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.idea
Megatron-LM-*
LM-Evaluation-Harness*
Bigcode-Evaluation-Harness*
**/__pycache__
.vscode
.gitmodules 0 → 100644
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[submodule "LM-Evaluation-Harness-240310"]
path = LM-Evaluation-Harness-240310
url = https://github.com/jerryli1981/lm-evaluation-harness.git
[submodule "Bigcode-Evaluation-Harness-240327"]
path = Bigcode-Evaluation-Harness-240327
url = https://github.com/jerryli1981/bigcode-evaluation-harness
[submodule "Megatron-LM-240405"]
path = Megatron-LM-240405
url = https://github.com/NVIDIA/Megatron-LM.git
[submodule "Megatron-LM-231007"]
path = Megatron-LM-231007
url = https://github.com/NVIDIA/Megatron-LM.git
[submodule "Megatron-LM-240126"]
path = Megatron-LM-240126
url = https://github.com/NVIDIA/Megatron-LM.git
[submodule "Megatron-LM-MegaBlocks"]
path = Megatron-LM-MegaBlocks
url = https://github.com/jerryli1981/Megatron-LM-MegaBlocks
[submodule "Megatron-LM-240612"]
path = Megatron-LM-240612
url = https://github.com/NVIDIA/Megatron-LM.git
[submodule "PAI-Megatron-LM-240718"]
path = PAI-Megatron-LM-240718
url = https://github.com/jerryli1981/PAI-Megatron-LM-240718
[submodule "Megatron-LM-241113"]
path = Megatron-LM-241113
url = https://github.com/NVIDIA/Megatron-LM.git
LICENSE 0 → 100644
View file @ deb8370c
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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See the License for the specific language governing permissions and
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NOTICE 0 → 100644
View file @ deb8370c
=============================================================
pai-megatron-patch is an open source tool developed by Alibaba PAI Team
Copyright (c) 2022-2023, Alibaba Group Holding Limited.
Licensed under the Apache License, Version 2.0
=============================================================
This toolkit implements some modules referring to some repositories under
the same/different open source licenses
-----------------------------
model, data, tokenization
Apache License, Version 2.0
Copyright (c) The HuggingFace Inc. team
------------------
model, toolkits, generation, tokenization
Apache License, Version 2.0
The Megatron-LM Team Authors
The Transformer Engine Team Authors
PAI-Megatron-LM-240718/.coveragerc 0 → 100644
View file @ deb8370c
[html]
directory = coverage
[run]
data_file = .coverage_$LOCAL_RANK
PAI-Megatron-LM-240718/.github/ISSUE_TEMPLATE/bug.md 0 → 100644
View file @ deb8370c
---
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.
PAI-Megatron-LM-240718/.github/ISSUE_TEMPLATE/enhancement.md 0 → 100644
View file @ deb8370c
---
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.
PAI-Megatron-LM-240718/.github/ISSUE_TEMPLATE/question.md 0 → 100644
View file @ deb8370c
---
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.
PAI-Megatron-LM-240718/.github/ISSUE_TEMPLATE/regression.md 0 → 100644
View file @ deb8370c
---
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.
PAI-Megatron-LM-240718/.github/workflows/stale.yml 0 → 100644
View file @ deb8370c
# 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
PAI-Megatron-LM-240718/.gitignore 0 → 100644
View file @ deb8370c
__pycache__
*.so
build
.coverage_*
*.egg-info
*~
slurm*
logs
.vscode
local/
\ No newline at end of file
PAI-Megatron-LM-240718/.gitlab-ci.yml 0 → 100644
View file @ deb8370c
workflow:
rules:
- if: $CI_PIPELINE_SOURCE == "schedule"
variables:
FUNCTIONAL_TEST: "yes"
- if: $CI_PIPELINE_SOURCE == "web"
- if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
variables:
FUNCTIONAL_TEST: "no"
- if: $CI_COMMIT_BRANCH =~ /^core_r/
variables:
FUNCTIONAL_TEST: "no"
- if: $CI_MERGE_REQUEST_LABELS =~ /Run tests and nightly/
variables:
FUNCTIONAL_TEST: "yes"
SLURM_CLUSTER: dgxa100_dracooci
SCOPE: mr-and-nightly
- if: $CI_MERGE_REQUEST_LABELS =~ /Run tests/
variables:
FUNCTIONAL_TEST: "yes"
SLURM_CLUSTER: dgxa100_dracooci
SCOPE: mr
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
