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Commit 5b6ef054 authored by yuguo's avatar yuguo
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Merge branch 'main' of https://github.com/NVIDIA/TransformerEngine

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docs/examples/te_llama/media/tellamadecoderlayer.svg 0 → 100644
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docs/examples/te_llama/media/weight_swap.svg 0 → 100644
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\ No newline at end of file
docs/examples/te_llama/te_llama.py 0 → 100644
View file @ 5b6ef054
# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import os
import re
import gc
from contextlib import contextmanager
import torch
from torch import nn
import transformer_engine as te
from transformer_engine.pytorch.dot_product_attention.rope import RotaryPositionEmbedding
from transformer_engine.pytorch.fp8 import fp8_model_init
import transformers
from transformers.models.llama.modeling_llama import (
LlamaModel,
LlamaForCausalLM,
LlamaRMSNorm,
LlamaConfig,
)
from transformers.modeling_utils import _add_variant, load_state_dict, _load_state_dict_into_model
from transformers.utils import WEIGHTS_INDEX_NAME
from transformers.utils.hub import get_checkpoint_shard_files
@contextmanager
def replace_decoder(te_decoder_cls):
"""
Replace `LlamaDecoderLayer` with custom `TELlamaDecoderLayer`.
"""
original_llama_decoder_cls = transformers.models.llama.modeling_llama.LlamaDecoderLayer
transformers.models.llama.modeling_llama.LlamaDecoderLayer = te_decoder_cls
try:
yield
finally:
transformers.models.llama.modeling_llama.LlamaDecoderLayer = original_llama_decoder_cls
class TELlamaDecoderLayer(te.pytorch.TransformerLayer):
"""
Wrapper class over TE's `TransformerLayer`. This makes the wrapper very
similar to HF's `LlamaDecoderLayer` and easier to replace it in the code.
Args:
config: LlamaConfig
args: positional args (for compatibility with `LlamaDecoderLayer`)
kwargs: keyword args (for compatibility with `LlamaDecoderLayer`)
"""
def __init__(self, config, *args, **kwargs):
super().__init__(
hidden_size=config.hidden_size,
ffn_hidden_size=config.intermediate_size,
num_attention_heads=config.num_attention_heads,
bias=False,
layernorm_epsilon=config.rms_norm_eps,
hidden_dropout=0,
attention_dropout=0,
fuse_qkv_params=False,
normalization="RMSNorm",
activation="swiglu",
attn_input_format="bshd",
num_gqa_groups=config.num_key_value_heads,
)
te_rope = RotaryPositionEmbedding(config.hidden_size // config.num_attention_heads)
self.te_rope_emb = te_rope(max_seq_len=config.max_position_embeddings).cuda()
def forward(self, hidden_states, *args, attention_mask, **kwargs):
"""
Custom forward to make sure we only pass relevant arguments to the
forward pass of the `TransformerLayer`. Also, make sure the output
format matches the output of the HF's `LlamaDecoderLayer`.
"""
return (
super().forward(
hidden_states, attention_mask=attention_mask, rotary_pos_emb=self.te_rope_emb
),
)
class TELlamaForCausalLM:
"""
Causal LM created with `LlamaModel`. The underlying `LlamaDecoderLayer`
class is monkey-patched with `TELlamaDecoderLayer` class before
initializing the causal LM with `LlamaForCausalLM`.
Args:
config: LlamaConfig
"""
def __new__(cls, config: LlamaConfig):
with replace_decoder(te_decoder_cls=TELlamaDecoderLayer):
llama_for_causal_lm = LlamaForCausalLM(config)
return llama_for_causal_lm
@classmethod
def from_pretrained_local(cls, pretrained_model_name_or_path, *args, config, **kwargs):
"""
Custom method adapted from `from_pretrained` method in HuggingFace
Transformers repo: https://github.com/huggingface/transformers/blob/f497f564bb76697edab09184a252fc1b1a326d1e/src/transformers/modeling_utils.py#L2579
"""
# Before loading the model, set the default dtype for torch
torch.set_default_dtype(kwargs["torch_dtype"])
# Load the vanilla model weights
vanilla_model = cls(config)
subfolder = ""
variant = None
if os.path.isfile(
os.path.join(
pretrained_model_name_or_path,
subfolder,
_add_variant("model.safetensors.index.json", variant),
)
):
# Load from a sharded PyTorch checkpoint
archive_file = os.path.join(
pretrained_model_name_or_path,
subfolder,
_add_variant("model.safetensors.index.json", variant),
)
is_sharded = True
elif os.path.isfile(
os.path.join(
pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
)
):
# Load from a sharded PyTorch checkpoint
archive_file = os.path.join(
pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
)
is_sharded = True
else:
raise AssertionError("Only sharded PyTorch ckpt format supported at the moment")
resolved_archive_file, _ = get_checkpoint_shard_files(
pretrained_model_name_or_path,
archive_file,
)
# If the checkpoint is not sharded, it's a trivial sharding case
if not is_sharded:
assert not isinstance(resolved_archive_file, list)
resolved_archive_file = [resolved_archive_file]
for shard_file in resolved_archive_file:
state_dict = load_state_dict(shard_file)
# replace_params copies parameters relevant only to TransformerEngine
replace_params(state_dict, vanilla_model.state_dict(), config)
# _load_state_dict_into_model copies parameters other than those in TransformerEngine
_load_state_dict_into_model(vanilla_model, state_dict, start_prefix="")
# Force mem release. Taken from huggingface code
del state_dict
gc.collect()
return vanilla_model
def replace_params(hf_state_dict, te_state_dict, config):
# collect all layer prefixes to update
all_layer_prefixes = set()
for param_key in hf_state_dict.keys():
layer_prefix_pat = "model.layers.\d+."
m = re.match(layer_prefix_pat, param_key)
if m is not None:
all_layer_prefixes.add(m.group())
for layer_prefix in all_layer_prefixes:
# When loading weights into models with less number of layers, skip the
# copy if the corresponding layer doesn't exist in HF model
if layer_prefix + "input_layernorm.weight" in hf_state_dict:
te_state_dict[layer_prefix + "self_attention.layernorm_qkv.layer_norm_weight"].data[
:
] = hf_state_dict[layer_prefix + "input_layernorm.weight"].data[:]
if layer_prefix + "self_attn.q_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "self_attention.layernorm_qkv.query_weight"].data[:] = (
hf_state_dict[layer_prefix + "self_attn.q_proj.weight"].data[:]
)
if layer_prefix + "self_attn.k_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "self_attention.layernorm_qkv.key_weight"].data[:] = (
hf_state_dict[layer_prefix + "self_attn.k_proj.weight"].data[:]
)
if layer_prefix + "self_attn.v_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "self_attention.layernorm_qkv.value_weight"].data[:] = (
hf_state_dict[layer_prefix + "self_attn.v_proj.weight"].data[:]
)
if layer_prefix + "self_attn.o_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "self_attention.proj.weight"].data[:] = hf_state_dict[
layer_prefix + "self_attn.o_proj.weight"
].data[:]
if layer_prefix + "post_attention_layernorm.weight" in hf_state_dict:
te_state_dict[layer_prefix + "layernorm_mlp.layer_norm_weight"].data[:] = hf_state_dict[
layer_prefix + "post_attention_layernorm.weight"
].data[:]
