
+- [4. Configuration](#4-configuration)
+- [5. Test Datasets](#5-test-datasets)
+- [6. FP8 Debugging](#6-fp8-debugging)
+
+## 1. Overview
+
+
+Train Llama models using FP8 precision with Megatron-Core.
+
+## 2. Prerequisites
+
+
+```bash
+# Clone repository
+export HOST_MEGATRON_LM_DIR="/path/to/your/host/megatron-lm"
+git clone https://github.com/NVIDIA/Megatron-LM.git "$HOST_MEGATRON_LM_DIR"
+cd "$HOST_MEGATRON_LM_DIR"
+git checkout "core_r0.12.0"
+
+# Set paths
+export HOST_CHECKPOINT_PATH="./checkpoints/llama3_8b_fp8"
+export HOST_TENSORBOARD_LOGS_PATH="./tensorboard_logs/llama3_8b_fp8"
+
+# Optional: For real data
+# export HOST_TOKENIZER_MODEL_PATH="/path/to/host/tokenizer.model"
+# export HOST_DATA_PREFIX="/path/to/host/mydata_prefix"
+```
+
+## 3. Training Setup
+
+
+### Using Mock Data
+```bash
+PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.03-py3"
+
+docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
+  -v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
+  -v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
+  -v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
+  --workdir /workspace/megatron-lm \
+  $PYTORCH_IMAGE \
+  bash examples/llama/train_llama3_8b_h100_fp8.sh \
+    /workspace/checkpoints \
+    /workspace/tensorboard_logs \
+  2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_mock_$(date +'%y-%m-%d_%H-%M-%S').log"
+```
+
+### Using Custom Data and Tokenizer
+```bash
+PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.03-py3"
+
+docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
+  -v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
+  -v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
+  -v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
+  -v "${HOST_TOKENIZER_MODEL_PATH}:/workspace/tokenizer_model" \
+  -v "$(dirname "${HOST_DATA_PREFIX}"):/workspace/data_dir" \
+  --workdir /workspace/megatron-lm \
+  $PYTORCH_IMAGE \
+  bash examples/llama/train_llama3_8b_h100_fp8.sh \
+    /workspace/checkpoints \
+    /workspace/tensorboard_logs \
+    /workspace/tokenizer_model \
+    "/workspace/data_dir/$(basename "${HOST_DATA_PREFIX}")" \
+  2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_custom_$(date +'%y-%m-%d_%H-%M-%S').log"
+```
+
+## 4. Configuration
+
+
+Default parallelism strategy:
+- Tensor Parallel: 1
+- Pipeline Parallel: 1
+- Context Parallel: 2
+
+Llama-3-8B architecture:
+- 32 layers
+- Hidden size: 4096
+- FFN hidden size: 14336
+- Attention heads: 32
+- Query groups: 8
+- Sequence length: 8192
+- RMSNorm normalization with SwiGLU and RoPE
+
+Key training parameters:
+- Micro-batch size: 1
+- Global batch size: 128
+- Learning rate: 1.5e-4
+- Min learning rate: 1.0e-5
+- Weight decay: 0.1
+- FP8 format: hybrid
+
+You can modify these parameters directly in the `train_llama3_8b_h100_fp8.sh` script.
+
+This configuration follows those defined in NeMo Framework's performance scripts, which can be found at [https://github.com/NVIDIA/NeMo/tree/main/scripts/performance](https://github.com/NVIDIA/NeMo/tree/main/scripts/performance). 
+
+### FP8 Performance
+
+| Model | #-GPUs | GBS | MBS | Seq Length | TP | PP | CP | VP | EP | GA | Tokens/sec/GPU | TFLOP/sec/GPU |
+|-------|--------|-----|-----|------------|----|----|----|----|----|----|----------------|---------------|
+| LLAMA3-8B | 8 | 128 | 1 | 8192 | 1 | 1 | 2 | 1 | 1 | 32 | 13812 | 800 |
+| LLAMA3-70B | 64 | 128 | 1 | 8192 | 4 | 8 | 1 | 5 | 1 | 64 | 1621 | 780 |
+| LLAMA3-405B | 1024 | 512 | 1 | 8192 | 8 | 8 | 2 | 8 | 1 | 64 | 315 | 834 |
+
+Legend:
+- GBS: Global Batch Size
+- MBS: Micro Batch Size
+- TP: Tensor Parallel size
+- PP: Pipeline Parallel size
+- CP: Context Parallel size
+- VP: Virtual Pipeline stages
+- EP: Expert Parallel size
+- GA: Gradient Accumulation steps
+
+As NeMo uses Megatron-Core, for the latest performance benchmarks, please refer to the official [NeMo documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/performance_summary.html).