variables:
FUNCTIONAL_TEST: "no"
- when: never
auto_cancel:
on_new_commit: interruptible
stages:
- build
- unit_tests
- functional_tests
default:
interruptible: true
variables:
FUNCTIONAL_TEST: "yes"
SCOPE:
value: "mr"
options:
- "mr"
- "nightly"
- "mr-and-nightly"
- "weekly"
- "release"
description: "Testsuite to run"
SLURM_CLUSTER:
value: "dgxa100_dracooci"
options:
- "dgxa100_dracooci"
- "dgxh100_eos"
description: '"dgxa100_dracooci" for OCI-IAD, "dgxh100_eos" for EOS'
# CI wide variables
CI_MCORE_IMAGE: gitlab-master.nvidia.com:5005/adlr/megatron-lm/mcore_ci
CI_NEMO_IMAGE: gitlab-master.nvidia.com:5005/adlr/megatron-lm/nemo_ci
LINTING_IMAGE: gitlab-master.nvidia.com:5005/adlr/megatron-lm/mcore_linting
metadata:
image: python:3.10
stage: .pre
tags:
- os/linux
script:
- set -x
- env
- JET_CUSTOM_FILTER="type == 'basic'"
- |
if [[ $SLURM_CLUSTER == dgxh100_eos ]]; then
JET_CI_BRANCH=mcore/eos
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and 'dgx_h100' in spec.platforms"
elif [[ $SLURM_CLUSTER == dgxa100_dracooci ]]; then
JET_CI_BRANCH=mcore/draco-oci
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and 'dgx_a100' in spec.platforms"
fi
- |
if [[ $SCOPE == mr ]]; then
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and 'mr' in spec.scope"
elif [[ $SCOPE == nightly ]]; then
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and 'nightly' in spec.scope"
elif [[ $SCOPE == mr-and-nightly ]]; then
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and ('mr' in spec.scope or 'nightly' in spec.scope)"
elif [[ $SCOPE == weekly ]]; then
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and 'weekly' in spec.scope"
elif [[ $SCOPE == release ]]; then
JET_CUSTOM_FILTER="$JET_CUSTOM_FILTER and 'release' in spec.scope"
fi
- |
if [[ "$JET_CUSTOM_FILTER" == "type == 'basic'" ]]; then
JET_CUSTOM_FILTER="False"
fi
- echo "JET_CI_BRANCH=$JET_CI_BRANCH" | tee -a build.env
- echo "JET_CUSTOM_FILTER=$JET_CUSTOM_FILTER" | tee -a build.env
artifacts:
reports:
dotenv: build.env
rules:
- if: '$FUNCTIONAL_TEST == "yes"'
ppp_capacity_statistics:
tags: [mcore-ssh-agent]
stage: .pre
script:
- |
set -x
ALL_USER=$(sshare -aP | grep coreai_dlalgo_mcore | tail -n +2 | awk -F '|' '{print $2}' | tr '\n' ',')
# Get the current year, month, and day
YEAR=$(date +%Y)
MONTH=$(date +%m)
DAY=$([[ $(date +%-d) -le 15 ]] && echo "01" || echo "15")
TIMESTAMP="${YEAR}-${MONTH}-${DAY}T00:00:01"
CLUSTER_ID=$(curl "${RESOURCE_ENDPOINT}/api/v1/clusters" \
-H "accept: application/json, text/plain, */*" \
-H "accept-language: en-US,en;q=0.9" \
-H "authorization: Bearer $CSRG_API_KEY" | jq '.[] | select(.name == "draco-oci-iad") | .id' | tr -d '"')
INITIATIVE_ITEM_ID=$(curl "${RESOURCE_ENDPOINT}/api/v1/initiative-items" \
-H "accept: application/json, text/plain, */*" \
-H "accept-language: en-US,en;q=0.9" \
-H "authorization: Bearer $CSRG_API_KEY" | jq '.[] | select(.name == "coreai_dlalgo_mcore") | .id' | tr -d '"')
QUOTA=$(curl "${RESOURCE_ENDPOINT}/api/v1/capacity-requests" \
-H "accept: application/json, text/plain, */*" \
-H "accept-language: en-US,en;q=0.9" \
-H "authorization: Bearer $CSRG_API_KEY" | jq --arg CLUSTER_ID $CLUSTER_ID --arg INITIATIVE_ITEM_ID $INITIATIVE_ITEM_ID '[.[] | select(.clusterId == $CLUSTER_ID and .initiativeItemId == $INITIATIVE_ITEM_ID)] | to_entries | [last] | .[0].value.quantity')
USED_CAPA=$(sacct \
-u ${ALL_USER} \
--partition batch_block1,batch_block3,batch_block4 \
--truncate \
-A coreai_dlalgo_mcore \
-S ${TIMESTAMP} \
-X \
--format JobID,JobName%20,Partition,AllocNodes,ElapsedRaw \
-p \
-n \
| awk -F "|" '{{sum+=$4*$5}} END {{print sum*8/3600}}')
TOTAL_CAPA=$(( $QUOTA*24*30 ))
USAGE=$(echo "$USED_CAPA $TOTAL_CAPA" | awk '{print (1 - $1/$2)*100}')%
echo "Usage left: $USAGE"
echo "Disclaimer: Please be careful with this number. Usage does not imply
what we are guaranteed to get a slot, SLURM scheduling is more complicated
than that. The number is rather a proxy to the FairShare that determines
our job-scheduling-priority.
