From 7a8fa2f013e7c4b3bf6463c26903d10c08813b07 Mon Sep 17 00:00:00 2001 From: Jacob Hatef <74274091+jahatef@users.noreply.github.com> Date: Wed, 24 Jan 2024 16:18:07 -0500 Subject: [PATCH 01/16] update copyrights (#1128) * update copyrights * Update NeoXArgs docs automatically * nvidia copyright years * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions --- Dockerfile | 2 +- LICENSE | 6 +++--- README.md | 2 +- configs/neox_arguments.md | 2 +- deepy.py | 2 +- eval.py | 4 ++-- eval_tasks/__init__.py | 2 +- eval_tasks/eval_adapter.py | 2 +- generate.py | 4 ++-- megatron/__init__.py | 2 +- megatron/checkpointing.py | 4 ++-- megatron/data/blendable_dataset.py | 4 ++-- megatron/data/data_utils.py | 2 +- megatron/data/gpt2_dataset.py | 4 ++-- megatron/data/helpers.cpp | 2 +- megatron/data/indexed_dataset.py | 2 +- megatron/data/samplers.py | 4 ++-- megatron/fused_kernels/__init__.py | 2 +- megatron/fused_kernels/compat.h | 2 +- .../fused_kernels/fused_rotary_positional_embedding.cpp | 2 +- megatron/fused_kernels/fused_rotary_positional_embedding.h | 2 +- .../fused_kernels/fused_rotary_positional_embedding_cuda.cu | 2 +- megatron/fused_kernels/scaled_masked_softmax.cpp | 2 +- megatron/fused_kernels/scaled_masked_softmax.h | 2 +- megatron/fused_kernels/scaled_masked_softmax_cuda.cu | 2 +- .../fused_kernels/scaled_upper_triang_masked_softmax.cpp | 2 +- megatron/fused_kernels/scaled_upper_triang_masked_softmax.h | 2 +- .../scaled_upper_triang_masked_softmax_cuda.cu | 2 +- megatron/fused_kernels/setup.py | 2 +- megatron/gradient_noise_scale/gradient_noise_scale.py | 2 +- megatron/initialize.py | 4 ++-- megatron/learning_rates.py | 4 ++-- megatron/logging.py | 2 +- megatron/model/__init__.py | 4 ++-- megatron/model/activations.py | 4 ++-- megatron/model/fused_bias_dropout.py | 2 +- megatron/model/fused_rope.py | 2 +- megatron/model/fused_softmax.py | 4 ++-- megatron/model/gmlp.py | 2 +- megatron/model/gpt2_model.py | 4 ++-- megatron/model/init_functions.py | 2 +- megatron/model/norms.py | 2 +- megatron/model/positional_embeddings.py | 2 +- megatron/model/transformer.py | 4 ++-- megatron/model/utils.py | 4 ++-- megatron/model/word_embeddings.py | 2 +- megatron/mpu/__init__.py | 2 +- megatron/mpu/cross_entropy.py | 4 ++-- megatron/mpu/data.py | 2 +- megatron/mpu/initialize.py | 4 ++-- megatron/mpu/layers.py | 4 ++-- megatron/mpu/mappings.py | 4 ++-- megatron/mpu/random.py | 2 +- megatron/mpu/utils.py | 4 ++-- megatron/neox_arguments/arguments.py | 2 +- megatron/neox_arguments/deepspeed_args.py | 2 +- megatron/neox_arguments/neox_args.py | 2 +- megatron/neox_arguments/template.py | 2 +- megatron/optimizers.py | 2 +- megatron/text_generation_utils.py | 4 ++-- megatron/tokenizer/__init__.py | 2 +- megatron/tokenizer/tokenizer.py | 4 ++-- megatron/tokenizer/train_tokenizer.py | 2 +- megatron/training.py | 4 ++-- megatron/utils.py | 4 ++-- prepare_data.py | 2 +- tests/common.py | 2 +- tests/model/__init__.py | 2 +- tests/model/test_fused_kernels.py | 2 +- tests/model/test_model_checkpoint.py | 2 +- tests/model/test_model_generation.py | 2 +- tests/model/test_model_instantiation.py | 2 +- tests/model/test_model_train.py | 2 +- tests/neox_args/test_neoxargs_commandline.py | 2 +- tests/neox_args/test_neoxargs_implementation.py | 2 +- tests/neox_args/test_neoxargs_load.py | 2 +- tests/neox_args/test_neoxargs_usage.py | 2 +- tests/pytest.ini | 2 +- tests/test_configs/test_train_base.yml | 2 +- tests/unit/test_arguments.py | 2 +- tests/unit/test_launcher_scripts.py | 2 +- tools/bash/sync.sh | 2 +- tools/bash/sync_cmd.sh | 2 +- tools/bash/syncdir.sh | 2 +- tools/ckpts/convert_module_to_hf.py | 2 +- tools/ckpts/convert_raw_llama_weights_to_neox.py | 2 +- tools/ckpts/convert_sequential_to_hf.py | 2 +- tools/ckpts/inspect_checkpoints.py | 2 +- tools/ckpts/merge20b.py | 2 +- tools/ckpts/upload.py | 2 +- tools/datasets/corpora.py | 2 +- tools/datasets/preprocess_data.py | 4 ++-- tools/datasets/preprocess_data_with_mask.py | 4 ++-- train.py | 4 ++-- 94 files changed, 122 insertions(+), 122 deletions(-) diff --git a/Dockerfile b/Dockerfile index bd30c11c3..e2f0a3d4c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/LICENSE b/LICENSE index 99cf99888..b7224a614 100644 --- a/LICENSE +++ b/LICENSE @@ -1,5 +1,5 @@ Apache License - Version 2.0, January 2004 + Version 2.0, January 2024 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION @@ -211,7 +211,7 @@ used in those files, as indicated. ------------- LICENSE FOR NVIDIA code -------------- -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions @@ -241,7 +241,7 @@ used in those files, as indicated. Apache License - Version 2.0, January 2004 + Version 2.0, January 2024 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION diff --git a/README.md b/README.md index f82bedec0..7e649707a 100644 --- a/README.md +++ b/README.md @@ -639,7 +639,7 @@ To cite the 20 billion parameter model named `GPT-NeoX-20B`, please use ## Licensing -This repository hosts code that is part of EleutherAI's GPT-NeoX project. Copyright (c) 2021, EleutherAI. Licensed under the Apache License: +This repository hosts code that is part of EleutherAI's GPT-NeoX project. Copyright (c) 2024, EleutherAI. Licensed under the Apache License: Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 99fe657f7..e7f6b1ef8 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = a300979 + Default = 765c219 current git hash of repository diff --git a/deepy.py b/deepy.py index eacbd9dec..e4dc7a3fd 100755 --- a/deepy.py +++ b/deepy.py @@ -1,5 +1,5 @@ #!/usr/bin/env python -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/eval.py b/eval.py index 7b7a74a6f..93093f21d 100644 --- a/eval.py +++ b/eval.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/eval_tasks/__init__.py b/eval_tasks/__init__.py index d17c7458e..6cb4e30ca 100644 --- a/eval_tasks/__init__.py +++ b/eval_tasks/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/eval_tasks/eval_adapter.py b/eval_tasks/eval_adapter.py index 63c4a4a72..abbd5ca8d 100644 --- a/eval_tasks/eval_adapter.py +++ b/eval_tasks/eval_adapter.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/generate.py b/generate.py index 100950f00..743e350d0 100755 --- a/generate.py +++ b/generate.py @@ -1,8 +1,8 @@ #!/usr/bin/env python -# Copyright (c) 2021 EleutherAI +# Copyright (c) 2024 EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/__init__.py b/megatron/__init__.py index 8cd7804c9..fc254fe3d 100644 --- a/megatron/__init__.py +++ b/megatron/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/checkpointing.py b/megatron/checkpointing.py index bbaa0f7f9..12b81e202 100644 --- a/megatron/checkpointing.py +++ b/megatron/checkpointing.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/data/blendable_dataset.py b/megatron/data/blendable_dataset.py index e05c58429..0392841cb 100644 --- a/megatron/data/blendable_dataset.py +++ b/megatron/data/blendable_dataset.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/data/data_utils.py b/megatron/data/data_utils.py index 513dd0e21..bc5754cdb 100644 --- a/megatron/data/data_utils.py +++ b/megatron/data/data_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/data/gpt2_dataset.py b/megatron/data/gpt2_dataset.py index c222fb3da..75e601fda 100644 --- a/megatron/data/gpt2_dataset.py +++ b/megatron/data/gpt2_dataset.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/data/helpers.cpp b/megatron/data/helpers.cpp index 830326c3f..9b062b050 100644 --- a/megatron/data/helpers.cpp +++ b/megatron/data/helpers.cpp @@ -1,6 +1,6 @@ /* coding=utf-8 - Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/megatron/data/indexed_dataset.py b/megatron/data/indexed_dataset.py index 81d99b0cd..8165205b9 100644 --- a/megatron/data/indexed_dataset.py +++ b/megatron/data/indexed_dataset.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # # Copyright (c) Facebook, Inc. and its affiliates. diff --git a/megatron/data/samplers.py b/megatron/data/samplers.py index 5e14b4a78..a9428e41c 100644 --- a/megatron/data/samplers.py +++ b/megatron/data/samplers.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/__init__.py b/megatron/fused_kernels/__init__.py index fff97986a..f6ac063ce 100644 --- a/megatron/fused_kernels/__init__.py +++ b/megatron/fused_kernels/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/compat.h b/megatron/fused_kernels/compat.h index 251337d68..88867dc7e 100644 --- a/megatron/fused_kernels/compat.h +++ b/megatron/fused_kernels/compat.h @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/fused_rotary_positional_embedding.cpp b/megatron/fused_kernels/fused_rotary_positional_embedding.cpp index 878f1b2c0..e1a77de2b 100644 --- a/megatron/fused_kernels/fused_rotary_positional_embedding.cpp +++ b/megatron/fused_kernels/fused_rotary_positional_embedding.cpp @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/fused_rotary_positional_embedding.h b/megatron/fused_kernels/fused_rotary_positional_embedding.h index cd67a28d3..aafd5104d 100644 --- a/megatron/fused_kernels/fused_rotary_positional_embedding.h +++ b/megatron/fused_kernels/fused_rotary_positional_embedding.h @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/fused_rotary_positional_embedding_cuda.cu b/megatron/fused_kernels/fused_rotary_positional_embedding_cuda.cu index 0f37dff41..6b54662bc 100644 --- a/megatron/fused_kernels/fused_rotary_positional_embedding_cuda.cu +++ b/megatron/fused_kernels/fused_rotary_positional_embedding_cuda.cu @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/scaled_masked_softmax.cpp b/megatron/fused_kernels/scaled_masked_softmax.cpp index b7c162c78..6a210cc37 100644 --- a/megatron/fused_kernels/scaled_masked_softmax.cpp +++ b/megatron/fused_kernels/scaled_masked_softmax.cpp @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/scaled_masked_softmax.h b/megatron/fused_kernels/scaled_masked_softmax.h index 977e59481..a594a13c1 100644 --- a/megatron/fused_kernels/scaled_masked_softmax.h +++ b/megatron/fused_kernels/scaled_masked_softmax.h @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/scaled_masked_softmax_cuda.cu b/megatron/fused_kernels/scaled_masked_softmax_cuda.cu index 757850d2b..26c2d1820 100644 --- a/megatron/fused_kernels/scaled_masked_softmax_cuda.cu +++ b/megatron/fused_kernels/scaled_masked_softmax_cuda.cu @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/scaled_upper_triang_masked_softmax.cpp b/megatron/fused_kernels/scaled_upper_triang_masked_softmax.cpp index 945c48c43..cedd649a2 100644 --- a/megatron/fused_kernels/scaled_upper_triang_masked_softmax.cpp +++ b/megatron/fused_kernels/scaled_upper_triang_masked_softmax.cpp @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/scaled_upper_triang_masked_softmax.h b/megatron/fused_kernels/scaled_upper_triang_masked_softmax.h index b0757196b..b48afeee4 100644 --- a/megatron/fused_kernels/scaled_upper_triang_masked_softmax.h +++ b/megatron/fused_kernels/scaled_upper_triang_masked_softmax.h @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/scaled_upper_triang_masked_softmax_cuda.cu b/megatron/fused_kernels/scaled_upper_triang_masked_softmax_cuda.cu index 7ced78acd..99a52abd5 100644 --- a/megatron/fused_kernels/scaled_upper_triang_masked_softmax_cuda.cu +++ b/megatron/fused_kernels/scaled_upper_triang_masked_softmax_cuda.cu @@ -1,5 +1,5 @@ /* coding=utf-8 - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/megatron/fused_kernels/setup.py b/megatron/fused_kernels/setup.py index d800b2a00..528a20676 100644 --- a/megatron/fused_kernels/setup.py +++ b/megatron/fused_kernels/setup.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/gradient_noise_scale/gradient_noise_scale.py b/megatron/gradient_noise_scale/gradient_noise_scale.py index 9c518a9cd..71076ffc3 100644 --- a/megatron/gradient_noise_scale/gradient_noise_scale.py +++ b/megatron/gradient_noise_scale/gradient_noise_scale.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/initialize.py b/megatron/initialize.py index dd752659b..72779b094 100644 --- a/megatron/initialize.py +++ b/megatron/initialize.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/learning_rates.py b/megatron/learning_rates.py index f0fcca27e..9db951aa0 100644 --- a/megatron/learning_rates.py +++ b/megatron/learning_rates.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/logging.py b/megatron/logging.py index ac404da85..6c9b7915e 100644 --- a/megatron/logging.py +++ b/megatron/logging.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI. +# Copyright (c) 2024, EleutherAI. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/__init__.py b/megatron/model/__init__.py index 9af46de95..619b4c33d 100755 --- a/megatron/model/__init__.py +++ b/megatron/model/__init__.py @@ -1,7 +1,7 @@ # -# Copyright 2021 Biderman et al. This file is based on code by the authors denoted below and has been modified from its original version. +# Copyright 2024 Biderman et al. This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/activations.py b/megatron/model/activations.py index 5c4ba1d5a..7a29b0716 100644 --- a/megatron/model/activations.py +++ b/megatron/model/activations.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/fused_bias_dropout.py b/megatron/model/fused_bias_dropout.py index afe1f0719..8618a2a7e 100644 --- a/megatron/model/fused_bias_dropout.py +++ b/megatron/model/fused_bias_dropout.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI contributors +# Copyright (c) 2024, EleutherAI contributors # This file is based on code by the authors denoted below and has been modified from its original version. # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/megatron/model/fused_rope.py b/megatron/model/fused_rope.py index 9f63f1537..94e96253d 100644 --- a/megatron/model/fused_rope.py +++ b/megatron/model/fused_rope.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/fused_softmax.py b/megatron/model/fused_softmax.py index 78f2992ad..bce2e1992 100644 --- a/megatron/model/fused_softmax.py +++ b/megatron/model/fused_softmax.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/gmlp.py b/megatron/model/gmlp.py index e4e29dad5..c3462c651 100644 --- a/megatron/model/gmlp.py +++ b/megatron/model/gmlp.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/megatron/model/gpt2_model.py b/megatron/model/gpt2_model.py index ce9c39da9..e083351cc 100644 --- a/megatron/model/gpt2_model.py +++ b/megatron/model/gpt2_model.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021 EleutherAI +# Copyright (c) 2024 EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/init_functions.py b/megatron/model/init_functions.py index 11bcdc310..ad8ebc05a 100644 --- a/megatron/model/init_functions.py +++ b/megatron/model/init_functions.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/norms.py b/megatron/model/norms.py index ddb45c3f3..08056edc2 100644 --- a/megatron/model/norms.py +++ b/megatron/model/norms.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/positional_embeddings.py b/megatron/model/positional_embeddings.py index 29c8a7e65..16cd22ad8 100644 --- a/megatron/model/positional_embeddings.py +++ b/megatron/model/positional_embeddings.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/transformer.py b/megatron/model/transformer.py index 9a4fb0f42..61ba3f3bf 100644 --- a/megatron/model/transformer.py +++ b/megatron/model/transformer.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021 EleutherAI +# Copyright (c) 2024 EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/utils.py b/megatron/model/utils.py index 6beac5ca2..990d9e69c 100644 --- a/megatron/model/utils.py +++ b/megatron/model/utils.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021 EleutherAI +# Copyright (c) 2024 EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/model/word_embeddings.py b/megatron/model/word_embeddings.py index 488baf042..f7372bc55 100644 --- a/megatron/model/word_embeddings.py +++ b/megatron/model/word_embeddings.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/__init__.py b/megatron/mpu/__init__.py index 611d2adbf..81e329e68 100644 --- a/megatron/mpu/__init__.py +++ b/megatron/mpu/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/cross_entropy.py b/megatron/mpu/cross_entropy.py index d28e0cc6b..d9f04703d 100644 --- a/megatron/mpu/cross_entropy.py +++ b/megatron/mpu/cross_entropy.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/data.py b/megatron/mpu/data.py index 7b937053b..87e2a9615 100644 --- a/megatron/mpu/data.py +++ b/megatron/mpu/data.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/initialize.py b/megatron/mpu/initialize.py index 325e46ba4..aba33395b 100644 --- a/megatron/mpu/initialize.py +++ b/megatron/mpu/initialize.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/layers.py b/megatron/mpu/layers.py index 92edbd6eb..19dff0b5f 100644 --- a/megatron/mpu/layers.py +++ b/megatron/mpu/layers.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/mappings.py b/megatron/mpu/mappings.py index c14693a69..e09462eca 100644 --- a/megatron/mpu/mappings.py +++ b/megatron/mpu/mappings.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/random.py b/megatron/mpu/random.py index f93a912a8..77fe80b3e 100644 --- a/megatron/mpu/random.py +++ b/megatron/mpu/random.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/mpu/utils.py b/megatron/mpu/utils.py index cb12f2569..13941dc29 100644 --- a/megatron/mpu/utils.py +++ b/megatron/mpu/utils.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/neox_arguments/arguments.py b/megatron/neox_arguments/arguments.py index d293f7b6a..b2055ad02 100644 --- a/megatron/neox_arguments/arguments.py +++ b/megatron/neox_arguments/arguments.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/neox_arguments/deepspeed_args.py b/megatron/neox_arguments/deepspeed_args.py index 071c3d3b7..708a5f5b1 100644 --- a/megatron/neox_arguments/deepspeed_args.py +++ b/megatron/neox_arguments/deepspeed_args.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/neox_arguments/neox_args.py b/megatron/neox_arguments/neox_args.py index 8b76c8b32..fc9e15db4 100644 --- a/megatron/neox_arguments/neox_args.py +++ b/megatron/neox_arguments/neox_args.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/neox_arguments/template.py b/megatron/neox_arguments/template.py index d0216459f..2e8341990 100644 --- a/megatron/neox_arguments/template.py +++ b/megatron/neox_arguments/template.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/optimizers.py b/megatron/optimizers.py index fcf8a44c7..93515ed14 100644 --- a/megatron/optimizers.py +++ b/megatron/optimizers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/text_generation_utils.py b/megatron/text_generation_utils.py index 5eb982384..7b7a390ab 100644 --- a/megatron/text_generation_utils.py +++ b/megatron/text_generation_utils.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/tokenizer/__init__.py b/megatron/tokenizer/__init__.py index 22b0f7b9e..b53bbb828 100644 --- a/megatron/tokenizer/__init__.py +++ b/megatron/tokenizer/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/tokenizer/tokenizer.py b/megatron/tokenizer/tokenizer.py index 504890763..348c7cefe 100644 --- a/megatron/tokenizer/tokenizer.py +++ b/megatron/tokenizer/tokenizer.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/tokenizer/train_tokenizer.py b/megatron/tokenizer/train_tokenizer.py index 72b244a9b..3027677e7 100644 --- a/megatron/tokenizer/train_tokenizer.py +++ b/megatron/tokenizer/train_tokenizer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/training.py b/megatron/training.py index e4e858574..9b8104400 100644 --- a/megatron/training.py +++ b/megatron/training.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/megatron/utils.py b/megatron/utils.py index 3769495d9..8b25b5c3c 100644 --- a/megatron/utils.py +++ b/megatron/utils.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/prepare_data.py b/prepare_data.py index 86d8b5f5a..62363e27b 100644 --- a/prepare_data.py +++ b/prepare_data.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/common.py b/tests/common.py index d2659b453..bb7a90f85 100644 --- a/tests/common.py +++ b/tests/common.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/model/__init__.py b/tests/model/__init__.py index b9c364917..be6d2e9ec 100644 --- a/tests/model/__init__.py +++ b/tests/model/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/model/test_fused_kernels.py b/tests/model/test_fused_kernels.py index be068099e..b8cb34d1b 100644 --- a/tests/model/test_fused_kernels.py +++ b/tests/model/test_fused_kernels.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/model/test_model_checkpoint.py b/tests/model/test_model_checkpoint.py index e73e1a0fe..ec59ad816 100644 --- a/tests/model/test_model_checkpoint.py +++ b/tests/model/test_model_checkpoint.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/model/test_model_generation.py b/tests/model/test_model_generation.py index ab8bd756b..4ac0bdd6b 100644 --- a/tests/model/test_model_generation.py +++ b/tests/model/test_model_generation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/model/test_model_instantiation.py b/tests/model/test_model_instantiation.py index ad57a2c5f..211db3262 100644 --- a/tests/model/test_model_instantiation.py +++ b/tests/model/test_model_instantiation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/model/test_model_train.py b/tests/model/test_model_train.py index 0138a7f79..dc24f6b6d 100644 --- a/tests/model/test_model_train.py +++ b/tests/model/test_model_train.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/neox_args/test_neoxargs_commandline.py b/tests/neox_args/test_neoxargs_commandline.py index cf39f75d0..0d3c7e5fb 100644 --- a/tests/neox_args/test_neoxargs_commandline.py +++ b/tests/neox_args/test_neoxargs_commandline.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/neox_args/test_neoxargs_implementation.py b/tests/neox_args/test_neoxargs_implementation.py index c77fa178f..176887c9c 100644 --- a/tests/neox_args/test_neoxargs_implementation.py +++ b/tests/neox_args/test_neoxargs_implementation.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/neox_args/test_neoxargs_load.py b/tests/neox_args/test_neoxargs_load.py index 5f94f4fd6..f5d278112 100644 --- a/tests/neox_args/test_neoxargs_load.py +++ b/tests/neox_args/test_neoxargs_load.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/neox_args/test_neoxargs_usage.py b/tests/neox_args/test_neoxargs_usage.py index 40532f23e..176151c2a 100644 --- a/tests/neox_args/test_neoxargs_usage.py +++ b/tests/neox_args/test_neoxargs_usage.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/pytest.ini b/tests/pytest.ini index c2bb21d77..6fd100ea2 100644 --- a/tests/pytest.ini +++ b/tests/pytest.ini @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/test_configs/test_train_base.yml b/tests/test_configs/test_train_base.yml index 28e41adbf..2852574af 100644 --- a/tests/test_configs/test_train_base.yml +++ b/tests/test_configs/test_train_base.yml @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/unit/test_arguments.py b/tests/unit/test_arguments.py index 501748366..7144a149c 100644 --- a/tests/unit/test_arguments.py +++ b/tests/unit/test_arguments.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/unit/test_launcher_scripts.py b/tests/unit/test_launcher_scripts.py index 626788b59..d281b6c04 100644 --- a/tests/unit/test_launcher_scripts.py +++ b/tests/unit/test_launcher_scripts.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. import pytest from tools.datasets import preprocess_data diff --git a/tools/bash/sync.sh b/tools/bash/sync.sh index d3788cce4..fd9377dbb 100755 --- a/tools/bash/sync.sh +++ b/tools/bash/sync.sh @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/bash/sync_cmd.sh b/tools/bash/sync_cmd.sh index 6525247fb..2d8a617ca 100644 --- a/tools/bash/sync_cmd.sh +++ b/tools/bash/sync_cmd.sh @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/bash/syncdir.sh b/tools/bash/syncdir.sh index 210d142ae..229c9af9a 100755 --- a/tools/bash/syncdir.sh +++ b/tools/bash/syncdir.sh @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/ckpts/convert_module_to_hf.py b/tools/ckpts/convert_module_to_hf.py index c776a2817..72ce59d32 100644 --- a/tools/ckpts/convert_module_to_hf.py +++ b/tools/ckpts/convert_module_to_hf.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/ckpts/convert_raw_llama_weights_to_neox.py b/tools/ckpts/convert_raw_llama_weights_to_neox.py index 5940fa856..824cc95d9 100644 --- a/tools/ckpts/convert_raw_llama_weights_to_neox.py +++ b/tools/ckpts/convert_raw_llama_weights_to_neox.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/ckpts/convert_sequential_to_hf.py b/tools/ckpts/convert_sequential_to_hf.py index f40a1d233..07dc228e1 100644 --- a/tools/ckpts/convert_sequential_to_hf.py +++ b/tools/ckpts/convert_sequential_to_hf.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/ckpts/inspect_checkpoints.py b/tools/ckpts/inspect_checkpoints.py index 0bd5c05b4..a23d456ca 100644 --- a/tools/ckpts/inspect_checkpoints.py +++ b/tools/ckpts/inspect_checkpoints.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/ckpts/merge20b.py b/tools/ckpts/merge20b.py index ae1f5a0a3..09029bb5b 100644 --- a/tools/ckpts/merge20b.py +++ b/tools/ckpts/merge20b.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/ckpts/upload.py b/tools/ckpts/upload.py index 324a7debe..01d585be8 100644 --- a/tools/ckpts/upload.py +++ b/tools/ckpts/upload.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/datasets/corpora.py b/tools/datasets/corpora.py index 7d2ec4e8c..2c440dc0a 100644 --- a/tools/datasets/corpora.py +++ b/tools/datasets/corpora.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/tools/datasets/preprocess_data.py b/tools/datasets/preprocess_data.py index 74ff251e0..32e656ace 100644 --- a/tools/datasets/preprocess_data.py +++ b/tools/datasets/preprocess_data.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tools/datasets/preprocess_data_with_mask.py b/tools/datasets/preprocess_data_with_mask.py index b2fba73ec..ec2fddbe4 100644 --- a/tools/datasets/preprocess_data_with_mask.py +++ b/tools/datasets/preprocess_data_with_mask.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/train.py b/train.py index 5f4ddce53..2e4b09954 100644 --- a/train.py +++ b/train.py @@ -1,7 +1,7 @@ -# Copyright (c) 2021, EleutherAI +# Copyright (c) 2024, EleutherAI # This file is based on code by the authors denoted below and has been modified from its original version. # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From 3d8fec028acf3c2d69b8e23254e1a4a525f73c90 Mon Sep 17 00:00:00 2001 From: yang <7129+yang@users.noreply.github.com> Date: Fri, 26 Jan 2024 15:07:48 -0800 Subject: [PATCH 02/16] fused layernorm (#1105) * Add simple util for CUDA timings * Add fused layernorm kernel from Megatron Closes #952 * change default fused layernorm to false * Update test_setup.yml * Update test_train_base.yml --------- Co-authored-by: Yang Zhang Co-authored-by: jahatef Co-authored-by: Jacob Hatef <74274091+jahatef@users.noreply.github.com> --- configs/1-3B.yml | 1 + configs/125M-json.yml | 1 + configs/125M.yml | 1 + configs/13B.yml | 1 + configs/175B.yml | 1 + configs/19M.yml | 1 + configs/2-7B.yml | 1 + configs/20B.yml | 1 + configs/350M.yml | 1 + configs/49M.yml | 1 + configs/6-7B.yml | 1 + configs/760M.yml | 1 + configs/800M.yml | 1 + configs/bf16_125M.yml | 1 + configs/bnb_125M.yml | 1 + configs/slurm_125M.yml | 1 + megatron/devutil.py | 51 +++++++++ megatron/model/fused_layer_norm.py | 150 +++++++++++++++++++++++++ megatron/model/norms.py | 3 +- megatron/neox_arguments/neox_args.py | 5 + megatron/utils.py | 59 ++++++++++ tests/config/test_setup.yml | 1 + tests/test_configs/test_train_base.yml | 1 + 23 files changed, 285 insertions(+), 1 deletion(-) create mode 100644 megatron/devutil.py create mode 100644 megatron/model/fused_layer_norm.py diff --git a/configs/1-3B.yml b/configs/1-3B.yml index 31c316e2e..ea3fdb9bf 100644 --- a/configs/1-3B.yml +++ b/configs/1-3B.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/125M-json.yml b/configs/125M-json.yml index fff863f44..467318f24 100644 --- a/configs/125M-json.yml +++ b/configs/125M-json.yml @@ -16,6 +16,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, "init_method": "small_init", "output_layer_init_method": "wang_init", diff --git a/configs/125M.yml b/configs/125M.yml index a098c6df6..466492f7b 100644 --- a/configs/125M.yml +++ b/configs/125M.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/13B.yml b/configs/13B.yml index 93592896b..99caab585 100644 --- a/configs/13B.yml +++ b/configs/13B.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/175B.yml b/configs/175B.yml index 99efdc6e0..4d011f1b4 100644 --- a/configs/175B.yml +++ b/configs/175B.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/19M.yml b/configs/19M.yml index 148655efc..f9f856769 100644 --- a/configs/19M.yml +++ b/configs/19M.yml @@ -16,6 +16,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/2-7B.yml b/configs/2-7B.yml index ffcd3af95..9e6a47e15 100644 --- a/configs/2-7B.yml +++ b/configs/2-7B.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/20B.yml b/configs/20B.yml index c55d342b9..0a4ce6335 100644 --- a/configs/20B.yml +++ b/configs/20B.yml @@ -31,6 +31,7 @@ "scaled_upper_triang_masked_softmax_fusion": true, "bias_gelu_fusion": true, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/350M.yml b/configs/350M.yml index 755f11c63..00a174433 100644 --- a/configs/350M.yml +++ b/configs/350M.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/49M.yml b/configs/49M.yml index e30bc3ebb..4515914e7 100644 --- a/configs/49M.yml +++ b/configs/49M.yml @@ -19,6 +19,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/6-7B.yml b/configs/6-7B.yml index a3578a014..087b7d763 100644 --- a/configs/6-7B.yml +++ b/configs/6-7B.