+
+## 5. Test Datasets
+
+
+Recommended datasets:
+1. **WikiText-103**: https://huggingface.co/datasets/Salesforce/wikitext
+
+Preprocess datasets:
+```bash
+python "${HOST_MEGATRON_LM_DIR}/tools/preprocess_data.py" \
+       --input your_dataset.json \
+       --output-prefix test_dataset \
+       --tokenizer-type HuggingFaceTokenizer \
+       --tokenizer-model /path/to/tokenizer.model \
+       --append-eod
+```
+
+## 6. FP8 Training Considerations
+
+
+- **Hardware**: Requires NVIDIA Hopper, Ada, or Blackwell GPUs for FP8 support
+   
+- **Troubleshooting**: If you encounter NaN values or instability with FP8 training, please refer to [Transformer Engine](https://github.com/NVIDIA/TransformerEngine).
diff --git a/examples/llama/train_llama3_8b_h100_fp8.sh b/examples/llama/train_llama3_8b_h100_fp8.sh
new file mode 100644
index 0000000000..f791996308
--- /dev/null
+++ b/examples/llama/train_llama3_8b_h100_fp8.sh
@@ -0,0 +1,195 @@
+#!/bin/bash
+
+# Environment variables for performance tuning
+export CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS:-1}
+#export LOG_LEVEL=${LOG_LEVEL:-INFO}
+#export NCCL_IB_TIMEOUT=${NCCL_IB_TIMEOUT:-19}
+#export NVTE_FWD_LAYERNORM_SM_MARGIN=${NVTE_FWD_LAYERNORM_SM_MARGIN:-16}
+#export NVTE_BWD_LAYERNORM_SM_MARGIN=${NVTE_BWD_LAYERNORM_SM_MARGIN:-16}
+#export NCCL_P2P_NET_CHUNKSIZE=${NCCL_P2P_NET_CHUNKSIZE:-2097152}
+#export NCCL_AVOID_RECORD_STREAMS=${NCCL_AVOID_RECORD_STREAMS:-1}
+
+CHECKPOINT_PATH=${1:-"checkpoints/llama3_8b_fp8"}
+TENSORBOARD_LOGS_PATH=${2:-"tensorboard_logs/llama3_8b_fp8"}
+TOKENIZER_ARG=${3:-"MOCK"} # Path to tokenizer model, or "MOCK"
+DATA_ARG=${4:-"MOCK"}     # Data prefix, or "MOCK"
+
+# Create directories if they don't exist
+mkdir -p "$(dirname "$CHECKPOINT_PATH")"
+mkdir -p "$(dirname "$TENSORBOARD_LOGS_PATH")"
+
+# Distributed training setup
+GPUS_PER_NODE=8
+NUM_NODES=1
+MASTER_ADDR=${MASTER_ADDR:-localhost}
+MASTER_PORT=${MASTER_PORT:-6000}
+NODE_RANK=${NODE_RANK:-0}
+WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
+
+# Path to the pretrain_gpt.py script, assuming this script is run from the root of the Megatron-LM repository
+PRETRAIN_SCRIPT_PATH="pretrain_gpt.py"
+
+# Fixed model and training parameters
+TP_SIZE=1     
+CP_SIZE=1     
+PP_SIZE=1     
+MICRO_BATCH_SIZE=1
+GLOBAL_BATCH_SIZE=128
+NUM_LAYERS=32  
+DTYPE="fp8"
+SEQ_LENGTH=8192
+MAX_POSITION_EMBEDDINGS=8192
+
+# Data cache path (useful for both mock and real data)
+DATA_CACHE_PATH="${PWD}/benchmark_cache_llama3_8b_fp8"
+mkdir -p "$DATA_CACHE_PATH"
+
+DISTRIBUTED_ARGS=(
+    --nproc_per_node $GPUS_PER_NODE
+    --nnodes $NUM_NODES
+    --node_rank $NODE_RANK
+    --master_addr $MASTER_ADDR
+    --master_port $MASTER_PORT
+)
+
+MODEL_ARGS=(
+    --use-mcore-models
+    --num-layers $NUM_LAYERS
+    --hidden-size 4096
+    --ffn-hidden-size 14336
+    --num-attention-heads 32
+    --group-query-attention
+    --num-query-groups 8
+    --kv-channels 128