Most important take-away of this number is to get a sense how much much
we are eating up our budget such that we can discuss this with capacity planning.
"
build_image:
tags:
- mcore-docker-node
image: docker:26.1.4-dind
needs: [] # May start ASAP
stage: build
timeout: 45m
parallel:
matrix:
- IMAGE: CI_MCORE_IMAGE
FILE: Dockerfile.ci
BASE_IMAGE: nvcr.io/nvidia/pytorch:24.01-py3
- IMAGE: CI_NEMO_IMAGE
FILE: Dockerfile.ci
BASE_IMAGE: nvcr.io/nvidian/nemo:nightly
- IMAGE: LINTING_IMAGE
FILE: Dockerfile.linting
BASE_IMAGE: python:3.10
before_script:
- echo "$NGC_API_KEY" | docker login nvcr.io -u '$oauthtoken' --password-stdin
- echo "$CI_REGISTRY_PASSWORD" | docker login $CI_REGISTRY -u $CI_REGISTRY_USER --password-stdin
script:
- |
set -x
eval "IMAGE=\$$IMAGE"
OLD_IMAGES=$(docker image ls --format "{{.ID}} {{.Repository}}:{{.Tag}}" \
| grep -v 'nvcr.io/nvidia/pytorch:24.01-py3' \
| grep -v 'gitlab-master.nvidia.com:5005/adlr/megatron-lm/mcore_ci:buildcache' \
| grep -v 'gitlab-master.nvidia.com:5005/adlr/megatron-lm/mcore_nemo:buildcache' \
| grep -v 'gitlab-master.nvidia.com:5005/adlr/megatron-lm/mcore_linting:buildcache' \
| grep -v 'nvcr.io/nvidian/nemo:nightly' \
| grep -v 'python:3.10' | awk '{ print $1 }'
)
docker rmi $OLD_IMAGES || true
docker builder prune -a --filter "until=24h" -f
if [[ "$CI_COMMIT_BRANCH" == "$CI_DEFAULT_BRANCH" ]]; then
ADDITIONAL_PARAMS="--pull"
fi
docker build \
-f $FILE \
-t ${IMAGE}:${CI_PIPELINE_ID} \
--cache-to type=inline \
--cache-from type=registry,ref=${IMAGE}:buildcache \
--build-arg FROM_IMAGE_NAME=$BASE_IMAGE \
${ADDITIONAL_PARAMS} .
docker push ${IMAGE}:${CI_PIPELINE_ID}
if [[ "$CI_COMMIT_BRANCH" == "$CI_DEFAULT_BRANCH" ]]; then
docker tag ${IMAGE}:${CI_PIPELINE_ID} ${IMAGE}:buildcache
docker push ${IMAGE}:buildcache
fi
if [[ $CI_COMMIT_BRANCH == core_r* ]]; then
docker tag ${IMAGE}:${CI_PIPELINE_ID} ${IMAGE}:v${CI_COMMIT_BRANCH#core_r}-${CI_PIPELINE_ID}
docker push ${IMAGE}:v${CI_COMMIT_BRANCH#core_r}-${CI_PIPELINE_ID}
fi
.unit_test_common:
image: ${CI_MCORE_IMAGE}:${CI_PIPELINE_ID}
stage: unit_tests
needs: [build_image]
tags:
- 8xL40S
variables:
MOE_GROUPED_GEMM: 0 # Set to 1 to enable grouped gemm for MoE
retry:
max: 2
when: job_execution_timeout
unit_tests:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s --cov-report=term --cov-report=html --cov=megatron/core --no-cov-on-fail tests/unit_tests
coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
artifacts:
paths:
- coverage
expire_in: 30 days
rules:
- if: '$FUNCTIONAL_TEST == "yes" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "yes"'
unit_tests-data:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/data
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-dist-checkpointing:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/dist_checkpointing
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-fusions:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/fusions
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-inference:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/inference
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-models:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/models
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-pipeline-parallel:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/pipeline_parallel
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-tensor-parallel:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/tensor_parallel
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-transformer:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/transformer
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
unit_tests-top-py:
extends: [.unit_test_common]
script:
- torchrun --nproc_per_node=8 -m pytest -x -v -s tests/unit_tests/*.py
rules:
- if: '$FUNCTIONAL_TEST == "no" && $CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- if: '$FUNCTIONAL_TEST == "no"'
docs_build_test:
image: gitlab-master.nvidia.com:5005/adlr/megatron-lm/python-format:0.0.1
stage: unit_tests
tags:
- os/linux
script:
- cd ..