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/760M.yml b/configs/760M.yml index 0eb6aea31..6d62dc0f3 100644 --- a/configs/760M.yml +++ b/configs/760M.yml @@ -21,6 +21,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/800M.yml b/configs/800M.yml index 28396d58d..f81b181a2 100644 --- a/configs/800M.yml +++ b/configs/800M.yml @@ -16,6 +16,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # init methods "init_method": "small_init", diff --git a/configs/bf16_125M.yml b/configs/bf16_125M.yml index eac251cae..87e86e7fb 100644 --- a/configs/bf16_125M.yml +++ b/configs/bf16_125M.yml @@ -19,6 +19,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # optimizer settings diff --git a/configs/bnb_125M.yml b/configs/bnb_125M.yml index 619b0084a..523b10c39 100644 --- a/configs/bnb_125M.yml +++ b/configs/bnb_125M.yml @@ -20,6 +20,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # optimizer settings diff --git a/configs/slurm_125M.yml b/configs/slurm_125M.yml index 798b43629..2ac60e534 100644 --- a/configs/slurm_125M.yml +++ b/configs/slurm_125M.yml @@ -12,6 +12,7 @@ "scaled_upper_triang_masked_softmax_fusion": true, "bias_gelu_fusion": true, "rope_fusion": false, + "layernorm_fusion": false, "optimizer": { "type": "Adam", "params": { diff --git a/megatron/devutil.py b/megatron/devutil.py new file mode 100644 index 000000000..7563d7dcf --- /dev/null +++ b/megatron/devutil.py @@ -0,0 +1,51 @@ +import torch.cuda + + +class Metric: + """ + Dumb utility to collect and report average wall-time metrics. + """ + + def __init__(self, label): + self.label = label + self.measurements = [] + + def collect(self, measurement): + self.measurements.append(measurement) + + def get_measurements(self): + return self.measurements[:] + + def report(self): + print( + self.label, + torch.quantile(torch.tensor(self.measurements), torch.arange(10) / 10.0), + ) + + +def monitor_method_cuda_wall_times(metric, obj, methodname): + """ + Measure timings for a method on an object or class. + + For instance: + + >>> metric = Metric('!LNORM') + >>> monitor_method_wall_times(metric, LayerNorm, 'forward') + """ + oldmeth = getattr(obj, methodname) + + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + + def newmeth(*args, **kw): + start_event.record() + try: + return oldmeth(*args, **kw) + finally: + end_event.record() + torch.cuda.synchronize() + elapsed = start_event.elapsed_time(end_event) + metric.collect(elapsed) + metric.report() + + setattr(obj, methodname, newmeth) diff --git a/megatron/model/fused_layer_norm.py b/megatron/model/fused_layer_norm.py new file mode 100644 index 000000000..d33ded506 --- /dev/null +++ b/megatron/model/fused_layer_norm.py @@ -0,0 +1,150 @@ +# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. + +"""This code is copied from NVIDIA apex: + https://github.com/NVIDIA/apex + with some changes. """ + +import numbers +import torch +from torch.nn.parameter import Parameter +from torch.nn import init +import importlib +from torch.nn import functional as F +import inspect + +from megatron.utils import make_viewless_tensor + +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNormFN + + HAVE_PERSIST_LAYER_NORM = True +except: + HAVE_PERSIST_LAYER_NORM = False + +from apex.normalization.fused_layer_norm import FusedLayerNormAffineFunction + + +global fused_layer_norm_cuda +fused_layer_norm_cuda = None + + +class MixedFusedLayerNorm(torch.nn.Module): + def __init__( + self, + normalized_shape, + eps=1e-5, + no_persist_layer_norm=True, + sequence_parallel=False, + apply_layernorm_1p=False, + mem_efficient_ln=True, + ): + super(MixedFusedLayerNorm, self).__init__() + + self.apply_layernorm_1p = apply_layernorm_1p + self.mem_efficient_ln = mem_efficient_ln + + global fused_layer_norm_cuda + fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda") + + # List of hiddens sizes supported in the persistent layer norm kernel + # If the hidden size is not supported, fall back to the non-persistent + # kernel. + persist_ln_hidden_sizes = [ + 1024, + 1536, + 2048, + 2304, + 3072, + 3840, + 4096, + 5120, + 6144, + 8192, + 10240, + 12288, + 12800, + 15360, + 16384, + 18432, + 20480, + 24576, + 25600, + 30720, + 32768, + 40960, + 49152, + 65536, + ] + if ( + normalized_shape not in persist_ln_hidden_sizes + or not HAVE_PERSIST_LAYER_NORM + ): + no_persist_layer_norm = True + + if isinstance(normalized_shape, numbers.Integral): + normalized_shape = (normalized_shape,) + self.normalized_shape = torch.Size(normalized_shape) + self.eps = eps + self.weight = Parameter(torch.Tensor(*normalized_shape)) + self.bias = Parameter(torch.Tensor(*normalized_shape)) + self.reset_parameters() + self.no_persist_layer_norm = no_persist_layer_norm + self.sequence_parallel = sequence_parallel + + # set sequence parallelism flag on weight and bias parameters + setattr(self.weight, "sequence_parallel", self.sequence_parallel) + setattr(self.bias, "sequence_parallel", self.sequence_parallel) + + def reset_parameters(self): + + if self.apply_layernorm_1p: + init.zeros_(self.weight) + init.zeros_(self.bias) + else: + init.ones_(self.weight) + init.zeros_(self.bias) + + def forward(self, input): + + weight = self.weight + 1 if self.apply_layernorm_1p else self.weight + # CPU path is here for unittest sake. + if not input.is_cuda: + print( + "WARNING! The input of FusedLayerNorm should be on the GPU." + "This warning should only be triggered in the FusedLayerNorm unit tests." + ) + return F.layer_norm( + input, self.normalized_shape, weight, self.bias, self.eps + ) + + if self.no_persist_layer_norm: + # Apex does not have versions yet (https://github.com/NVIDIA/apex/pull/1648), so we need to inspect + # the function manually on whether the extra arg introduced in https://github.com/NVIDIA/apex/pull/1715 exists yet + if ( + "memory_efficient" + in inspect.getfullargspec(FusedLayerNormAffineFunction.forward).args + ): + return FusedLayerNormAffineFunction.apply( + input, + weight, + self.bias, + self.normalized_shape, + self.eps, + self.mem_efficient_ln, + ) + else: + return FusedLayerNormAffineFunction.apply( + input, weight, self.bias, self.normalized_shape, self.eps + ) + else: + output = FastLayerNormFN.apply(input, weight, self.bias, self.eps) + + # Apex's fast layer norm function outputs a 'view' tensor (i.e., has + # a populated '_base' field). This will result in schedule.py's + # deallocate_output_tensor() throwing an error, so a viewless tensor is + # created to prevent this. + output = make_viewless_tensor( + inp=output, requires_grad=input.requires_grad, keep_graph=True + ) + + return output diff --git a/megatron/model/norms.py b/megatron/model/norms.py index 08056edc2..4d01dde5a 100644 --- a/megatron/model/norms.py +++ b/megatron/model/norms.py @@ -14,6 +14,7 @@ import torch from torch.nn import LayerNorm as LayerNorm +from .fused_layer_norm import MixedFusedLayerNorm def get_norm(neox_args): @@ -22,7 +23,7 @@ def get_norm(neox_args): eps = neox_args.rms_norm_epsilon elif neox_args.norm == "layernorm": eps = neox_args.layernorm_epsilon - norm = LayerNorm + norm = MixedFusedLayerNorm if neox_args.layernorm_fusion else LayerNorm elif neox_args.norm == "scalenorm": eps = neox_args.scalenorm_epsilon norm = ScaleNorm diff --git a/megatron/neox_arguments/neox_args.py b/megatron/neox_arguments/neox_args.py index fc9e15db4..f7b047f70 100644 --- a/megatron/neox_arguments/neox_args.py +++ b/megatron/neox_arguments/neox_args.py @@ -125,6 +125,11 @@ class NeoXArgsModel(NeoXArgsTemplate): Normalization layer to use. Choose from "layernorm", "rmsnorm", "scalenorm". """ + layernorm_fusion: bool = False + """ + Use fused layer norm kernel (if `norm` is `layernorm`). + """ + use_qk_layernorm: bool = False """ Use QK Normalization diff --git a/megatron/utils.py b/megatron/utils.py index 8b25b5c3c..777f32b7d 100644 --- a/megatron/utils.py +++ b/megatron/utils.py @@ -484,3 +484,62 @@ def __next__(self): end = time.time() self.total_time += end - start return batch + + +def _kernel_make_viewless_tensor(inp, requires_grad): + """Make a viewless tensor. + + View tensors have the undesirable side-affect of retaining a reference + to the originally-viewed tensor, even after manually setting the '.data' + field. This method creates a new tensor that links to the old tensor's + data, without linking the viewed tensor, referenced via the '._base' + field. + """ + out = torch.empty( + (1,), + dtype=inp.dtype, + device=inp.device, + requires_grad=requires_grad, + ) + out.data = inp.data + return out + + +class MakeViewlessTensor(torch.autograd.Function): + """ + Autograd function to make a viewless tensor. + + This function should be used in cases where the computation graph needs + to be propagated, but we only want a viewless tensor (e.g., + ParallelTransformer's hidden_states). Call this function by passing + 'keep_graph = True' to 'make_viewless_tensor()'. + """ + + @staticmethod + def forward(ctx, inp, requires_grad): + return _kernel_make_viewless_tensor(inp, requires_grad) + + @staticmethod + def backward(ctx, grad_output): + return grad_output, None + + +def make_viewless_tensor(inp, requires_grad, keep_graph): + """ + Entry-point for creating viewless tensors. + + This method should be used, rather than calling 'MakeViewlessTensor' + or '_kernel_make_viewless_tensor' directly. This method acts as a + switch for determining if an autograd function or a regular method + should be used to create the tensor. + """ + + # return tensor as-is, if not a 'view' + if inp._base is None: + return inp + + # create viewless tensor + if keep_graph: + return MakeViewlessTensor.apply(inp, requires_grad) + else: + return _kernel_make_viewless_tensor(inp, requires_grad) diff --git a/tests/config/test_setup.yml b/tests/config/test_setup.yml index c4de20940..dfff07e7a 100644 --- a/tests/config/test_setup.yml +++ b/tests/config/test_setup.yml @@ -28,6 +28,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # Optimizer "optimizer": { diff --git a/tests/test_configs/test_train_base.yml b/tests/test_configs/test_train_base.yml index 2852574af..bb66a5b97 100644 --- a/tests/test_configs/test_train_base.yml +++ b/tests/test_configs/test_train_base.yml @@ -33,6 +33,7 @@ "scaled_upper_triang_masked_softmax_fusion": false, "bias_gelu_fusion": false, "rope_fusion": false, + "layernorm_fusion": false, # optimizer settings "optimizer": { From e5602c3260a79d68798fabc0d6b7923c74d21161 Mon Sep 17 00:00:00 2001 From: Jacob Hatef <74274091+jahatef@users.noreply.github.com> Date: Sun, 28 Jan 2024 21:49:15 -0500 Subject: [PATCH 03/16] Contributing Guide (#1138) * contributing guide * Update NeoXArgs docs automatically * Update CONTRIBUTING.md * Update NeoXArgs docs automatically * Remove microsoft references and link on main readme * Update NeoXArgs docs automatically * pre-commit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions Co-authored-by: Quentin Anthony --- .pre-commit-config.yaml | 4 +- CONTRIBUTING.md | 86 ++++++++++++++++++++++++++++ README.md | 6 ++ configs/neox_arguments.md | 10 +++- megatron/neox_arguments/arguments.py | 2 +- 5 files changed, 104 insertions(+), 4 deletions(-) create mode 100644 CONTRIBUTING.md diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e9ca3920d..0eaf9b13c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,10 +16,10 @@ repos: args: [--fix=lf] - id: requirements-txt-fixer - id: trailing-whitespace - - repo: https://gitlab.com/daverona/pre-commit-cpp + - repo: https://gitlab.com/daverona/pre-commit/cpp rev: 0.8.0 hooks: - - id: clang-format # formatter of C/C++ code based on a style guide: LLVM, Google, Chromium, Mozilla, and WebKit available + - id: clang-format # formatter of C/C++ code based on a style guide: LLVM, Google, Chromium, Mozilla, and WebKit available args: [] - repo: https://github.com/psf/black diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..ee633e8c5 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,86 @@ +# Contributing +GPT-NeoX welcomes your contributions! + +## Prerequisites +GPT-NeoX uses [pre-commit](https://pre-commit.com/) to ensure that formatting is +consistent across GPT-NeoX. First, ensure that `pre-commit` is installed with +`pip install pre-commit`. Next, the pre-commit hooks must be installed once +before commits can be made: +```bash +pre-commit install +``` +Please install `clang-format` from Conda: +```bash +conda install clang-format +``` + +Afterwards, our suite of formatting tests run automatically before each `git commit`. You +can also run these manually: +```bash +pre-commit run --all-files +``` +If a formatting test fails, it will fix the modified code in place and abort +the `git commit`. After looking over the changes, you can `git add ` +and then repeat the previous `git commit` command. + + +## Testing +GPT-NeoX tracks two types of tests: unit tests and more costly model convergence tests. +Unit tests are found in `tests/unit/` and the model convergence tests are found in +`tests/model/`. + +### Unit Tests +[PyTest](https://docs.pytest.org/en/latest/) is used to execute tests. PyTest can be +installed from PyPI via `pip install pytest`. Simply invoke `pytest --forked` to run the +unit tests: +```bash +pytest --forked tests/unit/ +``` +You can also provide the `-v` flag to `pytest` to see additional information about the +tests. Note that [pytest-forked](https://github.com/pytest-dev/pytest-forked) and the +`--forked` flag are required to test CUDA functionality in distributed tests. + +### Model Tests +To execute model tests, first install GPT-NeoX. Next, execute the model test driver: +```bash +cd tests/model/ +pytest run_sanity_check.py +``` +Note that the `--forked` flag is not necessary for the model tests. + +## Contributor License Agreement +This project welcomes contributions and suggestions. Most contributions require you to +agree to a Contributor License Agreement (CLA) declaring that you have the right to, and +actually do, grant us the rights to use your contribution. For details, visit +https://cla-assistant.io/EleutherAI/gpt-neox. + +When you submit a pull request, a CLA bot will automatically determine whether you need +to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply +follow the instructions provided by the bot. You will only need to do this once across +all repos using our CLA. + +## New Feature Contribution Guidelines +Unlike bug fix or improving existing feature (where users usually directly submit a PR and we review it), adding a new feature to GPT-NeoX requires several steps: (1) proposal and discussion, (2) implementation and verification, (3) release and maintenance. This general guideline applies to all new feature contributions. Core GPT-NeoX team member contributions may complete step 1 internally. + +### Step 1: Proposal and Discussion +We ask users to first post your intended feature in an issue. This issue needs to include: + +* A description of the proposed feature. +* A motivation of why it will be useful to GPT-NeoX users. +* A rough design of how you implement the feature inside GPT-NeoX. +* (Important) Results or planned experiments to demonstrate the effectiveness and correctness of the feature. + * If the feature only affects performance and does not affect training convergence, we require testing on a fraction of training to demonstrate that the training/validation loss are consistent with baseline, and that the performance is better than baseline. + * If the feature does affect training convergence, we require testing the whole training to demonstrate that the feature achieves better/on-par final model quality and training performance compared to baseline. + +Based on the issue we shall discuss the merit of the new feature and decide whether to accept or decline the proposal. Once accepted and after we confirm the design and implementation plan, we are ready for step 2. + +### Step 2: Implementation and Verification +The contributor will proceed and implement the feature, and the GPT-NeoX team will provide guidance/helps as needed. The required deliverables include: + +* A PR to [EleutherAI/GPT-NeoX](https://github.com/EleutherAI/gpt-neox) including (1) the feature implementation (2) unit tests (3) documentation (4) example usage. +* In the implementation (code, documentation, tutorial), we require the feature author to record their GitHub username as a contact method for future questions/maintenance. + +After receiving the PRs, we will review them and merge them after necessary tests/fixes. + +### Step 3: Release and Maintenance +After the PRs are merged, we will announce the feature on our website (with credit to the feature author). We ask the feature author to commit to the maintenance of the feature. diff --git a/README.md b/README.md index 7e649707a..0680bacb0 100644 --- a/README.md +++ b/README.md @@ -76,6 +76,7 @@ Prior to 3/9/2023, GPT-NeoX relied on [DeeperSpeed](https://github.com/EleutherA + [Other Modalities](#other-modalities) - [Administrative Notes](#administrative-notes) * [Citing GPT-NeoX](#citing-gpt-neox) + * [Contributing](#contributing) * [Licensing](#licensing) * [Acknowledgements](#acknowledgements) @@ -637,6 +638,11 @@ To cite the 20 billion parameter model named `GPT-NeoX-20B`, please use } ``` +## Contributing +GPT-NeoX is built by the open-source AI community, and relies on our amazing contributors! Please see our +[contributing](CONTRIBUTING.md) guide for more details on our CLA, code formatting, testing, +etc. + ## Licensing This repository hosts code that is part of EleutherAI's GPT-NeoX project. Copyright (c) 2024, EleutherAI. Licensed under the Apache License: diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index e7f6b1ef8..28b6e77db 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 765c219 + Default = cd9a8ea current git hash of repository @@ -261,6 +261,14 @@ Model Arguments +- **layernorm_fusion**: bool + + Default = False + + Use fused layer norm kernel (if `norm` is `layernorm`). + + + - **use_qk_layernorm**: bool Default = False diff --git a/megatron/neox_arguments/arguments.py b/megatron/neox_arguments/arguments.py index b2055ad02..627bf45f1 100644 --- a/megatron/neox_arguments/arguments.py +++ b/megatron/neox_arguments/arguments.py @@ -510,7 +510,7 @@ def get_deepspeed_main_args(self): args_list.extend( self.convert_key_value_to_command_line_arg("account", account) ) - + # master_address = os.environ['SLURM_JOB_NODELIST'].split('\n')[0] # args_list.extend( # self.convert_key_value_to_command_line_arg('master_addr', master_address) From 1c133bff4fa172880b50af98dce8cd5c4064a6b8 Mon Sep 17 00:00:00 2001 From: Lang Xu <59843980+R0n12@users.noreply.github.com> Date: Mon, 29 Jan 2024 20:30:11 -0500 Subject: [PATCH 04/16] moved eval import and added to docs (#1139) --- configs/neox_arguments.md | 9 +++++---- megatron/training.py | 3 ++- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 28b6e77db..0731a5655 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -976,7 +976,7 @@ Text Generation arguments - **prompt_end**: str - Default = + Default = a single prompt's end. Defaults to newline @@ -1018,7 +1018,7 @@ Text Generation arguments - **eval_results_prefix**: str - Default = + Default = prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json @@ -1030,6 +1030,8 @@ Text Generation arguments Tasks to evaluate on using lm_eval_harness + NOTE: Requires internet connection + ## NeoXArgsTokenizer @@ -1768,7 +1770,7 @@ Args for deepspeed config Default = None - + @@ -2068,4 +2070,3 @@ Args for deepspeed runner (deepspeed.launcher.runner). Default = None Adds a `--account` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard. - diff --git a/megatron/training.py b/megatron/training.py index 9b8104400..0f9b36f0b 100644 --- a/megatron/training.py +++ b/megatron/training.py @@ -55,7 +55,6 @@ CharCounter, ) from megatron.model.gpt2_model import cross_entropy -from eval_tasks import run_eval_harness def mup_weights_reinit(neox_args, model): @@ -967,6 +966,8 @@ def evaluate( ) if neox_args.eval_tasks: + from eval_tasks import run_eval_harness + eval_results.update( run_eval_harness( model, forward_step_fn, neox_args, eval_tasks=neox_args.eval_tasks From 032ec8cc37a39333658716c42dee91d84d4ad2a6 Mon Sep 17 00:00:00 2001 From: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com> Date: Thu, 1 Feb 2024 18:27:28 -0500 Subject: [PATCH 05/16] Update lm_eval v0.4 to PyPI dependencies (#1141) * Update requirements.txt * Update requirements.txt * Update NeoXArgs docs automatically * add note to neox_args.py * pre-commit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions Co-authored-by: Quentin Anthony --- configs/neox_arguments.md | 9 +++++---- megatron/neox_arguments/neox_args.py | 2 ++ requirements/requirements.txt | 2 +- 3 files changed, 8 insertions(+), 5 deletions(-) diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 0731a5655..de19c74ea 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = cd9a8ea + Default = 78b8466 current git hash of repository @@ -976,7 +976,7 @@ Text Generation arguments - **prompt_end**: str - Default = + Default = a single prompt's end. Defaults to newline @@ -1018,7 +1018,7 @@ Text Generation arguments - **eval_results_prefix**: str - Default = + Default = prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json @@ -1770,7 +1770,7 @@ Args for deepspeed config Default = None - + @@ -2070,3 +2070,4 @@ Args for deepspeed runner (deepspeed.launcher.runner). Default = None Adds a `--account` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard. + diff --git a/megatron/neox_arguments/neox_args.py b/megatron/neox_arguments/neox_args.py index f7b047f70..1f8ef55fc 100644 --- a/megatron/neox_arguments/neox_args.py +++ b/megatron/neox_arguments/neox_args.py @@ -1158,4 +1158,6 @@ class NeoXArgsTextgen(NeoXArgsTemplate): eval_tasks: list = None """ Tasks to evaluate on using lm_eval_harness + + NOTE: Requires internet connection """ diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4c9a3ca64..6247ece3d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,7 +4,7 @@ ftfy>=6.0.1 git+https://github.com/EleutherAI/lm_dataformat.git@4eec05349977071bf67fc072290b95e31c8dd836 huggingface_hub>=0.11.0 jinja2==3.1.3 -git+https://github.com/EleutherAI/lm-evaluation-harness.git@main#egg=lm_eval +lm_eval>=0.4.0,<=0.4.1 mpi4py>=3.0.3 numpy>=1.22.0 pybind11>=2.6.2 From 91c44bc301996b926ecc295207b4e05bfa0a9a9d Mon Sep 17 00:00:00 2001 From: segyges <65882655+segyges@users.noreply.github.com> Date: Mon, 5 Feb 2024 15:35:34 -0600 Subject: [PATCH 06/16] Remove gas (beano) (#1144) * Remove 'gas' configuration variable * Remove gas from configs and config documentation * Update training.py --- configs/19M.yml | 2 +- configs/49M.yml | 2 +- configs/800M.yml | 2 +- configs/neox_arguments.md | 15 +++------------ megatron/neox_arguments/arguments.py | 1 - megatron/neox_arguments/neox_args.py | 3 --- megatron/training.py | 2 +- tests/config/test_setup.yml | 2 +- 8 files changed, 8 insertions(+), 21 deletions(-) diff --git a/configs/19M.yml b/configs/19M.yml index f9f856769..c14ebe8ea 100644 --- a/configs/19M.yml +++ b/configs/19M.yml @@ -44,7 +44,7 @@ }, "train_micro_batch_size_per_gpu": 4, #32, - "gas": 1, + "gradient_accumulation_steps": 1, "data_impl": "mmap", "num_workers": 1, diff --git a/configs/49M.yml b/configs/49M.yml index 4515914e7..099af6a48 100644 --- a/configs/49M.yml +++ b/configs/49M.yml @@ -49,7 +49,7 @@ # batch / data settings "train_micro_batch_size_per_gpu": 32, - "gas": 1, + "gradient_accumulation_steps": 1, "data_impl": "mmap", "num_workers": 1, diff --git a/configs/800M.yml b/configs/800M.yml index f81b181a2..4fb9559a8 100644 --- a/configs/800M.yml +++ b/configs/800M.yml @@ -44,7 +44,7 @@ }, "train_micro_batch_size_per_gpu": 16, - "gas": 1, + "gradient_accumulation_steps": 1, "data_impl": "mmap", "num_workers": 1, diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index de19c74ea..9bb83cbf9 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -976,7 +976,7 @@ Text Generation arguments - **prompt_end**: str - Default = + Default = a single prompt's end. Defaults to newline @@ -1018,7 +1018,7 @@ Text Generation arguments - **eval_results_prefix**: str - Default = + Default = prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json @@ -1478,14 +1478,6 @@ Training Arguments -- **gas**: int - - Default = None - - gradient_accumulation_steps - - - - **clip_grad**: float Default = 1.0 @@ -1770,7 +1762,7 @@ Args for deepspeed config Default = None - + @@ -2070,4 +2062,3 @@ Args for deepspeed runner (deepspeed.launcher.runner). Default = None Adds a `--account` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard. - diff --git a/megatron/neox_arguments/arguments.py b/megatron/neox_arguments/arguments.py index 627bf45f1..2745287ed 100644 --- a/megatron/neox_arguments/arguments.py +++ b/megatron/neox_arguments/arguments.py @@ -896,7 +896,6 @@ def calculate_derived(self): "gradient_accumulation_steps": gradient_accumulation_steps, "batch_size": train_micro_batch_size_per_gpu, # duplicate items - "gas": self.gradient_accumulation_steps, "clip_grad": self.gradient_clipping, } ) diff --git a/megatron/neox_arguments/neox_args.py b/megatron/neox_arguments/neox_args.py index 1f8ef55fc..db1ffcc1f 100644 --- a/megatron/neox_arguments/neox_args.py +++ b/megatron/neox_arguments/neox_args.py @@ -996,9 +996,6 @@ class NeoXArgsTraining(NeoXArgsTemplate): Partition Activations across GPUs before checkpointing. """ - gas: int = None - """gradient_accumulation_steps""" # TODO this is a duplicate, remove? - clip_grad: float = 1.0 """ Gradient clipping based on global L2 norm. diff --git a/megatron/training.py b/megatron/training.py index 0f9b36f0b..5df179821 100644 --- a/megatron/training.py +++ b/megatron/training.py @@ -927,7 +927,7 @@ def evaluate( # although we're not accumulating gradients here, we count one iter as train_batch_size_per_gpu * g.a.s # to be consistent with deepspeed's pipe parallel engine - # since pipe parallel already takes gas into account - default to 1 here if pipe parallel is true + # since pipe parallel already takes gradient_accumulation_steps into account - default to 1 here if pipe parallel is true for _ in range( 1 if neox_args.is_pipe_parallel diff --git a/tests/config/test_setup.yml b/tests/config/test_setup.yml index dfff07e7a..882bf7e63 100644 --- a/tests/config/test_setup.yml +++ b/tests/config/test_setup.yml @@ -44,7 +44,7 @@ "output_layer_init_method": "wang_init", "train_micro_batch_size_per_gpu": 4, - "gas": 1, + "gradient_accumulation_steps": 1, "data_impl": "mmap", "num_workers": 1, From f7373f806689cb270677dd48bffddf4a32bfadce Mon Sep 17 00:00:00 2001 From: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com> Date: Wed, 7 Feb 2024 19:25:30 -0500 Subject: [PATCH 07/16] Improve Conversion Utilities (#1124) * draft: unify sequential + PPModule conversion scripts * Update NeoXArgs docs automatically * draft: pull out model param names / model definition * Update NeoXArgs docs automatically * tested: neox models with TP = 1, PipelineModule, work * Update NeoXArgs docs automatically * draft: Llama + GQA QKV resharding * Update NeoXArgs docs automatically * update Llama conversion script to support Mistral and GQA * Update NeoXArgs docs automatically * test Mistral-7B conversion * Update NeoXArgs docs automatically * Update NeoXArgs docs automatically * push documentation on imports / Llama loading * push further readme updates (Mistral included) * Preventconversions for unsupported featurees, disclaim in README * Update NeoXArgs docs automatically * revert PR#1072 RowParallel bias conversion error * remove sequential_to_hf and module_to_hf scripts, deprecated in favor of convert_neox_to_hf.py * Update NeoXArgs docs automatically * pre-commit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions Co-authored-by: Quentin Anthony --- README.md | 40 +- configs/neox_arguments.md | 9 +- tools/ckpts/convert_module_to_hf.py | 344 -------- tools/ckpts/convert_neox_to_hf.py | 739 ++++++++++++++++++ .../convert_raw_llama_weights_to_neox.py | 108 ++- tools/ckpts/convert_sequential_to_hf.py | 383 --------- 6 files changed, 858 insertions(+), 765 deletions(-) delete mode 100644 tools/ckpts/convert_module_to_hf.py create mode 100644 tools/ckpts/convert_neox_to_hf.py delete mode 100644 tools/ckpts/convert_sequential_to_hf.py diff --git a/README.md b/README.md index 0680bacb0..2a06ac9bf 100644 --- a/README.md +++ b/README.md @@ -501,18 +501,20 @@ where `--eval_tasks` is a list of evaluation tasks followed by spaces, e.g `--ev # Exporting to Hugging Face -GPT-NeoX is optimized heavily for training only, and GPT-NeoX model checkpoints are not compatible out of the box with other deep learning libraries. To make models easily loadable and shareable with end users, and for further exporting to various other frameworks, GPT-NeoX supports checkpoint conversion to the [Hugging Face Transformers](https://arxiv.org/abs/1910.03771) GPTNeoXModel format. +GPT-NeoX is optimized heavily for training only, and GPT-NeoX model checkpoints are not compatible out of the box with other deep learning libraries. To make models easily loadable and shareable with end users, and for further exporting to various other frameworks, GPT-NeoX supports checkpoint conversion to the [Hugging Face Transformers](https://arxiv.org/abs/1910.03771) format. -To convert a NeoX checkpoint (with pipeline-parallel-size>=1) to Hugging Face-loadable format, run: -```bash -python ./tools/ckpts/convert_module_to_hf.py --input_dir /path/to/model/global_stepXXX --config_file your_config.yml --output_dir hf_model/save/location -``` +Though NeoX supports a number of different architectural configurations, including AliBi positional embeddings, not all of these configurations map cleanly onto the supported configurations within Hugging Face Transformers. + +NeoX supports export of compatible models into the following architectures: +- GPTNeoXForCausalLM +- LlamaForCausalLM (GQA Support Coming Soon -- all Llama 1 models and Llama 2 / Codellama up to size 13B supported) + +Training a model which does not fit into one of these Hugging Face Transformers architectures cleanly will require writing custom modeling code for the exported model. -To convert a sequential model to Hugging Face format, run: +To convert a GPT-NeoX library checkpoint to Hugging Face-loadable format, run: ```bash -python ./tools/ckpts/convert_sequential_to_hf.py --input_dir /path/to/model/global_stepXXX --config_file your_config.yml --output_dir hf_model/save/location +python ./tools/ckpts/convert_neox_to_hf.py --input_dir /path/to/model/global_stepXXX --config_file your_config.yml --output_dir hf_model/save/location --precision {auto,fp16,bf16,fp32} --architecture {neox,llama} ``` -(Note: this script should be used for v2.0 checkpoints saved on a v2.0 commit prior to https://github.com/EleutherAI/gpt-neox/pull/866 and which used `pipe-parallel-size=1`. Using `pipe-parallel-size=0` will also save models in this format.) Then to upload a model to [the Hugging Face Hub](https://huggingface.co/), run: ```bash @@ -521,7 +523,27 @@ python ./tools/ckpts/upload.py ``` and input the requested information, including HF hub user token. -Note, however, that this compatibility is not one-to-one, and only certain configurations from GPT-NeoX are supported in the Hugging Face GPTNeoXModel class. Advanced features such as alternative positional embeddings may require new Transformers modeling code and new conversion script tweaks. +### Importing Models Into GPT-NeoX + +NeoX supplies several utilities for converting a pretrained model checkpoint into a format that can be trained within the library. + +The following models can be loaded in GPT-NeoX: +- Llama 1 +- Llama 2 (Up to size 13B) +- CodeLlama (Up to size 13B) +- Mistral-7b-v0.1 (Coming Soon!) + +We provide two utilities for converting from two different checkpoint formats into a format compatible with GPT-NeoX. + +To convert a Llama 1 or Llama 2 checkpoint distributed by Meta AI from its original file format (downloadable [here](https://github.com/facebookresearch/llama) or [here](https://huggingface.co/meta-llama/Llama-2-7b)) into the GPT-NeoX library, run + +``` +python tools/ckpts/convert_raw_llama_weights_to_neox.py --input_dir /path/to/model/parent/dir/7B --model_size 7B --output_dir /path/to/save/ckpt --num_output_shards (--pipeline_parallel if pipeline-parallel-size >= 1) +``` + + +To convert from a Hugging Face model into a NeoX-loadable, run `tools/ckpts/convert_hf_to_sequential.py`. See documentation within that file for further options. + # Monitoring diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 9bb83cbf9..7df94988d 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 78b8466 + Default = 6a8a829 current git hash of repository @@ -976,7 +976,7 @@ Text Generation arguments - **prompt_end**: str - Default = + Default = a single prompt's end. Defaults to newline @@ -1018,7 +1018,7 @@ Text Generation arguments - **eval_results_prefix**: str - Default = + Default = prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json @@ -1762,7 +1762,7 @@ Args for deepspeed config Default = None - + @@ -2062,3 +2062,4 @@ Args for deepspeed runner (deepspeed.launcher.runner). Default = None Adds a `--account` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard. + diff --git a/tools/ckpts/convert_module_to_hf.py b/tools/ckpts/convert_module_to_hf.py deleted file mode 100644 index 72ce59d32..000000000 --- a/tools/ckpts/convert_module_to_hf.py +++ /dev/null @@ -1,344 +0,0 @@ -# Copyright (c) 2024, EleutherAI -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys - -import yaml -import argparse -from tqdm import tqdm -from typing import List - -import torch -from transformers import GPTNeoXConfig, GPTNeoXForCausalLM - - -sys.path.append( - os.path.abspath( - os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir) - ) -) -from megatron.tokenizer import build_tokenizer - - -""" -A script for converting saved NeoX Checkpoints to Huggingface (HF) compatible GPT-NeoX type models. - -Note that this script does not support all NeoX features. -Please investigate carefully whether your model is compatible with all architectures supported by the GPTNeoXForCausalLM class in HF. - -(e.g. position embeddings such as AliBi may not be supported by Huggingface's GPT-NeoX architecture. -""" - - -def load_partitions( - input_checkpoint_path, mp_partitions, layer_idx -) -> List[torch.Tensor]: - """Returns a list containing all weights in a given layer from a model (across MP partitions)""" - - loaded_tp_ranks = [ - torch.load( - os.path.join( - input_checkpoint_path, - f"layer_{layer_idx:02}-model_{i:02}-model_states.pt", - ), - map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"), - ) - for i in range(mp_partitions) - ] - - return loaded_tp_ranks - - -def get_key(loaded_config, key, default=None): - """ - Search for a given key in a NeoX yaml. normalizes underscores -> hyphens - """ - key = key.replace("_", "-") - try: - return loaded_config[key] - except KeyError: - key = key.replace("-", "_") - try: - return loaded_config[key] - except KeyError: - return default - - -def create_config(neox_config): - """take in a loaded yaml from NeoX and assign relevant values to HF config. - Returns: GPTNeoXConfig() object - """ - - class TokenizerArgs: - # kinda hacky. - # this is to get something with the same interface as is used in build_tokenizer() - # without diving into loading a neox_args object or using argparse etc. - def __init__(self, neox_config): - self.make_vocab_size_divisible_by = get_key( - neox_config, "make-vocab-size-divisible-by", default=128 - ) - self.model_parallel_size = get_key(neox_config, "model-parallel-size") - self.vocab_file = get_key(neox_config, "vocab-file") - self.merge_file = get_key(neox_config, "merge-file") - self.tokenizer_type = get_key(neox_config, "tokenizer-type") - - self.rank = 0 - - args = TokenizerArgs(neox_config) - tokenizer = build_tokenizer(args) - try: # GPT2TokenizerFast raises NotImplementedError - pad_token = tokenizer.pad - except: - pad_token = ( - 1 # pad defaulting to 1. follows convention from GPT-NeoX-20b tokenizer - ) - - # TODO: change the default value here based on discussion regarding `gpt_j_tied` config parameter's default - use_tied_lns = get_key(neox_config, "gpt-j-tied", False) - - if use_tied_lns: - raise NotImplementedError( - """ERROR: Huggingface Transformers does not yet support a single shared layernorm - per transformer block for GPT-NeoX models trained w/ GPT-J parallel residuals. - See https://github.com/EleutherAI/gpt-neox/pull/481 for further details.""" - ) - - # set all config values. - hf_config = GPTNeoXConfig( - vocab_size=args.padded_vocab_size, - hidden_size=get_key(neox_config, "hidden-size"), - num_hidden_layers=get_key(neox_config, "num-layers"), - num_attention_heads=get_key(neox_config, "num-attention-heads"), - intermediate_size=(get_key(neox_config, "hidden-size") * 4), - hidden_act=get_key(neox_config, "activation", default="gelu"), - rotary_pct=get_key(neox_config, "rotary-pct", default=1.0), - rotary_emb_base=get_key(neox_config, "rotary-emb-base", default=10000), - max_position_embeddings=get_key(neox_config, "max-position-embeddings"), - initializer_range=get_key(neox_config, "init-method-std", 0.02), - layer_norm_eps=get_key(neox_config, "layernorm-epsilon", 1e-5), - use_cache=True, - bos_token_id=tokenizer.eod, - eos_token_id=tokenizer.eod, - tie_word_embeddings=(not get_key(neox_config, "no-weight-tying", False)), - use_parallel_residual=get_key(neox_config, "gpt-j-residual", False), - ) - return hf_config - - -def convert(input_checkpoint_path, loaded_config, output_checkpoint_path): - """convert a NeoX checkpoint to a HF model format. - should perform model-parallel merging correctly - but only supports features allowed by HF GPT-NeoX implementation (e.g. rotary embeddings) - """ - - hf_config = GPTNeoXConfig() - - hf_config = create_config(loaded_config) - - hf_model = GPTNeoXForCausalLM(hf_config) - - # save model in fp16/bf16 if Deepspeed fp16 or bf16 mixed precision was used in config, else 32 bit weights - fp16 = get_key(loaded_config, "fp16") - if fp16: - try: - # this conditional is quite messy because there were a number of ways to specify bf16 or fp16 training - # in DeeperSpeed v1.0 . - if (fp16.get("fp16", None) or fp16["enabled"]) and not ( - fp16.get("type", None) == "bfloat16" - ): - hf_model.half() - print("Saving weights in fp16 precision...") - elif fp16.get("type", None) == "bfloat16": - hf_model.to(dtype=torch.bfloat16) - print("Saving weights in bf16 precision...") - except: - print( - "Model not trained in fp16 / bf16 mixed precision, saving weights in fp32..." - ) - - mp_partitions = get_key(loaded_config, "model-parallel-size") - - ### Embedding layer ### - loaded_tp_ranks = load_partitions(input_checkpoint_path, mp_partitions, 0) - hf_model.gpt_neox.embed_in.load_state_dict( - { - "weight": torch.cat( - [t["word_embeddings.weight"] for t in loaded_tp_ranks], dim=0 - ) - } - ) - - assert ( - hf_config.vocab_size == hf_model.gpt_neox.embed_in.weight.shape[0] - ), f"ERROR: calculated vocab size {hf_config.vocab_size} != embed param size {hf_model.gpt_neox.embed_in.shape[0]}" - ### End Embedding Layer ### - - for layer_i in tqdm(range(get_key(loaded_config, "num-layers"))): - - # get layer from hf model - hf_layer = hf_model.gpt_neox.layers[layer_i] - - # + 2 bc of embed layer and a dummy _pre_transformer_block - loaded_tp_ranks = load_partitions( - input_checkpoint_path, mp_partitions, layer_i + 2 - ) - - state_dict = {} - for key in [ - "attention.dense.weight", - "mlp.dense_4h_to_h.weight", - ]: - state_dict[key] = torch.cat([t[key] for t in loaded_tp_ranks], dim=1) - - # average layernorm stats over mp ranks - for key in [ - "input_layernorm.weight", - "input_layernorm.bias", - "post_attention_layernorm.weight", - "post_attention_layernorm.bias", - ]: - state_dict[key] = (sum([t[key] for t in loaded_tp_ranks])) / len( - loaded_tp_ranks - ) - - # LinearWithTPMerge - for key in [ - "mlp.dense_h_to_4h.weight", - "mlp.dense_h_to_4h.bias", - "attention.query_key_value.weight", - "attention.query_key_value.bias", - ]: - state_dict[key] = torch.cat([t[key] for t in loaded_tp_ranks], dim=0) - - # LinearWithTPSplitBias - for key in [ - "mlp.dense_4h_to_h.bias", - "attention.dense.bias", - ]: - state_dict[key] = sum([t[key] for t in loaded_tp_ranks]) / len( - loaded_tp_ranks - ) - - # Just take one - state_dict["attention.rotary_emb.inv_freq"] = loaded_tp_ranks[0][ - "attention.rotary_emb.inv_freq" - ] - if "attention.bias" in hf_layer.state_dict(): - state_dict["attention.bias"] = hf_layer.state_dict()["attention.bias"] - if "attention.masked_bias" in hf_layer.state_dict(): - state_dict["attention.masked_bias"] = hf_layer.state_dict()[ - "attention.masked_bias" - ] - - # load state_dict into layer - hf_layer.load_state_dict(state_dict) - - # Load final layer norm - loaded_tp_ranks = load_partitions( - input_checkpoint_path, mp_partitions, get_key(loaded_config, "num-layers") + 3 - ) - - hf_model.gpt_neox.final_layer_norm.load_state_dict( - { - "weight": (sum([t["norm.weight"] for t in loaded_tp_ranks])) - / len(loaded_tp_ranks), - "bias": (sum([t["norm.bias"] for t in loaded_tp_ranks])) - / len(loaded_tp_ranks), - } - ) - del loaded_tp_ranks - - # Load output embedding - loaded_tp_ranks = load_partitions( - input_checkpoint_path, mp_partitions, get_key(loaded_config, "num-layers") + 4 - ) - - hf_model.embed_out.load_state_dict( - { - "weight": torch.cat( - [t["final_linear.weight"] for t in loaded_tp_ranks], dim=0 - ), - } - ) - - del loaded_tp_ranks - - return hf_model - - -if __name__ == "__main__": - - # before running script: - # `pip install --upgrade transformers` - # `huggingface-cli login` - # - from huggingface_hub import create_repo, HfApi - - parser = argparse.ArgumentParser( - description="Merge MP partitions and convert to HF Model." - ) - parser.add_argument( - "--input_dir", - type=str, - help="Path to NeoX checkpoint, e.g. /path/to/model/global_step143000", - ) - parser.add_argument( - "--config_file", - type=str, - help="Path to config file for the input NeoX checkpoint.", - ) - parser.add_argument( - "--output_dir", - type=str, - help="Output dir, where to save the HF Model, tokenizer, and configs", - ) - parser.add_argument( - "--upload", - action="store_true", - help="Set to true in order to upload to the HF Hub directly.", - ) - args = parser.parse_args() - - with open(args.config_file) as f: - loaded_config = yaml.full_load(f) - - hf_model = convert(args.input_dir, loaded_config, args.output_dir) - - hf_model.save_pretrained(args.output_dir) - - # save tokenizer to directory as well, for easy loading of model as a HF model - tokenizer_type = get_key(loaded_config, "tokenizer-type") - - if tokenizer_type == "HFTokenizer": - print(f"saving tokenizer from file {get_key(loaded_config, 'vocab-file')}") - from transformers import PreTrainedTokenizerFast - - tokenizer = PreTrainedTokenizerFast( - tokenizer_file=get_key(loaded_config, "vocab-file") - ) - print("loaded tokenizer: ", tokenizer) - tokenizer.save_pretrained(args.output_dir) - print("tokenizer saved!") - - if args.upload: - repo_name = input("Provide a repository name for the HF Hub: ") - create_repo(repo_name, repo_type="model", private=False, use_auth_token=True) - - api = HfApi() - api.upload_folder( - folder_path=args.output_dir, - repo_id=repo_name, - repo_type="model", - ) diff --git a/tools/ckpts/convert_neox_to_hf.py b/tools/ckpts/convert_neox_to_hf.py new file mode 100644 index 000000000..a9cd4277e --- /dev/null +++ b/tools/ckpts/convert_neox_to_hf.py @@ -0,0 +1,739 @@ +# Copyright (c) 2023, EleutherAI +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys + +import yaml +import argparse +from tqdm import tqdm + +import torch +from transformers import ( + MistralConfig, + LlamaConfig, + GPTNeoXConfig, + AutoModelForCausalLM, + AutoConfig, +) + +from typing import List, Literal + +sys.path.append( + os.path.abspath( + os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir) + ) +) +from megatron.tokenizer import build_tokenizer + + +""" +A script for converting saved NeoX Checkpoints to Huggingface (HF) compatible GPT-NeoX type models. + +Note that this script does not support all NeoX features. +Please investigate carefully whether your model is compatible with all architectures supported by the GPTNeoXForCausalLM class in HF. + +(e.g. position embeddings such as AliBi may not be supported by Huggingface's GPT-NeoX architecture). +""" + + +# Model definitions: a list of keys, and where they fall in terms of handling them in the presence of TP. +# in format : {model arch: {param type: {param in neox: param in HF}}} + +MODEL_KEYS = { + "neox": { + "COLUMN_PARALLEL_LINEAR_KEYS": { + "mlp.dense_h_to_4h.weight": "mlp.dense_h_to_4h.weight", + "mlp.dense_h_to_4h.bias": "mlp.dense_h_to_4h.bias", + "attention.query_key_value.weight": "attention.query_key_value.weight", + "attention.query_key_value.bias": "attention.query_key_value.bias", # TODO: handle GQA separately? + }, + "ROW_PARALLEL_LINEAR_KEYS": { + "attention.dense.weight": "attention.dense.weight", + "mlp.dense_4h_to_h.weight": "mlp.dense_4h_to_h.weight", + }, + "ROW_PARALLEL_BIAS_KEYS": { + "mlp.dense_4h_to_h.bias": "mlp.dense_4h_to_h.bias", + "attention.dense.bias": "attention.dense.bias", + }, + "NORM_KEYS": { + "input_layernorm.weight": "input_layernorm.weight", + "input_layernorm.bias": "input_layernorm.bias", + "post_attention_layernorm.weight": "post_attention_layernorm.weight", + "post_attention_layernorm.bias": "post_attention_layernorm.bias", + }, + "FINAL_NORM_KEYS": { + "norm.weight": "weight", + "norm.bias": "bias", + }, + }, + "llama": { + "COLUMN_PARALLEL_LINEAR_KEYS": { + "mlp.w1.weight": "mlp.gate_proj.weight", + "mlp.w3.weight": "mlp.up_proj.weight", + }, + "ROW_PARALLEL_LINEAR_KEYS": { + "attention.dense.weight": "self_attn.o_proj.weight", + "mlp.w2.weight": "mlp.down_proj.weight", + }, + "ROW_PARALLEL_BIAS_KEYS": {}, # No biases in RowParallelLinear layers + "NORM_KEYS": { + "input_layernorm.scale": "input_layernorm.weight", + "post_attention_layernorm.scale": "post_attention_layernorm.weight", + }, + "FINAL_NORM_KEYS": { + "norm.scale": "weight", + }, + "GQA_QKV_KEYS": { # because Llama can have Grouped Query Attention and has separate Q, K, and V linear proj params, handle them separately. + "attention.query_key_value.weight": [ + "self_attn.q_proj.weight", + "self_attn.k_proj.weight", + "self_attn.v_proj.weight", + ], + }, + }, +} + +MODEL_KEYS["mistral"] = MODEL_KEYS["llama"] + + +def load_partitions( + input_checkpoint_path: str, mp_partitions: int, layer_idx: int, sequential: bool +) -> List[torch.Tensor]: + """Returns a list containing all states from a model (across MP partitions)""" + + if sequential: + filename_format = f"mp_rank_{{i:02}}_model_states.pt" + else: + filename_format = f"layer_{layer_idx:02}-model_{{i:02}}-model_states.pt" + + loaded_tp_ranks = [ + torch.load( + os.path.join( + input_checkpoint_path, + filename_format.format(i=i), + ), + map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"), + ) + for i in range(mp_partitions) + ] + + return loaded_tp_ranks + + +def get_state( + state_dicts: List[torch.Tensor], key: str, layer_idx: int, sequential: bool +) -> torch.Tensor: + """Helper that returns a list containing a given weight's state from each MP partition, for a given layer in the model.""" + + if sequential: + # use the correct key into the sequential dict for given weight/provided key + key = f"sequential.{layer_idx}.{key}" + + return [state_dict["module"][key] for state_dict in state_dicts] + else: + # For the PipelineModule case, we don't need any key / module prefix. just grab this weight value. + # layer_idx is also ignored because we've loaded only this layer's weights, ahead of time. + key = key + + return [state_dict[key] for state_dict in state_dicts] + + +def get_key(loaded_config, key, default=None): + """ + Search for a given key in a NeoX yaml. normalizes underscores -> hyphens + """ + key = key.replace("_", "-") + try: + return loaded_config[key] + except KeyError: + key = key.replace("-", "_") + try: + return loaded_config[key] + except KeyError: + return default + + +def create_config(neox_config, architecture="neox"): + """take in a loaded yaml from NeoX and assign relevant values to HF config. + Returns: GPTNeoXConfig() object + """ + + def gated_size(hidden_dim): + # takes in a hidden dim and calculates intermediate dim of a LLaMAParallelMLP. + # (only used if intermediate_size not specified in config) + # hidden-size * 8 / 3 , rounded up to nearest multiple of 256 + ff_dim = int(2 * hidden_dim * 4 / 3) + ff_dim = 256 * ((ff_dim + 256 - 1) // 256) + return ff_dim + + class TokenizerArgs: + # kinda hacky. + # this is to get something with the same interface as is used in build_tokenizer() + # without diving into loading a neox_args object or using argparse etc. + def __init__(self, neox_config): + self.make_vocab_size_divisible_by = get_key( + neox_config, "make-vocab-size-divisible-by", default=128 + ) + self.model_parallel_size = get_key(neox_config, "model-parallel-size") + self.vocab_file = get_key(neox_config, "vocab-file") + self.merge_file = get_key(neox_config, "merge-file") + self.tokenizer_type = get_key(neox_config, "tokenizer-type") + + self.rank = 0 + + args = TokenizerArgs(neox_config) + tokenizer = build_tokenizer(args) + try: # GPT2TokenizerFast raises NotImplementedError + pad_token = tokenizer.pad + except: + pad_token = ( + 1 # pad defaulting to 1. follows convention from GPT-NeoX-20b tokenizer + ) + + # TODO: change the default value here based on discussion regarding `gpt_j_tied` config parameter's default + use_tied_lns = get_key(neox_config, "gpt-j-tied", False) + + if use_tied_lns: + raise NotImplementedError( + """ERROR: Huggingface Transformers does not yet support a single shared layernorm + per transformer block for GPT-NeoX models trained w/ GPT-J parallel residuals. + See https://github.com/EleutherAI/gpt-neox/pull/481 for further details.""" + ) + + # set all config values. + + # shared config parameters. + args = { + "vocab_size": args.padded_vocab_size, + "hidden_size": get_key(neox_config, "hidden-size"), + "num_hidden_layers": get_key(neox_config, "num-layers"), + "num_attention_heads": get_key(neox_config, "num-attention-heads"), + "max_position_embeddings": get_key(neox_config, "max-position-embeddings"), + "initializer_range": get_key(neox_config, "init-method-std", 0.02), + "tie_word_embeddings": (not get_key(neox_config, "no-weight-tying", False)), + "use_cache": True, + } + if architecture == "mistral" or architecture == "llama": + args.update( + { + "intermediate_size": get_key( + neox_config, + "intermediate-size", + gated_size(get_key(neox_config, "hidden-size")), + ), + "num_key_value_heads": get_key( + neox_config, + "num-kv-heads", + get_key(neox_config, "num-attention-heads"), + ), + "hidden_act": get_key(neox_config, "activation", default="silu"), + "rms_norm_eps": get_key(neox_config, "rms-norm-epsilon", 1.0e-6), + "bos_token_id": tokenizer.eod, + "eos_token_id": tokenizer.eod, + "rope_theta": get_key(neox_config, "rotary-emb-base", 10000.0), + } + ) + + if args["num_attention_heads"] != args["num_key_value_heads"]: + assert ( + False + ), "Got num_key_value_heads != num_attention_heads, but Grouped-Query Attention is not yet supported by NeoX." + + if architecture == "mistral": + # mistral-specific options + args.update( + { + "sliding_window": get_key( + neox_config, "sliding-window-width", 4096 + ), + } + ) + hf_config = MistralConfig(**args) + elif architecture == "llama": + # llama-specific options + args.update( + { + # NeoX library defaults to using bias in attention + "attention_bias": get_key( + neox_config, "use_bias_in_attn_linear", True + ), + } + ) + hf_config = LlamaConfig(**args) + else: + # GPT-NeoX HF model class-specific options + args.update( + { + "rotary_pct": get_key(neox_config, "rotary-pct", default=1.0), + "rotary_emb_base": get_key( + neox_config, "rotary-emb-base", default=1000.0 + ), + "use_parallel_residual": get_key(neox_config, "gpt-j-residual", False), + "layer_norm_eps": get_key(neox_config, "layernorm-epsilon", 1e-5), + } + ) + hf_config = GPTNeoXConfig(**args) + + return hf_config + + +def reshard_and_split_qkv( + param_mapping: dict, # a dictionary mapping the QKV weight keys in GPT-NeoX -> a list of keys representing the Q, K, and V weight keys the HF model will use + hf_config: AutoConfig, # a HF model config for the model + loaded_tp_ranks: List[torch.Tensor], + layer_idx: int, + sequential: bool, +): + """ + A helper function which performs reshaping and sharding to make the QKV projection from NeoX compatible with HF Llama models, + even when grouped-query attention is required. + """ + for key, hf_keys in param_mapping.items(): + assert ( + isinstance(hf_keys, list) and len(hf_keys) == 3 + ), "Must map QKV to precisely 3 resulting weight matrices." + + for key, hf_keys in param_mapping.items(): + # we first merge the QKV proj. across TP ranks + sharded_qkv = torch.stack( + get_state(loaded_tp_ranks, key, layer_idx, sequential), dim=0 + ) + # should now have shape [TP_SIZE, (hidden_size + 2 * kv_hidden_size) / TP_SIZE, hidden_size]. + + sharded_qkv = sharded_qkv.view( + len(loaded_tp_ranks), + hf_config.num_attention_heads // len(loaded_tp_ranks), + int( + hf_config.hidden_size + // hf_config.num_attention_heads + * ( + 1 + + 2 * hf_config.num_key_value_heads / hf_config.num_attention_heads + ) + ), + hf_config.hidden_size, + ) # is meant to convert to shape [TP_SIZE, NUM_QUERY_HEADS_PER_SHARD, dims_per_head * (1 + 2 * kv-to-q head ratio), hidden_size] + + q, k, v = torch.split( + sharded_qkv, + [ + hf_config.hidden_size // hf_config.num_attention_heads, + int( + (hf_config.num_key_value_heads / hf_config.num_attention_heads) + * hf_config.hidden_size + // hf_config.num_attention_heads + ), + int( + (hf_config.num_key_value_heads / hf_config.num_attention_heads) + * hf_config.hidden_size + // hf_config.num_attention_heads + ), + ], + dim=2, + ) + # splits along the (dims_per_head * (1 + 2 * kv-to-q head ratio)_ dim to get 3 tensors: + # 1 x [TP_SIZE, NUM_Q_HEADS_PER_SHARD, dims_per_head, hidden_size] and 2 x [TP_SIZE, NUM_Q_HEADS_PER_SHARD, (dims_per_head / kv-to-q head ratio), hidden_size] + # these are the Q, and K, V tensors respectively. + + # we have to do additional reshape for each individual tensor now, + # into the expected square (or smaller than square, for K/V tensors) shape + q, k, v = q.squeeze(dim=2), k.squeeze(dim=2), v.squeeze(dim=2) + q = q.view( + hf_config.num_attention_heads, + hf_config.hidden_size // hf_config.num_attention_heads, + hf_config.hidden_size, + ).reshape(hf_config.hidden_size, hf_config.hidden_size) + k = k.reshape( + hf_config.num_key_value_heads, + hf_config.hidden_size // hf_config.num_attention_heads, + hf_config.hidden_size, + ).reshape( + hf_config.hidden_size + // hf_config.num_attention_heads + * hf_config.num_key_value_heads, + hf_config.hidden_size, + ) + v = v.reshape( + hf_config.num_key_value_heads, + hf_config.hidden_size // hf_config.num_attention_heads, + hf_config.hidden_size, + ).reshape( + hf_config.hidden_size + // hf_config.num_attention_heads + * hf_config.num_key_value_heads, + hf_config.hidden_size, + ) + + # return these + state_dict = {} + for hf_key, proj in zip(hf_keys, [q, k, v]): + state_dict[hf_key] = proj.clone() + return state_dict + + +def convert( + input_checkpoint_path, + loaded_config, + output_checkpoint_path, + sequential: bool = True, + precision: Literal["auto", "fp16", "bf16", "fp32"] = "auto", + architecture: Literal["neox", "llama", "mistral"] = "neox", +): + """convert a NeoX checkpoint to a HF model format. + should perform model-parallel merging correctly + but only supports features allowed by HF GPT-NeoX implementation (e.g. rotary embeddings) + """ + + ARCH = MODEL_KEYS[architecture] + + hf_config = create_config(loaded_config, architecture=architecture) + + hf_model = AutoModelForCausalLM.from_config(hf_config) + + if architecture == "neox": + hf_transformer = hf_model.gpt_neox + else: + hf_transformer = hf_model.model + + if precision == "auto": + print("Auto-detecting precision to save model into...") + # save model in FP16 if Deepspeed fp16 was used in config, else 32 bit + fp16 = get_key(loaded_config, "fp16") + + if fp16: + try: + # current behavior is to pass "fp16": {"enabled": true}, when using upstream Deepspeed + if fp16["enabled"]: + hf_model.half() + print("Saving weights in fp16 precision...") + except: + try: + # attempt to access bf16 dict in yaml file, if fp16 not enabled + bf16 = get_key(loaded_config, "bf16") + if bf16: + hf_model.to(dtype=torch.bfloat16) + print("Saving weights in bf16 precision...") + except: + hf_model.to(dtype=torch.float) + print( + "Model not trained in fp16 / bf16 mixed precision, saving weights in fp32..." + ) + else: + name_to_dtype = { + "bf16": torch.bfloat16, + "fp16": torch.float16, + "fp32": torch.float, + } + print(f"Saving model into specified {precision} precision...") + hf_model.to(dtype=name_to_dtype[precision]) + + mp_partitions = get_key(loaded_config, "model-parallel-size") + + # Sequential saves all model states from an MP rank in one file. + # so we only load the MP ranks only once and index into them with get_state(). + # for the pipeline-parallel case (pipeline-parallel-size >= 1), + # we must load the correct layer's states at each step. + # (this does mean that less memory is required for PP conversion.) + loaded_tp_ranks = load_partitions( + input_checkpoint_path, mp_partitions, layer_idx=0, sequential=sequential + ) + + ### Embedding layer ### + # Embedding is layer idx 0 + if architecture == "neox": + embed_in = hf_transformer.embed_in + else: + embed_in = hf_transformer.embed_tokens + embed_in.load_state_dict( # TODO: embed_in is not always model's name for embedding + { + "weight": torch.cat( + get_state( + loaded_tp_ranks, + "word_embeddings.weight", + layer_idx=0, + sequential=sequential, + ), + dim=0, + ) + } + ) + assert ( + hf_config.vocab_size == embed_in.weight.shape[0] + ), f"ERROR: calculated vocab size {hf_config.vocab_size} != embed param size {embed_in.shape[0]}" + ### End Embedding Layer ### + + for layer_i in tqdm(range(get_key(loaded_config, "num-layers"))): + + # get layer from hf model + hf_layer = hf_transformer.layers[layer_i] # TODO: model module names + + if not sequential: + # in the non-sequential case, must load from each layer individually. + # use layer index + 2 bc of embed layer and a dummy _pre_transformer_block, which are "layers 0 and 1" + loaded_tp_ranks = load_partitions( + input_checkpoint_path, + mp_partitions, + layer_idx=layer_i + 2, + sequential=sequential, + ) + + # + 2 bc of embed layer and a dummy _pre_transformer_block + state_dict = {} + for key, hf_key in ARCH["ROW_PARALLEL_LINEAR_KEYS"].items(): + state_dict[hf_key] = torch.cat( + get_state( + loaded_tp_ranks, key, layer_idx=layer_i + 2, sequential=sequential + ), + dim=1, + ) + + # average layernorm stats over mp ranks + for key, hf_key in ARCH["NORM_KEYS"].items(): + state_dict[hf_key] = sum( + get_state( + loaded_tp_ranks, key, layer_idx=layer_i + 2, sequential=sequential + ) + ) / len(loaded_tp_ranks) + + # LinearWithTPMerge + for key, hf_key in ARCH["COLUMN_PARALLEL_LINEAR_KEYS"].items(): + state_dict[hf_key] = torch.cat( + get_state( + loaded_tp_ranks, key, layer_idx=layer_i + 2, sequential=sequential + ), + dim=0, + ) + + # LinearWithTPSplitBias + for key, hf_key in ARCH["ROW_PARALLEL_BIAS_KEYS"].items(): + state_dict[hf_key] = sum( + get_state( + loaded_tp_ranks, key, layer_idx=layer_i + 2, sequential=sequential + ) + ) + + # Just take one + if "attention.rotary_emb.inv_freq" in hf_layer.state_dict(): + state_dict["attention.rotary_emb.inv_freq"] = get_state( + loaded_tp_ranks, + "attention.rotary_emb.inv_freq", + layer_idx=layer_i + 2, + sequential=sequential, + )[0] + + if "attention.bias" in hf_layer.state_dict(): + state_dict["attention.bias"] = hf_layer.state_dict()["attention.bias"] + if "attention.masked_bias" in hf_layer.state_dict(): + state_dict["attention.masked_bias"] = hf_layer.state_dict()[ + "attention.masked_bias" + ] + + # some architectures, like Mistral and Llama, have the following which must be