# It may happen that gate_proj.weight and up_proj.weight will be in the different files, so we need to
# load them separately.
if layer_prefix + "mlp.gate_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "layernorm_mlp.fc1_weight"].data[
: config.intermediate_size
] = hf_state_dict[layer_prefix + "mlp.gate_proj.weight"].data
if layer_prefix + "mlp.up_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "layernorm_mlp.fc1_weight"].data[
config.intermediate_size :
] = hf_state_dict[layer_prefix + "mlp.up_proj.weight"].data
if layer_prefix + "mlp.down_proj.weight" in hf_state_dict:
te_state_dict[layer_prefix + "layernorm_mlp.fc2_weight"].data[:] = hf_state_dict[
layer_prefix + "mlp.down_proj.weight"
].data[:]
return all_layer_prefixes
docs/examples/te_llama/tutorial_accelerate_hf_llama_with_te.ipynb 0 → 100644
View file @ 5b6ef054
{
"cells": [
{
"cell_type": "markdown",
"id": "6a5b2993",
"metadata": {},
"source": [
"# Accelerating a Hugging Face Llama 2 and Llama 3 models with Transformer Engine\n",
"\n",
"<div class=\"alert alert-info\">\n",
"\n",
"<b>Goal</b>\n",
"\n",
"This tutorial showcases how to accelerate finetuning a full [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf) or [Llama 3](https://huggingface.co/meta-llama/Meta-Llama-3-8B) models from Hugging Face by using `TransformerLayer` from the [Transformer Engine library](https://github.com/NVIDIA/TransformerEngine) in `BF16` and `FP8` precisions.\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "markdown",
"id": "331f476a",
"metadata": {},
"source": [
"## Dependencies for this tutorial\n",
"\n",
"Following files and media are necessary to effectively run this tutorial:\n",
"\n",
"1. `te_llama.py`\n",
" - This file contains the code to load a Hugging Face Llama 2 or Llama 3 checkpoint in Transformer Engine's `TransformerLayer` instead of Hugging Face's `LlamaDecoderLayer`. This is used in the following two sections of the tutorial - \"Improvement 1\" and \"Improvement 2\".\n",
"2. `utils.py`\n",
" - This file contains the code related to dataloading, hyperparameters, setting up model/optimizers/accelerator, model training and other miscellaneous tasks like restarting the jupyter notebook from within the cell. \n",
"3. `media/`\n",
" - This directory contains the images used in the following tutorial.\n",
"\n",
"These packages are necessary to run this tutorial:\n",
"`pytorch`, `transformer_engine`, `accelerate`, `transformers`, `peft`, `datasets`.\n",
"\n",
"\n",
"<div class=\"alert alert-info\">\n",
"\n",
"<b>Note on running the tutorial with Llama 3 weights</b>\n",
"\n",
"This tutorial shows the cell outputs when run with Llama 2 7B weights. It can be run with Llama 3 8B weights simply by providing the directory with those weights (in Hugging Face format) instead of Llama 2 7B weights. These two models are almost identical, the biggest difference being the model dimension (the smallest Llama 3 model has 8B parameters, whereas the smallest Llama 2 has 7B), which enables this tutorial to work for both of them.\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "markdown",
"id": "44abae4f",
"metadata": {},
"source": [
"## Table of contents\n",
"1. From \"Transformer\" to \"Llama\"\n",
"2. Hugging Face's `LlamaModel`\n",
" - Hugging Face's `LlamaDecoderLayer`\n",
"3. [Baseline] Running HF `LlamaModel` (Precision: `BF16`)\n",
"6. [Improvement 1] Replace HF's `LlamaDecoderLayer` with TE's `TransformerLayer` (Precision: `BF16`)\n",
" - Transformer Engine's `TransformerLayer`\n",
" - `TransformerLayer` options explained\n",
" - Mapping weights from HF's `LlamaDecoderLayer` to TE's `TransformerLayer`\n",
"7. [Improvement 2] Replace HF's `LlamaDecoderLayer` with TE's `TransformerLayer` (Precision: `FP8`)\n",
"8. Conclusion"
]
},
{
"cell_type": "markdown",
"id": "e37e2cc1",
"metadata": {},
"source": [
"## From \"Transformer\" to \"Llama\" \n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/transformer_llama.png\">\n",
" <figcaption> Fig 1: Llama visualized as a transformer. (generated with [Nvidia's AI-foundation models](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/sdxl))</figcaption>\n",
"</figure>\n",
"\n",
"A flashback:\n",
"\n",
"- 2017: [\"Attention Is All You Need\"](https://arxiv.org/abs/1706.03762) paper introduced pioneering \"Transformer\" architecture and changed the NLP field forever.\n",
"- 2018-2020: Emergence of GPT model series that showed causal decoder architectures are great fit for pretraining, few-shot and zero-shot learning.\n",
"- Fast forward to 2023-2024: Following GPT-3/GPT-4 success stories, researchers and companies raced to produce the next best pretrained model that could further be finetuned for application-specific use-cases.\n",
"- February 2023: Meta releases [Llama 2](https://llama.meta.com/llama2) models (Large Language Model Meta AI). \n",
" - These models range from 7B to 70B parameters.\n",
" - LLaMA 2 was pretrained on 2 trillion tokens.\n",
"- April 2024: Meta releases [Llama 3](https://llama.meta.com/llama3) models.\n",
" - These models range from 8B to 70B parameters.\n",
" - LLaMA 3 was pretrained on 15 trillion tokens.\n",
"\n",
"For more information on Llama 2 consider reading the [Huggingface tutorial](https://huggingface.co/blog/llama2). As a quick summary, here are some of the important differences b/w the conventional transformer decoder architecture vs Llama 2 architecture:\n",
"\n",
"1. Decoder only model (causal language modeling and next word prediction)\n",
"2. RMSNorm in place of the LayerNorm\n",
"3. SwiGLU activation function\n",
"4. RoPE as positional embeddings \n",
"5. Grouped Query Attention for the 70B model\n",
"6. Trained on 4K context length\n",
"\n",
"Hugging Face also released a [tutorial about Llama 3](https://huggingface.co/blog/llama3). The key points are:\n",
"\n",
"1. Use of bigger tokenizer - 128256 vs 32K.\n",
"2. Grouped Query Attention is used also by smaller 8B model.\n",
"3. The context length increased to 8K for all models.\n",
"3. Llama 3 was trained on 8x more data than Llama 2.\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/transformer_vs_llama.svg\">\n",
" <figcaption> Fig 2: Comparing GPT and Llama architectures. </figcaption>\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"id": "a110de1a",
"metadata": {},
"source": [
"## Hugging Face's `LlamaModel`\n",
"Hugging Face provides an open-source implementation of `Llama` model in [modeling_llama.py](https://github.com/huggingface/transformers/blob/3d2900e829ab16757632f9dde891f1947cfc4be0/src/transformers/models/llama/modeling_llama.py#L4).\n",
"\n",
"Here's a block diagram that shows how Llama model is implemented in the Hugging Face repo. Notice the modular encapsulated form and `LlamaDecoderLayer` at the core of the model implementation.\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/llama_for_causal_lm.svg\">\n",
" <figcaption> Fig 3: Causal Llama Model Block Diagram. </figcaption>\n",
"</figure>\n",
"\n",
"The above diagram translates to the following text output of the model in PyTorch. Notice that the core of the model has 32 `LlamaDecoderLayer`s. \n",
"\n",
"```\n",
"LlamaForCausalLM(\n",
" (model): LlamaModel(\n",
" (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
" (layers): ModuleList(\n",
" (0-31): 32 x LlamaDecoderLayer(\n",
" (self_attn): LlamaFlashAttention2(\n",
" (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (mlp): LlamaMLP(\n",
" (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
" (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
" (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
" (act_fn): SiLU()\n",
" )\n",
" (input_layernorm): LlamaRMSNorm()\n",
" (post_attention_layernorm): LlamaRMSNorm()\n",
" )\n",
" )\n",
" (norm): LlamaRMSNorm()\n",
" )\n",
" (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
")\n",
"```\n",
"\n",
"#### Hugging Face's `LlamaDecoderLayer`\n",
"\n",
"Let's take a closer look at `LlamaDecoderLayer`. It is composed of `input_layernorm`, `self_attn`, `post_attention_layernorm` and `mlp` modules. Each module has associated weights as shown in the diagram.\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/llama_zoom.svg\">\n",
" <figcaption> Fig 4: Causal Llama Model Block Diagram (with simplified illustration of the [LlamaDecoderLayer](https://github.com/huggingface/transformers/blob/e770f0316d2a9b787c9d1440f204fcb65e176682/src/transformers/models/llama/modeling_llama.py#L695)). </figcaption>\n",
"</figure>\n",
"\n",
"##### Self_Attn Layer\n",
"For simplicity in the block diagram illustration of the \"self_attn\" box, we omit the \"Grouped Query Attention\" operation and only showcase the modules which have associated weights.\n",
" \n",
"##### MLP Layer\n",
"\n",
"SwiGLU is an activation defined as follows in the [modeling_llama.py](https://github.com/huggingface/transformers/blob/7c4995f93d8d24aae05e1e43279c96dce736e5c8/src/transformers/models/llama/modeling_llama.py#L236) file in the Hugging Face github repo:\n",