- rm -rf documentation && git clone https://gitlab-ci-token:${CI_JOB_TOKEN}@gitlab-master.nvidia.com/nemo-megatron-core-tme/documentation.git
- mv megatron-lm/ documentation/
- cd documentation/
- ./repo docs
allow_failure: true
except:
- main
interruptible: true
formatting:
image: ${LINTING_IMAGE}:${CI_PIPELINE_ID}
tags:
- os/linux
stage: unit_tests
before_script:
- git fetch origin main
script:
- CHECK_ONLY=true bash tools/autoformat.sh
rules:
- if: '$CI_PIPELINE_SOURCE == "merge_request_event" && ($CI_MERGE_REQUEST_TARGET_BRANCH_NAME != $CI_DEFAULT_BRANCH && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME !~ /^core_r/)'
allow_failure: true
- when: always
interruptible: true
include:
- jet-tests.yml
PAI-Megatron-LM-240718/CODEOWNERS 0 → 100644
View file @ deb8370c
[MCORE][3]
megatron/core/ @shanmugamr @jcasper @eharper @terryk @okoenig
[TESTS]
tests/ @shanmugamr @terryk @okoenig
[MODELOPT]
examples/inference/quantization @chenhany @kmorabia
PAI-Megatron-LM-240718/CONTRIBUTING.md 0 → 100644
View file @ deb8370c
# 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
PAI-Megatron-LM-240718/Dockerfile.ci 0 → 100644
View file @ deb8370c
# syntax=docker/dockerfile:experimental
ARG FROM_IMAGE_NAME
FROM $FROM_IMAGE_NAME
ENV DEBIAN_FRONTEND=noninteractive
RUN sed -i -e 's/^APT/# APT/' -e 's/^DPkg/# DPkg/' \
/etc/apt/apt.conf.d/docker-clean
RUN apt-get update && \
apt-get install -y --no-install-recommends gettext && \
apt-get clean
RUN 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
RUN pip3 install --no-cache-dir \
einops \
flask-restful \
nltk \
pytest \
pytest-cov \
pytest_mock \
sentencepiece \
wrapt \
git+https://github.com/fanshiqing/grouped_gemm@v1.1.2 \
zarr \
tensorstore==0.1.45
##### For Mamba begin #####
RUN pip uninstall -y triton && \
pip install triton==2.1.0
# 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 && \
pip uninstall -y causal-conv1d && \
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 && \
pip uninstall -y mamba-ssm && \
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
##### For Mamba end #####
COPY . /workspace/megatron-lm
RUN cp -r /workspace/megatron-lm /opt && \
pip install /opt/megatron-lm
PAI-Megatron-LM-240718/Dockerfile.linting 0 → 100644
View file @ deb8370c
# syntax=docker/dockerfile:experimental
ARG FROM_IMAGE_NAME
FROM $FROM_IMAGE_NAME
ENV DEBIAN_FRONTEND=noninteractive
RUN sed -i -e 's/^APT/# APT/' -e 's/^DPkg/# DPkg/' \
/etc/apt/apt.conf.d/docker-clean
RUN pip3 install --no-cache-dir \
black==24.4.2 \
isort
COPY . /opt/megatron-lm
WORKDIR /opt/megatron-lm
\ No newline at end of file
PAI-Megatron-LM-240718/LICENSE 0 → 100644
View file @ deb8370c
The following applies to all files unless otherwise noted:
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
--
This repository also contains code from Hugging Face Inc., Google Research,
Facebook (from their Fairseq, Dino, and ParlAI projects), Microsoft (from their
Swin-Transformer project), Philip Popien, and the Mamba project (Tri Dao and
Albert Gu). 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, Google Research, LLaVA, and Mamba code --
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------------- LICENSE FOR various code from Facebook --------------
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------------- LICENSE FOR Mircrosoft Swin transformer code --------------
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PAI-Megatron-LM-240718/MANIFEST.in 0 → 100644
View file @ deb8370c
include megatron/core/requirements.txt
PAI-Megatron-LM-240718/README.md 0 → 100644
View file @ deb8370c
<div align="center">
Megatron-LM & Megatron-Core
===========================
<h4>GPU optimized techniques for training transformer models at-scale</h4>
[![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)
<div align="left">
# Latest News
- **[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 Overview](#megatron-overview)
* [Megatron-LM](#megatron-lm)
* [Megatron-Core](#megatron-core)
* [Training Speed and Scalability](#training-speed-and-scalability)
* [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 and InstructRetro](#retro-and-instructretro)
* [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)
* [Llama-2 Inference and Finetuning](#llama-2-inference-and-finetuning)
* [Datasets](#datasets)
* [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data)
* [Collecting GPT Webtext Data](#collecting-gpt-webtext-data)
* [Reproducibility](#reproducibility)
* [Projects using Megatron](#projects-using-megatron)
# Megatron Overview
This repository comprises two essential components: **Megatron-LM** and **Megatron-Core**. Megatron-LM serves as a ressearch-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:
```
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
```
## 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 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:
<pre>
BERT-345M-uncased: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_uncased/zip -O megatron_bert_345m_v0.1_uncased.zip
BERT-345M-cased: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O megatron_bert_345m_v0.1_cased.zip
GPT-345M: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O megatron_lm_345m_v0.0.zip
</pre>
The models require vocabulary files to run. The BERT WordPiece vocab file can be extracted from Google's pretrained BERT models: [uncased](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt), [cased](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt). The GPT [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.