+    --seq-length $SEQ_LENGTH
+    --max-position-embeddings $MAX_POSITION_EMBEDDINGS
+    --position-embedding-type rope
+    --rotary-base 1000000 
+    --rotary-percent 1.0
+    --attention-dropout 0.0
+    --hidden-dropout 0.0
+    --swiglu
+    --init-method-std 0.0134
+    --attention-backend fused
+    --apply-layernorm-1p 
+    --untie-embeddings-and-output-weights
+    --disable-bias-linear 
+)
+
+TRAINING_ARGS=(
+    --micro-batch-size $MICRO_BATCH_SIZE
+    --global-batch-size $GLOBAL_BATCH_SIZE
+    --train-samples 1953125000
+    --lr-decay-samples 1949218748
+    --lr-warmup-samples 3906252
+    --lr 0.00015
+    --min-lr 0.00001
+    --decoupled-lr 5.0e-4      # Specific to decoupled AdamW, ensure optimizer is compatible
+    --decoupled-min-lr 4.5e-5  # Specific to decoupled AdamW
+    --lr-decay-style cosine
+    --clip-grad 1.0
+    --weight-decay 0.1
+    --adam-beta1 0.9
+    --adam-beta2 0.95
+    --bf16
+    --grad-reduce-in-bf16
+    --cross-entropy-loss-fusion
+    --calculate-per-token-loss 
+    --manual-gc 
+    --empty-unused-memory-level 1 
+    --exit-duration-in-mins 235 
+)
+
+# Conditional arguments based on DTYPE (FP8)
+DTYPE_ARGS=()
+if [[ "$DTYPE" == "fp8" ]]; then
+    DTYPE_ARGS+=(
+        "--fp8-format hybrid"
+        "--fp8-amax-history-len 1024"
+        "--fp8-amax-compute-algo max"
+        "--fp8-param-gather"
+    )
+fi
+
+# Model parallelism arguments
+MODEL_PARALLEL_ARGS=(
+    --tensor-model-parallel-size $TP_SIZE
+    --context-parallel-size $CP_SIZE
+    # --pipeline-model-parallel-size $PP_SIZE # Not explicitly set in llama script options, assume 1 if not multi-node PP
+    --sequence-parallel  # Always enable sequence parallelism with TP_SIZE=2
+)
+
+# Distributed Data Parallel (DDP) arguments
+# From original script's ddp_args
+DDP_ARGS=(
+    --use-distributed-optimizer
+    --overlap-grad-reduce
+    --overlap-param-gather
+)
+TRAINING_ARGS+=("${DDP_ARGS[@]}")
+
+
+# Data arguments (conditional for mock vs real data)
+DATA_ARGS_LIST=()
+if [[ "$TOKENIZER_ARG" == "MOCK" ]] || [[ "$DATA_ARG" == "MOCK" ]] || [[ -z "$TOKENIZER_ARG" ]]; then
+    DATA_ARGS_LIST+=(
+        "--mock-data"
+        "--tokenizer-type NullTokenizer"
+        "--vocab-size 128256" 
+        "--data-cache-path ${DATA_CACHE_PATH}"
+        "--tiktoken-pattern v2" 
+        "--split '99,1,0'"
+        "--no-create-attention-mask-in-dataloader"
+        "--no-mmap-bin-files"
+        "--num-workers 1"
+    )
+else
+    # Settings for real data
+    DATA_ARGS_LIST+=(
+        "--data-path $DATA_ARG"
+        "--tokenizer-type HuggingFaceTokenizer" 
+        "--tokenizer-model $TOKENIZER_ARG"
+        "--data-cache-path ${DATA_CACHE_PATH}"
+        "--split '99,1,0'"
+        "--no-create-attention-mask-in-dataloader"
+        "--no-mmap-bin-files"
+        "--num-workers 1"
+        # Note: --vocab-size might be inferred by HuggingFaceTokenizer or might need to be explicit.