"```\n",
"\"\"\"\n",
"1. `self.up_proj`, `self.gate_proj` and `self.down_proj` are \"Linear\" layers\n",
"2. `self.act_fn` is a \"Swish\" function\n",
"\n",
"\"\"\"\n",
"down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n",
"```\n",
"It requires a set of 3 weights as compared to 2 weights in conventional \"MLP\" layers e.g. in the traditional transformer or GPT architectures. This is also illustrated in the following figure:\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/swiglu.svg\">\n",
" <figcaption> Fig 5: A look inside the feedforward layer with <code>swiglu</code> activation function. </figcaption>\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"id": "c9529229",
"metadata": {},
"source": [
"## [Baseline] Running HF `LlamaModel` (Precision: `BF16`)\n",
"\n",
"Llama 2 weights are loaded into the Hugging Face native implementation `LlamaForCausalLM` (refer to [modeling_llama.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py)). \n",
"\n",
"For this and other subsequent runs, the `batch_size` is `8`. The `LlamaDecoderLayer` is left unchanged in the baseline as follows:\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/llamadecoderlayer.svg\">\n",
" <figcaption> Fig 6: Revisiting \"LlamaDecoderLayer\". </figcaption>\n",
"</figure>\n",
"\n",
"<div class=\"alert alert-info\">\n",
"<b>Note</b>\n",
"\n",
"The baseline implementation will be run in `BF16` precision.\n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "b38eb3ac",
"metadata": {},
"source": [
"<div class=\"alert alert-info\">\n",
"\n",
"<b>Note</b>\n",
" \n",
"This tutorial loads and trains a Llama 3 8B or a Llama 2 7B model which takes up most of the GPU memory and therefore, we need to restart the jupyter notebook each time before running the following sections. A small utility method `restart_jupyter_notebook` is defined in the accompanying `utils.py` file. This function restarts the jupyter notebook so that the GPU memory is flushed before the model is loaded again from the checkpoint in order to avoid running into OOM (Out Of Memory) errors.\n",
"\n",
"If the utility doesn't work, comment this line `restart_jupyter_notebook()` in the following cell and manually restart the jupyter notebook before running the cell. Repeat the same for other sections in this tutorial.\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2e9d7a8c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 finetuning steps complete!\n",
"Average time taken per step: 248 milliseconds\n"
]
}
],
"source": [
"# Restart the notebook (to flush the GPU memory)\n",
"from utils import restart_jupyter_notebook\n",
"restart_jupyter_notebook()\n",
"\n",
"\n",
"# Import necessary packages, methods and variables\n",
"from utils import *\n",
"\n",
"\n",
"# Provide Huggingface Access Token\n",
"hyperparams.hf_access_token = \"\"\n",
"assert hyperparams.hf_access_token, \"Provide a HF API Access Token!\"\n",
"\n",
"# Provide a directory to cache weights in to avoid downloading them every time.\n",
"# (By default, weights are cached in `~/.cache/huggingface/hub/models`)\n",
"hyperparams.weights_cache_dir = \"\"\n",
"\n",
"# For Llama 2, uncomment this line (also set by default)\n",
"hyperparams.model_name = \"meta-llama/Llama-2-7b-hf\"\n",
"\n",
"# For Llama 3, uncomment this line\n",
"# hyperparams.model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
"\n",
"hyperparams.mixed_precision = \"bf16\"\n",
"\n",
"\n",
"# Init the model and accelerator wrapper\n",
"model = init_baseline_model(hyperparams)\n",
"accelerator, model, optimizer, train_dataloader, lr_scheduler = wrap_with_accelerator(model, hyperparams)\n",
"\n",
"\n",
"# Finetune the model\n",
"finetune_model(model, hyperparams, accelerator, train_dataloader, optimizer, lr_scheduler)"
]
},
{
"cell_type": "markdown",
"id": "4035ccb7",
"metadata": {},
"source": [
"Let's add this information in a table and keep comparing it with a few possible improvements in future sections:\n",
"\n",
"| Models | Precision | Step Time (or ms per batch) | Speedup (over baseline) |\n",
"|-------------------------------------------------------------|-----------|-----------------------------|-------------------------|\n",
"| HF (baseline) | BF16 | 248 | 1 |"
]
},
{
"cell_type": "markdown",
"id": "3db90dff",
"metadata": {},
"source": [
"## [Improvement 1] Replace HF's `LlamaDecoderLayer` with TE's `TransformerLayer` (Precision: `BF16`)\n",
"\n",
"In addition to basic layers like `Linear` and `LayerNorm`, Transformer Engine offers larger modules like `MultiheadAttention` (combines \"LayerNorm\" and \"Self Attention\") and `LayerNormMLP` (combines \"LayerNorm\" and \"MLP\") that could replace their counterparts in the `LlamaDecoderLayer` and potentially provide a speedup. Transformer Engine also offers a full `TransformerLayer` (which further combines `MultiheadAttention` and `LayerNormMLP` layers) which could replace `LlamaDecoderLayer` and provide a speedup (with careful mapping of the weights since the name of the weights are different for those two layers). Let's take a closer look at Transformer Engine's `TransformerLayer`. \n",
"\n",
"#### Transformer Engine's `TransformerLayer`\n",
"\n",
"At a higher level, TE's `TransformerLayer` could be visualized as an apt replacement for the `LlamaDecoderLayer`. But the internals of the `TransformerLayer` are organized a bit differently. \n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/tellamadecoderlayer.svg\">\n",
" <figcaption> Fig 7: Transformer Engine's `TransformerLayer` </figcaption>\n",
"</figure>\n",
"\n",
"Just like Hugging Face's `LlamaDecoderLayer`, Transformer Engine's `TransformerLayer` encapsulates `self_attention` (as `MultiheadAttention`) and `mlp` (as `LayerNormMLP`). A major difference is that the two `Norm`s are included in the `MultiheadAttention` and `LayerNormMLP` layers as shown in the following output prompt:\n",
"\n",
"```\n",
"TransformerLayer(\n",
" (self_attention): MultiheadAttention(\n",
" (layernorm_qkv): LayerNormLinear()\n",
" (core_attention): DotProductAttention()\n",
" (proj): Linear()\n",
" )\n",
" (layernorm_mlp): LayerNormMLP()\n",
")\n",
"```\n",
"\n",
"Another difference is that Transformer Engine implements an efficient version of feedforward layer with SwiGLU in which the weights from the `up_proj` and `gate_proj` modules are merged together and SwiGLU is applied using a custom fused kernel. This is done so that only one big and efficient Matrix Multiplication operation is issued to the GPU instead of two smaller ones.\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/swiglu_te.svg\">\n",
" <figcaption> Fig 8: Abstract illustration of the SwiGLU implementation in Transformer Engine. </figcaption>\n",
"</figure>\n",
"\n",
"#### `TransformerLayer` options explained\n",
"\n",
"<div class=\"alert alert-info\">\n",
"\n",
"<b>Note</b>\n",
" \n",
"Here, we go over some of the options in `TransformerLayer` that are needed for the tutorial. For a complete list of options, refer the [TransformerLayer API documentation](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/pytorch.html?highlight=transformerlayer#transformer_engine.pytorch.TransformerLayer).\n",
"\n",
"</div>\n",
"\n",
"In the accompanying `te_llama.py` file, `TELlamaDecoderLayer` is defined as a wrapper over TE's `TransformerLayer` with a few needed options that make `TransformerLayer` a plug-in replacement for the HF's `LlamaDecoderLayer`.\n",
"\n",
"```\n",
"class TELlamaDecoderLayer(te.pytorch.TransformerLayer):\n",
" def __init__(self, config):\n",
" super().__init__(\n",
" config.hidden_size,\n",
" config.intermediate_size,\n",
" config.num_attention_heads,\n",
" bias=False,\n",
" layernorm_epsilon=config.rms_norm_eps,\n",
" hidden_dropout=0,\n",
" attention_dropout=0,\n",
" fuse_qkv_params=False,\n",
" normalization=\"RMSNorm\",\n",
" activation=\"swiglu\",\n",
" attn_input_format=\"bshd\",\n",
" num_gqa_groups=config.num_key_value_heads,\n",
" )\n",
" te_rope = RotaryPositionEmbedding(config.hidden_size//config.num_attention_heads)\n",
" self.te_rope_emb = te_rope(max_seq_len=config.max_position_embeddings).cuda()\n",
"```\n",
"\n",
"Here's a list summarizing each option briefly:\n",
"\n",
"1. `hidden_size`: size of each input sample.\n",
"2. `ffn_hidden_size`: intermediate size to which samples are projected.\n",
"3. `num_attention_heads`: number of attention heads in the transformer layer.\n",
"4. `bias`: switch to add additive biases to the submodule layers.\n",
"5. `layernorm_epsilon`: a value added to the denominator of layer normalization for numerical stability. Default is `1e-5`.\n",
"6. `hidden_dropout`: dropout probability for the dropout op after FC2 layer (fully connected layer no. 2). Default is `0.1`.\n",
"7. `attention_dropout`: dropout probability for the dropout op during multi-head attention. Default is `0.1`. \n",