# Usage
After installation, there are several possible workflows. The most comprehensive is:
1. Data preprocessing
2. Pretraining
3. Finetuning (Optional for zero-shot tasks)
4. Downstream task evaluation or text generation
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 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:
<pre>
{"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
{"src": "The Internet", "text": "jumps over the lazy dog", "type": "Eng", "id": "42", "title": "Second Part"}
</pre>
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 format use `preprocess_data.py`. An example script to prepare data for BERT training is:
<pre>
python tools/preprocess_data.py \
--input my-corpus.json \
--output-prefix my-bert \
--vocab-file bert-vocab.txt \
--tokenizer-type BertWordPieceLowerCase \
--split-sentences
</pre>
The output will be two files named, in this case, `my-bert_text_sentence.bin` and `my-bert_text_sentence.idx`. The `--data-path` specified in later BERT training is the full path and new filename, but without the file extension.
For T5 use the same preprocessing as BERT, perhaps renaming it to:
<pre>
--output-prefix my-t5 \
</pre>
Some minor modifications are required for GPT data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type:
<pre>
python tools/preprocess_data.py \
--input my-corpus.json \
--output-prefix my-gpt2 \
--vocab-file gpt2-vocab.json \
--tokenizer-type GPT2BPETokenizer \
--merge-file gpt2-merges.txt \
--append-eod
</pre>
Here the output files are named `my-gpt2_text_document.bin` and `my-gpt2_text_document.idx`. As before, in GPT training, use the longer name without the extension as `--data-path`.
Further command line arguments are described in the source file [`preprocess_data.py`](./tools/preprocess_data.py).
## BERT Pretraining
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 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/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.
## 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.
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/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.
## 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:
* `--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.
* `--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.
## 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.
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 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`.
Other than these minor changes, the distributed training is identical to the training on a single GPU.
The interleaved pipelining schedule (more details in Section 2.2.2 of [our paper](https://arxiv.org/pdf/2104.04473.pdf)) can be enabled using the `--num-layers-per-virtual-pipeline-stage` argument, which controls the number of transformer layers in a virtual stage (by default with the non-interleaved schedule, each GPU will execute a single virtual stage with `NUM_LAYERS / PIPELINE_MP_SIZE` transformer layers). The total number of layers in the transformer model should be divisible by this argument value. Additionally, the number of microbatches in the pipeline (computed as `GLOBAL_BATCH_SIZE / (DATA_PARALLEL_SIZE * MICRO_BATCH_SIZE)`) should be divisible by the `PIPELINE_MP_SIZE` when using this schedule (this condition is checked in an assertion in the code). The interleaved schedule is not supported for pipelines with 2 stages (`PIPELINE_MP_SIZE=2`).
## Activation Checkpointing and Recomputation
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.
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`.
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.
* 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
Usage: `--use-distributed-optimizer`. Compatible with all model and data types.
The distributed optimizer is a memory savings technique, whereby the optimizer state is evenly distributed across data parallel ranks (versus the traditional method of replicating the optimizer state across data parallel ranks). As described in [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/abs/1910.02054), our implementation distributes all optimizer state that does not overlap with the model state. For example, when using fp16 model params, the distributed optimizer maintains its own separate copy of fp32 main params & grads, which are distributed across DP ranks. When using bf16 model params, however, the distributed optimizer's fp32 main grads are the same as the model's fp32 grads, and so the grads in this case are not distributed (although the fp32 main params are still distributed, as they are separate from the bf16 model params).
Theoretical memory savings vary depending on the combination of the model's param dtype and grad dtype. In our implementation, the theoretical number of bytes per parameter is (where 'd' is the data parallel size):
| | Non-distributed optim | Distributed optim |
|-|-|-|
| fp16 param, fp16 grads | 20 | 4 + 16/d |
| 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.
[FlashAttention](https://github.com/HazyResearch/flash-attention) is a fast and
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 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.
In this repo, we provide an end-to-end reproduction guide to implement Retro and InstructRetro, covering
- **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.
Please see [tools/retro/README.md](tools/retro/README.md) for a detailed overview.
## Mamba-based Language Models
Please see [examples/mamba](./examples/mamba) for details.
<!--
## REALM Pipeline
We are working on implementing the [REALM](https://arxiv.org/pdf/2002.08909.pdf) system. The following sections (will) reflect the three stages of training it. For now it's just the ICT code.