+        "--vocab-size 128256"
+    )
+fi
+
+EVAL_AND_LOGGING_ARGS=(
+    --log-interval 1
+    --eval-iters 32
+    --eval-interval 100
+    --save-interval 1000
+    --log-throughput
+    --profile
+    --profile-step-start 4
+    --profile-step-end 6
+    --ckpt-format torch_dist 
+    --distributed-timeout-minutes 60
+    --save "$CHECKPOINT_PATH"
+    --load "$CHECKPOINT_PATH" 
+    --tensorboard-dir "$TENSORBOARD_LOGS_PATH"
+)
+
+# Ensure pretrain_gpt.py is found
+if [ ! -f "$PRETRAIN_SCRIPT_PATH" ]; then
+    echo "Error: pretrain_gpt.py not found at $PRETRAIN_SCRIPT_PATH"
+    echo "Please ensure you are running this script from the root of the Megatron-LM repository, and pretrain_gpt.py is present."
+    exit 1
+fi
+
+# Run the training command
+torchrun ${DISTRIBUTED_ARGS[@]} \
+    "$PRETRAIN_SCRIPT_PATH" \
+    ${MODEL_ARGS[@]} \
+    ${TRAINING_ARGS[@]} \
+    ${DTYPE_ARGS[@]} \
+    ${MODEL_PARALLEL_ARGS[@]} \
+    ${DATA_ARGS_LIST[@]} \
+    ${EVAL_AND_LOGGING_ARGS[@]}
+
+set +x
\ No newline at end of file
diff --git a/examples/mamba/.gitignore b/examples/mamba/.gitignore
new file mode 100644
index 0000000000..940f4797e4
--- /dev/null
+++ b/examples/mamba/.gitignore
@@ -0,0 +1,4 @@
+checkpoints/
+data-cache/
+tensorboard/
+triton-cache/
diff --git a/examples/mamba/Dockerfile b/examples/mamba/Dockerfile
new file mode 100644
index 0000000000..2e194095b7
--- /dev/null
+++ b/examples/mamba/Dockerfile
@@ -0,0 +1,32 @@
+FROM nvcr.io/nvidia/pytorch:24.01-py3
+
+RUN pip uninstall -y triton && \
+    pip install triton==2.1.0 sentencepiece==0.1.99 flask-restful
+
+# The causal-conv1d and mamba-ssm packages below are built from scratch here
+# (which takes significant time) because there are no wheels available on PyPI
+# for these relatively newer versions of the packages that are compatible with
+# the older NGC-variant PyTorch version (e.g. version 2.2.0.dev231106) that we
+# are using (in the NGC base container). Generally, if the package is not
+# compatible with the PyTorch version, then it will generate a Python import
+# error. The package authors tend to only release wheels for new versions of
+# these pacakges which are compatible with the versions of regular PyTorch and
+# NGC-variant PyTorch that are newer at the time of release. So, to use newer
+# versions of these packages with relatively older versions of the NGC PyTorch
+# container, we tend to have to build the packages from scratch.
+
+RUN cd /tmp && \
+    git clone https://github.com/Dao-AILab/causal-conv1d.git && \
+    cd causal-conv1d && \
+    git checkout v1.2.2.post1 && \
+    CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install . && \
+    cd .. && \
+    rm -rf causal-conv1d
+
+RUN cd /tmp && \
+    git clone https://github.com/state-spaces/mamba.git && \
+    cd mamba && \
+    git checkout v2.0.3 && \
+    MAMBA_FORCE_BUILD=TRUE pip install . && \
+    cd .. && \
+    rm -rf mamba
diff --git a/examples/mamba/README.md b/examples/mamba/README.md
new file mode 100644
index 0000000000..f8f6d79683
--- /dev/null
+++ b/examples/mamba/README.md
@@ -0,0 +1,94 @@
+# Mamba-based Language Models
+
+## Introduction
+
+This document is an entrypoint into the code used for
+[An Empirical Study of Mamba-based Language Models](https://arxiv.org/abs/2406.07887).
