GitLab

* FusedRMSNorm/"T5LayerNorm" based on FusedLayerNorm (#1274)

* FusedRMSNorm based on FusedLayerNorm

* refactor duplicated kernels

* delete comments

* delete comments

* cleanup

* cleanup

* cleanup, fixed clobbering forward_affine_mixed_dtypes

* fix pybind naming and add MixedFused test

* undo skipping

* check elementwise_affine

* Update tests/L0/run_fused_layer_norm/test_fused_layer_norm.py

Oof, nice catch, thanks
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>

* fix and generate docs for FusedRMSNorm (#1285)

* [FusedRMSNorm doc] document where epsilon is added (#1295)

* [FusedRMSNorm doc] add epsilon to formula

* correct

* better wording

* Fix some bugs

* Optimize HostRMSNormGradient and HostApplyRMSNorm for AMD GPUs

* Fix NaN issues in FusedRMSNorm

* Update test_fused_layer_norm.py

* Skip test_fused_layer_norm.TestAutocastFusedRMSNorm on ROCm

* Use at::cuda::warp_size() instead of at::cuda::getCurrentDeviceProperties()->warpSize
Co-authored-by: eqy <eddiey@nvidia.com>
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* add temporary dispatch of double, float, half, bfloat16

* fusedadam of bfloat16

* Add bfloat16 path to FusedAdam

* Make rocblas_gemm_flags_fp16_alt_impl backward-compat for new naming

* Use BACKWARD_PASS_GUARD_CLASS to prevent lengthy if-statement

* FusedRMSNorm based on FusedLayerNorm

* refactor duplicated kernels

* delete comments

* delete comments

* cleanup

* cleanup

* cleanup, fixed clobbering forward_affine_mixed_dtypes

* fix pybind naming and add MixedFused test

* undo skipping

* check elementwise_affine

* Update tests/L0/run_fused_layer_norm/test_fused_layer_norm.py

Oof, nice catch, thanks
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>

* Add setup_simple.py for debugging the compiling issue of scaled_masked_softmax_cuda

* Comment out CUDA-specific implementations

* Resolve filename collision of *cpp files with to-hipify code and *cu files

Make rocblas_gemm_flags_fp16_alt_impl in MHA and MLP backward compatible with old PyTorch versions (#74)

* First attempt to make rocblas flag backward compatible

* Fix some bugs

* Fix some bugs

* Make rocblas_gemm_flags_fp16_alt_impl in MHA backward compatible with old PyTorch versions

* Add groupbn extension unit tests for ROCm

* Fix some bugs

* Add rocblas_alt_impl flag in MLP

* Refactor rocblas_alt_impl implementation and only use it for backprop

* fuse grad accumulation w/ weight grad
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* fp32 training path

* not using *args, **kwargs

* backward: moved the tensor dimension cnversion
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* move files to csrc/megatron

* fix fp32 path

* fix typo

* add  to  in order to select the correct custom extension

* fix typo

* comment on import guard

* update test: enable gradient_accumulation_fusion

* 86

* remove redundant call of `test_column_parallel_linear`
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* FusedRMSNorm based on FusedLayerNorm

* refactor duplicated kernels

* delete comments

* delete comments

* cleanup

* cleanup

* cleanup, fixed clobbering forward_affine_mixed_dtypes

* fix pybind naming and add MixedFused test

* undo skipping

* check elementwise_affine

* Update tests/L0/run_fused_layer_norm/test_fused_layer_norm.py

Oof, nice catch, thanks
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>
Co-authored-by: Masaki Kozuki <masaki.kozuki.2014@gmail.com>

* Optimize fused layer normalization for MI100

* Optimize cuComputePartGradGammaBeta for AMD GPUs

* Add fused mixed precision lamb optimizer.

* Fix device usage in constructor.

* Fix sending param_group tensor state to device.

* Remove unneeded device set.

* Add fused mixed precision lamb optimizer.

* Fix device usage in constructor.

* Fix sending param_group tensor state to device.

* Remove unneeded device set.

* Init apex.ppu (pipeline model parallel utility)

Reference commit:

```
commit 5ab646376d67831601d5552c193241d017f1b35c (HEAD -> main, internal/main)
Merge: 14f2c684 7b293d9b
Author: Mohammad Shoeybi <mshoeybi@nvidia.com>
Date:   Wed Sep 22 22:57:54 2021 -0700

    Merge branch 'add_BOS' into 'main'

    Add Beginning of Sentence token option and adding semaphore while multi-threading to prevent crashes and hangs due to connection keep-alives

    See merge request ADLR/megatron-lm!328
```

* removing get_args and replace import - phase 1

* removing get_args and replace import - phase 2

* move ppu to apex.transformer.pipeline_parallel

* update two __init__.py

* update READMEs

* mpu -> parallel_state & tensor_parallel

* fix

* remove not pipeline files

* separate schedules.py - phase 1

* dissect schedules.py

* data_iterators -> batch

* remove optimizer from forward_backward_step funcs

* init test

* Apply 2 suggestion(s...

Co-authored-by: Piotr Bialecki <pbialecki@nvidia.com>
Co-authored-by: Eddie Yan <eddiey@nvidia.com>
Co-authored-by: Rishi Puri <riship@nvidia.com>
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* support for fused dense layer with cublasLt, fusion in both fprop and bprop

* fix typo causing syntax error

* add fused GEMM+gelu+GEMM modue

* fix typo for workspace size

* update cublas check for 11600

* add tests for fused dense layer

* fix CUDA 10.x path

* safer guard around CUBLAS constants, remove unreferenced variable

* more guard changes

* guard against cublas version instead of cuda
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* fuse norm into scale

* add fused norm into dlamb
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* support for fused dense layer with cublasLt, fusion in both fprop and bprop

* fix typo causing syntax error

* add fused GEMM+gelu+GEMM modue

* fix typo for workspace size

* update cublas check for 11600

* add tests for fused dense layer

* fix CUDA 10.x path
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* don't create cublasLt handle, fix zero block size case

* cleanup

* initial cublaslt support

* 64 bit input

* add license headers

* cleanup

* remove license
Co-authored-by: pbialecki <pbialecki@nvidia.com>

* Add unit tests for fused-novograd

* Fix: tensors should reside on the same device

* Fix: Cudastream should be called on the same device on which the tensors reside on. Found this during debugging fused novograd multi-device unit test

* fixed issues mentioned in the comments

This reverts commit bdd481d1.

- incorrect use of __shfl_down
- fix warp size assumptions
- update unit tests to exit on failure

use __launch_bounds__(1024) for multi_tensor_apply, re-enable skipped tests

* fix warp size in WARP_SHFL* in layernorm

* enable fused_layer_norm tests on ROCm