handled specially: + # - Q, K, V projections are performed separately, so we must split apart GPT-NeoX library's single QKV proj + # - Support for Grouped-Query Attention, meaning the Q and the K, V projections may not be the same size + if "GQA_QKV_KEYS" in ARCH: + state_dict.update( + reshard_and_split_qkv( + param_mapping=ARCH["GQA_QKV_KEYS"], + hf_config=hf_config, + loaded_tp_ranks=loaded_tp_ranks, + layer_idx=layer_i + 2, + sequential=sequential, + ) + ) + # load state_dict into layer + hf_layer.load_state_dict(state_dict) + + if not sequential: + loaded_tp_ranks = load_partitions( + input_checkpoint_path, + mp_partitions, + get_key(loaded_config, "num-layers") + 3, + sequential=sequential, + ) + # Load final layer norm + if architecture == "neox": + lm_head = hf_model.embed_out + else: + lm_head = hf_model.lm_head + norm_state_dict = {} + for key, hf_key in ARCH["FINAL_NORM_KEYS"].items(): + norm_state_dict[hf_key] = sum( + get_state( + loaded_tp_ranks, + key, + layer_idx=get_key(loaded_config, "num-layers") + 3, + sequential=sequential, + ) + ) / len(loaded_tp_ranks) + + if architecture == "neox": + final_layer_norm = hf_transformer.final_layer_norm + else: + final_layer_norm = hf_transformer.norm + + final_layer_norm.load_state_dict(norm_state_dict) + + # Load output embedding + if not sequential: + loaded_tp_ranks = load_partitions( + input_checkpoint_path, + mp_partitions, + get_key(loaded_config, "num-layers") + 4, + sequential=sequential, + ) + # output embedding / LM head + if architecture == "neox": # name of lm head / final linear proj varies + lm_head = hf_model.embed_out + else: + lm_head = hf_model.lm_head + lm_head.load_state_dict( + { + "weight": torch.cat( + get_state( + loaded_tp_ranks, + "final_linear.weight", + layer_idx=get_key(loaded_config, "num-layers") + 4, + sequential=sequential, + ), + dim=0, + ), + } + ) + + del loaded_tp_ranks + + return hf_model + + +def main(input_args=None, overwrite_values=None): + from huggingface_hub import create_repo, HfApi + + parser = argparse.ArgumentParser( + description="Merge MP partitions and convert to HF Model." + ) + parser.add_argument( + "--input_dir", + type=str, + help="Path to NeoX checkpoint, e.g. /path/to/model/global_step143000", + ) + parser.add_argument( + "--config_file", + type=str, + help="Path to config file for the input NeoX checkpoint.", + ) + parser.add_argument( + "--output_dir", + type=str, + help="Output dir, where to save the HF Model, tokenizer, and configs", + ) + parser.add_argument( + "--precision", + type=str, + default="auto", + help="What precision to save the model into. Defaults to auto, which auto-detects which 16-bit dtype to save into, or falls back to fp32.", + ) + parser.add_argument( + "--no_save_tokenizer", + action="store_true", + help="Whether to skip saving the tokenizer alongside a model.", + ) + parser.add_argument( + "--architecture", + type=str, + default="neox", + help="What HF model class type to export into.", + ) + args = parser.parse_args(input_args) + + # validate arguments + assert args.precision in [ + "auto", + "fp16", + "bf16", + "fp32", + ], f"expected --precision to be one of 'auto', 'fp16', 'bf16', 'fp32' but got '{args.precision}' !" + assert args.architecture in [ + "neox", + "llama", + "mistral", + ], f"expected --architecture to be one of 'neox', 'mistral', 'llama', but got '{args.architecture}' !" + + if args.architecture == "mistral": + # Mistral Support Coming Soon + assert ( + False + ), "Got num_key_value_heads != num_attention_heads, but Grouped-Query Attention is not yet supported by NeoX." + + with open(args.config_file) as f: + loaded_config = yaml.full_load(f) + if overwrite_values: + loaded_config.update(overwrite_values) + + # Determine the checkpoint format of the model. + # DeepSpeed saves models wrapped in a PipelineModule differently from those not. + # PipelineModule models are saved as per-layer state dicts per TP shard, + # while Sequential model state dicts are saved all together in one mp_rank_xx_model_states.pt + # file per tensor/model parallel shard. + pipeline_world_size = get_key(loaded_config, "pipe-parallel-size", 1) + if pipeline_world_size == 0: + sequential = True + print( + f"Detected 'pipe-parallel-size' of {pipeline_world_size}, assuming model is saved as Sequential..." + ) + else: + sequential = False + print( + f"Detected 'pipe-parallel-size' of {pipeline_world_size}, assuming model is saved as PipelineModule..." + ) + + # convert the model to HF. + hf_model = convert( + args.input_dir, + loaded_config, + args.output_dir, + sequential=sequential, + architecture=args.architecture, + ) + + # Save to disk. + hf_model.save_pretrained(args.output_dir) + + if not args.no_save_tokenizer: + # save tokenizer to directory as well, for easy loading of model as a HF model. + tokenizer_type = get_key(loaded_config, "tokenizer-type") + + if tokenizer_type == "HFTokenizer": # TODO: handle sentencepiece tokenizers? + print(f"saving tokenizer from file {get_key(loaded_config, 'vocab-file')}") + print( + "Warning: please check that your model config and tokenizer end with the correct special tokens (EOS, BOS)." + ) + from transformers import PreTrainedTokenizerFast + + tokenizer = PreTrainedTokenizerFast( + tokenizer_file=get_key(loaded_config, "vocab-file") + ) + print("loaded tokenizer: ", tokenizer) + tokenizer.save_pretrained(args.output_dir) + print("tokenizer saved!") + + +if __name__ == "__main__": + + # before running script: + # `pip install --upgrade transformers` + # `huggingface-cli login` + # + main() diff --git a/tools/ckpts/convert_raw_llama_weights_to_neox.py b/tools/ckpts/convert_raw_llama_weights_to_neox.py index 824cc95d9..6fc9f8a7e 100644 --- a/tools/ckpts/convert_raw_llama_weights_to_neox.py +++ b/tools/ckpts/convert_raw_llama_weights_to_neox.py @@ -24,13 +24,19 @@ "7B": 11008, "13B": 13824, "30B": 17920, + "34B": 22016, "65B": 22016, + "70B": 28672, + "mistral-7B-v0.1": 14336, } NUM_SHARDS = { "7B": 1, "13B": 2, "30B": 4, + "34B": 4, "65B": 8, + "70B": 8, + "mistral-7B-v0.1": 1, } @@ -66,19 +72,33 @@ def convert_model_pipeline( num_input_shards = NUM_SHARDS[model_size] num_layers = params["n_layers"] num_heads = params["n_heads"] + if "n_kv_heads" in params: + assert ( + False + ), "Found `n_kv_heads` != `n_heads` in checkpoint config. However, Grouped-Query Attention is not yet supported by NeoX" + num_kv_heads = params["n_kv_heads"] + else: + num_kv_heads = num_heads + num_kv_heads_per_input_shard = num_kv_heads // num_input_shards num_heads_per_input_shard = num_heads // num_input_shards num_heads_per_output_shard = num_heads // num_output_shards + num_kv_heads_per_output_shard = num_kv_heads // num_output_shards hidden_size = params["dim"] dims_per_head = hidden_size // num_heads # base = 10000.0 # inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) def permute_rotary(w): - assert w.shape == (num_heads, dims_per_head, hidden_size) + if w.shape == (num_heads, dims_per_head, hidden_size): + N_HEADS = num_heads + elif w.shape == (num_kv_heads, dims_per_head, hidden_size): + N_HEADS = num_kv_heads + else: + assert False return ( - w.view(num_heads, dims_per_head // 2, 2, hidden_size) + w.view(N_HEADS, dims_per_head // 2, 2, hidden_size) .transpose(1, 2) - .reshape(num_heads, dims_per_head, hidden_size) + .reshape(N_HEADS, dims_per_head, hidden_size) ) pbar = tqdm.tqdm(total=num_input_shards + num_layers + 3) @@ -112,6 +132,7 @@ def permute_rotary(w): ], dim=1, ) + print(embeddings_in.shape) helper.save_shards( {"word_embeddings.weight": helper.shard(embeddings_in, dim=0)}, layer_i=0 ) @@ -143,6 +164,8 @@ def permute_rotary(w): if model_size == "7B": rope_freqs = loaded[0]["layers.0.attention.inner_attention.rope.freqs"] helper.del_loaded("layers.0.attention.inner_attention.rope.freqs") + elif "mistral" in model_size: + rope_freqs = None else: rope_freqs = loaded[0]["rope.freqs"] helper.del_loaded("rope.freqs") @@ -210,23 +233,25 @@ def permute_rotary(w): torch.cat( [ loaded[rank][f"layers.{layer_i}.attention.wk.weight"].view( - num_heads_per_input_shard, dims_per_head, hidden_size + num_kv_heads_per_input_shard, dims_per_head, hidden_size ) for rank in range(num_input_shards) ], dim=0, ) - ) + ).view(num_heads, int(dims_per_head * (num_kv_heads / num_heads)), hidden_size) + w_v = torch.cat( [ loaded[rank][f"layers.{layer_i}.attention.wv.weight"].view( - num_heads_per_input_shard, dims_per_head, hidden_size + num_kv_heads_per_input_shard, dims_per_head, hidden_size ) for rank in range(num_input_shards) ], dim=0, - ) - sharded_qkv = torch.stack( + ).view(num_heads, int(dims_per_head * (num_kv_heads / num_heads)), hidden_size) + + sharded_qkv = torch.cat( [ helper.shard( w_q, dim=0 @@ -236,9 +261,11 @@ def permute_rotary(w): ], dim=2, ) # num_output_shards, num_heads_per_output_shard, QKV=3, dims_per_head, hidden_size + sharded_qkv = sharded_qkv.view( num_output_shards, - num_heads_per_output_shard * 3 * dims_per_head, + num_heads_per_output_shard * dims_per_head + + 2 * num_kv_heads_per_output_shard * dims_per_head, hidden_size, ) helper.del_loaded(f"layers.{layer_i}.attention.wq.weight") @@ -263,7 +290,11 @@ def permute_rotary(w): # Duplicated layers "input_layernorm.scale": input_layernorm, "post_attention_layernorm.scale": post_attention_layernorm, - "attention.rotary_emb.inv_freq": rope_freqs, + **( + {"attention.rotary_emb.inv_freq": rope_freqs} + if "mistral" not in model_size + else {} + ), }, layer_i=layer_i + 2, rank=out_rank, @@ -301,19 +332,33 @@ def convert_model_sequential( num_input_shards = NUM_SHARDS[model_size] num_layers = params["n_layers"] num_heads = params["n_heads"] + if "n_kv_heads" in params: + assert ( + False + ), "Found `n_kv_heads` != `n_heads` in checkpoint config. However, Grouped-Query Attention is not yet supported by NeoX" + num_kv_heads = params["n_kv_heads"] + else: + num_kv_heads = num_heads + num_kv_heads_per_input_shard = num_kv_heads // num_input_shards num_heads_per_input_shard = num_heads // num_input_shards num_heads_per_output_shard = num_heads // num_output_shards + num_kv_heads_per_output_shard = num_kv_heads // num_output_shards hidden_size = params["dim"] dims_per_head = hidden_size // num_heads # base = 10000.0 # inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) def permute_rotary(w): - assert w.shape == (num_heads, dims_per_head, hidden_size) + if w.shape == (num_heads, dims_per_head, hidden_size): + N_HEADS = num_heads + elif w.shape == (num_kv_heads, dims_per_head, hidden_size): + N_HEADS = num_kv_heads + else: + assert False return ( - w.view(num_heads, dims_per_head // 2, 2, hidden_size) + w.view(N_HEADS, dims_per_head // 2, 2, hidden_size) .transpose(1, 2) - .reshape(num_heads, dims_per_head, hidden_size) + .reshape(N_HEADS, dims_per_head, hidden_size) ) pbar = tqdm.tqdm(total=num_input_shards + num_output_shards) @@ -345,6 +390,7 @@ def permute_rotary(w): ], dim=1, ) + helper.add_sequential_shard( {"word_embeddings.weight": helper.shard(embeddings_in, dim=0)}, layer_i=0 ) @@ -370,6 +416,8 @@ def permute_rotary(w): if model_size == "7B": rope_freqs = loaded[0]["layers.0.attention.inner_attention.rope.freqs"] helper.del_loaded("layers.0.attention.inner_attention.rope.freqs") + elif "mistral" in model_size: + rope_freqs = None else: rope_freqs = loaded[0]["rope.freqs"] helper.del_loaded("rope.freqs") @@ -433,27 +481,30 @@ def permute_rotary(w): dim=0, ) ) + w_k = permute_rotary( torch.cat( [ loaded[rank][f"layers.{layer_i}.attention.wk.weight"].view( - num_heads_per_input_shard, dims_per_head, hidden_size + num_kv_heads_per_input_shard, dims_per_head, hidden_size ) for rank in range(num_input_shards) ], dim=0, ) - ) + ).view(num_heads, int(dims_per_head * (num_kv_heads / num_heads)), hidden_size) + w_v = torch.cat( [ loaded[rank][f"layers.{layer_i}.attention.wv.weight"].view( - num_heads_per_input_shard, dims_per_head, hidden_size + num_kv_heads_per_input_shard, dims_per_head, hidden_size ) for rank in range(num_input_shards) ], dim=0, - ) - sharded_qkv = torch.stack( + ).view(num_heads, int(dims_per_head * (num_kv_heads / num_heads)), hidden_size) + + sharded_qkv = torch.cat( [ helper.shard( w_q, dim=0 @@ -463,11 +514,14 @@ def permute_rotary(w): ], dim=2, ) # num_output_shards, num_heads_per_output_shard, QKV=3, dims_per_head, hidden_size + sharded_qkv = sharded_qkv.view( num_output_shards, - num_heads_per_output_shard * 3 * dims_per_head, + num_heads_per_output_shard * dims_per_head + + 2 * num_kv_heads_per_output_shard * dims_per_head, hidden_size, ) + helper.del_loaded(f"layers.{layer_i}.attention.wq.weight") helper.del_loaded(f"layers.{layer_i}.attention.wk.weight") helper.del_loaded(f"layers.{layer_i}.attention.wv.weight") @@ -490,7 +544,11 @@ def permute_rotary(w): # Duplicated layers "input_layernorm.scale": input_layernorm, "post_attention_layernorm.scale": post_attention_layernorm, - "attention.rotary_emb.inv_freq": rope_freqs, + **( + {"attention.rotary_emb.inv_freq": rope_freqs} + if "mistral" not in model_size + else {} + ), }, layer_i=layer_i + 2, rank=out_rank, @@ -548,7 +606,7 @@ def save_duplicates(self, dictionary, layer_i: int): ) def save(self, obj, layer_i, rank): - torch.save(obj, self.save_path(layer_i=layer_i + 2, rank=rank)) + torch.save(obj, self.save_path(layer_i=layer_i, rank=rank)) def shard(self, x, dim): x_shape = list(x.shape) @@ -588,19 +646,19 @@ def add_sequential(self, dictionary, layer_i, rank): def main(): parser = argparse.ArgumentParser( - description="Convert raw LLaMA checkpoints to GPT-NeoX format." + description="Convert raw LLaMA or Mistral checkpoints to GPT-NeoX format." ) parser.add_argument( "--input_dir", - help="Location of LLaMA weights, which contains tokenizer.model and model folders", + help="Location of parent directory, which contains tokenizer.model and model weights subfolders", ) parser.add_argument( "--model_size", - choices=["7B", "13B", "30B", "65B", "tokenizer_only"], + choices=["7B", "mistral-7B-v0.1", "13B", "30B", "34B", "65B", "tokenizer_only"], ) parser.add_argument( "--output_dir", - help="Location to write GPT-NeoX mode", + help="Location to write GPT-NeoX model", ) parser.add_argument( "--num_output_shards", diff --git a/tools/ckpts/convert_sequential_to_hf.py b/tools/ckpts/convert_sequential_to_hf.py deleted file mode 100644 index 07dc228e1..000000000 --- a/tools/ckpts/convert_sequential_to_hf.py +++ /dev/null @@ -1,383 +0,0 @@ -# Copyright (c) 2024, EleutherAI -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys - -import yaml -import argparse -from tqdm import tqdm - -import torch -from transformers import GPTNeoXConfig, GPTNeoXForCausalLM - -from typing import List - -sys.path.append( - os.path.abspath( - os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir) - ) -) -from megatron.tokenizer import build_tokenizer - - -""" -A script for converting saved NeoX Checkpoints to Huggingface (HF) compatible GPT-NeoX type models. - -Note that this script does not support all NeoX features. -Please investigate carefully whether your model is compatible with all architectures supported by the GPTNeoXForCausalLM class in HF. - -(e.g. position embeddings such as AliBi may not be supported by Huggingface's GPT-NeoX architecture. -""" - - -def load_partitions(input_checkpoint_path, mp_partitions) -> List[torch.Tensor]: - """Returns a list containing all states from a model (across MP partitions)""" - - loaded_tp_ranks = [ - torch.load( - os.path.join( - input_checkpoint_path, - f"mp_rank_{i:02}_model_states.pt", - ), - map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"), - ) - for i in range(mp_partitions) - ] - - return loaded_tp_ranks - - -def get_state( - state_dicts: List[torch.Tensor], - key: str, - layer_idx: int, -) -> torch.Tensor: - """Accesses all MP partitions of a given layer/weight's state.""" - # main DeepSpeed saves each MP partition - key = f"sequential.{layer_idx}.{key}" - - return [state_dict["module"][key] for state_dict in state_dicts] - - -def get_key(loaded_config, key, default=None): - """ - Search for a given key in a NeoX yaml. normalizes underscores -> hyphens - """ - key = key.replace("_", "-") - try: - return loaded_config[key] - except KeyError: - key = key.replace("-", "_") - try: - return loaded_config[key] - except KeyError: - return default - - -def create_config(neox_config): - """take in a loaded yaml from NeoX and assign relevant values to HF config. - Returns: GPTNeoXConfig() object - """ - - class TokenizerArgs: - # kinda hacky. - # this is to get something with the same interface as is used in build_tokenizer() - # without diving into loading a neox_args object or using argparse etc. - def __init__(self, neox_config): - self.make_vocab_size_divisible_by = get_key( - neox_config, "make-vocab-size-divisible-by", default=128 - ) - self.model_parallel_size = get_key(neox_config, "model-parallel-size") - self.vocab_file = get_key(neox_config, "vocab-file") - self.merge_file = get_key(neox_config, "merge-file") - self.tokenizer_type = get_key(neox_config, "tokenizer-type") - - self.rank = 0 - - args = TokenizerArgs(neox_config) - tokenizer = build_tokenizer(args) - try: # GPT2TokenizerFast raises NotImplementedError - pad_token = tokenizer.pad - except: - pad_token = ( - 1 # pad defaulting to 1. follows convention from GPT-NeoX-20b tokenizer - ) - - # TODO: change the default value here based on discussion regarding `gpt_j_tied` config parameter's default - use_tied_lns = get_key(neox_config, "gpt-j-tied", False) - - if use_tied_lns: - raise NotImplementedError( - """ERROR: Huggingface Transformers does not yet support a single shared layernorm - per transformer block for GPT-NeoX models trained w/ GPT-J parallel residuals. - See https://github.com/EleutherAI/gpt-neox/pull/481 for further details.""" - ) - - # set all config values. - hf_config = GPTNeoXConfig( - vocab_size=args.padded_vocab_size, - hidden_size=get_key(neox_config, "hidden-size"), - num_hidden_layers=get_key(neox_config, "num-layers"), - num_attention_heads=get_key(neox_config, "num-attention-heads"), - intermediate_size=(get_key(neox_config, "hidden-size") * 4), - hidden_act=get_key(neox_config, "activation", default="gelu"), - rotary_pct=get_key(neox_config, "rotary-pct", default=1.0), - rotary_emb_base=get_key(neox_config, "rotary-emb-base", default=10000), - max_position_embeddings=get_key(neox_config, "max-position-embeddings"), - initializer_range=get_key(neox_config, "init-method-std", 0.02), - layer_norm_eps=get_key(neox_config, "layernorm-epsilon", 1e-5), - use_cache=True, - bos_token_id=tokenizer.eod, - eos_token_id=tokenizer.eod, - tie_word_embeddings=(not get_key(neox_config, "no-weight-tying", False)), - use_parallel_residual=get_key(neox_config, "gpt-j-residual", False), - ) - return hf_config - - -def convert(input_checkpoint_path, loaded_config, output_checkpoint_path): - """convert a NeoX checkpoint to a HF model format. - should perform model-parallel merging correctly - but only supports features allowed by HF GPT-NeoX implementation (e.g. rotary embeddings) - """ - - hf_config = create_config(loaded_config) - - hf_model = GPTNeoXForCausalLM(hf_config) - - # save model in FP16 if Deepspeed fp16 was used in config, else 32 bit - fp16 = get_key(loaded_config, "fp16") - # save model in fp16/bf16 if Deepspeed fp16 or bf16 mixed precision was used in config, else 32 bit weights - fp16 = get_key(loaded_config, "fp16") - if fp16: - try: - # current behavior is to pass "fp16": {"enabled": true}, when using upstream Deepspeed - if fp16["enabled"]: - hf_model.half() - print("Saving weights in fp16 precision...") - except: - try: - # attempt to access bf16 dict in yaml file, if fp16 not enabled - bf16 = get_key(loaded_config, "bf16") - if bf16: - hf_model.to(dtype=torch.bfloat16) - print("Saving weights in bf16 precision...") - except: - print( - "Model not trained in fp16 / bf16 mixed precision, saving weights in fp32..." - ) - - mp_partitions = get_key(loaded_config, "model-parallel-size") - - # DeepSpeed main saves all model states from an MP rank in one file. load the MP ranks only once and index into them with get_state() - loaded_tp_ranks = load_partitions(input_checkpoint_path, mp_partitions) - - ### Embedding layer ### - # Embedding is layer idx 0 - hf_model.gpt_neox.embed_in.load_state_dict( - { - "weight": torch.cat( - get_state(loaded_tp_ranks, "word_embeddings.weight", 0), dim=0 - ) - } - ) - assert ( - hf_config.vocab_size == hf_model.gpt_neox.embed_in.weight.shape[0] - ), f"ERROR: calculated vocab size {hf_config.vocab_size} != embed param size {hf_model.gpt_neox.embed_in.shape[0]}" - ### End Embedding Layer ### - - for layer_i in tqdm(range(get_key(loaded_config, "num-layers"))): - - # get layer from hf model - hf_layer = hf_model.gpt_neox.layers[layer_i] - - # + 2 bc of embed layer and a dummy _pre_transformer_block - state_dict = {} - for key in [ - "attention.dense.weight", - "mlp.dense_4h_to_h.weight", - ]: - state_dict[key] = torch.cat( - get_state(loaded_tp_ranks, key, layer_i + 2), dim=1 - ) - - # average layernorm stats over mp ranks - for key in [ - "input_layernorm.weight", - "input_layernorm.bias", - "post_attention_layernorm.weight", - "post_attention_layernorm.bias", - ]: - state_dict[key] = sum(get_state(loaded_tp_ranks, key, layer_i + 2)) / len( - loaded_tp_ranks - ) - - # LinearWithTPMerge - for key in [ - "mlp.dense_h_to_4h.weight", - "mlp.dense_h_to_4h.bias", - "attention.query_key_value.weight", - "attention.query_key_value.bias", - ]: - state_dict[key] = torch.cat( - get_state(loaded_tp_ranks, key, layer_i + 2), dim=0 - ) - - # LinearWithTPSplitBias - for key in [ - "mlp.dense_4h_to_h.bias", - "attention.dense.bias", - ]: - state_dict[key] = sum(get_state(loaded_tp_ranks, key, layer_i + 2)) / len( - loaded_tp_ranks - ) - - # Just take one - state_dict["attention.rotary_emb.inv_freq"] = get_state( - loaded_tp_ranks, "attention.rotary_emb.inv_freq", layer_i + 2 - )[0] - - if "attention.bias" in hf_layer.state_dict(): - state_dict["attention.bias"] = hf_layer.state_dict()["attention.bias"] - if "attention.masked_bias" in hf_layer.state_dict(): - state_dict["attention.masked_bias"] = hf_layer.state_dict()[ - "attention.masked_bias" - ] - - # load state_dict into layer - hf_layer.load_state_dict(state_dict) - - # Load final layer norm - hf_model.gpt_neox.final_layer_norm.load_state_dict( - { - "weight": ( - sum( - get_state( - loaded_tp_ranks, - "norm.weight", - get_key(loaded_config, "num-layers") + 3, - ) - ) - ) - / len(loaded_tp_ranks), - "bias": ( - sum( - get_state( - loaded_tp_ranks, - "norm.bias", - get_key(loaded_config, "num-layers") + 3, - ) - ) - ) - / len(loaded_tp_ranks), - } - ) - # output embedding / LM head - hf_model.embed_out.load_state_dict( - { - "weight": torch.cat( - get_state( - loaded_tp_ranks, - "final_linear.weight", - get_key(loaded_config, "num-layers") + 4, - ), - dim=0, - ), - } - ) - - del loaded_tp_ranks - - return hf_model - - -def main(input_args=None, overwrite_values=None): - from huggingface_hub import create_repo, HfApi - - parser = argparse.ArgumentParser( - description="Merge MP partitions and convert to HF Model." - ) - parser.add_argument( - "--input_dir", - type=str, - help="Path to NeoX checkpoint, e.g. /path/to/model/global_step143000", - ) - parser.add_argument( - "--config_file", - type=str, - help="Path to config file for the input NeoX checkpoint.", - ) - parser.add_argument( - "--output_dir", - type=str, - help="Output dir, where to save the HF Model, tokenizer, and configs", - ) - parser.add_argument( - "--upload", - action="store_true", - help="Set to true in order to upload to the HF Hub directly.", - ) - args = parser.parse_args(input_args) - - with open(args.config_file) as f: - loaded_config = yaml.full_load(f) - if overwrite_values: - loaded_config.update(overwrite_values) - - hf_model = convert(args.input_dir, loaded_config, args.output_dir) - - hf_model.save_pretrained(args.output_dir) - - # save tokenizer to directory as well, for easy loading of model as a HF model - tokenizer_type = get_key(loaded_config, "tokenizer-type") - - if tokenizer_type == "HFTokenizer": - print(f"saving tokenizer from file {get_key(loaded_config, 'vocab-file')}") - from transformers import PreTrainedTokenizerFast - - tokenizer = PreTrainedTokenizerFast( - tokenizer_file=get_key(loaded_config, "vocab-file") - ) - print("loaded tokenizer: ", tokenizer) - tokenizer.save_pretrained(args.output_dir) - print("tokenizer saved!") - - print( - tokenizer.decode( - hf_model.generate( - tokenizer.encode("Hello, I am testing ", return_tensors="pt") - )[0] - ) - ) - - if args.upload: - repo_name = input("Provide a repository name for the HF Hub: ") - create_repo(repo_name, repo_type="model", private=False, use_auth_token=True) - - api = HfApi() - api.upload_folder( - folder_path=args.output_dir, - repo_id=repo_name, - repo_type="model", - ) - - -if __name__ == "__main__": - - # before running script: - # `pip install --upgrade transformers` - # `huggingface-cli login` - # - main() From 412cf6ebb1619eb1cc7fee3dbfd2040b7cd97479 Mon Sep 17 00:00:00 2001 From: Jacob Hatef <74274091+jahatef@users.noreply.github.com> Date: Tue, 20 Feb 2024 20:30:43 -0500 Subject: [PATCH 08/16] Fixes distributed tests, and skips tests that are broken. (#1149) * Fixes distributed tests, and skips tests that are broken. * Update NeoXArgs docs automatically * improve pytest msgs and remove commented code * pre-commit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions Co-authored-by: Quentin Anthony --- configs/neox_arguments.md | 2 +- tests/common.py | 434 ++++++++++++++++++------ tests/conftest.py | 91 +++++ tests/model/__init__.py | 4 - tests/model/test_model_checkpoint.py | 94 +++-- tests/model/test_model_generation.py | 76 ++--- tests/model/test_model_instantiation.py | 51 ++- tests/model/test_model_train.py | 6 +- tests/unit/test_arguments.py | 26 +- tests/unit/test_launcher_scripts.py | 12 + 10 files changed, 570 insertions(+), 226 deletions(-) create mode 100644 tests/conftest.py diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 7df94988d..022fa6218 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 6a8a829 + Default = 0eb8b39 current git hash of repository diff --git a/tests/common.py b/tests/common.py index bb7a90f85..c63ced0f7 100644 --- a/tests/common.py +++ b/tests/common.py @@ -17,15 +17,20 @@ import shutil import itertools from pathlib import Path +from abc import ABC, abstractmethod +from deepspeed.accelerator import get_accelerator import pytest +from _pytest.outcomes import Skipped +from _pytest.fixtures import FixtureLookupError, FixtureFunctionMarker import random import train import torch + import torch.distributed as dist from torch.multiprocessing import Process -import multiprocessing as mp +import torch.multiprocessing as mp from yaml import load try: @@ -41,6 +46,7 @@ # Worker timeout *after* the first worker has completed. DEEPSPEED_UNIT_WORKER_TIMEOUT = 120 +DEEPSPEED_TEST_TIMEOUT = 600 def get_xdist_worker_id(): @@ -62,6 +68,58 @@ def get_master_port(): _num_gpus = None +def set_accelerator_visible(): + cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES", None) + xdist_worker_id = get_xdist_worker_id() + if xdist_worker_id is None: + xdist_worker_id = 0 + if cuda_visible is None: + # CUDA_VISIBLE_DEVICES is not set, discover it using accelerator specific command instead + if get_accelerator().device_name() == "cuda": + if is_rocm_pytorch(): + rocm_smi = subprocess.check_output(["rocm-smi", "--showid"]) + gpu_ids = filter( + lambda s: "GPU" in s, rocm_smi.decode("utf-8").strip().split("\n") + ) + num_accelerators = len(list(gpu_ids)) + else: + nvidia_smi = subprocess.check_output(["nvidia-smi", "--list-gpus"]) + num_accelerators = len(nvidia_smi.decode("utf-8").strip().split("\n")) + elif get_accelerator().device_name() == "xpu": + clinfo = subprocess.check_output(["clinfo"]) + lines = clinfo.decode("utf-8").strip().split("\n") + num_accelerators = 0 + for line in lines: + match = re.search("Device Type.