+
+We are releasing the parameters for some of the models described in that
+technical report via
+[HuggingFace](https://huggingface.co/collections/nvidia/ssms-666a362c5c3bb7e4a6bcfb9c).
+The code in the `main` branch is no longer compatible with the `Mamba2-*`
+checkpoints. You can load them using the
+[fixed snapshot of the code used for the technical report](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba).
+
+## Installation
+
+Create and run a Docker container using the [Dockerfile](./Dockerfile).
+
+```
+docker build -t your_image_name:your_tag .
+docker run --gpus all -it --rm \
+  -v /path/to/megatron:/workspace/megatron \
+  -v /path/to/dataset:/workspace/dataset \
+  -v /path/to/checkpoints:/workspace/checkpoints \
+  -w /workspace/megatron/examples/mamba \
+  your_image_name:your_tag
+```
+
+## Train
+
+[`train.sh`](./train.sh) is an example pretraining script, showing how to run on
+a single node. Select between 800M-scale and 8B-scale models by setting the
+`MODEL_SCALE` variable. The 8B-scale hybrid model architecture is the same as
+the one described in the technical report.
+
+## Text Generation
+
+Use [`run_text_gen_server_8b.sh`](./run_text_gen_server_8b.sh) to start a text
+generation server using an 8B hybrid checkpoint. This is configured to run the
+8B hybrid model described in the technical report, with tensor model parallel
+set to 1.
+
+The arguments in the script will need to be changed if using a checkpoint with a
+different model parallel configuration or other differences, such as model
+architecture. For example, to run the 8B pure Mamba-2 model, change
+`--hybrid-attention-ratio` and `--hybrid-mlp-ratio` to 0.0, or remove them.
+
+Use [`run_text_gen_server_8b_gpt3.sh`](./run_text_gen_server_8b_gpt3.sh) to start
+a text generation server using the 8B reference Transformer checkpoint.
+
+## Checkpoint Formats
+
+For inference, the model must be configured to match the checkpoint file used,
+including the hybrid layer configuration and model parallel configuration.
+
+If you need to convert a hybrid checkpoint file to a different tensor parallel
+or pipeline parallel size, use
+[the hybrid conversion script](../../tools/checkpoint/hybrid_conversion.py).
+There is an example run command at the end of that file.
+
+Before running that script, you will need to set `PYTHONPATH` to include the
+root directory of your Megatron-LM repository clone.
+
+```
+export PYTHONPATH=:PYTHONPATH
+```
+
+## Hybrid Options
+
+`--hybrid-attention-ratio ATT` specifies a target ratio of attention layers
+to total layers. For example, 4 attention layers out of 48 total layers is
+specified by `--hybrid-attention-ratio 0.08`.
+
+`--hybrid-mlp-ratio MLP` specifies a target ratio of MLP layers to total
+layers. For example, 24 MLP layers out of 48 total layers is specified by
+`--hybrid-mlp-ratio 0.5`.
+
+* (`ATT` + `MLP`) must be less than or equal to 1.0.
+* (1.0 - `ATT` - `MLP`) is the hybrid mamba ratio, the ratio of mamba layers to
+total layers.
+* `ATT` = `MLP` = 0 is a pure Mamba model.
+* `ATT` = `MLP` = 0.5 is a transfomer model.
+
+If either `ATT` or `MLP` is greater than 0.0 or if `--hybrid-override-pattern`
+is specified, the logfile will include information about the hybrid layer
+pattern used. `--hybrid-override-pattern` can be used to specify a different
+pattern than the default, algorithmically-generated one.