"8. `fuse_qkv_params`: if set to True, TransformerLayer module exposes a single fused parameter for query-key-value. This enables optimizations such as QKV fusion without concatentations/splits and also enables the argument fuse_wgrad_accumulation.\n",
"9. `normalization`: type of normalization applied. Default is `LayerNorm`.\n",
"10. `activation`: type of activation used in the MLP block. Default is `gelu`.\n",
"11. `attn_input_format`: controls whether the dimensions of the intermediate hidden states is 'batch first' ('bshd') or 'sequence first' ('sbhd'). `s` stands for the sequence length, `b` batch size, `h` the number of heads, `d` head size. Note that these formats are very closely related to the `qkv_format` in the `MultiHeadAttention` and `DotProductAttention` modules.\n",
"12. `num_gqa_groups`: number of GQA groups in the transformer layer. Grouped Query Attention is described in [this paper](https://arxiv.org/pdf/2305.13245.pdf). This only affects the keys and values, not the querys. GQA-1 is equivalent to Multi-Query Attention ([MQA](https://arxiv.org/pdf/1911.02150.pdf)), while GQA-H is equivalent to MultiHead Attention, i.e. `num_gqa_groups = num_attention_heads`.\n",
"\n",
"\n",
"Further, note that `RotaryPositionEmbedding` is defined as part of the `TELlamaDecoderLayer` (wrapper around TE's `TransformerLayer`) itself since it expects this rope cache if RoPE is used in the model. \n",
"\n",
"Let's revisit how `LlamaDecoderLayer`s form the core of the decoder layer stack in HF's llama implementation:\n",
"```\n",
"ModuleList(\n",
" (0-31): 32 x LlamaDecoderLayer(\n",
" (self_attn): LlamaAttention(\n",
" (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (mlp): LlamaMLP(\n",
" (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
" (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
" (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
" (act_fn): SiLU()\n",
" )\n",
" (input_layernorm): LlamaRMSNorm()\n",
" (post_attention_layernorm): LlamaRMSNorm()\n",
" )\n",
")\n",
"```\n",
"\n",
"A major portion of the Hugging Face model implementation (32 `LlamaDecoderLayer` layers) could be potentially replaced with Transformer Engine's `TransformerLayer` layers. Let's see how it is made possible.\n",
"\n",
"\n",
"#### Mapping weights from HF's `LlamaDecoderLayer` to TE's `TransformerLayer`\n",
"\n",
"Refer the accompanying file `te_llama.py` which provides a reference to create a Llama 2 model with TE's `TransformerLayer` after replacing HF's `LlamaDecoderLayer`.\n",
"\n",
"Briefly, following pieces of code are put together:\n",
"\n",
"1. `TELlamaDecoderLayer` is added as a wrapper for `TransformerLayer`. \n",
"```\n",
"class TELlamaDecoderLayer(te.pytorch.TransformerLayer):\n",
" \"\"\"\n",
" Wrapper class over TE's `TransformerLayer`. This makes the wrapper very\n",
" similar to HF's `LlamaDecoderLayer` and easier to replace it in the code.\n",
"\n",
" Args:\n",
" config: LlamaConfig\n",
" args: positional args (for compatibility with `LlamaDecoderLayer`)\n",
" kwargs: keyword args (for compatibility with `LlamaDecoderLayer`)\n",
" \"\"\"\n",
" def __init__(self, config, *args, **kwargs):\n",
" super().__init__(\n",
" hidden_size=config.hidden_size,\n",
" ffn_hidden_size=config.intermediate_size,\n",
" num_attention_heads=config.num_attention_heads,\n",
" bias=False,\n",
" layernorm_epsilon=config.rms_norm_eps,\n",
" hidden_dropout=0,\n",
" attention_dropout=0,\n",
" fuse_qkv_params=False,\n",
" normalization=\"RMSNorm\",\n",
" activation=\"swiglu\",\n",
" attn_input_format=\"bshd\",\n",
" )\n",
" te_rope = RotaryPositionEmbedding(config.hidden_size//config.num_attention_heads)\n",
" self.te_rope_emb = te_rope(max_seq_len=config.max_position_embeddings).cuda()\n",
"\n",
" def forward(self,\n",
" hidden_states,\n",
" *args,\n",
" attention_mask,\n",
" **kwargs):\n",
" \"\"\"\n",
" Custom forward to make sure we only pass relevant arguments to the\n",
" forward pass of the `TransformerLayer`. Also, make sure the output\n",
" format matches the output of the HF's `LlamaDecoderLayer`.\n",
" \"\"\"\n",
" return (super().forward(hidden_states, attention_mask=attention_mask, rotary_pos_emb=self.te_rope_emb),)\n",
"```\n",
"\n",
"2. Before creating a `LlamaForCausalLM`, `replace_decoder` context manager is used to monkey-patch `LlamaDecoderLayer` with `TELlamaDecoderLayer`.\n",
"\n",
"```\n",
"@contextmanager\n",
"def replace_decoder(te_decoder_cls):\n",
" \"\"\"\n",
" Replace `LlamaDecoderLayer` with custom `TELlamaDecoderLayer`.\n",
" \"\"\"\n",
" original_llama_decoder_cls = transformers.models.llama.modeling_llama.LlamaDecoderLayer\n",
" transformers.models.llama.modeling_llama.LlamaDecoderLayer = te_decoder_cls\n",
" try:\n",
" yield\n",
" finally:\n",
" transformers.models.llama.modeling_llama.LlamaDecoderLayer = original_llama_decoder_cls\n",
".\n",
".\n",
".\n",
"class TELlamaForCausalLM:\n",
" \"\"\"\n",
" Causal LM created with `LlamaModel`. The underlying `LlamaDecoderLayer`\n",
" class is monkey-patched with `TELlamaDecoderLayer` class before\n",
" initializing the causal LM with `LlamaForCausalLM`.\n",
"\n",
" Args:\n",
" config: LlamaConfig\n",
" \"\"\"\n",
"\n",
" def __new__(cls, config: LlamaConfig):\n",
" with replace_decoder(te_decoder_cls=TELlamaDecoderLayer):\n",
" llama_for_causal_lm = LlamaForCausalLM(config)\n",
" return llama_for_causal_lm\n",
".\n",
".\n",
".\n",
"```\n",
"\n",
"3. A custom `pretrained_from_local` method is added that copies the weights from the checkpoint (which is meant for HF Llama implementation) to the modified `TELlamaForCausalLM` by carefully mapping the weights from the `LlamaDecoderLayer` (HF) to `TransformerLayer` (TE). The method `replace_params` maps and copies apt weights from `LlamaDecoderLayer` to the `TransformerLayer`. Refer to the following diagram for more details.\n",
"\n",
"```\n",
"def replace_params(hf_state_dict, te_state_dict):\n",
" # collect all layer prefixes to update\n",
" all_layer_prefixes = set()\n",
" for param_key in hf_state_dict.keys():\n",
" layer_prefix_pat = 'model.layers.\\d+.'\n",
" m = re.match(layer_prefix_pat, param_key)\n",
" if m is not None:\n",
" all_layer_prefixes.add(m.group())\n",
"\n",
" for layer_prefix in all_layer_prefixes:\n",
" # When loading weights into models with less number of layers, skip the\n",
" # copy if the corresponding layer doesn't exist in TE model\n",
" if layer_prefix + 'self_attention.layernorm_qkv.layer_norm_weight' in te_state_dict:\n",
" te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.layer_norm_weight'].data[:] = hf_state_dict[layer_prefix + 'input_layernorm.weight'].data[:]\n",
"\n",
" if layer_prefix + 'self_attention.layernorm_qkv.query_weight' in te_state_dict:\n",
" te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.query_weight'].data[:] = hf_state_dict[layer_prefix + 'self_attn.q_proj.weight'].data[:]\n",
"\n",
" if layer_prefix + 'self_attention.layernorm_qkv.key_weight' in te_state_dict:\n",
" te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.key_weight'].data[:] = hf_state_dict[layer_prefix + 'self_attn.k_proj.weight'].data[:]\n",
" .\n",
" .\n",
" .\n",
"\n",
" return all_layer_prefixes\n",
"```\n",
"\n",
"The following figure shows how the weights get mapped from the HF's `LlamaDecoderLayer` to TE's `TransformerLayer`.\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/weight_swap.svg\">\n",
" <figcaption> Fig 9: Replace `LlamaDecoderLayer` with `TransformerLayer`. </figcaption>\n",
"</figure>\n",
"\n",
"After initializing the modified Llama model this way, the core decoder layers get changed to `TELlamaDecoderLayer` (wrapper around `TransformerLayer`) as shown in the following output:\n",
"```\n",
"ModuleList(\n",
" (0-31): 32 x TELlamaDecoderLayer(\n",
" (self_attention): MultiheadAttention(\n",
" (layernorm_qkv): LayerNormLinear()\n",
" (core_attention): DotProductAttention(\n",
" (flash_attention): FlashAttention()\n",
" (fused_attention): FusedAttention()\n",
" (unfused_attention): UnfusedDotProductAttention(\n",
" (scale_mask_softmax): FusedScaleMaskSoftmax()\n",
" (attention_dropout): Dropout(p=0, inplace=False)\n",
" )\n",
" )\n",
" (proj): Linear()\n",
" )\n",
" (layernorm_mlp): LayerNormMLP()\n",
" )\n",
")\n",
"```\n",
"\n",
"In summary, the model gets changed as follows with a large chunk of the implementation (core decoder layers) coming from Transformer Engine.\n",
"\n",
"<figure align=\"center\">\n",
"<img src=\"media/model_change.svg\">\n",
" <figcaption> Fig 10: Language model after the HF's `LlamaDecoderLayer`s are replaced with TE's `TransformerLayer`s. </figcaption>\n",
"</figure>\n",
"\n",
"\n",
"<div class=\"alert alert-info\">\n",
"<b>Note</b>\n",
"\n",
"Let's first run this \"TELlama\" implementation in `BF16` precision.\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bdb34b91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 finetuning steps complete!\n",
"Average time taken per step: 185 milliseconds\n"
]
}
],
"source": [
"# Restart the notebook (to flush the GPU memory)\n",