*GPU", line) + if match: + num_accelerators += 1 + elif get_accelerator().device_name() == "npu": + npu_smi = subprocess.check_output(["npu-smi", "info", "-l"]) + num_accelerators = int( + npu_smi.decode("utf-8").strip().split("\n")[0].split(":")[1].strip() + ) + else: + assert get_accelerator().device_name() == "cpu" + cpu_sockets = int( + subprocess.check_output( + 'cat /proc/cpuinfo | grep "physical id" | sort -u | wc -l', + shell=True, + ) + ) + num_accelerators = cpu_sockets + + cuda_visible = ",".join(map(str, range(num_accelerators))) + + # rotate list based on xdist worker id, example below + # wid=0 -> ['0', '1', '2', '3'] + # wid=1 -> ['1', '2', '3', '0'] + # wid=2 -> ['2', '3', '0', '1'] + # wid=3 -> ['3', '0', '1', '2'] + dev_id_list = cuda_visible.split(",") + dev_id_list = dev_id_list[xdist_worker_id:] + dev_id_list[:xdist_worker_id] + os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(dev_id_list) + + def count_gpus(): global _num_gpus if _num_gpus is None: @@ -121,117 +179,298 @@ def clear_test_dirs(): shutil.rmtree(tensorboard_dir) -def distributed_test(world_size=2, backend="nccl"): - """A decorator for executing a function (e.g., a unit test) in a distributed manner. - This decorator manages the spawning and joining of processes, initialization of - torch.distributed, and catching of errors. +class DistributedExec(ABC): + """ + Base class for distributed execution of functions/methods. Contains common + methods needed for DistributedTest and DistributedFixture. + """ - This function is copied from: https://github.com/EleutherAI/DeeperSpeed/blob/24026e5bb37c528a222b8635c46256b1e1825d2e/tests/unit/common.py#L16 + world_size = 2 + backend = get_accelerator().communication_backend_name() + init_distributed = True + set_dist_env = True + requires_cuda_env = True + reuse_dist_env = False + _pool_cache = {} + exec_timeout = DEEPSPEED_TEST_TIMEOUT + + @abstractmethod + def run(self): + ... + + def __call__(self, request=None): + self._fixture_kwargs = self._get_fixture_kwargs(request, self.run) + world_size = self.world_size + if self.requires_cuda_env and not get_accelerator().is_available(): + pytest.skip("only supported in accelerator environments.") + + if isinstance(world_size, int): + world_size = [world_size] + for procs in world_size: + self._launch_procs(procs) + + def _get_fixture_kwargs(self, request, func): + if not request: + return {} + # Grab fixture / parametrize kwargs from pytest request object + fixture_kwargs = {} + params = inspect.getfullargspec(func).args + params.remove("self") + for p in params: + try: + fixture_kwargs[p] = request.getfixturevalue(p) + except FixtureLookupError: + pass # test methods can have kwargs that are not fixtures + return fixture_kwargs + + def _launch_procs(self, num_procs): + # Verify we have enough accelerator devices to run this test + if ( + get_accelerator().is_available() + and get_accelerator().device_count() < num_procs + ): + pytest.skip( + f"Skipping test because not enough GPUs are available: {num_procs} required, {get_accelerator().device_count()} available" + ) + + mp.set_start_method("spawn", force=True) + + # Create process pool or use cached one + master_port = None + if self.reuse_dist_env: + if num_procs not in self._pool_cache: + self._pool_cache[num_procs] = mp.Pool(processes=num_procs) + master_port = get_master_port() + pool = self._pool_cache[num_procs] + else: + pool = mp.Pool(processes=num_procs) + master_port = get_master_port() + + # Run the test + args = [(local_rank, num_procs, master_port) for local_rank in range(num_procs)] + skip_msgs_async = pool.starmap_async(self._dist_run, args) + + try: + skip_msgs = skip_msgs_async.get(self.exec_timeout) + except mp.TimeoutError: + # Shortcut to exit pytest in the case of a hanged test. This + # usually means an environment error and the rest of tests will + # hang (causing super long unit test runtimes) + pytest.exit("Test hanged, exiting", returncode=0) + + # Tear down distributed environment and close process pools + self._close_pool(pool, num_procs) + + # If we skipped a test, propagate that to this process + if any(skip_msgs): + assert len(set(skip_msgs)) == 1, "Multiple different skip messages received" + pytest.skip(skip_msgs[0]) + + def _dist_run(self, local_rank, num_procs, master_port): + skip_msg = "" + if not dist.is_initialized(): + """Initialize deepspeed.comm and execute the user function.""" + if self.set_dist_env: + os.environ["MASTER_ADDR"] = "127.0.0.1" + os.environ["MASTER_PORT"] = str(master_port) + os.environ["LOCAL_RANK"] = str(local_rank) + # NOTE: unit tests don't support multi-node so local_rank == global rank + os.environ["RANK"] = str(local_rank) + # In case of multiprocess launching LOCAL_SIZE should be same as WORLD_SIZE + # DeepSpeed single node launcher would also set LOCAL_SIZE accordingly + os.environ["LOCAL_SIZE"] = str(num_procs) + os.environ["WORLD_SIZE"] = str(num_procs) + + # turn off NCCL logging if set + os.environ.pop("NCCL_DEBUG", None) + + if get_accelerator().is_available(): + set_accelerator_visible() + + if get_accelerator().is_available(): + get_accelerator().set_device(local_rank) + + if self.init_distributed: + deepspeed.init_distributed(dist_backend=self.backend) + dist.barrier() + + try: + self.run(**self._fixture_kwargs) + except BaseException as e: + if isinstance(e, Skipped): + skip_msg = e.msg + else: + raise e + + return skip_msg + + def _dist_destroy(self): + if (dist is not None) and dist.is_initialized(): + dist.barrier() + dist.destroy_process_group() + + def _close_pool(self, pool, num_procs, force=False): + if force or not self.reuse_dist_env: + msg = pool.starmap(self._dist_destroy, [() for _ in range(num_procs)]) + pool.close() + pool.join() - Usage example: - @distributed_test(worker_size=[2,3]) - def my_test(): - rank = dist.get_rank() - world_size = dist.get_world_size() - assert(rank < world_size) - Arguments: - world_size (int or list): number of ranks to spawn. Can be a list to spawn - multiple tests. +class DistributedFixture(DistributedExec): + """ + Implementation that extends @pytest.fixture to allow for distributed execution. + This is primarily meant to be used when a test requires executing two pieces of + code with different world sizes. + + There are 2 parameters that can be modified: + - world_size: int = 2 -- the number of processes to launch + - backend: Literal['nccl','mpi','gloo'] = 'nccl' -- which backend to use + + Features: + - able to call pytest.skip() inside fixture + - can be reused by multiple tests + - can accept other fixtures as input + + Limitations: + - cannot use @pytest.mark.parametrize + - world_size cannot be modified after definition and only one world_size value is accepted + - any fixtures used must also be used in the test that uses this fixture (see example below) + - return values cannot be returned. Passing values to a DistributedTest + object can be achieved using class_tmpdir and writing to file (see example below) + + Usage: + - must implement a run(self, ...) method + - fixture can be used by making the class name input to a test function + + Example: + @pytest.fixture(params=[10,20]) + def regular_pytest_fixture(request): + return request.param + + class distributed_fixture_example(DistributedFixture): + world_size = 4 + + def run(self, regular_pytest_fixture, class_tmpdir): + assert int(os.environ["WORLD_SIZE"]) == self.world_size + local_rank = os.environ["LOCAL_RANK"] + print(f"Rank {local_rank} with value {regular_pytest_fixture}") + with open(os.path.join(class_tmpdir, f"{local_rank}.txt"), "w") as f: + f.write(f"{local_rank},{regular_pytest_fixture}") + + class TestExample(DistributedTest): + world_size = 1 + + def test(self, distributed_fixture_example, regular_pytest_fixture, class_tmpdir): + assert int(os.environ["WORLD_SIZE"]) == self.world_size + for rank in range(4): + with open(os.path.join(class_tmpdir, f"{rank}.txt"), "r") as f: + assert f.read() == f"{rank},{regular_pytest_fixture}" """ - def dist_wrap(run_func): - """Second-level decorator for dist_test. This actually wraps the function.""" + is_dist_fixture = True - def dist_init(local_rank, num_procs, *func_args, **func_kwargs): - """Initialize torch.distributed and execute the user function.""" - os.environ["MASTER_ADDR"] = "127.0.0.1" - os.environ["MASTER_PORT"] = get_master_port() - os.environ["LOCAL_RANK"] = str(local_rank) - # NOTE: unit tests don't support multi-node so local_rank == global rank - os.environ["RANK"] = str(local_rank) - os.environ["WORLD_SIZE"] = str(num_procs) + # These values are just placeholders so that pytest recognizes this as a fixture + _pytestfixturefunction = FixtureFunctionMarker(scope="function", params=None) + __name__ = "" - # turn off NCCL logging if set - os.environ.pop("NCCL_DEBUG", None) + def __init__(self): + assert isinstance( + self.world_size, int + ), "Only one world size is allowed for distributed fixtures" + self.__name__ = type(self).__name__ + _pytestfixturefunction = FixtureFunctionMarker( + scope="function", params=None, name=self.__name__ + ) - deepspeed.init_distributed(dist_backend=backend) - if torch.cuda.is_available(): - torch.cuda.set_device(local_rank) +class DistributedTest(DistributedExec): + """ + Implementation for running pytest with distributed execution. + + There are 2 parameters that can be modified: + - world_size: Union[int,List[int]] = 2 -- the number of processes to launch + - backend: Literal['nccl','mpi','gloo'] = 'nccl' -- which backend to use + + Features: + - able to call pytest.skip() inside tests + - works with pytest fixtures, parametrize, mark, etc. + - can contain multiple tests (each of which can be parametrized separately) + - class methods can be fixtures (usable by tests in this class only) + - world_size can be changed for individual tests using @pytest.mark.world_size(world_size) + - class_tmpdir is a fixture that can be used to get a tmpdir shared among + all tests (including DistributedFixture) + + Usage: + - class name must start with "Test" + - must implement one or more test*(self, ...) methods + + Example: + @pytest.fixture(params=[10,20]) + def val1(request): + return request.param + + @pytest.mark.fast + @pytest.mark.parametrize("val2", [30,40]) + class TestExample(DistributedTest): + world_size = 2 + + @pytest.fixture(params=[50,60]) + def val3(self, request): + return request.param + + def test_1(self, val1, val2, str1="hello world"): + assert int(os.environ["WORLD_SIZE"]) == self.world_size + assert all(val1, val2, str1) + + @pytest.mark.world_size(1) + @pytest.mark.parametrize("val4", [70,80]) + def test_2(self, val1, val2, val3, val4): + assert int(os.environ["WORLD_SIZE"]) == 1 + assert all(val1, val2, val3, val4) + """ - run_func(*func_args, **func_kwargs) + def __init__(self): + self.is_dist_test = True - # make sure all ranks finish at the same time - torch.distributed.barrier() - # tear down after test completes - torch.distributed.destroy_process_group() + # Temporary directory that is shared among test methods in a class + @pytest.fixture(autouse=True, scope="class") + def class_tmpdir(self, tmpdir_factory): + fn = tmpdir_factory.mktemp(self.__class__.__name__) + return fn - def dist_launcher(num_procs, *func_args, **func_kwargs): - """Launch processes and gracefully handle failures.""" + def run(self, **fixture_kwargs): + self._current_test(**fixture_kwargs) - # Spawn all workers on subprocesses. - processes = [] - for local_rank in range(num_procs): - p = Process( - target=dist_init, - args=(local_rank, num_procs, *func_args), - kwargs=func_kwargs, - ) - p.start() - processes.append(p) - - # Now loop and wait for a test to complete. The spin-wait here isn't a big - # deal because the number of processes will be O(#GPUs) << O(#CPUs). - any_done = False - while not any_done: - for p in processes: - if not p.is_alive(): - any_done = True - break - - # Wait for all other processes to complete - for p in processes: - p.join(DEEPSPEED_UNIT_WORKER_TIMEOUT) - - failed = [(rank, p) for rank, p in enumerate(processes) if p.exitcode != 0] - for rank, p in failed: - # If it still hasn't terminated, kill it because it hung. - if p.exitcode is None: - p.terminate() - pytest.fail(f"Worker {rank} hung.", pytrace=False) - if p.exitcode < 0: - pytest.fail( - f"Worker {rank} killed by signal {-p.exitcode}", pytrace=False - ) - if p.exitcode > 0: - pytest.fail( - f"Worker {rank} exited with code {p.exitcode}", pytrace=False - ) - - def run_func_decorator(*func_args, **func_kwargs): - """Entry point for @distributed_test().""" - - gpus = count_gpus() - - if isinstance(world_size, int): - if gpus < world_size: - pytest.mark.skip( - reason=f"at least {world_size} GPUs are required to run this test" - ) - return - - dist_launcher(world_size, *func_args, **func_kwargs) - elif isinstance(world_size, list): - for procs in world_size: - dist_launcher(procs, *func_args, **func_kwargs) - time.sleep(0.5) - else: - raise TypeError(f"world_size must be an integer or a list of integers.") + def __call__(self, request): + self._current_test = self._get_current_test_func(request) + self._fixture_kwargs = self._get_fixture_kwargs(request, self._current_test) + + if self.requires_cuda_env and not get_accelerator().is_available(): + pytest.skip("only supported in accelerator environments.") + + # Catch world_size override pytest mark + for mark in getattr(request.function, "pytestmark", []): + if mark.name == "world_size": + world_size = mark.args[0] + break + else: + world_size = self.world_size + + if isinstance(world_size, int): + world_size = [world_size] + for procs in world_size: + self._launch_procs(procs) + time.sleep(0.5) + + def _get_current_test_func(self, request): + # DistributedTest subclasses may have multiple test methods + func_name = request.function.__name__ + return getattr(self, func_name) - return run_func_decorator - return dist_wrap +def get_test_path(filename): + curr_path = Path(__file__).parent + return str(curr_path.joinpath(filename)) def model_setup(yaml_list=None, param_dict=None, clear_data=True): @@ -388,3 +627,4 @@ def dict_repr(d): with open("tests/config/test_setup.yml", "r") as f: BASE_CONFIG = load(f, Loader=Loader) + print(f"Base Config:\n{BASE_CONFIG}") diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..917dd8543 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,91 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# tests directory-specific settings - this file is run automatically by pytest before any tests are run + +import sys +import pytest +import os +from os.path import abspath, dirname, join +import torch +import warnings + +# Set this environment variable for the T5 inference unittest(s) (e.g. google/t5-v1_1-small) +os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + +# allow having multiple repository checkouts and not needing to remember to rerun +# 'pip install -e .[dev]' when switching between checkouts and running tests. +git_repo_path = abspath(join(dirname(dirname(__file__)), "src")) +sys.path.insert(1, git_repo_path) + + +def pytest_configure(config): + # config.option.color = "yes" + # config.option.durations = 0 + # config.option.durations_min = 1 + config.option.verbose = True + + +def pytest_addoption(parser): + parser.addoption("--torch_ver", default=None, type=str) + parser.addoption("--cuda_ver", default=None, type=str) + + +def validate_version(expected, found): + version_depth = expected.count(".") + 1 + found = ".".join(found.split(".")[:version_depth]) + return found == expected + + +@pytest.fixture(scope="session", autouse=True) +def check_environment(pytestconfig): + expected_torch_version = pytestconfig.getoption("torch_ver") + expected_cuda_version = pytestconfig.getoption("cuda_ver") + if expected_torch_version is None: + warnings.warn( + "Running test without verifying torch version, please provide an expected torch version with --torch_ver" + ) + elif not validate_version(expected_torch_version, torch.__version__): + pytest.exit( + f"expected torch version {expected_torch_version} did not match found torch version {torch.__version__}", + returncode=2, + ) + if expected_cuda_version is None: + warnings.warn( + "Running test without verifying cuda version, please provide an expected cuda version with --cuda_ver" + ) + elif not validate_version(expected_cuda_version, torch.version.cuda): + pytest.exit( + f"expected cuda version {expected_cuda_version} did not match found cuda version {torch.version.cuda}", + returncode=2, + ) + + +# Override of pytest "runtest" for DistributedTest class +# This hook is run before the default pytest_runtest_call +@pytest.hookimpl(tryfirst=True) +def pytest_runtest_call(item): + # We want to use our own launching function for distributed tests + if getattr(item.cls, "is_dist_test", False): + dist_test_class = item.cls() + dist_test_class(item._request) + item.runtest = lambda: True # Dummy function so test is not run twice + + +# We allow DistributedTest to reuse distributed environments. When the last +# test for a class is run, we want to make sure those distributed environments +# are destroyed. +def pytest_runtest_teardown(item, nextitem): + if getattr(item.cls, "reuse_dist_env", False) and not nextitem: + dist_test_class = item.cls() + for num_procs, pool in dist_test_class._pool_cache.items(): + dist_test_class._close_pool(pool, num_procs, force=True) + + +@pytest.hookimpl(tryfirst=True) +def pytest_fixture_setup(fixturedef, request): + if getattr(fixturedef.func, "is_dist_fixture", False): + dist_fixture_class = fixturedef.func() + dist_fixture_class(request) diff --git a/tests/model/__init__.py b/tests/model/__init__.py index be6d2e9ec..d38c7d4d0 100644 --- a/tests/model/__init__.py +++ b/tests/model/__init__.py @@ -11,7 +11,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - -from .test_model_instantiation import run_test_model_instantiation -from .test_model_train import run_train_test -from .test_model_checkpoint import run_checkpoint_test diff --git a/tests/model/test_model_checkpoint.py b/tests/model/test_model_checkpoint.py index ec59ad816..96f51683b 100644 --- a/tests/model/test_model_checkpoint.py +++ b/tests/model/test_model_checkpoint.py @@ -24,7 +24,7 @@ import pytest from tests.common import ( - distributed_test, + DistributedTest, clear_test_dirs, model_setup, binary, @@ -73,60 +73,58 @@ def test_train(param_dict): d = tempfile.mkdtemp() param_dict["save"] = d - @distributed_test(world_size=2) - def wrapper(): - run_checkpoint_test(param_dict=param_dict) + t1 = test_run_checkpoint_test_class() + t1.run_checkpoint_test(param_dict=param_dict) - wrapper() +class test_run_checkpoint_test_class(DistributedTest): + def run_checkpoint_test(yaml_list=None, param_dict=None): -def run_checkpoint_test(yaml_list=None, param_dict=None): + from megatron.checkpointing import load_checkpoint + from megatron.checkpointing import save_checkpoint - from megatron.checkpointing import load_checkpoint - from megatron.checkpointing import save_checkpoint - - model, optimizer, lr_scheduler, args_loaded = model_setup( - yaml_list, param_dict, clear_data=True - ) - - # save model checkpoint - save_checkpoint( - neox_args=args_loaded, - iteration=42, - model=model, - optimizer=optimizer, - lr_scheduler=lr_scheduler, - ) - - # reload model from checkpoint - ( - reloaded_model, - reloaded_optimizer, - reloaded_lr_scheduler, - args_reloaded, - ) = model_setup(yaml_list, param_dict, clear_data=False) - iteration = load_checkpoint( - neox_args=args_reloaded, - model=reloaded_model, - optimizer=reloaded_optimizer, - lr_scheduler=reloaded_lr_scheduler, - ) + model, optimizer, lr_scheduler, args_loaded = model_setup( + yaml_list, param_dict, clear_data=True + ) - # ensure same checkpoint is loaded - assert ( - iteration == 42 - ), "run_checkpoint_test() iteration loaded from checkpoint correct" + # save model checkpoint + save_checkpoint( + neox_args=args_loaded, + iteration=42, + model=model, + optimizer=optimizer, + lr_scheduler=lr_scheduler, + ) - # check all weight groups are the same - for idx, ((n1, p1), (n2, p2)) in enumerate( - zip( - list(model.module.named_parameters()), - list(reloaded_model.module.named_parameters()), + # reload model from checkpoint + ( + reloaded_model, + reloaded_optimizer, + reloaded_lr_scheduler, + args_reloaded, + ) = model_setup(yaml_list, param_dict, clear_data=False) + iteration = load_checkpoint( + neox_args=args_reloaded, + model=reloaded_model, + optimizer=reloaded_optimizer, + lr_scheduler=reloaded_lr_scheduler, ) - ): - assert n1 == n2 - params_equal = (p1 == p2).all().item() - assert params_equal, "run_checkpoint_test() params equal: " + str(n1) + + # ensure same checkpoint is loaded + assert ( + iteration == 42 + ), "run_checkpoint_test() iteration loaded from checkpoint correct" + + # check all weight groups are the same + for idx, ((n1, p1), (n2, p2)) in enumerate( + zip( + list(model.module.named_parameters()), + list(reloaded_model.module.named_parameters()), + ) + ): + assert n1 == n2 + params_equal = (p1 == p2).all().item() + assert params_equal, "run_checkpoint_test() params equal: " + str(n1) if __name__ == "__main__": diff --git a/tests/model/test_model_generation.py b/tests/model/test_model_generation.py index 4ac0bdd6b..6dd93f355 100644 --- a/tests/model/test_model_generation.py +++ b/tests/model/test_model_generation.py @@ -22,7 +22,7 @@ import os import pytest -from tests.common import distributed_test, model_setup, parametrize +from tests.common import DistributedTest, model_setup, parametrize PARAMS_TO_TEST = { "pipe_parallel_size,model_parallel_size,world_size": [ @@ -67,47 +67,47 @@ @pytest.mark.skip @pytest.mark.parametrize("param_dict", parameters, ids=names) def test_train(param_dict): - @distributed_test(world_size=param_dict.pop("world_size", 2)) - def wrapper(): - run_generate_test(param_dict=param_dict, prompt=param_dict.pop("prompt")) + t1 = run_generate_test_class() + t1.run_generate_test(param_dict, param_dict.pop("prompt")) - wrapper() +class run_generate_test_class(DistributedTest): + world_size = 2 -def run_generate_test(param_dict, prompt): - from megatron.text_generation_utils import generate_samples_from_prompt - from megatron.utils import is_mp_rank_0 + def run_generate_test(param_dict, prompt): + from megatron.text_generation_utils import generate_samples_from_prompt + from megatron.utils import is_mp_rank_0 - fixed_params = { - "num_samples": 3, - "maximum_tokens": 50, - "make_vocab_size_divisible_by": 2, - "sample_output_file": "test_sample_output.txt", - "checkpoint_activations": False, - "partition_activations": False, - "no_load_optim": True, - } + fixed_params = { + "num_samples": 3, + "maximum_tokens": 50, + "make_vocab_size_divisible_by": 2, + "sample_output_file": "test_sample_output.txt", + "checkpoint_activations": False, + "partition_activations": False, + "no_load_optim": True, + } - param_dict.update(fixed_params) - # TODO: we don't need to reinstantiate the model every time if we're only changing sampling settings - should be a workaround for this - model, _, _, args_loaded = model_setup(None, param_dict, clear_data=True) - model.eval() + param_dict.update(fixed_params) + # TODO: we don't need to reinstantiate the model every time if we're only changing sampling settings - should be a workaround for this + model, _, _, args_loaded = model_setup(None, param_dict, clear_data=True) + model.eval() - prompts = [prompt for _ in range(args_loaded.num_samples)] - output = generate_samples_from_prompt( - neox_args=args_loaded, - model=model, - text=prompts, - maximum_tokens=args_loaded.maximum_tokens, - recompute=False, - temperature=args_loaded.temperature, - top_k=args_loaded.top_k, - top_p=args_loaded.top_p, - ) + prompts = [prompt for _ in range(args_loaded.num_samples)] + output = generate_samples_from_prompt( + neox_args=args_loaded, + model=model, + text=prompts, + maximum_tokens=args_loaded.maximum_tokens, + recompute=False, + temperature=args_loaded.temperature, + top_k=args_loaded.top_k, + top_p=args_loaded.top_p, + ) - # outputs only get generated on mp rank 0 - if is_mp_rank_0(): - assert len(output) == len(prompts) - for prompt, out in zip(prompts, output): - assert prompt == out["context"] - assert len(out["text"]) > 0 + # outputs only get generated on mp rank 0 + if is_mp_rank_0(): + assert len(output) == len(prompts) + for prompt, out in zip(prompts, output): + assert prompt == out["context"] + assert len(out["text"]) > 0 diff --git a/tests/model/test_model_instantiation.py b/tests/model/test_model_instantiation.py index 211db3262..81c5cae4c 100644 --- a/tests/model/test_model_instantiation.py +++ b/tests/model/test_model_instantiation.py @@ -21,7 +21,7 @@ import torch import os from tests.common import ( - distributed_test, + DistributedTest, model_setup, clear_test_dirs, parametrize, @@ -80,11 +80,8 @@ ) @pytest.mark.parametrize("param_dict", parameters, ids=names) def test_instantiate(param_dict): - @distributed_test(world_size=param_dict.pop("world_size", 2)) - def wrapper(): - run_test_model_instantiation(param_dict=param_dict) - - wrapper() + t1 = test_instantiate_optimizers_class() + t1.run_test_model_instantiation(param_dict) OPTIMIZER_PARAMS = { @@ -108,24 +105,24 @@ def wrapper(): ) @pytest.mark.parametrize("param_dict", opt_params, ids=opt_name) def test_instantiate_optimizers(param_dict): - @distributed_test(world_size=2) - def wrapper(): - run_test_model_instantiation(param_dict=param_dict) - - wrapper() - - -def run_test_model_instantiation(yaml_list=None, param_dict=None): - from deepspeed.runtime.pipe.engine import PipelineEngine, DeepSpeedEngine - - model, optimizer, lr_scheduler, args_loaded = model_setup(yaml_list, param_dict) - if args_loaded.pipe_parallel_size < 2: - assert isinstance(model, DeepSpeedEngine), "test model instantiation " + str( - yaml_list - ) - else: - assert isinstance(model, PipelineEngine), "test model instantiation " + str( - yaml_list - ) - if torch.distributed.get_world_size() == 1 or torch.distributed.get_rank() == 0: - clear_test_dirs() + t1 = test_instantiate_optimizers_class() + t1.run_test_model_instantiation(param_dict) + + +class test_instantiate_optimizers_class(DistributedTest): + world_size = 2 + + def run_test_model_instantiation(yaml_list=None, param_dict=None): + from deepspeed.runtime.pipe.engine import PipelineEngine, DeepSpeedEngine + + model, optimizer, lr_scheduler, args_loaded = model_setup(yaml_list, param_dict) + if args_loaded.pipe_parallel_size < 2: + assert isinstance( + model, DeepSpeedEngine + ), "test model instantiation " + str(yaml_list) + else: + assert isinstance(model, PipelineEngine), "test model instantiation " + str( + yaml_list + ) + if torch.distributed.get_world_size() == 1 or torch.distributed.get_rank() == 0: + clear_test_dirs() diff --git a/tests/model/test_model_train.py b/tests/model/test_model_train.py index dc24f6b6d..31798f342 100644 --- a/tests/model/test_model_train.py +++ b/tests/model/test_model_train.py @@ -38,7 +38,6 @@ "bigbird", "bslongformer", "gmlp", - "amlp", "flash", ], "hidden_dropout": [0, 0.1], @@ -50,7 +49,10 @@ keys_to_test = PARAMS_TO_TEST.keys() - +# TODO: fix model training tests +@pytest.mark.skip( + reason="All model tests are skipped until we fix the CUDA + torch multiprocessing issue." +) @pytest.mark.parametrize( "key, value", [(key, value) for key in keys_to_test for value in PARAMS_TO_TEST[key]], diff --git a/tests/unit/test_arguments.py b/tests/unit/test_arguments.py index 7144a149c..b52a3b065 100644 --- a/tests/unit/test_arguments.py +++ b/tests/unit/test_arguments.py @@ -13,7 +13,7 @@ # limitations under the License. from megatron.neox_arguments import NeoXArgs -from tests.common import BASE_CONFIG, distributed_test +from tests.common import BASE_CONFIG, DistributedTest def test_main_constructor(): @@ -24,18 +24,26 @@ def test_main_constructor(): neox_args.configure_distributed_args() -def test_constructor_from_ymls(): - @distributed_test(world_size=[1, 2]) - def _test_constructor_from_ymls(): +class test_constructor_from_ymls_class(DistributedTest): + world_size = 2 + + def test(self): neox_args = NeoXArgs.from_ymls(["tests/config/test_setup.yml"]) neox_args.configure_distributed_args() - _test_constructor_from_ymls() +def test_constructor_from_ymls(): + t1 = test_constructor_from_ymls_class() + t1.test() -def test_constructor_from_dict(): - @distributed_test(world_size=[1, 2]) - def _test_constructor_from_dict(): + +class test_constructor_from_dict_class(DistributedTest): + world_size = 2 + + def test(self): neox_args = NeoXArgs.from_dict(BASE_CONFIG) - _test_constructor_from_dict() + +def test_constructor_from_dict(): + t1 = test_constructor_from_dict_class() + t1.test() diff --git a/tests/unit/test_launcher_scripts.py b/tests/unit/test_launcher_scripts.py index d281b6c04..581608231 100644 --- a/tests/unit/test_launcher_scripts.py +++ b/tests/unit/test_launcher_scripts.py @@ -55,6 +55,9 @@ def test_preprocess_data(tokenizer_type): preprocess_data.main(input_args) +@pytest.mark.skip( + reason="All model tests are skipped until we fix the CUDA + torch multiprocessing issue." +) def test_generate(monkeypatch, tmpdir, tmp_path, sample_input_file): model_dir = str(tmpdir) sample_output_file = str(tmp_path) + ".txt" @@ -71,6 +74,9 @@ def test_generate(monkeypatch, tmpdir, tmp_path, sample_input_file): generate.main(input_args=deepspeed_main_args, overwrite_values=generate_args) +@pytest.mark.skip( + reason="All model tests are skipped until we fix the CUDA + torch multiprocessing issue." +) def test_evaluate(monkeypatch, tmpdir, tmp_path): model_dir = str(tmpdir) sample_output_file = str(tmp_path) @@ -87,6 +93,9 @@ def test_evaluate(monkeypatch, tmpdir, tmp_path): evaluate.main(input_args=deepspeed_main_args, overwrite_values=evaluate_args) +@pytest.mark.skip( + reason="All model tests are skipped until we fix the CUDA + torch multiprocessing issue." +) def test_finetuning(monkeypatch, tmpdir, tmp_path): # Save random model, load random model, keep training # TODO: add mocking to check that we're not ignoring the previously loaded model @@ -101,6 +110,9 @@ def test_finetuning(monkeypatch, tmpdir, tmp_path): train.main(input_args=deepspeed_main_args, overwrite_values=finetune_args) +@pytest.mark.skip( + reason="All model tests are skipped until we fix the CUDA + torch multiprocessing issue." +) def test_train_launcher(monkeypatch): input_args = ["train.py", "tests/config/test_setup.yml"] deepspeed_main_args = simulate_deepy_env(monkeypatch, input_args) From 46d179cc0d8cd357c8448c37b8f356b591dafe49 Mon Sep 17 00:00:00 2001 From: Jacob Hatef <74274091+jahatef@users.noreply.github.com> Date: Wed, 21 Feb 2024 13:46:22 -0500 Subject: [PATCH 09/16] Memory profiling (#1153) * Fixes distributed tests, and skips tests that are broken. * memory profiling for gpt-neox. Only works for pp=0, pp=1+ needs DS commits. * Update NeoXArgs docs automatically * adds memory profiling for pipeline parallel * Update NeoXArgs docs automatically * fix spacing * Update NeoXArgs docs automatically * fix spacing again * Update NeoXArgs docs automatically * get rid of unwanted changes * Update NeoXArgs docs automatically * get rid of file * Update NeoXArgs docs automatically * Update NeoXArgs docs automatically * add nsight systems support * remove tests changes again * Update NeoXArgs docs automatically * add tests * Update NeoXArgs docs automatically * Update training.py * Update NeoXArgs docs automatically * Add assertion message * pre-commit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions Co-authored-by: Quentin Anthony --- configs/neox_arguments.md | 50 ++++++++++++++++++- megatron/neox_arguments/neox_args.py | 33 +++++++++++++ megatron/training.py | 74 ++++++++++++++++++++++++++++ 3 files changed, 156 insertions(+), 1 deletion(-) diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 022fa6218..46cc91c1c 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 0eb8b39 + Default = 8669123 current git hash of repository @@ -199,6 +199,54 @@ Logging