+
+## Mamba vs Mamba-2
+
+This codebase currently only supports Mamba-2, and not the original version of
+Mamba. However, the
+[fixed snapshot of the code used for the technical report](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba)
+can be configured to run the original version of Mamba.
diff --git a/examples/mamba/run_text_gen_server_8b.sh b/examples/mamba/run_text_gen_server_8b.sh
new file mode 100755
index 0000000000..8d3137f244
--- /dev/null
+++ b/examples/mamba/run_text_gen_server_8b.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+
+# Use: ./run_text_gen_server_8b.sh  
+# To launch the client: python ../../tools/text_generation_cli.py 
+
+CHECKPOINT_PATH=$1
+TOKENIZER_PATH=$2
+
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export NCCL_IB_TIMEOUT=19
+export NCCL_IB_QPS_PER_CONNECTION=4
+
+export TRITON_CACHE_DIR="./triton-cache/"
+export TRITON_CACHE_MANAGER="megatron.core.ssm.triton_cache_manager:ParallelFileCacheManager"
+
+torchrun $DISTRIBUTED_ARGS ../../tools/run_mamba_text_generation_server.py \
+       --tensor-model-parallel-size 1  \
+       --pipeline-model-parallel-size 1  \
+       --untie-embeddings-and-output-weights \
+       --num-layers 56  \
+       --hidden-size 4096  \
+       --load ${CHECKPOINT_PATH}  \
+       --num-attention-heads 32  \
+       --group-query-attention \
+       --num-query-groups 8 \
+       --hybrid-attention-ratio 0.08 \
+       --hybrid-mlp-ratio 0.5 \
+       --attention-dropout 0.0 \
+       --hidden-dropout 0.0 \
+       --disable-bias-linear \
+       --normalization RMSNorm \
+       --seq-length 4096  \
+       --max-position-embeddings 4096  \
+       --position-embedding-type none \
+       --tokenizer-type GPTSentencePieceTokenizer  \
+       --tokenizer-model ${TOKENIZER_PATH} \
+       --distributed-backend nccl \
+       --distributed-timeout-minutes 1440 \
+       --bf16  \
+       --micro-batch-size 1  \
+       --use-mcore-models \
+       --spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
+       --seed 42
diff --git a/examples/mamba/run_text_gen_server_8b_gpt3.sh b/examples/mamba/run_text_gen_server_8b_gpt3.sh
new file mode 100644
index 0000000000..5413b245ed
--- /dev/null
+++ b/examples/mamba/run_text_gen_server_8b_gpt3.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+
+# Use: ./run_text_gen_server_8b_gpt3.sh  
+# To launch the client: python ../../tools/text_generation_cli.py 
+
+CHECKPOINT_PATH=$1
+TOKENIZER_PATH=$2
+
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export NCCL_IB_TIMEOUT=19
+export NCCL_IB_QPS_PER_CONNECTION=4
+
+torchrun $DISTRIBUTED_ARGS ../../tools/run_text_generation_server.py \
+       --tensor-model-parallel-size 1  \
+       --pipeline-model-parallel-size 1  \
+       --use-flash-attn \
+       --apply-layernorm-1p \
+       --untie-embeddings-and-output-weights \
+       --num-layers 32  \
+       --hidden-size 4096  \
+       --load ${CHECKPOINT_PATH}  \
+       --num-attention-heads 32  \
+       --attention-dropout 0.0 \
+       --hidden-dropout 0.0 \
+       --disable-bias-linear \
+       --seq-length 4096  \
+       --max-position-embeddings 4096  \
+       --position-embedding-type rope \
+       --rotary-percent 0.5 \
+       --squared-relu \
+       --tokenizer-type GPTSentencePieceTokenizer  \
+       --tokenizer-model ${TOKENIZER_PATH} \
+       --distributed-backend nccl \
+       --distributed-timeout-minutes 1440 \
+       --bf16  \
+       --micro-batch-size 1  \
+       --use-mcore-models \
+       --transformer-impl local \
+       --seed 42
diff --git a/examples/mamba/train.sh b/examples/mamba/train.sh
new file mode 100755
index 0000000000..3952a997d4
--- /dev/null
+++ b/examples/mamba/train.sh
@@ -0,0 +1,105 @@
+#!/bin/bash
+
+# Use: ./train.sh  
+
+MODEL_SCALE="800M" # or "8B"
+
+case "${MODEL_SCALE}" in