"from utils import restart_jupyter_notebook\n",
"restart_jupyter_notebook()\n",
"\n",
"\n",
"# Import necessary packages, methods and variables\n",
"from utils import *\n",
"\n",
"\n",
"# Provide Huggingface Access Token\n",
"hyperparams.hf_access_token = \"\"\n",
"assert hyperparams.hf_access_token, \"Provide a HF API Access Token!\"\n",
"\n",
"# Provide a directory to cache weights in to avoid downloading them every time.\n",
"# (By default, weights are cached in `~/.cache/huggingface/hub/models`)\n",
"hyperparams.weights_cache_dir = \"\"\n",
"\n",
"# For Llama 2, uncomment this line (also set by default)\n",
"hyperparams.model_name = \"meta-llama/Llama-2-7b-hf\"\n",
"\n",
"# For Llama 3, uncomment this line\n",
"# hyperparams.model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
"\n",
"hyperparams.mixed_precision = \"bf16\"\n",
"\n",
"\n",
"# Init the model and accelerator wrapper\n",
"model = init_te_llama_model(hyperparams)\n",
"accelerator, model, optimizer, train_dataloader, lr_scheduler = wrap_with_accelerator(model, hyperparams)\n",
"\n",
"\n",
"# Finetune the model\n",
"finetune_model(model, hyperparams, accelerator, train_dataloader, optimizer, lr_scheduler)"
]
},
{
"cell_type": "markdown",
"id": "0c9fbd65",
"metadata": {},
"source": [
"Compared to the \"baseline\" implementation, we see that using Transformer Engine's `TransformerLayer` in place of Huggging Face's `LlamaDecoderLayer` gives a speedup of **34%** even when using only BF16 precision!\n",
"\n",
"| Models | Precision | Step Time (or ms per batch) | Speedup (over baseline) |\n",
"|-------------------------------------------------------------|-----------|-----------------------------|-------------------------|\n",
"| HF (baseline) | BF16 | 248 | 1 |\n",
"| TE (replace `LlamaDecoderLayer` with `TE.TransformerLayer`) | BF16 | 185 | 1.34 |"
]
},
{
"cell_type": "markdown",
"id": "98cd8efb",
"metadata": {},
"source": [
"## [Improvement 2] Replace HF's `LlamaDecoderLayer` with TE's `TransformerLayer` (Precision: `FP8`)\n",
"\n",
"Now that most of the HF Llama model implementation (`LlamaDecoderLayer`s) has been swapped with Transformer Engine implementation (`TELlamaDecoderLayer` or `TransformerLayer`), let's see how finetuning in `FP8` precision helps improve performance.\n",
"\n",
"#### How to run the model in `FP8` precision\n",
"\n",
"After the substitution, the model can be run in `FP8` precision by the following change over the previous BF16 runs. (For more information, refer the corresponding `wrap_with_accelerator` function in the accompanying `utils.py` file).\n",
"\n",
"```\n",
"# Specify the `FP8RecipeKwargs` (additional argument required to run in `fp8` precision)\n",
"fp8_kwarg_handler = [FP8RecipeKwargs(backend=\"te\")]\n",
"\n",
"# Pass the `FP8RecipeKwargs` to the `Accelerator` init call\n",
"accelerator = Accelerator(\n",
" ...\n",
" kwargs_handlers=fp8_kwarg_handler\n",
")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "772c6f22",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 finetuning steps complete!\n",
"Average time taken per step: 160 milliseconds\n"
]
}
],
"source": [
"# Restart the notebook (to flush the GPU memory)\n",
"from utils import restart_jupyter_notebook\n",
"restart_jupyter_notebook()\n",
"\n",
"\n",
"# Import necessary packages, methods and variables\n",
"from utils import *\n",
"\n",
"\n",
"# Provide Huggingface Access Token\n",
"hyperparams.hf_access_token = \"\"\n",
"assert hyperparams.hf_access_token, \"Provide a HF API Access Token!\"\n",
"\n",
"# Provide a directory to cache weights in to avoid downloading them every time.\n",
"# (By default, weights are cached in `~/.cache/huggingface/hub/models`)\n",
"hyperparams.weights_cache_dir = \"\"\n",
"\n",
"# For Llama 2, uncomment this line (also set by default)\n",
"hyperparams.model_name = \"meta-llama/Llama-2-7b-hf\"\n",
"\n",
"# For Llama 3, uncomment this line\n",
"# hyperparams.model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
"\n",
"hyperparams.mixed_precision = \"fp8\"\n",
"\n",
"\n",
"# Init the model and accelerator wrapper\n",
"model = init_te_llama_model(hyperparams)\n",
"accelerator, model, optimizer, train_dataloader, lr_scheduler = wrap_with_accelerator(model, hyperparams)\n",
"\n",
"\n",
"# Finetune the model\n",
"finetune_model(model, hyperparams, accelerator, train_dataloader, optimizer, lr_scheduler)"
]
},
{
"cell_type": "markdown",
"id": "e7cf9c3a",
"metadata": {},
"source": [
"| Models | Precision | Step Time (or ms per batch) | Speedup (over baseline) |\n",
"|-------------------------------------------------------------|-----------|-----------------------------|-------------------------|\n",
"| HF (baseline) | BF16 | 248 | 1 |\n",
"| TE (replace `LlamaDecoderLayer` with `TE.TransformerLayer`) | BF16 | 185 | 1.34 |\n",
"| TE (replace `LlamaDecoderLayer` with `TE.TransformerLayer`) | FP8 | 160 | 1.55 |\n",
"\n",
"\n",
"After turning on FP8 precision, we get even more speedup of **55%** (with Llama 2 7B)!\n",
"\n",
"#### Llama 3 performance results\n",
"Running the same tutorial with **Llama 3 8B** yields the following performance numbers:\n",
"\n",
"| Models | Precision | Step Time (or ms per batch) | Speedup (over baseline) |\n",
"|-------------------------------------------------------------|-----------|-----------------------------|-------------------------|\n",
"| HF (baseline) | BF16 | 270 | 1 |\n",
"| TE (replace `LlamaDecoderLayer` with `TE.TransformerLayer`) | BF16 | 217 | 1.24 |\n",
"| TE (replace `LlamaDecoderLayer` with `TE.TransformerLayer`) | FP8 | 185 | 1.46 |\n",
"\n",
"For Llama 3 8B, we get the most speedup of **46%** with FP8 precision!\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "95d6c42b",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"Using `TransformerLayer` module from Transformer Engine as a substitute for Hugging Face's `LlamaDecoderLayer` provides a speedup over Hugging Face's native Llama 2 and Llama 3 implementations. This needs careful initialization of the model such that the model weights (which are meant for `LlamaDecoderLayer`) are correctly mapped to their counterparts in TE's `TransformerLayer`. Even with `BF16` precision, `TransformerLayer` provides a speedup over the baseline implementation. With `FP8` precision, the speed up is even more pronounced!"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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docs/examples/te_llama/utils.py 0 → 100644
View file @ 5b6ef054
# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import time
import sys
import IPython
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
get_linear_schedule_with_warmup,
AutoConfig,
)
from transformers import DataCollatorForLanguageModeling
from datasets import load_dataset
from accelerate import Accelerator
from accelerate.utils.dataclasses import FP8RecipeKwargs
class HyperParameters:
def __init__(self):
self.mixed_precision = "bf16"
# Set to Meta Llama 2 by default.
self.model_name = "meta-llama/Llama-2-7b-hf"
self.dataset_name = "timdettmers/openassistant-guanaco"
self.dataset_text_field = "text"
self.learning_rate = 1.41e-5
self.batch_size = 8
self.max_seq_length = 256
self.gradient_accumulation_steps = 1
self.num_warmup_steps = 5
self.num_training_steps = 10
# This is either provided by the user or it will be set when the
# model weights are downloaded.
self.weights_cache_dir = ""
hyperparams = HyperParameters()
def get_dataloaders(accelerator: Accelerator, hyperparams):
dataset = load_dataset(hyperparams.dataset_name, split="train")
tokenizer = AutoTokenizer.from_pretrained(hyperparams.model_name)
if getattr(tokenizer, "pad_token", None) is None:
tokenizer.pad_token = tokenizer.eos_token
def tokenize(element):
outputs = tokenizer(
element["text"],
truncation=True,
padding=False,
max_length=hyperparams.max_seq_length,
return_overflowing_tokens=False,
return_length=False,
)
return {"input_ids": outputs["input_ids"], "attention_mask": outputs["attention_mask"]}
with accelerator.main_process_first():
dataset = dataset.map(tokenize, batched=True, remove_columns=dataset.column_names)
# Simply pad to the multiple of 16 for both FP8 and BF16 precision
pad_to_multiple_of = 16
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False,
pad_to_multiple_of=pad_to_multiple_of,
)
dataloader_params = {
"batch_size": hyperparams.batch_size,
"collate_fn": data_collator,
"drop_last": True,
}
train_dataloader = DataLoader(dataset, **dataloader_params)
return train_dataloader
def ensure_model_is_downloaded(hyperparams):
assert hyperparams.model_name in [
"meta-llama/Meta-Llama-3-8B",
"meta-llama/Llama-2-7b-hf",
], "Only Meta Llama 2 7B and Meta Llama 3 8B models are supported!"
# Login using Huggingface Hub API
from huggingface_hub import login
try:
login(hyperparams.hf_access_token)
except Exception as e:
if "Invalid token passed!" in str(e):
print(
"Please pass a valid HF Access Token! More info at"
" https://huggingface.co/docs/hub/en/security-tokens."