Loosely, they are pretraining the retriever modules, then jointly training the language model and the retriever, and then finetuning a question answering head on the language model with fixed retriever.
### Inverse Cloze Task (ICT) Pretraining
1. Have a corpus in loose JSON format with the intention of creating a collection of fixed-size blocks of text as the fundamental units of data. For a corpus like Wikipedia, this will mean multiple sentences per block but also multiple blocks per document.
Run `tools/preprocess_data.py` to construct one or more indexed datasets with the `--split-sentences` argument to make sentences the basic unit. For the original REALM system, we construct two datasets, one with the title of every document, and another with the body.
Refer to the following script
<pre>
python preprocess_data.py \
--input /path/to/corpus.json \
--json-keys text title \
--split-sentences \
--tokenizer-type BertWordPieceLowerCase \
--vocab-file /path/to/vocab.txt \
--output-prefix corpus_indexed \
--workers 5 # works well for 10 CPU cores. Scale up accordingly.
</pre>
2. Use a custom samples mapping function in place of `megatron/legacy/data/realm_dataset_utils.get_block_samples_mapping` if required. To do this, you will need to implement a new function in C++ inside of `megatron/core/datasets/helpers.cpp`. The samples mapping data structure is used to select the data that will constitute every training sample in advance of the training loop.
The samples mapping is responsible for holding all of the required metadata needed to construct the sample from one or more indexed datasets. In REALM, the samples mapping contains the start and end sentence indices, as well as the document index (to find the correct title for a body) and a unique ID for every block.
3. Pretrain a BERT language model using `pretrain_bert.py`, with the sequence length equal to the block size in token ids. This model should be trained on the same indexed dataset that is used to supply the blocks for the information retrieval task.
In REALM, this is an uncased bert base model trained with the standard hyperparameters.
4. Use `pretrain_ict.py` to train an `ICTBertModel` which uses two BERT-based encoders to encode queries and blocks to perform retrieval with.
The script below trains the ICT model from REALM. It references a pretrained BERT model (step 3) in the `--bert-load` argument. The batch size used in the paper is 4096, so this would need to be run with data parallel world size 32.
<pre>
python pretrain_ict.py \
--num-layers 12 \
--num-attention-heads 12 \
--hidden-size 768 \
--batch-size 128 \
--seq-length 256 \
--max-position-embeddings 256 \
--ict-head-size 128 \
--train-iters 100000 \
--bert-load /path/to/pretrained_bert \
--load checkpoints \
--save checkpoints \
--data-path /path/to/indexed_dataset \
--titles-data-path /path/to/titles_indexed_dataset \
--vocab-file /path/to/vocab.txt \
--lr 0.0001 \
--num-workers 2 \
--lr-decay-style linear \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction .01 \
--save-interval 3000 \
--query-in-block-prob 0.1 \
--fp16
</pre>
### Building an Index of Block Embeddings
After having trained an ICT model, you can now embed an entire dataset of blocks by creating a `BlockData` structure. After that has been saved, you can load it
and wrap it with a `FaissMIPSIndex` to do fast similarity search which is key in the learned information retrieval pipeline. The initial index can be built with the following script, meant to be run in an interactive session. It can leverage multiple GPUs on multiple nodes to index large datasets much more quickly.
<pre>
python tools/create_doc_index.py \
--num-layers 12 \
--hidden-size 768 \
--ict-head-size 128 \
--num-attention-heads 12 \
--batch-size 128 \
--seq-length 256 \
--max-position-embeddings 256 \
--ict-load /path/to/pretrained_ict \
--data-path /path/to/indexed_dataset \
--titles-data-path /path/to/titles_indexed_dataset \
--block-data-path embedded_blocks.pkl \
--indexer-log-interval 1000 \
--indexer-batch-size 128 \
--vocab-file /path/to/vocab.txt \
--num-workers 2 \
--fp16
</pre>
-->
# 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.
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.
<pre>
python tools/checkpoint/convert.py \
--model-type GPT \
--load-dir checkpoints/gpt3_tp4_pp4 \
--save-dir checkpoints/gpt3_tp2_pp2 \
--target-tensor-parallel-size 2 \
--target-pipeline-parallel-size 2
</pre>
Several downstream tasks are described for both GPT and BERT models below. They can be run in distributed and model parallel modes with the same changes used in the training scripts.
## 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.
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.
<pre>
tools/text_generation_cli.py localhost:5000
</pre>
You can also use CURL or any other tools to query the server directly:
<pre>
curl 'http://localhost:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8' -d '{"prompts":["Hello world"], "tokens_to_generate":1}'
</pre>
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.
