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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
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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
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=============================================================
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
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---
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
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---
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.
--------------------------------------------------------------------------------
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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
This diff is collapsed.
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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