)
else:
print(f"Exception is {e}")
# Download the model if it doesn't exist
from huggingface_hub import snapshot_download
supplied_cache_dir = (
hyperparams.weights_cache_dir if hyperparams.weights_cache_dir != "" else None
)
hyperparams.weights_cache_dir = snapshot_download(
repo_id=hyperparams.model_name, cache_dir=supplied_cache_dir
)
print(f"Model cache directory : {hyperparams.weights_cache_dir}")
def init_baseline_model(hyperparams):
# Download and cache the weights
ensure_model_is_downloaded(hyperparams)
# Init the model
config = AutoConfig.from_pretrained(hyperparams.weights_cache_dir)
# make sure to use flash_attention to do iso comparison with TELlamaModel
config._attn_implementation = "flash_attention_2"
model = AutoModelForCausalLM.from_pretrained(
hyperparams.weights_cache_dir,
config=config,
torch_dtype=torch.bfloat16,
)
model = model.cuda()
# Needed for the cases when using TELlamaForCausalLM. So adding here for 1:1 comparison
model.config.use_cache = False
return model
def init_te_llama_model(hyperparams):
# Download and cache the weights
ensure_model_is_downloaded(hyperparams)
# Init the model
from te_llama import TELlamaForCausalLM
config = AutoConfig.from_pretrained(hyperparams.weights_cache_dir)
config._attn_implementation = "flash_attention_2"
model = TELlamaForCausalLM.from_pretrained_local(
hyperparams.weights_cache_dir,
config=config,
torch_dtype=torch.bfloat16,
)
model = model.cuda()
# Needed for the cases when using TELlamaForCausalLM
model.config.use_cache = False
return model
def wrap_with_accelerator(model, hyperparams):
# Create FP8 kwarg handler if required
fp8_kwarg_handler = (
[FP8RecipeKwargs(backend="te")] if hyperparams.mixed_precision == "fp8" else None
)
# Init HF accelerator that's used for training
accelerator = Accelerator(
log_with="wandb",
gradient_accumulation_steps=hyperparams.gradient_accumulation_steps,
mixed_precision=hyperparams.mixed_precision,
kwargs_handlers=fp8_kwarg_handler,
)
# accelerator.print(f'State: {accelerator.state}')
train_dataloader = get_dataloaders(accelerator, hyperparams)
# Wrap model, optimizer/scheduler, dataloaders in accelerate
optimizer = AdamW(params=model.parameters(), lr=hyperparams.learning_rate, fused=True)
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=100,
num_training_steps=hyperparams.num_training_steps,
)
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
return accelerator, model, optimizer, train_dataloader, lr_scheduler
def finetune_model(model, hyperparams, accelerator, train_dataloader, optimizer, lr_scheduler):
model.train()
total_loss = 0
optimizer.zero_grad()
train_dataloader = enumerate(train_dataloader)
# Warmup iters
for _ in range(hyperparams.num_warmup_steps):
step, batch = next(train_dataloader)
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Get the timers ready
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
torch.cuda.synchronize()
start.record()
# Training iters
for _ in range(hyperparams.num_training_steps):
step, batch = next(train_dataloader)
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
torch.cuda.synchronize()
end.record()
accelerator.end_training()
print(
f"{hyperparams.num_training_steps} finetuning steps complete!\nAverage time taken per step:"
f" {(start.elapsed_time(end)/hyperparams.num_training_steps):.0f} milliseconds"
)
def restart_jupyter_notebook():
# Try restarting the Jupyter kernel
IPython.Application.instance().kernel.do_shutdown(True)
# Check whether the device memory has been flushed
if torch.cuda.memory_allocated() != 0:
import warnings
warnings.warn("The device memory hasn't been flushed, trying with a second method!")
# Try restarting the Jupyter kernel another way
# Restart the kernel
from IPython.core.display import HTML
HTML("<script>Jupyter.notebook.kernel.restart()</script>")
if torch.cuda.memory_allocated() != 0:
print(
"The device memory hasn't been flushed, try manually restarting the Jupyter kernel!"
)
# Suppress the warnings
if not sys.warnoptions:
import warnings
warnings.simplefilter("ignore")
torch.set_warn_always(False)
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..
Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
See LICENSE for license information.
Frequently Asked Questions (FAQ)
================================
FP8 checkpoint compatibility
----------------------------
Transformer Engine starts to support FP8 attention in 1.6. It stores the FP8 metadata, i.e. scaling factors and amax histories, under a `._extra_state` key in the checkpoint. As the FP8 attention support expands from one backend to multiple backends, the location of the `._extra_state` key has also shifted.
Here, we take the `MultiheadAttention` module as an example. Its FP8 attention metadata in Transformer Engine 1.11 is stored as `core_attention._extra_state` as shown below.
.. code-block:: python
>>> from transformer_engine.pytorch import MultiheadAttention, fp8_model_init
>>> with fp8_model_init(enabled=True):
... mha = MultiheadAttention(
... hidden_size=1024,
... num_attention_heads=16,
... bias=True,
... params_dtype=torch.bfloat16,
... input_layernorm=False,
... fuse_qkv_params=True,
... attention_type="self",
... qkv_weight_interleaved=True,
... ).to(dtype=torch.bfloat16, device="cuda")
...
>>> state_dict = mha.state_dict()
>>> print(state_dict.keys())
odict_keys(['qkv.weight', 'qkv.bias', 'qkv._extra_state', 'core_attention._extra_state', 'proj.weight', 'proj.bias', 'proj._extra_state'])
Here is a full list of the checkpoint save/load behaviors from all Transformer Engine versions.
.. list-table::
* - **Version: <= 1.5**
- Saves no FP8 metadata since FP8 attention is not supported
- Loading behavior for checkpoints created by the following versions:
:<= 1.5: Loads no FP8 metadata
:> 1.5: Error: unexpected key
* - **Version: 1.6, 1.7**
- Saves FP8 metadata to `core_attention.fused_attention._extra_state`
- Loading behavior for checkpoints created by the following versions:
:<= 1.5: Initializes FP8 metadata to the default, i.e. 1s for scaling factors, and 0s for amaxes
:1.6, 1.7: Loads FP8 metadata from checkpoint
:>= 1.8: Error: unexpected key
* - **Version: >=1.8, <= 1.11**
- Saves FP8 metadata to `core_attention._extra_state`
- Loading behavior for checkpoints created by the following versions:
:<= 1.5: Initializes FP8 metadata to the default, i.e. 1s for scaling factors, and 0s for amaxes
:1.6, 1.7: This save/load combination relies on users to map the 1.6/1.7 key to the 1.8-1.11 key. Otherwise, it initializes FP8 metadata to the default, i.e. 1s for scaling factors, and 0s for amaxes. The mapping can be done, in this `MultiheadAttention` example, by
.. code-block:: python
>>> state_dict["core_attention._extra_state"] = \
state_dict["core_attention.fused_attention._extra_state"]
>>> del state_dict["core_attention.fused_attention._extra_state"]
:>= 1.8: Loads FP8 metadata from checkpoint
* - **Version: >=1.12**
- Saves FP8 metadata to `core_attention._extra_state`
- Loading behavior for checkpoints created by the following versions:
:<= 1.5: Initializes FP8 metadata to the default, i.e. 1s for scaling factors, and 0s for amaxes
:>= 1.6: Loads FP8 metadata from checkpoint
docs/index.rst 0 → 100644
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..
Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
See LICENSE for license information.
Transformer Engine documentation
==============================================
.. ifconfig:: "dev" in release
.. warning::
You are currently viewing unstable developer preview of the documentation.
To see the documentation for the latest stable release, refer to:
* `Release Notes <https://docs.nvidia.com/deeplearning/transformer-engine/release-notes/index.html>`_
* `Developer Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html>`_ (stable version of this page)
.. include:: ../README.rst
:start-after: overview-begin-marker-do-not-remove
:end-before: overview-end-marker-do-not-remove
.. toctree::
:hidden:
Home <self>
.. toctree::
:hidden:
:caption: Getting Started
installation
examples/quickstart.ipynb
faq
.. toctree::
:hidden:
:caption: Python API documentation
api/common
api/framework
.. toctree::
:hidden:
:caption: Examples and Tutorials
examples/fp8_primer.ipynb
examples/advanced_optimizations.ipynb
examples/te_llama/tutorial_accelerate_hf_llama_with_te.ipynb
.. toctree::
:hidden:
:caption: Advanced
api/c/index
examples/attention/attention.ipynb
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..
Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
See LICENSE for license information.
Installation
============
Prerequisites
-------------
.. |driver link| replace:: NVIDIA Driver
.. _driver link: https://www.nvidia.com/drivers
1. Linux x86_64
2. `CUDA 12.1+ (12.8+ for Blackwell support) <https://developer.nvidia.com/cuda-downloads>`__
3. |driver link|_ supporting CUDA 12.1 or later.
4. `cuDNN 9.3 <https://developer.nvidia.com/cudnn>`__ or later.
If the CUDA Toolkit headers are not available at runtime in a standard
installation path, e.g. within `CUDA_HOME`, set
`NVTE_CUDA_INCLUDE_PATH` in the environment.
Transformer Engine in NGC Containers
------------------------------------
Transformer Engine library is preinstalled in the PyTorch container in versions 22.09 and later
on `NVIDIA GPU Cloud <https://ngc.nvidia.com>`_.
pip - from PyPI
-----------------------
Transformer Engine can be directly installed from `our PyPI <https://pypi.org/project/transformer-engine/>`_, e.g.