## 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.
<pre>
TASK="WIKITEXT103"
VALID_DATA=&#60;wikitext path&#62;.txt
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
CHECKPOINT_PATH=checkpoints/gpt2_345m
COMMON_TASK_ARGS="--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--fp16 \
--vocab-file $VOCAB_FILE"
python tasks/main.py \
--task $TASK \
$COMMON_TASK_ARGS \
--valid-data $VALID_DATA \
--tokenizer-type GPT2BPETokenizer \
--merge-file $MERGE_FILE \
--load $CHECKPOINT_PATH \
--micro-batch-size 8 \
--log-interval 10 \
--no-load-optim \
--no-load-rng
</pre>
### 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. Ensure that `lambada` is part of the file path.
<pre>
TASK="LAMBADA"
VALID_DATA=&#60;lambada path&#62;.json
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
CHECKPOINT_PATH=checkpoints/gpt2_345m
COMMON_TASK_ARGS=&#60;same as those in <a href="#wikitext-perplexity-evaluation">WikiText Perplexity Evaluation</a> above&#62;
python tasks/main.py \
--task $TASK \
$COMMON_TASK_ARGS \
--valid-data $VALID_DATA \
--tokenizer-type GPT2BPETokenizer \
--strict-lambada \
--merge-file $MERGE_FILE \
--load $CHECKPOINT_PATH \
--micro-batch-size 8 \
--log-interval 10 \
--no-load-optim \
--no-load-rng
</pre>
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.
<pre>
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
COMMON_TASK_ARGS="--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--fp16 \
--vocab-file $VOCAB_FILE"
COMMON_TASK_ARGS_EXT="--train-data $TRAIN_DATA \
--valid-data $VALID_DATA \
--pretrained-checkpoint $PRETRAINED_CHECKPOINT \
--save-interval 10000 \
--save $CHECKPOINT_PATH \
--log-interval 100 \
--eval-interval 1000 \
--eval-iters 10 \
--weight-decay 1.0e-1"
python tasks/main.py \
--task RACE \
$COMMON_TASK_ARGS \
$COMMON_TASK_ARGS_EXT \
--tokenizer-type BertWordPieceLowerCase \
--epochs 3 \
--micro-batch-size 4 \
--lr 1.0e-5 \
--lr-warmup-fraction 0.06
</pre>
### 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.
<pre>
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
COMMON_TASK_ARGS=&#60;same as those in <a href="#race-evaluation">RACE Evaluation</a> above&#62;
COMMON_TASK_ARGS_EXT=&#60;same as those in <a href="#race-evaluation">RACE Evaluation</a> above&#62;
python tasks/main.py \
--task MNLI \
$COMMON_TASK_ARGS \
$COMMON_TASK_ARGS_EXT \
--tokenizer-type BertWordPieceLowerCase \
--epochs 5 \
--micro-batch-size 8 \
--lr 5.0e-5 \
--lr-warmup-fraction 0.065
</pre>
## 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 https://arxiv.org/pdf/2307.09288.pdf).
The Llama-2 checkpoints can be loaded into Megatron for inference and finetuning. See documentation [here](docs/llama2.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 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 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.
## 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)
PAI-Megatron-LM-240718/docs/llama_mistral.md 0 → 100644
View file @ deb8370c
# Llama, Mistral and other Llama-like model support in Megatron-LM
NOTE: Llama-3 and Mistral support in Megatron is currently experimental and we are still evaluting benchmark results to confirm model conversion, training and inference correctness.
The [Llama-2](https://ai.meta.com/llama/) and [Llama-3](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.
# 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.
## Contents
* [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)
* [Megatron](#launch-megatron)
* [Meta](#launch-meta)
* [Huggingface](#launch-hf)
* [Benchmark results](#benchmark-results)
## 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 \
> --saver megatron \
> --checkpoint-type meta \
> --model-size llama2-7B \
> --load-dir $LLAMA_META_FORMAT_DIR \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --tokenizer-model ${TOKENIZER_MODEL} \
> --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 \
> --saver megatron \
> --target-tensor-parallel-size ${TP} \
> --checkpoint-type hf
> --load-dir ${HF_FORMAT_DIR} \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --tokenizer-model ${TOKENIZER_MODEL}
```
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
```
### 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: `|<llama_score> - <megatron_score>| / <llama_score>`, 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
Llama-3 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. Clone the llama3 loading code from Meta.
3. Install the llama package from source.
4. Convert the checkpoints from Meta/Huggingface format to Megatron format.
5. Setup arguments for launching the model.
The following sections detail these steps.
## Contents
* [Download Meta or Huggingface checkpoints](#download-meta-or-huggingface-checkpoints)
* [Install tiktoken](#install-tiktoken)
* [Install llama package from Meta](#install-llama-package)
* [Convert checkpoint format](#convert-checkpoint-format)
* [Meta format](#meta-format)
* [Huggingface format](#huggingface-format)
* [Launch model](#launch-model)
* [Megatron](#launch-megatron)
* [Meta](#launch-meta)
* [Huggingface](#launch-hf)
* [Benchmark results](#benchmark-results)
## Download Meta or Huggingface checkpoints
Users must first apply for access to download the Llama-3 checkpoints either directly from [Meta](https://llama.meta.com/llama-downloads) or through [Huggingface](https://huggingface.co/meta-llama) (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.