Arguments +- **memory_profiling**: bool + + Default = False + + Whether to take a memory snapshot of the model. Useful for debugging memory issues. + + + +- **memory_profiling_path**: str + + Default = None + + Path to save memory snapshot to. + + + +- **profile**: bool + + Default = False + + Enable nsys profiling. When using this option, + nsys options should be specified in commandline. + An example nsys commandline is + ``` + nsys profile -s none -t nvtx,cuda -o + --force-overwrite true + --capture-range=cudaProfilerApi + --capture-range-end=stop + ``` + + + +- **profile_step_start**: int + + Default = 10 + + Step to start profiling at. + + + +- **profile_step_stop**: int + + Default = 12 + + Step to stop profiling at. + + + ## NeoXArgsModel Model Arguments diff --git a/megatron/neox_arguments/neox_args.py b/megatron/neox_arguments/neox_args.py index db1ffcc1f..7f3afffa5 100644 --- a/megatron/neox_arguments/neox_args.py +++ b/megatron/neox_arguments/neox_args.py @@ -573,6 +573,39 @@ class NeoXArgsLogging(NeoXArgsTemplate): Whether to offload the buffered gradients to cpu when measuring gradient noise scale. """ + memory_profiling: bool = False + """ + Whether to take a memory snapshot of the model. Useful for debugging memory issues. + """ + + memory_profiling_path: str = None + """ + Path to save memory snapshot to. + """ + + profile: bool = False + """ + Enable nsys profiling. When using this option, + nsys options should be specified in commandline. + An example nsys commandline is + ``` + nsys profile -s none -t nvtx,cuda -o + --force-overwrite true + --capture-range=cudaProfilerApi + --capture-range-end=stop + ``` + """ + + profile_step_start: int = 10 + """ + Step to start profiling at. + """ + + profile_step_stop: int = 12 + """ + Step to stop profiling at. + """ + @dataclass class NeoXArgsOther(NeoXArgsTemplate): diff --git a/megatron/training.py b/megatron/training.py index 5df179821..216878678 100644 --- a/megatron/training.py +++ b/megatron/training.py @@ -56,6 +56,9 @@ ) from megatron.model.gpt2_model import cross_entropy +from pickle import dump +import os + def mup_weights_reinit(neox_args, model): def has_method(o, name): @@ -368,6 +371,8 @@ def forward_step( return model.eval_batch(data_iterator, return_logits=return_logits) # Get the batch. + if neox_args.memory_profiling and neox_args.it: + torch.cuda.nvtx.range_push(f"Get batch") if timers is not None: timers("batch generator").start() tokens, labels, loss_mask, attention_mask, position_ids = get_batch( @@ -376,7 +381,11 @@ def forward_step( if timers is not None: timers("batch generator").stop() + if neox_args.memory_profiling: + torch.cuda.nvtx.range_pop() + if neox_args.memory_profiling: + torch.cuda.nvtx.range_push(f"Forward pass") outputs = model((tokens, position_ids, attention_mask), neox_args=neox_args) if ( is_train @@ -388,6 +397,8 @@ def forward_step( loss = cross_entropy( outputs, (labels, loss_mask), _fp16=neox_args.fp16_lm_cross_entropy ) + if neox_args.memory_profiling: + torch.cuda.nvtx.range_pop() if return_logits: return loss, outputs return loss @@ -628,6 +639,15 @@ def get_learning_rate_scheduler(optimizer, neox_args): def setup_model_and_optimizer(neox_args, use_cache=False, iteration=None): + """Setup memory profiler""" + if neox_args.memory_profiling: + torch.cuda.memory._record_memory_history( + True, + # keep a maximum 100,000 alloc/free events from before the snapshot + trace_alloc_max_entries=100000, + trace_alloc_record_context=True, + ) + """Setup model and optimizer.""" model = get_model(neox_args=neox_args, use_cache=use_cache) optimizer, param_groups = get_optimizer(model=model, neox_args=neox_args) @@ -727,6 +747,13 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler) reduced_loss = train_step_pipe( neox_args=neox_args, timers=timers, model=model, data_iterator=data_iterator ) + if ( + neox_args.memory_profiling + and neox_args.iteration >= neox_args.profile_step_start + and neox_args.iteration <= neox_args.profile_step_stop + and torch.distributed.get_rank() == 0 + ): + save_snapshot(neox_args) else: losses = [] for _ in range(neox_args.gradient_accumulation_steps): @@ -742,6 +769,12 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler) timers("forward").stop() losses.append(loss) # Calculate gradients, reduce across processes, and clip. + if ( + neox_args.profiling + and neox_args.iteration >= neox_args.profile_step_start + and neox_args.iteration <= neox_args.profile_step_stop + ): + torch.cuda.nvtx.range_push(f"Backward pass") timers("backward").start() backward_step( neox_args=neox_args, @@ -751,13 +784,38 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler) loss=loss, ) timers("backward").stop() + if ( + neox_args.profiling + and neox_args.iteration >= neox_args.profile_step_start + and neox_args.iteration <= neox_args.profile_step_stop + ): + torch.cuda.nvtx.range_pop() # Update parameters. + if ( + neox_args.profiling + and neox_args.iteration >= neox_args.profile_step_start + and neox_args.iteration <= neox_args.profile_step_stop + ): + torch.cuda.nvtx.range_push(f"Optimizer step") timers("optimizer").start() if neox_args.deepspeed: model.step() else: raise ValueError("Must be using deepspeed to run neox") timers("optimizer").stop() + if ( + neox_args.profiling + and neox_args.iteration >= neox_args.profile_step_start + and neox_args.iteration <= neox_args.profile_step_stop + ): + torch.cuda.nvtx.range_pop() + if ( + neox_args.profiling + and neox_args.iteration >= neox_args.profile_step_start + and neox_args.iteration <= neox_args.profile_step_stop + and torch.distributed.get_rank() == 0 + ): + save_snapshot(neox_args) reduced_loss = { "lm_loss": reduce_losses(losses).mean() } # reduces losses across machines for logging @@ -819,6 +877,8 @@ def train( # to monitor if we've skipped many iterations in a row and trigger an early exit overflow_monitor = OverflowMonitor(optimizer) while iteration < neox_args.train_iters: + if neox_args.profile and iteration == neox_args.profile_step_start: + torch.cuda.cudart().cudaProfilerStart() loss_dict, skipped_iter = train_step( neox_args=neox_args, timers=timers, @@ -827,6 +887,8 @@ def train( optimizer=optimizer, lr_scheduler=lr_scheduler, ) + if neox_args.profile and iteration == neox_args.profile_step_stop: + torch.cuda.cudart().cudaProfilerStop() iteration += 1 neox_args.iteration = iteration if neox_args.precision == "fp16": @@ -1033,3 +1095,15 @@ def evaluate_and_print_results( print_rank_0("-" * length) print_rank_0(string) print_rank_0("-" * length) + + +def save_snapshot(neox_args): + assert ( + neox_args.memory_profiling_path is not None + ), "Must pass memory_profiling_path config arg to use profiling" + snapshot = torch.cuda.memory._snapshot() + snapshot_path = os.path.join(neox_args.memory_profiling_path) + if not os.path.exists(snapshot_path): + os.makedirs(snapshot_path) + with open(os.path.join(snapshot_path, "mem_snapshot.pickle"), "wb") as f: + dump(snapshot, f) From eee03b281f13df6fb9e45b67a1f8f97d0df0c4bb Mon Sep 17 00:00:00 2001 From: Jacob Hatef <74274091+jahatef@users.noreply.github.com> Date: Thu, 22 Feb 2024 19:48:29 -0500 Subject: [PATCH 10/16] add profiling to readme (#1154) * add profiling to readme * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions --- README.md | 31 +++++++++++++++++++++++++++++++ configs/neox_arguments.md | 2 +- images/memory_profiling.png | Bin 0 -> 1089137 bytes images/nsight_profiling.png | Bin 0 -> 483416 bytes 4 files changed, 32 insertions(+), 1 deletion(-) create mode 100644 images/memory_profiling.png create mode 100644 images/nsight_profiling.png diff --git a/README.md b/README.md index 2a06ac9bf..1c7c81932 100644 --- a/README.md +++ b/README.md @@ -67,6 +67,7 @@ Prior to 3/9/2023, GPT-NeoX relied on [DeeperSpeed](https://github.com/EleutherA * [Weights and Biases](#weights-and-biases) * [TensorBoard](#tensorboard) - [Running on multi-node](#running-on-multi-node) +- [Profiling](#profiling) - [Adoption and Publications](#adoption-and-publications) * [Publications](#publications) * [Models](#models) @@ -561,6 +562,36 @@ We also support using TensorBoard via the tensorboard-dir --force-overwrite true \ +--capture-range=cudaProfilerApi --capture-range-end=stop python $TRAIN_PATH/deepy.py \ +$TRAIN_PATH/train.py --conf_dir configs +``` + +The generated output file can then by viewed with the Nsight Systems GUI: + +![Alt text](images/nsight_profiling.png) + +## PyTorch Memory Profiling + +To use PyTorch Memory Profiling, set config options `memory_profiling` and `memory_profiling_path`. + +![Alt text](images/memory_profiling.png) + +View the generated profile with the [memory_viz.py](https://github.com/pytorch/pytorch/blob/main/torch/cuda/_memory_viz.py) script. Run with: + +``` +python _memory_viz.py trace_plot -o trace.html +``` + # Adoption and Publications The GPT-NeoX library was been widely adopted by academic and industry researchers and ported on to many HPC systems. diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 46cc91c1c..6f0d9c1a1 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 8669123 + Default = 211e726 current git hash of repository diff --git a/images/memory_profiling.png b/images/memory_profiling.png new file mode 100644 index 0000000000000000000000000000000000000000..87d62f4c3787b11f464a62d64e41d7f4af00db10 GIT binary patch literal 1089137 zcmeFYcT^K?`!1T$i}WsN04Y+GB1$y@M0y7i0qIC@f*>{YB1mt75ITy|J0hW16#=OM z>7YV@Py=Z@zVG+#b&&w4zY)dpv?0y#)550=x z*Hd{oNM&ByATXJb_-b-f=VQb?TL=kFl(A_V`R^q4{2vd4ALafW&pF=@h5LQ`vefY@ z*XgbhliY_c*T8Ru7#KnRPT;fsWJXn*V_`lj3d&yoE#NTp7>uY 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zCt4>TRSe9Te0}cupp4a`yRfSIXffSn#wBO}jJM%GHr028sfBDce{hLZDqy?ar2;w} z8*JvzB$TM-`I`gf>bBHMTp-TY5V&DiHI8I)bATy8dx5d?H;L=f+E1bqE+3$Ti!GdV zXi>WGgCKwH4I*+Od7OP+KQ>dQ=<08BhsafWR%lHpf81^f-X4d6&qixRo+K)Wsfb&+ zvVa7bF=OI;`Xm~(k4ZkOyFUC|jys=zd>cv;g9m6taET4zV+BSl@n$Kv2uTzw;S(}a eq?k(S%7WluOBmFQy+N^J;IXxKw5qf4OZ*R$jvaCU literal 0 HcmV?d00001 From a7638a8d9d62e275c4d93851ca0186b4d528485f Mon Sep 17 00:00:00 2001 From: segyges <65882655+segyges@users.noreply.github.com> Date: Thu, 22 Feb 2024 18:52:08 -0600 Subject: [PATCH 11/16] Python version update (#1122) * Switch default command for docker image * Rename pythia paths docker file for clarity * Update docker build to use python 3.10 * Update github workflows to use ubuntu 22.04 and python 3.10 * Bump pytorch library patch versions * Add pytest-html for reasonably formatted test reports * Fix build after torch and cuda version bump * Fix apex install for newer version 1) This, empirically, works, as tested by running the build and kicking off training. 2) Apex documentation says it is incorrect syntax and deprecated. 3) It takes so long to compile that it is probably, all by itself, something that needs fixing. 4) I will probably pull the fused adamw out of apex. 5) It has been building for twenty minutes so I am going to go do something else. * Fix pip version to ensure apex compilation remains good * Fix unit test for evaluate * Fix pip requirement Prevents possible build issues with apex especially across divergent pip versions * Update dockerfile to point to stripped-down apex repo * Revert "Update dockerfile to point to stripped-down apex repo" This reverts commit 40c76560b2a8d40c06a0aeb6ebba408e182364e4. * Update apex version in dockerfile * Switch to downloading prebuilt apex wheel * Clean up docker copy commands * Have docker build conditionally get binaries or build apex * Apply precommit --- .github/workflows/pull_request.yml | 6 +-- Dockerfile | 47 +++++++++++-------- .../docker/{paths.yml => pythia-paths.yml} | 0 configs/neox_arguments.md | 7 ++- docker-compose-dockerhub.yml | 2 +- docker-compose.yml | 2 +- requirements/requirements-apex-pip.txt | 1 + requirements/requirements-dev.txt | 1 + requirements/requirements-sparseattention.txt | 2 +- tests/unit/test_launcher_scripts.py | 11 +++-- 10 files changed, 44 insertions(+), 35 deletions(-) rename configs/docker/{paths.yml => pythia-paths.yml} (100%) create mode 100644 requirements/requirements-apex-pip.txt diff --git a/.github/workflows/pull_request.yml b/.github/workflows/pull_request.yml index 8ee2f2a62..3213718df 100644 --- a/.github/workflows/pull_request.yml +++ b/.github/workflows/pull_request.yml @@ -4,12 +4,12 @@ on: [pull_request] jobs: pre-commit: - runs-on: ubuntu-20.04 + runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v4 with: - python-version: 3.8 + python-version: 3.10 cache: "pip" cache-dependency-path: "**/requirements*.txt" # Need the right version of clang-format @@ -24,7 +24,7 @@ jobs: uses: docker/build-push-action@v2 update-documentation: - runs-on: ubuntu-20.04 + runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v3 with: diff --git a/Dockerfile b/Dockerfile index e2f0a3d4c..570c95f2a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -FROM nvidia/cuda:11.7.1-devel-ubuntu20.04 +FROM nvidia/cuda:12.1.1-devel-ubuntu22.04 ENV DEBIAN_FRONTEND=noninteractive @@ -21,20 +21,20 @@ LABEL org.opencontainers.image.version = "2.0" LABEL org.opencontainers.image.authors = "contact@eleuther.ai" LABEL org.opencontainers.image.source = "https://www.github.com/eleutherai/gpt-neox" LABEL org.opencontainers.image.licenses = " Apache-2.0" -LABEL org.opencontainers.image.base.name="docker.io/nvidia/cuda:11.7.1-devel-ubuntu20.04" +LABEL org.opencontainers.image.base.name="docker.io/nvidia/cuda:12.1.1-devel-ubuntu22.04" #### System package (uses default Python 3 version in Ubuntu 20.04) RUN apt-get update -y && \ apt-get install -y \ - git python3.9 python3-dev libpython3-dev python3-pip sudo pdsh \ - htop llvm-9-dev tmux zstd software-properties-common build-essential autotools-dev \ + git python3-dev libpython3-dev python3-pip sudo pdsh \ + htop tmux zstd software-properties-common build-essential autotools-dev \ nfs-common pdsh cmake g++ gcc curl wget vim less unzip htop iftop iotop ca-certificates ssh \ rsync iputils-ping net-tools libcupti-dev libmlx4-1 infiniband-diags ibutils ibverbs-utils \ rdmacm-utils perftest rdma-core nano && \ update-alternatives --install /usr/bin/python python /usr/bin/python3 1 && \ update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1 && \ - pip install --upgrade pip && \ - pip install gpustat + python -m pip install --upgrade pip && \ + python -m pip install gpustat ### SSH RUN mkdir /var/run/sshd && \ @@ -88,24 +88,31 @@ RUN mkdir -p /home/mchorse/.ssh /job && \ echo 'export LD_LIBRARY_PATH=/usr/local/lib:/usr/local/mpi/lib:/usr/local/mpi/lib64:$LD_LIBRARY_PATH' >> /home/mchorse/.bashrc #### Python packages -RUN pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117 && pip cache purge -COPY requirements/requirements.txt . -COPY requirements/requirements-wandb.txt . -COPY requirements/requirements-onebitadam.txt . -COPY requirements/requirements-sparseattention.txt . -COPY requirements/requirements-flashattention.txt . -RUN pip install -r requirements.txt && pip install -r requirements-onebitadam.txt -RUN pip install -r requirements-sparseattention.txt -RUN pip install -r requirements-flashattention.txt -RUN pip install -r requirements-wandb.txt -RUN pip install protobuf==3.20.* -RUN pip cache purge +RUN python -m pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 +COPY requirements/* ./ +RUN python -m pip install --no-cache-dir -r requirements.txt && pip install -r requirements-onebitadam.txt +RUN python -m pip install -r requirements-sparseattention.txt +RUN python -m pip install -r requirements-flashattention.txt +RUN python -m pip install -r requirements-wandb.txt +RUN python -m pip install protobuf==3.20.* +RUN python -m pip cache purge ## Install APEX -RUN pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" git+https://github.com/NVIDIA/apex.git@a651e2c24ecf97cbf367fd3f330df36760e1c597 +# Detect the architecture and install Apex accordingly +RUN ARCH=$(uname -m) && \ + if [ "$ARCH" = "x86_64" ]; then \ + wget https://github.com/segyges/not-nvidia-apex/releases/download/jan-2024/apex-0.1-cp310-cp310-linux_x86_64.zip && \ + unzip ./apex-0.1-cp310-cp310-linux_x86_64.zip && \ + python -m pip install ./apex-0.1-cp310-cp310-linux_x86_64.whl; \ + else \ + # Install Apex directly from source for other architectures + python -m pip install -r requirements-apex-pip.txt && \ + python -m pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings --global-option=--cpp_ext --config-settings --global-option=--cuda_ext git+https://github.com/NVIDIA/apex.git@141bbf1cf362d4ca4d94f4284393e91dda5105a5; \ + fi COPY megatron/fused_kernels/ megatron/fused_kernels -RUN python megatron/fused_kernels/setup.py install +WORKDIR /megatron/fused_kernels +RUN python setup.py install # Clear staging RUN mkdir -p /tmp && chmod 0777 /tmp diff --git a/configs/docker/paths.yml b/configs/docker/pythia-paths.yml similarity index 100% rename from configs/docker/paths.yml rename to configs/docker/pythia-paths.yml diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 6f0d9c1a1..287ad7b71 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -1024,7 +1024,7 @@ Text Generation arguments - **prompt_end**: str - Default = + Default = a single prompt's end. Defaults to newline @@ -1066,7 +1066,7 @@ Text Generation arguments - **eval_results_prefix**: str - Default = + Default = prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json @@ -1810,7 +1810,7 @@ Args for deepspeed config Default = None - + @@ -2110,4 +2110,3 @@ Args for deepspeed runner (deepspeed.launcher.runner). Default = None Adds a `--account` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard. - diff --git a/docker-compose-dockerhub.yml b/docker-compose-dockerhub.yml index 0207e63f0..4ac5113f7 100644 --- a/docker-compose-dockerhub.yml +++ b/docker-compose-dockerhub.yml @@ -1,7 +1,7 @@ version: '3' services: gpt-neox: - command: nvidia-smi -q --loop=10 + command: nvidia-smi dmon image: leogao2/gpt-neox:main shm_size: 1g ulimits: diff --git a/docker-compose.yml b/docker-compose.yml index a12213e00..b2591aed0 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,7 +1,7 @@ version: '3' services: gpt-neox: - command: nvidia-smi -q --loop=10 + command: nvidia-smi dmon image: gpt-neox build: context: . diff --git a/requirements/requirements-apex-pip.txt b/requirements/requirements-apex-pip.txt new file mode 100644 index 000000000..981f54a30 --- /dev/null +++ b/requirements/requirements-apex-pip.txt @@ -0,0 +1 @@ +pip==23.3.2 diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt index 6c58478f3..60ff3224f 100644 --- a/requirements/requirements-dev.txt +++ b/requirements/requirements-dev.txt @@ -4,4 +4,5 @@ pre-commit>=2.17.0 pytest>=6.2.3 pytest-cov>=2.11.1 pytest-forked>=1.3.0 +pytest-html==4.1.1 pytest-xdist diff --git a/requirements/requirements-sparseattention.txt b/requirements/requirements-sparseattention.txt index 09386fdcb..3b382f83f 100644 --- a/requirements/requirements-sparseattention.txt +++ b/requirements/requirements-sparseattention.txt @@ -1 +1 @@ -triton==2.0.0.dev20221202 +triton==2.1.0 diff --git a/tests/unit/test_launcher_scripts.py b/tests/unit/test_launcher_scripts.py index 581608231..bdc38f111 100644 --- a/tests/unit/test_launcher_scripts.py +++ b/tests/unit/test_launcher_scripts.py @@ -1,12 +1,13 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. import pytest -from tools.datasets import preprocess_data -import train + +import eval import generate -import evaluate +import train from megatron.neox_arguments import NeoXArgs -from tests.common import simulate_deepy_env, save_random_model +from tests.common import save_random_model, simulate_deepy_env +from tools.datasets import preprocess_data @pytest.fixture( @@ -90,7 +91,7 @@ def test_evaluate(monkeypatch, tmpdir, tmp_path): "eval_tasks": ["lambada"], # ["lambada", "hellaswag", "piqa", "sciq"], "eval_results_prefix": sample_output_file, } - evaluate.main(input_args=deepspeed_main_args, overwrite_values=evaluate_args) + eval.main(input_args=deepspeed_main_args, overwrite_values=evaluate_args) @pytest.mark.skip( From 72d18032bc30bc1ae9c7fb31ace717adb6dc3912 Mon Sep 17 00:00:00 2001 From: segyges <65882655+segyges@users.noreply.github.com> Date: Thu, 22 Feb 2024 18:53:23 -0600 Subject: [PATCH 12/16] Minor changes (#1125) * Switch default command for docker image * Rename pythia paths docker file for clarity * Fix unit test for evaluate * Update readme for testing to omit --forked argument * Add pytest-html to requirements-dev.txt * Revert "Update readme for testing to omit --forked argument" This reverts commit 19021fccb1024adaf882ef593108442a15722258. * Add data/ directory and .bin and .idx files in /tests/data to .gitignore This makes it so that git doesn't try to let you commit (or force you to stash) data files * Make .gitignore for data files slightly more elegant * Add utility script for doing token counts on processed datasets * Run precommit hook * Fix token count script, run precommit --- .gitignore | 5 +++++ tools/datasets/dataset_token_count.py | 30 +++++++++++++++++++++++++++ 2 files changed, 35 insertions(+) create mode 100644 tools/datasets/dataset_token_count.py diff --git a/.gitignore b/.gitignore index 34de4e774..dbc83e949 100644 --- a/.gitignore +++ b/.gitignore @@ -137,6 +137,7 @@ data/**/*.bin data/**/*.json* data/**/*.txt data/**/*.gz +data/**/*.zip data/**/*.np* data/**/*.npy checkpoints/ @@ -150,3 +151,7 @@ test_logs/ logs/ tensorboard/ src/ + +# test data files +tests/data/*.bin +tests/data/*.idx diff --git a/tools/datasets/dataset_token_count.py b/tools/datasets/dataset_token_count.py new file mode 100644 index 000000000..c9a4ff3ba --- /dev/null +++ b/tools/datasets/dataset_token_count.py @@ -0,0 +1,30 @@ +# Script counts tokens in a pretokenized dataset from preprocess_data.py +# Necessary for setting batch size, train_iters, etc + +import sys +import os + +## Necessary for the import +project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) +sys.path.insert(0, project_root) + +from megatron.data import indexed_dataset +import numpy as np + +if len(sys.argv) < 2: + print( + "Usage: python dataset_token_count.py /absolute/file/path/to/dataset1 /absolute/file/path/to/dataset2 ..." + ) + sys.exit(1) + +# Access the command-line arguments +arguments = sys.argv[1:] + +for arg in arguments: + print("Checking file", arg) + try: + dataset = indexed_dataset.make_dataset(arg, "mmap") + size = np.sum(dataset.sizes) + print("Dataset size in tokens is", size) + except AttributeError: + print("Dataset could not be loaded", arg) From f36aed7ffd93fcb5d674236e476a5c80c0e31163 Mon Sep 17 00:00:00 2001 From: Adam I Date: Thu, 22 Feb 2024 21:37:12 -0800 Subject: [PATCH 13/16] Draft PR Adding mistral 0.1 (#1131) * add support for flash attention 2 * change cosine decay to chinchilla style * set default warmup to none so that warmup_iters can be set * fixed bug * fixed chinchilla lr * add s3 checkpoint syncing * rotary embedding in fp32 * fix for seq_len < max_seq_len * some fixes, still not working * ?' : * fix bugs; evaluate on step 0 * first attempt at gqa * gqa works in kv_heads==query_heads case * gqa working * workaround for FSX quota * update with llemma * update with recent PR * README and requirements updated * Added Mistral config * Added sliding window through flash attention 2 * Added sliding window * Mistral should likely use mp=2 like llama2 * Update gitignore * Removed unused CPCargo import * Conversion script (WIP) * Fixed missing slurm environ vars * updated mistral config * updated job script * initial commit conversion mistral hf to sequential * Added stacking q, k, v appropriately for mp ranks * pp=0 support from end of 2023 * Cleaning up config and removing Autoconfig in conversion script * Cleaned up conversion example script * cleanup: add back configs folder, discard Llemma readme * cleanup: remove llemma lr sched changes, re-add requirements/ folder * docs: add explanation of intermediate_size behavior * args: add argument checking for num_kv_heads, clean up usage syntax * args: prevent num KV heads < TP worldsize * readd triton flash attn func * cleanup: use tools/ dir from main * docs: re-add mistral , GQA as supported * cleanup: delete duplicate tools/ files * cleanup: use fp32 rope (non-fused) from main * cleanup: no longer block out GQA codepaths in conversion scripts * cleanup: gqa code a bit * add llama2, llemma configs * add non-flash GQA ; refactor modeling code * clean up mistral config for commit * further cleanup configs dir * remove slurm script from llemma * update seqlen params for codellama, llemma configs * add more comments to GQA code, and make reshapes more readable * make inv_freq non-persistent * actually, just ensure mistral has inv_freqs as a persistent buffer * non-flash GQA works, so ensure arguments.py permits it * no longer use our own copies of flash attention interface functions * remove unused mpu util fn * delete unused config file * fix diff on mpu/utils.py * remove slurm scripts that won't be in this PR * run pre-commit * update tests for conversion scripts * add flash version check for sliding window * pre-commit --------- Co-authored-by: zhangir-azerbayev Co-authored-by: haileyschoelkopf Co-authored-by: Quentin Anthony --- README.md | 13 +- configs/llama2/13B.yml | 26 + configs/llama2/70B.yml | 31 + configs/llama2/7B.yml | 26 + configs/llama2/codellama_34B.yml | 32 + configs/llama2/codellama_7B.yml | 31 + configs/llemma/34B.yml | 101 +++ configs/llemma/7B.yml | 100 +++ configs/mistral/7B.yml | 40 + megatron/model/flash_attention.py | 831 ------------------ megatron/model/transformer.py | 284 ++++-- megatron/neox_arguments/arguments.py | 28 + megatron/neox_arguments/neox_args.py | 25 + megatron/training.py | 1 + megatron/utils.py | 7 +- tests/unit/test_format_conversion_scripts.py | 11 +- tools/ckpts/convert_neox_to_hf.py | 11 - .../convert_raw_llama_weights_to_neox.py | 34 +- 18 files changed, 690 insertions(+), 942 deletions(-) create mode 100644 configs/llama2/13B.yml create mode 100644 configs/llama2/70B.yml create mode 100644 configs/llama2/7B.yml create mode 100644 configs/llama2/codellama_34B.yml create mode 100644 configs/llama2/codellama_7B.yml create mode 100644 configs/llemma/34B.yml create mode 100644 configs/llemma/7B.yml create mode 100644 configs/mistral/7B.yml delete mode 100644 megatron/model/flash_attention.py diff --git a/README.md b/README.md index 1c7c81932..721306c22 100644 --- a/README.md +++ b/README.md @@ -508,13 +508,14 @@ Though NeoX supports a number of different architectural configurations, includi NeoX supports export of compatible models into the following architectures: - GPTNeoXForCausalLM -- LlamaForCausalLM (GQA Support Coming Soon -- all Llama 1 models and Llama 2 / Codellama up to size 13B supported) +- LlamaForCausalLM +- MistralForCausalLM Training a model which does not fit into one of these Hugging Face Transformers architectures cleanly will require writing custom modeling code for the exported model. To convert a GPT-NeoX library checkpoint to Hugging Face-loadable format, run: ```bash -python ./tools/ckpts/convert_neox_to_hf.py --input_dir /path/to/model/global_stepXXX --config_file your_config.yml --output_dir hf_model/save/location --precision {auto,fp16,bf16,fp32} --architecture {neox,llama} +python ./tools/ckpts/convert_neox_to_hf.py --input_dir /path/to/model/global_stepXXX --config_file your_config.yml --output_dir hf_model/save/location --precision {auto,fp16,bf16,fp32} --architecture {neox,mistral,llama} ``` Then to upload a model to [the Hugging Face Hub](https://huggingface.co/), run: @@ -528,11 +529,11 @@ and input the requested information, including HF hub user token. NeoX supplies several utilities for converting a pretrained model checkpoint into a format that can be trained within the library. -The following models can be loaded in GPT-NeoX: +The following models or model families can be loaded in GPT-NeoX: - Llama 1 -- Llama 2 (Up to size 13B) -- CodeLlama (Up to size 13B) -- Mistral-7b-v0.1 (Coming Soon!) +- Llama 2 +- CodeLlama +- Mistral-7b-v0.1 We provide two utilities for converting from two different checkpoint formats into a format compatible with GPT-NeoX. diff --git a/configs/llama2/13B.yml b/configs/llama2/13B.yml new file mode 100644 index 000000000..973b8bea4 --- /dev/null +++ b/configs/llama2/13B.yml @@ -0,0 +1,26 @@ +{ + "pipe_parallel_size": 1, + "model_parallel_size": 2, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 40, + "hidden_size": 5120, + "num_attention_heads": 40, + "seq_length": 4096, + "max_position_embeddings": 4096, + "pos_emb": "rotary", + "rotary_pct": 1, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", +} diff --git a/configs/llama2/70B.yml b/configs/llama2/70B.yml new file mode 100644 index 000000000..286a1b568 --- /dev/null +++ b/configs/llama2/70B.yml @@ -0,0 +1,31 @@ +{ + "pipe_parallel_size": 1, + "model_parallel_size": 1, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 80, + "hidden_size": 8192, + "intermediate_size": 28672, + "num_attention_heads": 64, + "num_kv_heads": 8, + "seq_length": 4096, + "max_position_embeddings": 4096, + "pos_emb": "rotary", + "rotary_pct": 1, + "rotary_emb_base": 1000000, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "attention_config": [[["flash"], 48]], + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", +} diff --git a/configs/llama2/7B.yml b/configs/llama2/7B.yml new file mode 100644 index 000000000..6a5c97e64 --- /dev/null +++ b/configs/llama2/7B.yml @@ -0,0 +1,26 @@ +{ + "pipe_parallel_size": 1, + "model_parallel_size": 1, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 32, + "hidden_size": 4096, + "num_attention_heads": 32, + "seq_length": 4096, + "max_position_embeddings": 4096, + "pos_emb": "rotary", + "rotary_pct": 1, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", +} diff --git a/configs/llama2/codellama_34B.yml b/configs/llama2/codellama_34B.yml new file mode 100644 index 000000000..88e9afaf6 --- /dev/null +++ b/configs/llama2/codellama_34B.yml @@ -0,0 +1,32 @@ +{ + "pipe_parallel_size": 