.. code-block:: bash
pip3 install transformer_engine[pytorch]
To obtain the necessary Python bindings for Transformer Engine, the frameworks needed must be explicitly specified as extra dependencies in a comma-separated list (e.g. [jax,pytorch]). Transformer Engine ships wheels for the core library. Source distributions are shipped for the JAX and PyTorch extensions.
pip - from GitHub
-----------------------
Additional Prerequisites
^^^^^^^^^^^^^^^^^^^^^^^^
1. [For PyTorch support] `PyTorch <https://pytorch.org/>`__ with GPU support.
2. [For JAX support] `JAX <https://github.com/google/jax/>`__ with GPU support, version >= 0.4.7.
Installation (stable release)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Execute the following command to install the latest stable version of Transformer Engine:
.. code-block:: bash
pip3 install git+https://github.com/NVIDIA/TransformerEngine.git@stable
This will automatically detect if any supported deep learning frameworks are installed and build Transformer Engine support for them. To explicitly specify frameworks, set the environment variable `NVTE_FRAMEWORK` to a comma-separated list (e.g. `NVTE_FRAMEWORK=jax,pytorch`).
Installation (development build)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. warning::
While the development build of Transformer Engine could contain new features not available in
the official build yet, it is not supported and so its usage is not recommended for general
use.
Execute the following command to install the latest development build of Transformer Engine:
.. code-block:: bash
pip3 install git+https://github.com/NVIDIA/TransformerEngine.git@main
This will automatically detect if any supported deep learning frameworks are installed and build Transformer Engine support for them. To explicitly specify frameworks, set the environment variable `NVTE_FRAMEWORK` to a comma-separated list (e.g. `NVTE_FRAMEWORK=jax,pytorch`). To only build the framework-agnostic C++ API, set `NVTE_FRAMEWORK=none`.
In order to install a specific PR, execute (after changing NNN to the PR number):
.. code-block:: bash
pip3 install git+https://github.com/NVIDIA/TransformerEngine.git@refs/pull/NNN/merge
Installation (from source)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Execute the following commands to install Transformer Engine from source:
.. code-block:: bash
# Clone repository, checkout stable branch, clone submodules
git clone --branch stable --recursive https://github.com/NVIDIA/TransformerEngine.git
cd TransformerEngine
export NVTE_FRAMEWORK=pytorch # Optionally set framework
pip3 install . # Build and install
If the Git repository has already been cloned, make sure to also clone the submodules:
.. code-block:: bash
git submodule update --init --recursive
Extra dependencies for testing can be installed by setting the "test" option:
.. code-block:: bash
pip3 install .[test]
To build the C++ extensions with debug symbols, e.g. with the `-g` flag:
.. code-block:: bash
pip3 install . --global-option=--debug
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diff --git a/sphinx_rtd_theme/layout.html b/sphinx_rtd_theme/layout.html
index e6a38b1..579eaec 100644
--- a/sphinx_rtd_theme/layout.html
+++ b/sphinx_rtd_theme/layout.html
@@ -124,6 +124,16 @@
{%- endif %}
</a>
+ {# Show TE version and version selector #}
+ <div class="version">
+ {{ version }}
+ <br>
+ Version select: <select onChange="window.location.href = this.value" onFocus="this.selectedIndex = {0}">
+ <option value="https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html"{1}>Current release</option>
+ <option value="https://docs.nvidia.com/deeplearning/transformer-engine/documentation-archive.html">Older releases</option>
+ </select>
+ </div>
+
{%- if READTHEDOCS or DEBUG %}
{%- if theme_version_selector or theme_language_selector %}
<div class="switch-menus">
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# Examples
We provide a variety of examples for deep learning frameworks including [PyTorch](https://github.com/pytorch/pytorch) and [JAX](https://github.com/jax-ml/jax).
Additionally, we offer [Jupyter notebook tutorials](https://github.com/NVIDIA/TransformerEngine/tree/main/docs/examples) and a selection of [third-party examples](#third-party). Please be aware that these third-party examples might need specific, older versions of dependencies to function properly.
# PyTorch
- [Accelerate Hugging Face Llama models with TE](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/te_llama/tutorial_accelerate_hf_llama_with_te.ipynb)
- Provides code examples and explanations for integrating TE with the LLaMA2 and LLaMA2 models.
- [PyTorch FSDP with FP8](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/fsdp)
- **Distributed Training**: How to set up and run distributed training using PyTorch’s FullyShardedDataParallel (FSDP) strategy.
- **TE Integration**: Instructions on integrating TE/FP8 with PyTorch for optimized performance.
- **Checkpointing**: Methods for applying activation checkpointing to manage memory usage during training.
- [Attention backends in TE](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/attention/attention.ipynb)
- **Attention Backends**: Describes various attention backends supported by Transformer Engine, including framework-native, fused, and flash-attention backends, and their performance benefits.
- **Flash vs. Non-Flash**: Compares the flash algorithm with the standard non-flash algorithm, highlighting memory and computational efficiency improvements.
- **Backend Selection**: Details the logic for selecting the most appropriate backend based on availability and performance, and provides user control options for backend selection.
- [Overlapping Communication with GEMM](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/comm_gemm_overlap)
- Training a TE module with GEMM and communication overlap, including various configurations and command-line arguments for customization.
- [Performance Optimizations](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/advanced_optimizations.ipynb)
- **Multi-GPU Training**: How to use TE with data, tensor, and sequence parallelism.
- **Gradient Accumulation Fusion**: Utilizing Tensor Cores to accumulate outputs directly into FP32 for better numerical accuracy.
- **FP8 Weight Caching**: Avoiding redundant FP8 casting during multiple gradient accumulation steps to improve efficiency.
- [Introduction to FP8](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/fp8_primer.ipynb)
- Overview of FP8 datatypes (E4M3, E5M2), mixed precision training, delayed scaling strategies, and code examples for FP8 configuration and usage.
- [TE Quickstart](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb)
- Introduction to TE, building a Transformer Layer using PyTorch, and instructions on integrating TE modules like Linear and LayerNorm.
- [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/mnist)
# JAX
- [Basic Transformer Encoder Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/jax/encoder)
- Single GPU Training: Demonstrates setting up and training a Transformer model using a single GPU.
- Data Parallelism: Scale training across multiple GPUs using data parallelism.
- Model Parallelism: Divide a model across multiple GPUs for parallel training.
- Multiprocessing with Model Parallelism: Multiprocessing for model parallelism, including multi-node support and hardware affinity setup.
- [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/jax/mnist)
# Third party
- [Hugging Face Accelerate + TE](https://github.com/huggingface/accelerate/tree/main/benchmarks/fp8/transformer_engine)
- Scripts for training with Accelerate and TE. Supports single GPU, and multi-GPU via DDP, FSDP, and DeepSpeed ZeRO 1-3.
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# Transformer Engine Examples #
This folder contains simple examples introducing Transformer Engine and FP8 training usage.
**Examples Outline**
* MNIST training: Training MNIST dataset is a good start point to learn how use Transformer Engine and enable FP8 training
* Encoder training: The encoder examples introduce more about how to scale up training on multiple GPUs with Transformer Engine
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# Basic Transformer Encoder Example with Optional FP8 #
This example uses Transformer Encoder to demonstrate the Transformer Engine usage. And more focus on scaling up training on multiple GPUs. Highly recommend studying the [MNIST example of the Transformer Engine](/examples/jax/mnist) before reading this example. The Transformer Engine is built on top of [Flax](https://github.com/google/flax). Thus, examples use `jit` `in `in_shardings` and `out_shardings` parameters to set up multiple GPU training. The basic parallel jit usage can be referred to [Scale up Flax Modules on multiple devices](https://flax.readthedocs.io/en/latest/guides/flax_on_pjit.html).
## Single GPU ##
1. Setup dataset: This is done by using the `tfds` library to download the GLUE/CoLA dataset and using `nltk` to tokenize the sentences. This example focuses on Transformer Engine usage. Thus, a simple algorithm is used to convert tokens to INT32 tensors as input to the embedding layer. The `get_datasets` and `data_preprocess` routines are used for this purpose.
2. Define model: The `Net` class is a small Transformer Encoder model for sentence classification. The Transformer Engine provides `te.TransformerLayer` as encoder block and `te.DenseGeneral`. The structure of encoder block can be referred to [Scaling Up Models and Data with t5x and seqio](https://arxiv.org/abs/2203.17189)
3. Build training loop: The `train_and_evaluate` is the main routine to initialize the model and start training and evaluating. Use `fp8_autocast` context manager to enable FP8 training and check `var_collect` if the variable collection contains `Float8`.
4. Training process: In `train_step`, combine the FP8 metadata and latest model parameters into var_collect as a frozen dictionary and fill it to the gradient function.
5. Evaluating process: Same as the training process, the FP8 metadata needs to be in var_collect and fill it into a loss function, if enabling FP8 computing.
### Run ###
```bash
python test_single_gpu_encoder.py
python test_single_gpu_encoder.py --use-fp8
```
## Multiple GPU with Data Parallelism ##
1. The data parallelism (DP) divides a mini-batch for multiple devices, and each device has complete model parameters. In this example, the first dimension of input tensor is `batch_size` which is 64 by default, and uses 8 GPUs to train the model, so each device takes 8 sentences at once. The "dividing" is called "sharding" in the JAX documents.