+    "800M")
+        TENSOR_MODEL_PARALLEL_SIZE=1
+        NUM_LAYERS=48
+        HIDDEN_SIZE=1024
+        NUM_ATTENTION_HEADS=16
+        GLOBAL_BATCH_SIZE=32
+        ;;
+    "8B")
+        TENSOR_MODEL_PARALLEL_SIZE=4
+        NUM_LAYERS=56
+        HIDDEN_SIZE=4096
+        NUM_ATTENTION_HEADS=32
+        GLOBAL_BATCH_SIZE=8
+        ;;
+    *)
+        echo "Invalid version specified"
+        exit 1
+        ;;
+esac
+
+DATA_PATH=$1
+TOKENIZER_PATH=$2
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+export NCCL_IB_TIMEOUT=19
+export NCCL_IB_QPS_PER_CONNECTION=4
+
+CHECKPOINT_DIR="./checkpoints"
+DATACACHE_DIR="./data-cache"
+TENSORBOARD_DIR="./tensorboard"
+
+mkdir -p ${CHECKPOINT_DIR}
+mkdir -p ${DATACACHE_DIR}
+mkdir -p ${TENSORBOARD_DIR}
+
+export TRITON_CACHE_DIR="./triton-cache/"
+export TRITON_CACHE_MANAGER="megatron.core.ssm.triton_cache_manager:ParallelFileCacheManager"
+
+SEQ_LEN=4096
+TRAIN_SAMPLES=73242188  # 300B tokens / 4096
+LR_WARMUP_SAMPLES=50000
+LR_DECAY_SAMPLES=73192188 # TRAIN_SAMPLES - LR_WARMUP_SAMPLES
+
+options=" \
+       --tensor-model-parallel-size ${TENSOR_MODEL_PARALLEL_SIZE} \
+       --sequence-parallel \
+       --pipeline-model-parallel-size 1 \
+       --use-distributed-optimizer \
+       --overlap-param-gather \
+       --overlap-grad-reduce \
+       --untie-embeddings-and-output-weights \
+       --init-method-std 0.02 \
+       --position-embedding-type none \
+       --num-layers ${NUM_LAYERS} \
+       --hidden-size ${HIDDEN_SIZE} \
+       --num-attention-heads ${NUM_ATTENTION_HEADS} \
+       --group-query-attention \
+       --num-query-groups 8 \
+       --hybrid-attention-ratio 0.08 \
+       --hybrid-mlp-ratio 0.5 \
+       --seq-length ${SEQ_LEN} \
+       --max-position-embeddings ${SEQ_LEN} \
+       --train-samples ${TRAIN_SAMPLES} \
+       --lr-warmup-samples ${LR_WARMUP_SAMPLES} \
+       --lr-decay-samples ${LR_DECAY_SAMPLES} \
+       --save ${CHECKPOINT_DIR} \
+       --load ${CHECKPOINT_DIR} \
+       --data-path ${DATA_PATH} \
+       --data-cache-path ${DATACACHE_DIR} \
+       --split 99,1,0 \
+       --tokenizer-type GPTSentencePieceTokenizer \
+       --tokenizer-model ${TOKENIZER_PATH} \
+       --distributed-backend nccl \
+       --micro-batch-size 4 \
+       --global-batch-size ${GLOBAL_BATCH_SIZE} \
+       --lr 2.5e-4 \
+       --min-lr 2.5e-5 \
+       --lr-decay-style cosine \
+       --weight-decay 0.1 \
+       --clip-grad 1.0 \
+       --attention-dropout 0.0 \
+       --hidden-dropout 0.0 \
+       --disable-bias-linear \
+       --normalization RMSNorm \
+       --adam-beta1 0.9 \
+       --adam-beta2 0.95 \
+       --log-interval 10 \
+       --save-interval 2000 \
+       --eval-interval 2000 \
+       --eval-iters 32 \
+       --bf16 \
+       --use-mcore-models \
+       --spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
+       --no-create-attention-mask-in-dataloader \
+       --tensorboard-dir ${TENSORBOARD_DIR}"
+
+torchrun --nproc_per_node 8 ../../pretrain_mamba.py ${options}
diff --git a/examples/merge_mp_bert.sh b/examples/merge_mp_bert.sh
deleted file mode 100755
index 1383433284..0000000000
--- a/examples/merge_mp_bert.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash
-
-TENSOR_MODEL_PARALLEL_SIZE=2
-
-VOCAB_FILE=bert-vocab.txt
-CHECKPOINT_PATH=checkpoints/bert_345m
-
-WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
-                                --model-type BERT \
-                                --tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
-                                --tokenizer-type BertWordPieceLowerCase \
-                                --vocab-file $VOCAB_FILE \
-                                --num-layers 24 \
-                                --hidden-size 1024 \