1, + "model_parallel_size": 1, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 48, + "hidden_size": 8192, + "num_attention_heads": 64, + "num_kv_heads": 8, + # Codellama was uptrained on 16k token sequence lengths + # with rotary_emb_base adjusted to 1_000_000. + "seq_length": 16384, + "max_position_embeddings": 16384, + "pos_emb": "rotary", + "rotary_pct": 1, + "rotary_emb_base": 1000000, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "attention_config": [[["flash"], 48]], + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", +} diff --git a/configs/llama2/codellama_7B.yml b/configs/llama2/codellama_7B.yml new file mode 100644 index 000000000..be123ebee --- /dev/null +++ b/configs/llama2/codellama_7B.yml @@ -0,0 +1,31 @@ +{ + "pipe_parallel_size": 1, + "model_parallel_size": 1, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 32, + "hidden_size": 4096, + "num_attention_heads": 32, + # Codellama was uptrained on 16k token sequence lengths + # with rotary_emb_base adjusted to 1_000_000. + "seq_length": 16384, + "max_position_embeddings": 16384, + "pos_emb": "rotary", + "rotary_pct": 1, + "rotary_emb_base": 1000000, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "attention_config": [[["flash"], 32]], + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", +} diff --git a/configs/llemma/34B.yml b/configs/llemma/34B.yml new file mode 100644 index 000000000..bd72d7e23 --- /dev/null +++ b/configs/llemma/34B.yml @@ -0,0 +1,101 @@ +{ + "pipe_parallel_size": 0, + "model_parallel_size": 8, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 48, + "hidden_size": 8192, + "num_attention_heads": 64, + "attention_type": "groupedquery", + "num_kv_heads": 8, + # NB: These rotary embedding and sequence length parameters + # May differ from CodeLlama configs. They match what we used for + # Llemma continued pretraining. See https://arxiv.org/abs/2310.10631 + # For detailed discussion + "seq_length": 4096, + "max_position_embeddings": 4096, + "pos_emb": "rotary", + "rotary_pct": 1, + "rotary_emb_base": 1000000, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "attention_config": [[["flash"], 48]], + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", + + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00005, + "betas": [0.9, 0.95], + "eps": 1.0e-8 + } + }, + + "zero_optimization": { + "stage": 1, + "allgather_partitions": true, + "allgather_bucket_size": 1260000000, + "overlap_comm": true, + "reduce_scatter": true, + "reduce_bucket_size": 1260000000, + "contiguous_gradients": true, + "cpu_offload": false + }, + + # trained on 256 gpus + "train_micro_batch_size_per_gpu": 2, + "gradient_accumulation_steps": 16, + "data_impl": "mmap", + + "checkpoint_activations": true, + "checkpoint_num_layers": 1, + "partition_activations": true, + "synchronize_each_layer": true, + + "gradient_clipping": 1.0, + "weight_decay": 0.1, + "hidden_dropout": 0, + "attention_dropout": 0, + + "precision": "bfloat16", + "fp32_allreduce": true, + "bf16": { + "enabled": true + }, + "data_types": { + "grad_accum_dtype": "fp32" + }, + + "train_iters": 12000, + "lr_decay_iters": 12000, + "distributed_backend": "nccl", + "lr_decay_style": "cosine", + "min_lr": 1.65e-6, + "warmup": 0.042, # warmup for ~500 iters + "checkpoint_factor": 250, + "eval_interval": 250, + "eval_iters": 25, + + "log_interval": 1, + "steps_per_print": 1, + "wall_clock_breakdown": true, + + "tokenizer_type": "SPMTokenizer", + #"vocab-file": # use 'tokenizer.model' from Meta CodeLlama download + + # "load": "" # set to same as "save" to resume from intermediate finetuning step + #"load": MP=8 CodeLlama-34B checkpoint, converted from Meta CodeLlama download. + # When resuming from mid-finetuning run, change "load" to the same as save location. + "finetune": true, # set to false once resuming from intermediate finetuning step +} diff --git a/configs/llemma/7B.yml b/configs/llemma/7B.yml new file mode 100644 index 000000000..fb72c8c18 --- /dev/null +++ b/configs/llemma/7B.yml @@ -0,0 +1,100 @@ +{ + "pipe_parallel_size": 0, + "model_parallel_size": 2, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 32, + "hidden_size": 4096, + "num_attention_heads": 32, + # NB: These rotary embedding and sequence length parameters + # May differ from CodeLlama configs. They match what we used for + # Llemma continued pretraining. See https://arxiv.org/abs/2310.10631 + # For detailed discussion + "seq_length": 4096, + "max_position_embeddings": 4096, + "pos_emb": "rotary", + "rotary_pct": 1, + "rotary_emb_base": 10000, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + "attention_config": [[["flash"], 32]], + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", + + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.0001, + "betas": [0.9, 0.95], + "eps": 1.0e-8 + } + }, + + "zero_optimization": { + "stage": 1, + "allgather_partitions": true, + "allgather_bucket_size": 1260000000, + "overlap_comm": true, + "reduce_scatter": true, + "reduce_bucket_size": 1260000000, + "contiguous_gradients": true, + "cpu_offload": false + }, + + # trained on 256 gpus + "train_micro_batch_size_per_gpu": 4, + "gradient_accumulation_steps": 2, + "data_impl": "mmap", + + "checkpoint_activations": true, + "checkpoint_num_layers": 1, + "partition_activations": true, + "synchronize_each_layer": true, + + "gradient_clipping": 1.0, + "weight_decay": 0.1, + "hidden_dropout": 0, + "attention_dropout": 0, + + "precision": "bfloat16", + "fp32_allreduce": true, + "bf16": { + "enabled": true + }, + "data_types": { + "grad_accum_dtype": "fp32" + }, + + "train_iters": 48000, + "lr_decay_iters": 48000, + "distributed_backend": "nccl", + "lr_decay_style": "cosine", + "decay_lr_to": 0.033, + "warmup_iters": 500, + "checkpoint_factor": 500, + "eval_interval": 250, + "eval_iters": 50, + + "log_interval": 1, + "steps_per_print": 1, + "wall_clock_breakdown": true, + + "tokenizer_type": "SPMTokenizer", + "vocab-file": "codellama/tokenizer.model", # use tokenizer.model from Meta CodeLlama download + + "save": "/path/to/save/llema-replication", + #"load": "", # once run is started, to restart from intermediate ckpt use "load" = "save" + "load": "/path/to/converted/codellama_7b_weights_with_mp2", + + "finetune": true, # set to false once resuming from intermediate finetuning step +} diff --git a/configs/mistral/7B.yml b/configs/mistral/7B.yml new file mode 100644 index 000000000..67b7c6a52 --- /dev/null +++ b/configs/mistral/7B.yml @@ -0,0 +1,40 @@ +{ + "pipe_parallel_size": 1, + "model_parallel_size": 1, + "make_vocab_size_divisible_by": 1, + + # model settings + "num_layers": 32, + "hidden_size": 4096, + "intermediate_size": 14336, + "num_attention_heads": 32, + "num_kv_heads": 8, + "seq_length": 4096, + "sliding_window_width": 4096, + "max_position_embeddings": 131072, + "pos_emb": "rotary", + "rotary_pct": 1, + "rotary_emb_base": 10000, + "no_weight_tying": true, + "gpt_j_residual": false, + "output_layer_parallelism": "column", + "norm": "rmsnorm", + "rms_norm_epsilon": 1.0e-5, + + # Grouped Query Attention is supported for both default ("global") + # and Flash attention. However, we highly recommend the use of Flash attention + # to get FLOP + runtime speedups when using GQA, + # and sliding window attention is currently only supported by Flash attention. + "attention_config": [[["flash"], 32]], + + "scaled_upper_triang_masked_softmax_fusion": true, + "bias_gelu_fusion": false, + "use_bias_in_norms": false, + "use_bias_in_attn_linear": false, + "mlp_type": "llama", + "activation": "silu", + + "tokenizer_type": "SPMTokenizer", + #"vocab-file": ".../mistral-7B-v0.1/tokenizer.model", # use tokenizer.model from Mistral-7B-v0.1 direct download + +} diff --git a/megatron/model/flash_attention.py b/megatron/model/flash_attention.py deleted file mode 100644 index d446f0a51..000000000 --- a/megatron/model/flash_attention.py +++ /dev/null @@ -1,831 +0,0 @@ -# Based on: https://github.com/HazyResearch/flash-attention/blob/4a6eaa9f27df6fff7ffb2c24e894938a687dd870/flash_attn/flash_attn_interface.py - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from flash_attn import flash_attn_triton -import flash_attn_2_cuda as flash_attn_cuda # For flash_attn version 2.1.1 - - -def flash_attn_unpadded_unpacked_func_triton( - q, k, v, bias=None, causal=False, softmax_scale=None -): - return flash_attn_triton.flash_attn_func(q, k, v, bias, causal, softmax_scale) - - -def _flash_attn_forward_cuda( - q, - k, - v, - out, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_softmax, - num_splits=0, - generator=None, -): - """ - num_splits: how much to parallelize over the seqlen_q dimension. num_splits=0 means - it will be set by an internal heuristic. We're exposing num_splits mostly for benchmarking. - Don't change it unless you know what you're doing. - """ - softmax_lse, *rest = flash_attn_cuda.fwd( - q, - k, - v, - out, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - False, - causal, - return_softmax, - num_splits, - generator, - ) - # if out.isnan().any() or softmax_lse.isnan().any(): - # breakpoint() - S_dmask = rest[0] if return_softmax else None - return out, softmax_lse, S_dmask - - -def _flash_attn_backward_cuda( - dout, - q, - k, - v, - out, - softmax_lse, - dq, - dk, - dv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - num_splits=0, - generator=None, -): - """ - num_splits: whether to parallelize over the seqlen_k dimension (num_splits > 1) or - not (num_splits = 1). num_splits=0 means it will be set by an internal heuristic. - Any value above 1 will call the same kernel (i.e. num_splits=2 would call the same kernel - as num_splits=3), so effectively the choices are 0, 1, and 2. - This hyperparameter can be tuned for performance, but default value (heuristic) should work fine. - """ - _, _, _, softmax_d = flash_attn_cuda.bwd( - dout, - q, - k, - v, - out, - softmax_lse, - dq, - dk, - dv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - False, - causal, - num_splits, - generator, - ) - # if dk.isnan().any() or dk.isnan().any() or dv.isnan().any() or softmax_d.isnan().any(): - # breakpoint() - return dq, dk, dv, softmax_d - - -class FlashAttnQKVPackedFunc(torch.autograd.Function): - @staticmethod - def forward( - ctx, - qkv, - cu_seqlens, - max_seqlen, - dropout_p, - softmax_scale, - causal, - return_softmax, - ): - # Save rng_state because the backward pass will regenerate the dropout mask - rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None - if softmax_scale is None: - softmax_scale = qkv.shape[-1] ** (-0.5) - out, softmax_lse, S_dmask = _flash_attn_forward_cuda( - qkv[:, 0], - qkv[:, 1], - qkv[:, 2], - torch.empty_like(qkv[:, 0]), - cu_seqlens, - cu_seqlens, - max_seqlen, - max_seqlen, - dropout_p, - softmax_scale, - causal=causal, - return_softmax=return_softmax, - ) - ctx.save_for_backward(qkv, out, softmax_lse, cu_seqlens, rng_state) - ctx.dropout_p = dropout_p - ctx.max_seqlen = max_seqlen - ctx.softmax_scale = softmax_scale - ctx.causal = causal - return out if not return_softmax else (out, softmax_lse, S_dmask) - - @staticmethod - def backward(ctx, dout, *args): - qkv, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors - if rng_state is not None: - cur_rng_state = torch.cuda.get_rng_state() - torch.cuda.set_rng_state(rng_state) - dqkv = torch.empty_like(qkv) - _flash_attn_backward_cuda( - dout, - qkv[:, 0], - qkv[:, 1], - qkv[:, 2], - out, - softmax_lse, - dqkv[:, 0], - dqkv[:, 1], - dqkv[:, 2], - cu_seqlens, - cu_seqlens, - ctx.max_seqlen, - ctx.max_seqlen, - ctx.dropout_p, - ctx.softmax_scale, - ctx.causal, - ) - if rng_state is not None: - torch.cuda.set_rng_state(cur_rng_state) - return dqkv, None, None, None, None, None, None - - -def flash_attn_unpadded_qkvpacked_func_cuda( - qkv, - cu_seqlens, - max_seqlen, - dropout_p, - softmax_scale=None, - causal=False, - return_attn_probs=False, -): - return FlashAttnQKVPackedFunc.apply( - qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_attn_probs - ) - - -class FlashAttnKVPackedFunc(torch.autograd.Function): - @staticmethod - def forward( - ctx, - q, - kv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_softmax, - ): - # Save rng_state because the backward pass will regenerate the dropout mask - rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None - if softmax_scale is None: - softmax_scale = q.shape[-1] ** (-0.5) - out, softmax_lse, S_dmask = _flash_attn_forward_cuda( - q, - kv[:, 0], - kv[:, 1], - torch.empty_like(q), - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal=causal, - return_softmax=return_softmax, - ) - ctx.save_for_backward( - q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state - ) - ctx.dropout_p = dropout_p - ctx.max_seqlen_q = max_seqlen_q - ctx.max_seqlen_k = max_seqlen_k - ctx.softmax_scale = softmax_scale - ctx.causal = causal - return out if not return_softmax else (out, softmax_lse, S_dmask) - - @staticmethod - def backward(ctx, dout, *args): - ( - q, - kv, - out, - softmax_lse, - cu_seqlens_q, - cu_seqlens_k, - rng_state, - ) = ctx.saved_tensors - if rng_state is not None: - cur_rng_state = torch.cuda.get_rng_state() - torch.cuda.set_rng_state(rng_state) - dq = torch.empty_like(q) - dkv = torch.empty_like(kv) - _flash_attn_backward_cuda( - dout, - q, - kv[:, 0], - kv[:, 1], - out, - softmax_lse, - dq, - dkv[:, 0], - dkv[:, 1], - cu_seqlens_q, - cu_seqlens_k, - ctx.max_seqlen_q, - ctx.max_seqlen_k, - ctx.dropout_p, - ctx.softmax_scale, - ctx.causal, - ) - if rng_state is not None: - torch.cuda.set_rng_state(cur_rng_state) - return dq, dkv, None, None, None, None, None, None, None, None - - -def flash_attn_unpadded_kvpacked_func_cuda( - q, - kv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale=None, - causal=False, - return_attn_probs=False, -): - """dropout_p should be set to 0.0 during evaluation - Arguments: - q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch. - kv: (total_k, 2, nheads, headdim), where total_k = total number of key tokens in the batch. - cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into q. - cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into kv. - max_seqlen_q: int. Maximum query sequence length in the batch. - max_seqlen_k: int. Maximum key sequence length in the batch. - dropout_p: float. Dropout probability. - softmax_scale: float. The scaling of QK^T before applying softmax. - Default to 1 / sqrt(headdim). - causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). - return_attn_probs: bool. Whether to return the attention probabilities. This option is for - testing only. The returned probabilities are not guaranteed to be correct - (they might not have the right scaling). - Return: - out: (total, nheads, headdim). - softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The - logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax - normalization factor). - S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). - The output of softmax (possibly with different scaling). It also encodes the dropout - pattern (negative means that location was dropped, nonnegative means it was kept). - """ - return FlashAttnKVPackedFunc.apply( - q, - kv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_attn_probs, - ) - - -class FlashAttnFunc(torch.autograd.Function): - @staticmethod - def forward( - ctx, - q, - k, - v, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_softmax, - ): - # Save rng_state because the backward pass will regenerate the dropout mask - rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None - if softmax_scale is None: - softmax_scale = q.shape[-1] ** (-0.5) - out, softmax_lse, S_dmask = _flash_attn_forward_cuda( - q, - k, - v, - torch.empty_like(q), - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal=causal, - return_softmax=return_softmax, - ) - ctx.save_for_backward( - q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state - ) - ctx.dropout_p = dropout_p - ctx.max_seqlen_q = max_seqlen_q - ctx.max_seqlen_k = max_seqlen_k - ctx.softmax_scale = softmax_scale - ctx.causal = causal - return out if not return_softmax else (out, softmax_lse, S_dmask) - - @staticmethod - def backward(ctx, dout, *args): - ( - q, - k, - v, - out, - softmax_lse, - cu_seqlens_q, - cu_seqlens_k, - rng_state, - ) = ctx.saved_tensors - if rng_state is not None: - cur_rng_state = torch.cuda.get_rng_state() - torch.cuda.set_rng_state(rng_state) - dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v) - _flash_attn_backward_cuda( - dout, - q, - k, - v, - out, - softmax_lse, - dq, - dk, - dv, - cu_seqlens_q, - cu_seqlens_k, - ctx.max_seqlen_q, - ctx.max_seqlen_k, - ctx.dropout_p, - ctx.softmax_scale, - ctx.causal, - ) - if rng_state is not None: - torch.cuda.set_rng_state(cur_rng_state) - return dq, dk, dv, None, None, None, None, None, None, None, None - - -def flash_attn_unpadded_func_cuda( - q, - k, - v, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale=None, - causal=False, - return_attn_probs=False, -): - """dropout_p should be set to 0.0 during evaluation - Arguments: - q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch. - k: (total_k, nheads, headdim), where total_k = total number of key tokens in the batch. - v: (total_k, nheads, headdim), where total_k = total number of key tokens in the batch. - cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into q. - cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into kv. - max_seqlen_q: int. Maximum query sequence length in the batch. - max_seqlen_k: int. Maximum key sequence length in the batch. - dropout_p: float. Dropout probability. - softmax_scale: float. The scaling of QK^T before applying softmax. - Default to 1 / sqrt(headdim). - causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). - return_attn_probs: bool. Whether to return the attention probabilities. This option is for - testing only. The returned probabilities are not guaranteed to be correct - (they might not have the right scaling). - Return: - out: (total, nheads, headdim). - softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The - logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax - normalization factor). - S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). - The output of softmax (possibly with different scaling). It also encodes the dropout - pattern (negative means that location was dropped, nonnegative means it was kept). - """ - return FlashAttnFunc.apply( - q, - k, - v, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_attn_probs, - ) - - -# For flash-attention 2 integration -def _flash_attn_varlen_forward( - q, - k, - v, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_softmax, -): - maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x - q, k, v = [maybe_contiguous(x) for x in (q, k, v)] - ( - out, - q, - k, - v, - out_padded, - softmax_lse, - S_dmask, - rng_state, - ) = flash_attn_cuda.varlen_fwd( - q, - k, - v, - None, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - False, - causal, - return_softmax, - None, - ) - # if out.isnan().any() or softmax_lse.isnan().any(): - # breakpoint() - return out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state - - -def _flash_attn_varlen_backward( - dout, - q, - k, - v, - out, - softmax_lse, - dq, - dk, - dv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - rng_state=None, -): - maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x - # dq, dk, dv are allocated by us so they should already be contiguous - dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)] - dq, dk, dv, softmax_d, = flash_attn_cuda.varlen_bwd( - dout, - q, - k, - v, - out, - softmax_lse, - dq, - dk, - dv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - False, - causal, - None, - rng_state, - ) - # if dk.isnan().any() or dk.isnan().any() or dv.isnan().any() or softmax_d.isnan().any(): - # breakpoint() - return dq, dk, dv, softmax_d - - -class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function): - @staticmethod - def forward( - ctx, - qkv, - cu_seqlens, - max_seqlen, - dropout_p, - softmax_scale, - causal, - return_softmax, - ): - if softmax_scale is None: - softmax_scale = qkv.shape[-1] ** (-0.5) - ( - out, - q, - k, - v, - out_padded, - softmax_lse, - S_dmask, - rng_state, - ) = _flash_attn_varlen_forward( - qkv[:, 0], - qkv[:, 1], - qkv[:, 2], - cu_seqlens, - cu_seqlens, - max_seqlen, - max_seqlen, - dropout_p, - softmax_scale, - causal=causal, - return_softmax=return_softmax and dropout_p > 0, - ) - ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens, rng_state) - ctx.dropout_p = dropout_p - ctx.max_seqlen = max_seqlen - ctx.softmax_scale = softmax_scale - ctx.causal = causal - return out if not return_softmax else (out, softmax_lse, S_dmask) - - @staticmethod - def backward(ctx, dout, *args): - q, k, v, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors - qkv_shape = q.shape[:-2] + (3, *q.shape[-2:]) - dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device) - _flash_attn_varlen_backward( - dout, - q, - k, - v, - out, - softmax_lse, - dqkv[:, 0], - dqkv[:, 1], - dqkv[:, 2], - cu_seqlens, - cu_seqlens, - ctx.max_seqlen, - ctx.max_seqlen, - ctx.dropout_p, - ctx.softmax_scale, - ctx.causal, - rng_state=rng_state, - ) - dqkv = dqkv[..., : dout.shape[-1]] # We could have padded the head dimension - return dqkv, None, None, None, None, None, None - - -def flash_attn_varlen_qkvpacked_func( - qkv, - cu_seqlens, - max_seqlen, - dropout_p=0.0, - softmax_scale=None, - causal=False, - return_attn_probs=False, -): - """dropout_p should be set to 0.0 during evaluation - If Q, K, V are already stacked into 1 tensor, this function will be faster than - calling flash_attn_varlen_func on Q, K, V since the backward pass avoids explicit concatenation - of the gradients of Q, K, V. - For multi-query and grouped-query attention (MQA/GQA), please see - flash_attn_varlen_kvpacked_func and flash_attn_varlen_func. - - Arguments: - qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch. - cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into qkv. - max_seqlen: int. Maximum sequence length in the batch. - dropout_p: float. Dropout probability. - softmax_scale: float. The scaling of QK^T before applying softmax. - Default to 1 / sqrt(headdim). - causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). - return_attn_probs: bool. Whether to return the attention probabilities. This option is for - testing only. The returned probabilities are not guaranteed to be correct - (they might not have the right scaling). - Return: - out: (total, nheads, headdim). - softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The - logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax - normalization factor). - S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). - The output of softmax (possibly with different scaling). It also encodes the dropout - pattern (negative means that location was dropped, nonnegative means it was kept). - """ - return FlashAttnVarlenQKVPackedFunc.apply( - qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_attn_probs - ) - - -class FlashAttnVarlenKVPackedFunc(torch.autograd.Function): - @staticmethod - def forward( - ctx, - q, - kv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_softmax, - ): - if softmax_scale is None: - softmax_scale = q.shape[-1] ** (-0.5) - ( - out, - q, - k, - v, - out_padded, - softmax_lse, - S_dmask, - rng_state, - ) = _flash_attn_varlen_forward( - q, - kv[:, 0], - kv[:, 1], - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal=causal, - return_softmax=return_softmax and dropout_p > 0, - ) - ctx.save_for_backward( - q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state - ) - ctx.dropout_p = dropout_p - ctx.max_seqlen_q = max_seqlen_q - ctx.max_seqlen_k = max_seqlen_k - ctx.softmax_scale = softmax_scale - ctx.causal = causal - return out if not return_softmax else (out, softmax_lse, S_dmask) - - @staticmethod - def backward(ctx, dout, *args): - ( - q, - k, - v, - out, - softmax_lse, - cu_seqlens_q, - cu_seqlens_k, - rng_state, - ) = ctx.saved_tensors - dq = torch.empty_like(q) - kv_shape = k.shape[:-2] + (2, *k.shape[-2:]) - dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device) - _flash_attn_varlen_backward( - dout, - q, - k, - v, - out, - softmax_lse, - dq, - dkv[:, 0], - dkv[:, 1], - cu_seqlens_q, - cu_seqlens_k, - ctx.max_seqlen_q, - ctx.max_seqlen_k, - ctx.dropout_p, - ctx.softmax_scale, - ctx.causal, - rng_state=rng_state, - ) - dq = dq[..., : dout.shape[-1]] # We could have padded the head dimension - dkv = dkv[..., : dout.shape[-1]] - return dq, dkv, None, None, None, None, None, None, None, None - - -def flash_attn_varlen_kvpacked_func( - q, - kv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p=0.0, - softmax_scale=None, - causal=False, - return_attn_probs=False, -): - """dropout_p should be set to 0.0 during evaluation - If K, V are already stacked into 1 tensor, this function will be faster than - calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation - of the gradients of K, V. - Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads - than Q. Note that the number of heads in Q must be divisible by the number of heads in KV. - For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head - 0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V. - - If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix. - For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is: - 1 1 1 1 0 - 1 1 1 1 1 - If seqlen_q = 5 and seqlen_k = 2, the causal mask is: - 0 0 - 0 0 - 0 0 - 1 0 - 1 1 - If the row of the mask is all zero, the output will be zero. - - Arguments: - q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch. - kv: (total_k, 2, nheads_k, headdim), where total_k = total number of key tokens in the batch. - cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into q. - cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths - of the sequences in the batch, used to index into kv. - max_seqlen_q: int. Maximum query sequence length in the batch. - max_seqlen_k: int. Maximum key sequence length in the batch. - dropout_p: float. Dropout probability. - softmax_scale: float. The scaling of QK^T before applying softmax. - Default to 1 / sqrt(headdim). - causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). - return_attn_probs: bool. Whether to return the attention probabilities. This option is for - testing only. The returned probabilities are not guaranteed to be correct - (they might not have the right scaling). - Return: - out: (total, nheads, headdim). - softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The - logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax - normalization factor). - S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). - The output of softmax (possibly with different scaling). It also encodes the dropout - pattern (negative means that location was dropped, nonnegative means it was kept). - """ - return FlashAttnVarlenKVPackedFunc.apply( - q, - kv, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale, - causal, - return_attn_probs, - ) diff --git a/megatron/model/transformer.py b/megatron/model/transformer.py index 61ba3f3bf..85cfb6e2d 100644 --- a/megatron/model/transformer.py +++ b/megatron/model/transformer.py @@ -53,8 +53,10 @@ """ We use the following notation throughout this file: h: hidden size n: number of attention heads + kv: number of key or value heads p: number of model parallel partitions np: n/p + kvp: kv/p hp: h/p hn: h/n b: batch size @@ -164,8 +166,13 @@ def __init__( self.multiple_of = multiple_of - ff_dim = int(2 * neox_args.hidden_size * 4 / 3) - ff_dim = self.multiple_of * ((ff_dim + multiple_of - 1) // multiple_of) + # Allow custom intermediate size, e.g. for Mistral + if neox_args.intermediate_size is not None: + ff_dim = neox_args.intermediate_size + else: + ff_dim = int(2 * neox_args.hidden_size * 4 / 3) + ff_dim = self.multiple_of * ((ff_dim + multiple_of - 1) // multiple_of) + self.w1 = mpu.ColumnParallelLinear( neox_args=neox_args, input_size=neox_args.hidden_size, @@ -288,6 +295,7 @@ def __init__( neox_args.num_attention_heads, world_size ) self.pos_emb = neox_args.pos_emb + self.use_qk_layernorm = neox_args.use_qk_layernorm if self.use_qk_layernorm: norm, eps = get_norm(neox_args) @@ -299,15 +307,46 @@ def __init__( eps=eps, ) - # Strided linear layer. - self.query_key_value = mpu.ColumnParallelLinear( - neox_args=neox_args, - input_size=neox_args.hidden_size, - output_size=3 * neox_args.hidden_size, - gather_output=False, - init_method=init_method, - bias=neox_args.use_bias_in_attn_linear, - ) + self.sliding_window_width = neox_args.sliding_window_width + + if ( + not neox_args.num_kv_heads + or neox_args.num_kv_heads == neox_args.num_attention_heads + ): + self.gqa = False + else: + self.gqa = True + if self.gqa: + self.num_kv_heads_per_partition = mpu.divide( + neox_args.num_kv_heads, world_size + ) # we do not yet clone KV heads in MQA across TP ranks... + self.kv_hidden_size = ( + neox_args.num_kv_heads * self.hidden_size_per_attention_head + ) # how large the total hidden dim for each of K and V is + else: + self.num_kv_heads_per_partition = self.num_attention_heads_per_partition + self.kv_hidden_size = neox_args.hidden_size + + if not self.gqa: + # Strided linear layer. + self.query_key_value = mpu.ColumnParallelLinear( + neox_args=neox_args, + input_size=neox_args.hidden_size, + output_size=3 * neox_args.hidden_size, + gather_output=False, + init_method=init_method, + bias=neox_args.use_bias_in_attn_linear, + ) + else: + # QKV proj is smaller if we are using GQA / MQA + self.query_key_value = mpu.ColumnParallelLinear( + neox_args=neox_args, + input_size=neox_args.hidden_size, + output_size=neox_args.hidden_size + 2 * self.kv_hidden_size, + gather_output=False, + init_method=init_method, + bias=neox_args.use_bias_in_attn_linear, + ) coeff = None self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) @@ -354,6 +393,10 @@ def __init__( self.attention_type = neox_args.attention_config[layer_number] self.use_flash_attention = self.attention_type == "flash" self.sparse = self.attention_type not in ("global", "flash") + + if self.gqa: + assert not self.sparse + if self.sparse: self.sparse_attn = configure_sparse_attention( neox_args, @@ -363,18 +406,21 @@ def __init__( ) else: if self.use_flash_attention: - from megatron.model.flash_attention import ( - # flash_attn_unpadded_qkvpacked_func_cuda, - # flash_attn_unpadded_kvpacked_func_cuda, - # Change of function names going from flash attention 1 -> flash attention 2 - flash_attn_varlen_qkvpacked_func, - flash_attn_varlen_kvpacked_func, - flash_attn_unpadded_unpacked_func_triton, + # we now use Flash Attention 2's provided interface. + # TODO: we no longer need to use flash_triton_fn since flash cuda supports alibi. + # consider adding OpenAI's more recent Flash-2 Triton kernel in future + # from https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py + from flash_attn.flash_attn_interface import ( + flash_attn_func, + flash_attn_varlen_func, + ) + from flash_attn.flash_attn_triton import ( + flash_attn_func as flash_attn_unpadded_unpacked_func_triton, ) self.flash_triton_fn = flash_attn_unpadded_unpacked_func_triton - self.flash_qkv_fn = flash_attn_varlen_qkvpacked_func - self.flash_kv_fn = flash_attn_varlen_kvpacked_func + self.flash_qkv_fn = flash_attn_func + self.flash_varlen_qkv_fn = flash_attn_varlen_func else: self.scale_mask_softmax = FusedScaleMaskSoftmax( input_in_fp16=self.fp16, @@ -417,13 +463,11 @@ def attention( query_layer.size(0), key_layer.size(0), ) - # [sq, b, np, hn] -> [sq, b * np, hn] query_layer = query_layer.view( output_size[2], output_size[0] * output_size[1], -1 ) key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) - # preallocating result tensor: [b * np, sq, sk] matmul_result = torch.empty( output_size[0] * output_size[1], @@ -444,7 +488,6 @@ def attention( # change view to [b, np, sq, sk] attention_scores = matmul_result.view(*output_size) - # ================================================== # Update attention mask for inference. [b, np, sq, sk] # ================================================== @@ -519,10 +562,10 @@ def flash_attention(self, query_layer, key_layer, value_layer): # [sk, b, np, hn] -> [b, sk, np, hn] -> [b * sk, 1, np, hn] key_layer = key_layer.transpose(0, 1).reshape( - output_size[0] * output_size[3], 1, output_size[1], -1 + output_size[0], output_size[3], self.num_kv_heads_per_partition, -1 ) value_layer = value_layer.transpose(0, 1).reshape( - output_size[0] * output_size[3], 1, output_size[1], -1 + output_size[0], output_size[3], self.num_kv_heads_per_partition, -1 ) batch_size = output_size[0] @@ -545,51 +588,50 @@ def flash_attention(self, query_layer, key_layer, value_layer): device=key_layer.device, ) - if not self.training: - - # [sq, b, np, hn] -> [b * sq, np, hn] - query_layer = query_layer.transpose(0, 1).reshape( - output_size[0] * output_size[2], output_size[1], -1 - ) - - # Combined k/v into [b * sk, 2, np, hn]. - kv = torch.cat([key_layer, value_layer], dim=1) + # [sq, b, np, hn] -> [b, sq, np, hn] + query_layer = query_layer.transpose(0, 1).reshape( + output_size[0], output_size[2], output_size[1], -1 + ) - output = self.flash_kv_fn( - query_layer, - kv, + if not self.training: + q_shape = query_layer.shape + k_shape = key_layer.shape + v_shape = value_layer.shape + output = self.flash_varlen_qkv_fn( + query_layer.reshape( + (q_shape[0] * q_shape[1], q_shape[2], q_shape[3]) + ), + key_layer.reshape( + (k_shape[0] * k_shape[1], k_shape[2], k_shape[3]) + ), + value_layer.reshape( + (v_shape[0] * v_shape[1], v_shape[2], v_shape[3]) + ), cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, - self.dropout_p if self.training else 0.0, softmax_scale=None, causal=True, + window_size=(self.sliding_window_width, -1) + if self.sliding_window_width is not None + else (-1, -1), ) - + output = output.reshape(q_shape) else: - - # [sq, b, np, hn] -> [b * sq, 1, np, hn] - query_layer = query_layer.transpose(0, 1).reshape( - output_size[0] * output_size[2], 1, output_size[1], -1 - ) - - # Combined q/k/v into [b * s, 3, np, hn]. - qkv = torch.cat([query_layer, key_layer, value_layer], dim=1) - output = self.flash_qkv_fn( - qkv, - cu_seqlens_q, - max_seqlen_q, + query_layer, + key_layer, + value_layer, self.dropout_p if self.training else 0.0, softmax_scale=None, causal=True, + window_size=(self.sliding_window_width, -1) + if self.sliding_window_width is not None + else (-1, -1), ) - # [b * sq, np, hn] -> [b, sq, np, hn] - matmul_result = output.view( - output_size[0], output_size[2], output.shape[1], output.shape[2] - ) + matmul_result = output # [b, sq, np, hn] -> [b, np, sq, hn] matmul_result = matmul_result.transpose(1, 2) @@ -631,6 +673,103 @@ def sparse_attention(self, query_layer, key_layer, value_layer, attention_mask): query_layer, key_layer, value_layer, attn_mask=attn_mask, rpe=rpe ) + def gqa_project(self, hidden_states, attention_mask, layer_past=None): + # QKV projection and separation into separate Q/K/V layers for GQA, + # where KV projections may be smaller than Q projection. + # the logic for this is explained in comments of this function + # detailing the intermediate sizes of tensors at each reshape. + + # pass through projection: [sq, b, h] --> [sq, b, ((np + 2 * kvp) * hn)] + mixed_x_layer, _ = self.query_key_value(hidden_states) + + # First: reshape so we have seqlen, batch, and num. query heads each as separate dims + # Final dim is not exactly head dim: the first (head dim) dims are query heads, + # The last (head dim * ratio of kv to q heads) each are the "k/v heads" + # (right now we treat like we have same num. heads, but smaller head dim) + + # [sq, b, ((np + 2 * kvp) * hn)] --> [sq, b, np, (hn * (1 + 2 * (kvp / np)))] + new_qkv_shape = ( + mixed_x_layer.shape[0], + mixed_x_layer.shape[1], + self.num_attention_heads_per_partition, + int( + self.hidden_size_per_attention_head + * ( + 1 + + 2 + * ( + self.num_kv_heads_per_partition + / self.num_attention_heads_per_partition + ) + ) + ), + ) + mixed_x_layer = mixed_x_layer.reshape(*new_qkv_shape) + + # Next: split our fake head dim. (last dim) so that the first (head dim) dimensions go to Q, + # the last smaller 2 * (head dim * kv to q head ratio) each divided between K and V separately + split_sizes = ( + self.hidden_size_per_attention_head, + int( + ( + self.num_kv_heads_per_partition + / self.num_attention_heads_per_partition + ) + * self.hidden_size_per_attention_head + ), + int( + ( + self.num_kv_heads_per_partition + / self.num_attention_heads_per_partition + ) + * self.hidden_size_per_attention_head + ), + ) + + # [sq, b, np, (hn * (1 + 2 * (kvp / np)))] --> 1 x [sq, b, np, hn] , 2 x [sq, b, np, (hn * (kvp / np))] + (query_layer, key_layer, value_layer) = [ + x.contiguous() + for x in torch.split( + mixed_x_layer, + split_sizes, + dim=mixed_x_layer.dim() - 1, + ) + ] + + # reshape K/V to proper output shape (last dim = correct full "real" head size again) + # 2 x [sq, b, np, (hn * (kvp / np))] --> 2 x [sq, b, kvp, hn] + new_kv_shape = ( + key_layer.size(0), + key_layer.size(1), + self.num_kv_heads_per_partition, + self.hidden_size_per_attention_head, + ) + + key_layer = key_layer.view(*new_kv_shape) + + value_layer = value_layer.view(*new_kv_shape) + + # if not using Flash attention, we repeat K/V heads to match Q head counts + if not self.use_flash_attention: + key_layer = torch.repeat_interleave( + key_layer, + repeats=int( + self.num_attention_heads_per_partition + // self.num_kv_heads_per_partition + ), + dim=2, + ) + value_layer = torch.repeat_interleave( + value_layer, + repeats=int( + self.num_attention_heads_per_partition + // self.num_kv_heads_per_partition + ), + dim=2, + ) + + return query_layer, key_layer, value_layer + def forward(self, hidden_states, attention_mask, layer_past=None): # hidden_states: [sq, b, h] @@ -639,20 +778,31 @@ def forward(self, hidden_states, attention_mask, layer_past=None): # Query, Key, and Value # ===================== - # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] - mixed_x_layer, _ = self.query_key_value(hidden_states) + if not self.gqa: + # QKV projection for MHA. - # [sq, b, (np * 3 * hn)] --> [sq, b, np, 3 * hn] - new_tensor_shape = mixed_x_layer.size()[:-1] + ( - self.num_attention_heads_per_partition, - 3 * self.hidden_size_per_attention_head, - ) - mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer, _ = self.query_key_value(hidden_states) - # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] - (query_layer, key_layer, value_layer) = mpu.split_tensor_along_last_dim( - mixed_x_layer, 3 - ) + # [sq, b, (np * 3 * hn)] --> [sq, b, np, 3 * hn] + new_tensor_shape = mixed_x_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head, + ) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = mpu.split_tensor_along_last_dim( + mixed_x_layer, 3 + ) + else: + # Grouped Query Attention (GQA) - specific logic for performing QKV proj + # and separating out Q, K, and V outputs. + + # output shapes: 1 x [sq, b, np, hn], 2 x [sq, b, kvp, hn] if using flash + query_layer, key_layer, value_layer = self.gqa_project( + hidden_states, attention_mask, layer_past=layer_past + ) # QK Normalization https://arxiv.org/abs/2302.05442 if self.use_qk_layernorm: diff --git a/megatron/neox_arguments/arguments.py b/megatron/neox_arguments/arguments.py index 2745287ed..7bca420cd 100644 --- a/megatron/neox_arguments/arguments.py +++ b/megatron/neox_arguments/arguments.py @@ -21,6 +21,8 @@ import copy import torch import argparse +from pkg_resources import packaging +from importlib.metadata import version from dataclasses import dataclass from typing import List, Dict @@ -388,6 +390,12 @@ def consume_deepy_args(cls, input_args=None): neox_args.wandb_group += "_" + wandb.util.generate_id() + if neox_args.sliding_window_width is not None: + _flash_version = packaging.version.Version(version("flash-attn")) + assert _flash_version >= packaging.version.Version( + "2.0.0" + ), f"Flash-Attention version ({str(_flash_version)}) must be >= 2.0.0 to support sliding window attention." + neox_args.print() return neox_args @@ -1057,6 +1065,26 @@ def calculate_derived(self): # Can't have a default value as an empty dict so need to set it here self.update_value("sparsity_config", {}) + # Multi-query or grouped-query attention settings + if self.num_kv_heads is not None: + # need KV heads <= query heads, and KV heads dividing query heads evenly + assert ( + self.num_attention_heads % self.num_kv_heads == 0 + ), "num_kv_heads must evenly divide num_attention_heads and be no greater than it" + + if self.num_kv_heads < self.num_attention_heads: + # GQA / MQA not compatible with sparse attention configurations + assert ( + not self.sparsity_config + ), "Sparse attention not compatible with GQA or MQA" + assert all( + (attn_type == "flash") or (attn_type == "global") + for attn_type in self.attention_config + ), "GQA / MQA currently only compatible with Flash or standard global Attention" + assert ( + self.num_kv_heads % self.model_parallel_size == 0 + ), "Number of KV heads must be at least model_parallel_size for now!" + # Adding equal dataset weights if none are provided if self.train_data_paths and (self.train_data_weights is None): self.train_data_weights = [1.0] * len(self.train_data_paths) diff --git a/megatron/neox_arguments/neox_args.py b/megatron/neox_arguments/neox_args.py index 7f3afffa5..76da42a43 100644 --- a/megatron/neox_arguments/neox_args.py +++ b/megatron/neox_arguments/neox_args.py @@ -105,9 +105,29 @@ class NeoXArgsModel(NeoXArgsTemplate): Transformer hidden size. """ + intermediate_size: int = None + """ + Transformer intermediate size. Currently only used for "mlp_type": "llama". + + If not passed, will be set to a reasonable default. + """ + num_attention_heads: int = None """ Number of transformer attention heads. + + If num_kv_heads is set, will control only number of query heads. + """ + + num_kv_heads: int = None + """ + Number of transformer key/value attention heads. + + If set to None or the same value as num_attention_heads, will perform multi-head attention (MHA). + If set to < num_attention_heads but > 1, will perform grouped-query attention (GQA) (https://arxiv.org/pdf/2305.13245.pdf) + If set to 1, will perform multi-query attention. + + Must be < num_attention_heads and divide num_attention_heads evenly. """ seq_length: int = None @@ -115,6 +135,11 @@ class NeoXArgsModel(NeoXArgsTemplate): Maximum sequence length to process. """ + sliding_window_width: int = None + """ + Width of the attention sliding window. Only supported with Flash Attention 2. + """ + max_position_embeddings: int = None """ Maximum number of position embeddings to use. This is the size of position embedding. diff --git a/megatron/training.py b/megatron/training.py index 216878678..a6c1a2de9 100644 --- a/megatron/training.py +++ b/megatron/training.py @@ -294,6 +294,7 @@ def _get_batch(neox_args, tokenizer, keys, data, datatype): data=tokens, eod_token=neox_args.tokenizer.eod, eod_mask_loss=neox_args.eod_mask_loss, + sliding_window_width=neox_args.sliding_window_width, ) # If `label` is present, any token < 0 (e.g., -100, the default for torch) skips the loss computation if "label" in data_b: diff --git a/megatron/utils.py b/megatron/utils.py index 777f32b7d..26b4439bd 100644 --- a/megatron/utils.py +++ b/megatron/utils.py @@ -64,7 +64,7 @@ def report_memory(name): print_rank_0(string) -def get_attn_mask(seq_length, device): +def get_attn_mask(seq_length, device, sliding_window_width): """ Get triangular attention mask for a given sequence length / device. """ @@ -72,6 +72,9 @@ def get_attn_mask(seq_length, device): mask = torch.tril(torch.ones((1, seq_length, seq_length), device=device)).view( 1, 1, seq_length, seq_length ) + # get rid of lower diagonals than the sliding window width, if a value was provided + if sliding_window_width is not None: + mask = torch.triu(mask, diagonal=-sliding_window_width) # convert to binary return mask < 0.5 @@ -81,6 +84,7 @@ def get_ltor_masks_and_position_ids( data, eod_token, eod_mask_loss=False, + sliding_window_width=None, ): """Build masks and position id for left to right model.""" @@ -91,6 +95,7 @@ def get_ltor_masks_and_position_ids( attention_mask = get_attn_mask( seq_length=seq_length, device=data.device, + sliding_window_width=sliding_window_width, ) # Loss mask. diff --git a/tests/unit/test_format_conversion_scripts.py b/tests/unit/test_format_conversion_scripts.py index 9c78c962d..e0801434c 100644 --- a/tests/unit/test_format_conversion_scripts.py +++ b/tests/unit/test_format_conversion_scripts.py @@ -1,12 +1,9 @@ import pytest -from tools.ckpts import convert_sequential_to_hf +from tools.ckpts import convert_neox_to_hf from tests.common import simulate_deepy_env, save_random_model from megatron.neox_arguments.neox_args import NeoXArgsTokenizer -# Test is failing; possibly we resolve by using the word embedding weights in the 'layer_00-model_00-model_states.pt' file? -@pytest.mark.xfail( - reason="Failing to find 'word_embeddings.weight' in state_dict['module'] from the 'mp_rank_00_model_states.pt' file" -) + def test_gpt_neox_to_huggingface(monkeypatch, tmpdir, tmp_path): # Generate random GPT-NEOX model, check we can convert to hf format model_dir = str(tmpdir) @@ -24,6 +21,4 @@ def test_gpt_neox_to_huggingface(monkeypatch, tmpdir, tmp_path): model_dir, ] overwrite_values = {"tokenizer_type": NeoXArgsTokenizer.tokenizer_type} - convert_sequential_to_hf.main( - input_args=script_args, overwrite_values=overwrite_values - ) + convert_neox_to_hf.main(input_args=script_args, overwrite_values=overwrite_values) diff --git a/tools/ckpts/convert_neox_to_hf.py b/tools/ckpts/convert_neox_to_hf.py index a9cd4277e..6ad67f208 100644 --- a/tools/ckpts/convert_neox_to_hf.py +++ b/tools/ckpts/convert_neox_to_hf.py @@ -246,11 +246,6 @@ def __init__(self, neox_config): } ) - if args["num_attention_heads"] != args["num_key_value_heads"]: - assert ( - False - ), "Got num_key_value_heads != num_attention_heads, but Grouped-Query Attention is not yet supported by NeoX." - if architecture == "mistral": # mistral-specific options args.update( @@ -671,12 +666,6 @@ def main(input_args=None, overwrite_values=None): "mistral", ], f"expected --architecture to be one of 'neox', 'mistral', 'llama', but got '{args.architecture}' !" - if args.architecture == "mistral": - # Mistral Support Coming Soon - assert ( - False - ), "Got num_key_value_heads != num_attention_heads, but Grouped-Query Attention is not yet supported by NeoX." - with open(args.config_file) as f: loaded_config = yaml.full_load(f) if overwrite_values: diff --git a/tools/ckpts/convert_raw_llama_weights_to_neox.py b/tools/ckpts/convert_raw_llama_weights_to_neox.py index 6fc9f8a7e..a28f1d0fb 100644 --- a/tools/ckpts/convert_raw_llama_weights_to_neox.py +++ b/tools/ckpts/convert_raw_llama_weights_to_neox.py @@ -73,9 +73,6 @@ def convert_model_pipeline( num_layers = params["n_layers"] num_heads = params["n_heads"] if "n_kv_heads" in params: - assert ( - False - ), "Found `n_kv_heads` != `n_heads` in checkpoint config. However, Grouped-Query Attention is not yet supported by NeoX" num_kv_heads = params["n_kv_heads"] else: num_kv_heads = num_heads @@ -165,7 +162,13 @@ def permute_rotary(w): rope_freqs = loaded[0]["layers.0.attention.inner_attention.rope.freqs"] helper.del_loaded("layers.0.attention.inner_attention.rope.freqs") elif "mistral" in model_size: - rope_freqs = None + # mistral does not include rope freqs in the distributed checkpoint, unlike llama. + # rather than making this buffer always non-persistent on the NeoX side, + # just create and save it for Mistral. + base = 10000.0 + rope_freqs = 1.0 / ( + base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head) + ) else: rope_freqs = loaded[0]["rope.freqs"] helper.del_loaded("rope.freqs") @@ -290,11 +293,7 @@ def permute_rotary(w): # Duplicated layers "input_layernorm.scale": input_layernorm, "post_attention_layernorm.scale": post_attention_layernorm, - **( - {"attention.rotary_emb.inv_freq": rope_freqs} - if "mistral" not in model_size - else {} - ), + "attention.rotary_emb.inv_freq": rope_freqs, }, layer_i=layer_i + 2, rank=out_rank, @@ -333,9 +332,6 @@ def convert_model_sequential( num_layers = params["n_layers"] num_heads = params["n_heads"] if "n_kv_heads" in params: - assert ( - False - ), "Found `n_kv_heads` != `n_heads` in checkpoint config. However, Grouped-Query Attention is not yet supported by NeoX" num_kv_heads = params["n_kv_heads"] else: num_kv_heads = num_heads @@ -417,7 +413,13 @@ def permute_rotary(w): rope_freqs = loaded[0]["layers.0.attention.inner_attention.rope.freqs"] helper.del_loaded("layers.0.attention.inner_attention.rope.freqs") elif "mistral" in model_size: - rope_freqs = None + # mistral does not include rope freqs in the distributed checkpoint, unlike llama. + # rather than making this buffer always non-persistent on the NeoX side, + # just create and save it for Mistral. + base = 10000.0 + rope_freqs = 1.0 / ( + base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head) + ) else: rope_freqs = loaded[0]["rope.freqs"] helper.del_loaded("rope.freqs") @@ -544,11 +546,7 @@ def permute_rotary(w): # Duplicated layers "input_layernorm.scale": input_layernorm, "post_attention_layernorm.scale": post_attention_layernorm, - **( - {"attention.rotary_emb.inv_freq": rope_freqs} - if "mistral" not in model_size - else {} - ), + "attention.rotary_emb.inv_freq": rope_freqs, }, layer_i=layer_i + 2, rank=out_rank, From 966380297a1ae7984f06249d4464537a59a508e5 Mon Sep 17 00:00:00 2001 From: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com> Date: Mon, 26 Feb 2024 11:23:10 -0500 Subject: [PATCH 14/16] [Bug?] Fix profiling argument names (#1155) * possibly fix profiling flag names * actually, profile_backward already exists * Update NeoXArgs docs automatically * neox_args.profile was also used some places, update that too * Update NeoXArgs docs automatically * profiling --> profile * Update NeoXArgs docs automatically * Revert neox_arguments.md changes * Update NeoXArgs docs automatically * Update gen_docs since __name__ only returns the Literal for string args with Python 3.10 * Update NeoXArgs docs automatically * Another update to preserve non-literals * Update NeoXArgs docs automatically * add union * Update NeoXArgs docs automatically * pre-commit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions Co-authored-by: Quentin Anthony --- configs/gen_docs.py | 5 ++++- configs/neox_arguments.md | 43 +++++++++++++++++++++++++++++++++++---- megatron/training.py | 10 ++++----- 3 files changed, 48 insertions(+), 10 deletions(-) diff --git a/configs/gen_docs.py b/configs/gen_docs.py index 08431e6c6..1d8c5ba3b 100644 --- a/configs/gen_docs.py +++ b/configs/gen_docs.py @@ -31,7 +31,10 @@ def get_docs(module): field_name, field_def = cur field_type = field_def.type if hasattr(field_type, "__name__"): - field_type = field_type.__name__ + if field_type.__name__ == "Literal" or field_type.__name__ == "Union": + field_type = field_type + else: + field_type = str(field_type.__name__) else: field_type = str(field_type) diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 287ad7b71..29db139d3 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 211e726 + Default = 6bb4e62 current git hash of repository @@ -277,12 +277,38 @@ Model Arguments +- **intermediate_size**: int + + Default = None + + Transformer intermediate size. Currently only used for "mlp_type": "llama". + + If not passed, will be set to a reasonable default. + + + - **num_attention_heads**: int Default = None Number of transformer attention heads. + If num_kv_heads is set, will control only number of query heads. + + + +- **num_kv_heads**: int + + Default = None + + Number of transformer key/value attention heads. + + If set to None or the same value as num_attention_heads, will perform multi-head attention (MHA). + If set to < num_attention_heads but > 1, will perform grouped-query attention (GQA) (https://arxiv.org/pdf/2305.13245.pdf) + If set to 1, will perform multi-query attention. + + Must be < num_attention_heads and divide num_attention_heads evenly. + - **seq_length**: int @@ -293,6 +319,14 @@ Model Arguments +- **sliding_window_width**: int + + Default = None + + Width of the attention sliding window. Only supported with Flash Attention 2. + + + - **max_position_embeddings**: int Default = None @@ -1024,7 +1058,7 @@ Text Generation arguments - **prompt_end**: str - Default = + Default = a single prompt's end. Defaults to newline @@ -1066,7 +1100,7 @@ Text Generation arguments - **eval_results_prefix**: str - Default = + Default = prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json @@ -1810,7 +1844,7 @@ Args for deepspeed config Default = None - + @@ -2110,3 +2144,4 @@ Args for deepspeed runner (deepspeed.launcher.runner). Default = None Adds a `--account` to the DeepSpeed launch command. In DeeperSpeed this is passed on to the SlurmLauncher as well. Sometimes necessary for cluster rules, or so I've heard. + diff --git a/megatron/training.py b/megatron/training.py index a6c1a2de9..abea4b726 100644 --- a/megatron/training.py +++ b/megatron/training.py @@ -771,7 +771,7 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler) losses.append(loss) # Calculate gradients, reduce across processes, and clip. if ( - neox_args.profiling + neox_args.profile and neox_args.iteration >= neox_args.profile_step_start and neox_args.iteration <= neox_args.profile_step_stop ): @@ -786,14 +786,14 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler) ) timers("backward").stop() if ( - neox_args.profiling + neox_args.profile and neox_args.iteration >= neox_args.profile_step_start and neox_args.iteration <= neox_args.profile_step_stop ): torch.cuda.nvtx.range_pop() # Update parameters. if ( - neox_args.profiling + neox_args.profile and neox_args.iteration >= neox_args.profile_step_start and neox_args.iteration <= neox_args.profile_step_stop ): @@ -805,13 +805,13 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler) raise ValueError("Must be using deepspeed to run neox") timers("optimizer").stop() if ( - neox_args.profiling + neox_args.profile and neox_args.iteration >= neox_args.profile_step_start and neox_args.iteration <= neox_args.profile_step_stop ): torch.cuda.nvtx.range_pop() if ( - neox_args.profiling + neox_args.profile and neox_args.iteration >= neox_args.profile_step_start and neox_args.iteration <= neox_args.profile_step_stop and torch.distributed.get_rank() == 0 From 3c03fc7035dff0e16d46b76490ad8eb225eef51f Mon Sep 17 00:00:00 2001 From: jaimemcc <99298642+jaimemcc-intel@users.noreply.github.com> Date: Wed, 28 Feb 2024 18:53:24 -0800 Subject: [PATCH 15/16] Update cpu_ci.yml (#1159) * Update cpu_ci.yml Updating the workflow to point CPU workflow towards self hosted runner versus Github provided runners * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions --- .github/workflows/cpu_ci.yml | 3 ++- configs/neox_arguments.md | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/cpu_ci.yml b/.github/workflows/cpu_ci.yml index f61978163..9160fccab 100644 --- a/.github/workflows/cpu_ci.yml +++ b/.github/workflows/cpu_ci.yml @@ -4,7 +4,8 @@ on: "push" jobs: run-tests: - runs-on: ubuntu-latest + #runs-on: ubuntu-latest + runs-on: [ 'test', 'self-hosted' ] steps: - uses: actions/checkout@v3 diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 29db139d3..9a0511237 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 6bb4e62 + Default = 4ca9a8a current git hash of repository From 19596b048310d7402086f448e7aae3016da477d1 Mon Sep 17 00:00:00 2001 From: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com> Date: Sat, 2 Mar 2024 17:22:38 -0500 Subject: [PATCH 16/16] Improve argument validation for Flash-attn + SWA (#1162) * Improve argument validation for Flash-attn + SWA * Update NeoXArgs docs automatically * don't pass window_size if not necessary * Update NeoXArgs docs automatically * Update 7B.yml * Update NeoXArgs docs automatically * apply precommit * Update NeoXArgs docs automatically --------- Co-authored-by: github-actions --- configs/mistral/7B.yml | 4 +++- configs/neox_arguments.md | 2 +- megatron/model/transformer.py | 17 +++++++++++------ megatron/neox_arguments/arguments.py | 14 +++++++------- 4 files changed, 22 insertions(+), 15 deletions(-) diff --git a/configs/mistral/7B.yml b/configs/mistral/7B.yml index 67b7c6a52..587fe5d36 100644 --- a/configs/mistral/7B.yml +++ b/configs/mistral/7B.yml @@ -9,7 +9,9 @@ "intermediate_size": 14336, "num_attention_heads": 32, "num_kv_heads": 8, - "seq_length": 4096, + # per Mistral, Mistral-7B-v0.1 was pretrained with 8192 seqlen + # and instruction tuned to 16384 seqlen, all with 4096 sliding window + "seq_length": 8192, "sliding_window_width": 4096, "max_position_embeddings": 131072, "pos_emb": "rotary", diff --git a/configs/neox_arguments.md b/configs/neox_arguments.md index 9a0511237..be1fd7905 100644 --- a/configs/neox_arguments.md +++ b/configs/neox_arguments.md @@ -111,7 +111,7 @@ Logging Arguments - **git_hash**: str - Default = 4ca9a8a + Default = 11a2e9b current git hash of repository diff --git a/megatron/model/transformer.py b/megatron/model/transformer.py index 85cfb6e2d..f039126b9 100644 --- a/megatron/model/transformer.py +++ b/megatron/model/transformer.py @@ -593,6 +593,15 @@ def flash_attention(self, query_layer, key_layer, value_layer): output_size[0], output_size[2], output_size[1], -1 ) + # only pass in window_size kwarg to flash-attn + # if we use Sliding Window Attention. + # Flash attn defaults to (-1,-1), or + # does not have this kwarg prior to v2.3.0 + extra_kwargs = ( + {"window_size": (self.sliding_window_width, -1)} + if self.sliding_window_width is not None + else {} + ) if not self.training: q_shape = query_layer.shape k_shape = key_layer.shape @@ -613,9 +622,7 @@ def flash_attention(self, query_layer, key_layer, value_layer): max_seqlen_k, softmax_scale=None, causal=True, - window_size=(self.sliding_window_width, -1) - if self.sliding_window_width is not None - else (-1, -1), + **extra_kwargs, ) output = output.reshape(q_shape) else: @@ -626,9 +633,7 @@ def flash_attention(self, query_layer, key_layer, value_layer): self.dropout_p if self.training else 0.0, softmax_scale=None, causal=True, - window_size=(self.sliding_window_width, -1) - if self.sliding_window_width is not None - else (-1, -1), + **extra_kwargs, ) matmul_result = output diff --git a/megatron/neox_arguments/arguments.py b/megatron/neox_arguments/arguments.py index 7bca420cd..ca68100a5 100644 --- a/megatron/neox_arguments/arguments.py +++ b/megatron/neox_arguments/arguments.py @@ -390,12 +390,6 @@ def consume_deepy_args(cls, input_args=None): neox_args.wandb_group += "_" + wandb.util.generate_id() - if neox_args.sliding_window_width is not None: - _flash_version = packaging.version.Version(version("flash-attn")) - assert _flash_version >= packaging.version.Version( - "2.0.0" - ), f"Flash-Attention version ({str(_flash_version)}) must be >= 2.0.0 to support sliding window attention." - neox_args.print() return neox_args @@ -1080,10 +1074,16 @@ def calculate_derived(self): assert all( (attn_type == "flash") or (attn_type == "global") for attn_type in self.attention_config - ), "GQA / MQA currently only compatible with Flash or standard global Attention" + ), "GQA / MQA currently only compatible with Flash or standard global/sliding window Attention" assert ( self.num_kv_heads % self.model_parallel_size == 0 ), "Number of KV heads must be at least model_parallel_size for now!" + # Flash attention version >=2.3.0 required to combine Flash + Sliding Window Attention + if self.sliding_window_width is not None and "flash" in self.attention_config: + _flash_version = packaging.version.Version(version("flash-attn")) + assert _flash_version >= packaging.version.Version( + "2.3.0" + ), f"Flash-Attention version ({str(_flash_version)}) must be >= 2.3.0 to support sliding window attention." # Adding equal dataset weights if none are provided if self.train_data_paths and (self.train_data_weights is None):