2. In order to let JAX know how to do sharding, the `device_mesh` needs to be defined and each axis need to be named. A common way to annotate axis names is `data` which means the mesh dimension used for data-parallel sharding of the batch dimension of inputs and activations. And the first argument of `te.ShardingResource` is the name of the device axis which is used for data parallelism.
3. On the model side, the logical axis of each weight tensor of the model can be named. The `te.TransformerLayer` has the default names, which are stored in `abs_var_collect`, a collection of variables returned by `jax.eval_shape(encoder.init, ...)`. The key index is `params_axes`. The `te.DenseGeneral` doesn't have the default named axis because it is generic. Also, data-parallel sharding doesn't need to divide weight tensor, so named axis is not required for this case.
4. The next is to create sharding rules, mapping the device axis to the logical axis. The `te.extend_logical_axis_rules` under fp8_autocast will return a list of pairs of the mapping, such as `(('batch', 'data'), ...)`. The first is the logical axis and second is the device axis.
5. Refer structure of `abs_var_collect['params']` and `abs_var_collect['params_axes']` to set up `PartitionSpec` for parallel jit. All logical axes should be replaced by device axes. If the value of PartitionSpec is None, that means no sharding, broadcasting the data to every device. Note that the `params_axes` attribute is provided by Transformer Engine. The Flax's module doesn't have it, such as `nn.Embed`. For nn.Embed, assigning an empty PartitionSpec is fine because each device has its own embedding layer in DP mode. The `get_params_pspec` routine is used for this purpose. Because each device has a complete model in DP mode, all values of PartitionSpec in params_pspec should be None. This will be different in the model parallelism example.
6. Fill in `params_sharding` and `encoder.init` to jit to get a compiled function, `jit_encoder_init `, and use it to initialize the model, so JAX now can know how to do the sharding.
7. The `train_step` and `eval_step` also need to be compiled by jit. Thus, every input and output argument has to be set up `PartitionSpec` if the argument contains a tensor. For instance, the `input_pspec` is `PartitionSpec('data', None)` because the input shape is (batch size, sequence length). Then, the rest of the workflow is similar to the previous example.
8. Use `CUDA_VISIBLE_DEVICES` to control the number of GPUs used. For example, if the system has 8 GPUs but only 4 GPUs need to be used, then:
```sh
export CUDA_VISIBLE_DEVICES=0,1,2,3
python test_multigpu_encoder.py
```
Please refer to [CUDA Environment Variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#cuda-environment-variables) for more details.
### Run ###
```bash
python test_multigpu_encoder.py
python test_multigpu_encoder.py --use-fp8
```
## Multiple GPU with Model Parallelism ##
1. The model parallelism as known as tensor parallelism (TP) divides a model for multiple devices, and each device has part of model parameters. This example inherits previous DP example, but divides a model to two devices.
2. To set up device mesh for TP, adding a new named axis called `model`, which is used for sharding parameters of the model across devices. This example divides the model to two parts (`num_gpu_tp = 2`). One device only has half of the model.
3. On the model side, The `te.TransformerLayer` doesn't need additional settings because it has the default axis name already. It will be divided by `DEVICE_TP_AXIS` when model initialization. The first `te.DenseGeneral` is divided by columns and second one is divided by rows for TP. Because `te.DenseGeneral` doesn't have the default named axis, the names must be set manually by passing `kernel_axes` and `bias_axes` arguments. Then, the rest of the workflow is similar to the previous example.
4. The tips for debugging TP:
* Use [inspect_array_sharding](https://jax.readthedocs.io/en/latest/_autosummary/jax.debug.inspect_array_sharding.html) or [visualize_array_sharding](https://jax.readthedocs.io/en/latest/_autosummary/jax.debug.visualize_array_sharding.html) to check the shape of activations and weights.
* Check the shape of device buffer of weight tensor. For instance, `var_collect['params']['DenseGeneral_0']['kernel'].device_buffers[device_id].shape`. The `device_id` is an integer. If a weight tensor's shape is (256, 256) and you intend to divide it for two devices by second dimension, then the shape returned by device_buffers should be (256, 128).
* Dump XLA HLO by setting `XLA_FLAGS` and see whether it contains unexpected `all-gather` operations or not.
```python
import os
os.environ['XLA_FLAGS'] = "--xla_dump_hlo_as_proto --xla_dump_hlo_as_text --xla_dump_hlo_as_html --xla_dump_to=<path to store XLA HLO>"
```
5. If the model parallelism example is run in the container, it is recommended to add `--ipc=host` in launch arguments. Otherwise, it might trigger UCX errors.
```sh
docker run --gpus=all --ipc=host ...
```
### Run ###
```bash
python test_model_parallel_encoder.py
python test_model_parallel_encoder.py --use-fp8
```
## Multiple Processes with Model Parallelism ##
1. This example inherits previous model parallelism example, but uses multiprocessing instead of single-program multiple-data (SPMD). It uses 1 GPU per process.
2. There is two main benefits of multiprocessing: support multi-node and to setup hardware affinity for GPUs, such as NUMA binding. Affinity may help improve performance and stability. Please refer to [Best Practices When Benchmarking CUDA Applications](https://www.nvidia.com/en-us/on-demand/session/gtcsiliconvalley2019-s9956/) for more details.
3. The quick way to check system topology is to use `nvidia-smi`, for example:
```sh
$ nvidia-smi topo -mp
CPU Affinity NUMA Affinity
GPU0 48-63,176-191 3
GPU1 48-63,176-191 3
GPU2 16-31,144-159 1
GPU3 16-31,144-159 1
GPU4 112-127,240-255 7
GPU5 112-127,240-255 7
GPU6 80-95,208-223 5
GPU7 80-95,208-223 5
```
4. It is recommended to set the environment variable `CUDA_DEVICE_ORDER` to `PCI_BUS_ID` before running the example with the affinity setting. To ensure that the device order is aligned between CUDA and `nvidia-smi`. Please refer to [CUDA Environment Variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#cuda-environment-variables) for more details.
5. `jax.distributed.initialize` must be called before any other JAX or Flax API, otherwise `jax.local_devices` will be incorrect. `jax.distributed.shutdown` should be the last API call.
6. Unlike SPMD, the input tensor must be sharded manually and be wrapped by `jax.make_array_from_single_device_arrays`. Otherwise, the sharding will be incorrect. Using DP=4, TP=2 as an example, the device mesh looks like:
```python
mesh.device_ids = [[0, 1],
[2, 3],
[4, 5],
[6, 7]]
```
Assume that the process ID is mapped to GPU ID. The process 0 and process 1 are grouped for model parallelism, the process 2 and process 3 are grouped together too, and so on. Thus, process 0 and process 1 need to share the same micro-batch in the training step, process 0 and process 2, 4, and 6 have different micro-batch.
### Run ###
If the system has 8 GPUs, the basic commands are:
```bash
python test_multiprocessing_encoder.py --num-process 8 --process-id 0 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 1 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 2 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 3 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 4 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 5 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 6 &
python test_multiprocessing_encoder.py --num-process 8 --process-id 7 &
```
The correct setting for hardware affinity is system dependent. Taking the above system topology as an example, the command can be:
```bash
numactl --cpunodebind=48 --membind=3 python test_multiprocessing_encoder.py --num-process 8 --process-id 0 &
numactl --cpunodebind=49 --membind=3 python test_multiprocessing_encoder.py --num-process 8 --process-id 1 &
numactl --cpunodebind=16 --membind=1 python test_multiprocessing_encoder.py --num-process 8 --process-id 2 &
numactl --cpunodebind=17 --membind=1 python test_multiprocessing_encoder.py --num-process 8 --process-id 3 &
numactl --cpunodebind=112 --membind=7 python test_multiprocessing_encoder.py --num-process 8 --process-id 4 &
numactl --cpunodebind=113 --membind=7 python test_multiprocessing_encoder.py --num-process 8 --process-id 5 &
numactl --cpunodebind=80 --membind=5 python test_multiprocessing_encoder.py --num-process 8 --process-id 6 &
numactl --cpunodebind=81 --membind=5 python test_multiprocessing_encoder.py --num-process 8 --process-id 7 &
```
\ No newline at end of file
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# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""Shared functions for the encoder tests"""
from functools import lru_cache
from transformer_engine_jax import get_device_compute_capability
@lru_cache
def is_bf16_supported():
"""Return if BF16 has hardware supported"""
gpu_arch = get_device_compute_capability(0)
return gpu_arch >= 80
@lru_cache
def is_fp8_supported():
"""Return if FP8 has hardware supported"""
gpu_arch = get_device_compute_capability(0)
return gpu_arch >= 90
examples/jax/encoder/conftest.py 0 → 100644
View file @ 5b6ef054
# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""config for test_multiprocessing_encoder"""
import pytest
def pytest_addoption(parser):
"""Pytest hook for test_multiprocessing_encoder"""
parser.addoption("--num-process", action="store", default=0)
parser.addoption("--process-id", action="store", default=0)
@pytest.fixture(autouse=True)
def multiprocessing_parses(request):
"""Fixture for querying num-process and process-id"""
if request.cls:
request.cls.num_process = int(request.config.getoption("--num-process"))
request.cls.process_id = int(request.config.getoption("--process-id"))
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