## Install tiktoken
The Llama-3 tokenizer relies on the availability of the `tiktoken` module which can be installed through `pip`.
## Install llama package from Meta
1. In a location outside of the megatron-lm source directory, e.g `~`: `git clone https://github.com/meta-llama/llama3.git`
2. `cd $LLAMA3_SOURCE_DIR`
4. `pip install -e .`
## Convert checkpoint format
We recommend passing `--dtype bf16` for training or finetuning. Inference can be done in bfloat16 or float16.
### Meta format
The Meta format checkpoints are converted to HF format as an intermediate step before converting to Megatron format. The `transformers` package is required, and must have version >=4.31.0 (e.g., `pip install transformers>=4.31.0`). (**Note**: we have specifically tested with versions `4.31.0` and `4.32.0`; your experience may vary with newer versions.) Assuming the downloaded checkpoints are in `$CHECKPOINT_DIR` (with separate sub-directories for 8B, 70B, etc.), the following example command can be used to convert from Llama-3 format to HF format in bfloat16:
```
python tools/checkpoint/convert.py \
> --model-type GPT \
> --loader llama_mistral \
> --saver mcore \
> --checkpoint-type meta \
> --model-size llama3-8B \
> --load-dir $LLAMA_META_FORMAT_DIR \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --tokenizer-model ${TOKENIZER_MODEL} \
> --target-tensor-parallel-size ${TP} \
> --target-pipeline-parallel-size ${PP} \
> --bf16
```
Valid values for `--model_size` are `llama3-8B` and `llama3-70B` (for pretrained-only models), and `llama3-8Bf` and `llama3-70Bf` (for chat-finetuned models).
### Huggingface format
The HF checkpoints can be converted to Megatron format by using Megatron's own Llama-3 checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`). One important argument that must be set correctly is the tensor parallel size (`TP`) for each model. The following table shows these values:
| Model size | Tensor parallel size (`TP`) |
| ---------- | --------------------------- |
| 8B | 1 |
| 70B | 8 |
Using these values for `TP`, along with the path to the Llama-3 tokenizer model (automatically downloaded with original checkpoint download; see `${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 \
> --saver mcore \
> --target-tensor-parallel-size ${TP} \
> --checkpoint-type hf
> --load-dir ${HF_FORMAT_DIR} \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --tokenizer-model ${TOKENIZER_MODEL}
> --model-size llama3-8B \
```
Valid values for `--model-size` are `llama3-8B` and `llama3-70B` (for pretrained-only models), and `llama3-8Bf` and `llama3-70Bf` (for chat-finetuned models).
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 Llama3Tokenizer \
--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
```
### Launch Meta
Meta checkpoints can be launched with: https://github.com/meta-llama/llama3
### Launch Huggingface
Huggingface checkpoints can be launched by following the instructions here: https://huggingface.co/blog/llama3
## Benchmark results
Llama-3 support in Megatron is currently experimental and we are still carrying out benchmark evaluations.
# Mistral-7b
Megatron currently supports loading the v.03 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. Install the `mistral-common` package
3. Convert the checkpoints from HuggingFace format to Megatron format.
4. Setup arguments for launching the model.
The following sections detail these steps.
## Contents
* [Download Huggingface checkpoints](#download-huggingface-checkpoints)
* [Install mistral-common packgage](#install-mistral-common)
* [Convert checkpoint format](#convert-checkpoint-format)
* [Launch model](#launch-model)
* [Benchmark results](#benchmark-results)
## 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). Megatron does not currently support the v0.1 or v0.2 checkpoints, ensure you download v0.3. Megatron does not currently support using the raw weights directly from [Mistral](https://docs.mistral.ai/getting-started/open_weight_models/).
## Install the mistral-common package
`pip install mistral-common`
## 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 mcore format:
```
$>: python tools/checkpoint/convert.py \
> --model-type GPT \
> --loader llama_mistral \
> --saver mcore \
> --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-7B \
```
Valid values for `--model-size` are mistral-7B for the pretrained model or mistral-7Bf for the chat fine-tuned model.
After this conversion, we are ready to load the checkpoints into an mcore GPT model.
## 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 MistralTokenizer \
--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
```
## Benchmark results
Mistral-7B support in Megatron is currently experimental and we are still carrying out benchmark evaluations.
# Other Llama-like model support
*Note: Experimental*
Many models such as Yi-34B use the Llama architecture and may be converted from HuggingFace to Megatron using the commands in [Llama3](#llama-3).
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