-                                --num-attention-heads 16 \
-                                --seq-length 512 \
-                                --max-position-embeddings 512 \
-                                --load $CHECKPOINT_PATH
diff --git a/examples/mimo/__init__.py b/examples/mimo/__init__.py
new file mode 100644
index 0000000000..0519ecba6e
--- /dev/null
+++ b/examples/mimo/__init__.py
@@ -0,0 +1 @@
+ 
\ No newline at end of file
diff --git a/examples/mimo/avlm_inference.py b/examples/mimo/avlm_inference.py
new file mode 100644
index 0000000000..14ca2068ca
--- /dev/null
+++ b/examples/mimo/avlm_inference.py
@@ -0,0 +1,244 @@
+import argparse
+import os
+from pathlib import Path
+from typing import Union
+
+# hf path
+import requests
+import torch
+from PIL import Image
+from transformers import AutoProcessor
+from transformers import AutoTokenizer
+import soundfile as sf
+import io
+import numpy as np
+import scipy.signal as signal
+
+from examples.mimo.model_providers.llava_avlm import model_provider_llava_avlm
+from megatron.core import dist_checkpointing, parallel_state, tensor_parallel
+from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.training import print_rank_0
+from examples.mimo.data.utils.calculate_audio_tokens import calculate_num_audio_tokens
+
+def init_distributed(tp_size: int = 1, pp_size: int = 1):
+    if torch.distributed.is_initialized():
+        return
+    rank = int(os.environ.get("LOCAL_RANK", 0))
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    torch.cuda.set_device(rank % torch.cuda.device_count())
+    torch.distributed.init_process_group("nccl", rank=rank, world_size=world_size)
+    parallel_state.initialize_model_parallel(tp_size, pp_size)
+
+def get_input_data(
+    processor: AutoProcessor,
+    image_processor: AutoProcessor,
+    audio_processor: AutoProcessor,
+    audio_path: str,
+    image_path: str,
+    prompt: str,
+    device: Union[int, str] = 0):
+    """
+    Prepare inputs for the MIMO model forward pass.
+    """
+
+    def read_audio(audio_path):
+        """Process audio file and return tensor."""
+        with open(audio_path, 'rb') as f:
+            audio_bytes = f.read()
+        audio_io = io.BytesIO(audio_bytes)
+        waveform, sample_rate = sf.read(audio_io)
+        
+        # Resample if needed
+        fixed_sample_rate = 16000
+        if sample_rate != fixed_sample_rate:
+            num_samples = int(len(waveform) * fixed_sample_rate / sample_rate)
+            waveform = signal.resample(waveform, num_samples)
+        
+        # Convert to tensor
+        audio_tensor = torch.from_numpy(waveform).float()
+        return audio_tensor
+
+    def read_image(image_path):
+        """Process image file and return tensor."""
+        with open(image_path, 'rb') as f:
+            image_bytes = f.read()
+        image_io = io.BytesIO(image_bytes)
+        image = Image.open(image_io)
+        image_tensor = torch.from_numpy(np.array(image)).permute(2, 0, 1)  # Convert to CxHxW format
+        image_tensor = image_tensor.float() / 255.0  # rescale to [0,1] range
+        return image_tensor
+
+
+    # read audio and image
+    audio_tensor = read_audio(audio_path)
+    image_tensor = read_image(image_path)
+
+    # set up prompt
+    conversation = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+    prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+    # process audio
+    processed_audios = audio_processor(audio_tensor, sampling_rate=16000)
+    processed_audios = torch.tensor(processed_audios["input_features"])
+    processed_audios = processed_audios.squeeze(0) # remove batch dim
+    num_audio_tokens = calculate_num_audio_tokens(audio_tensor.unsqueeze(0), "openai/whisper-base")
+    audios_seq_lengths = torch.tensor(num_audio_tokens)
+    prompt = prompt.replace("