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Mixed precision training

The SageMaker model parallelism (SMP) library v2 supports mixed precision training out of the box by integrating with open source frameworks such as PyTorch FSDP and Transformer Engine. To learn more, see the following topics.

Mixed precision training with FP8 on P5 instances using Transformer Engine

Starting from the SageMaker model parallelism (SMP) library v2.2.0, the SMP library integrates with Transformer Engine and supports FP8 mixed precision training out of the box, keeping compatibility with PyTorch FSDP MixedPrecision. This means that you can use both PyTorch FSDP for mixed precision training and Transformer Engine for FP8 training. For model layers not supported by Transformer Engine's FP8 training feature, those layers fall back to PyTorch FSDP mixed precision.

FP8 (8-bit floating point precision) is a data type that has emerged as another paradigm to accelerate deep learning training of LLM models. With the release of NVIDIA H100 GPUs supporting FP8 data types, you can benefit from the advantages from the performance improvements on P5 instances equipped with the H100 GPUs, while accelerating distributed training with FP8 mixed precision training.

The FP8 data type further branches down to E4M3 and E5M2 formats. E4M3 offers a better precision, has a limited dynamic range, and is ideal for the forward pass in model training. E5M2 has a broader dynamic range, but reduced precision, and is better suited for the backward pass, where precision is less critical and a wider dynamic range becomes beneficial. Hence, we recommend that you use the hybrid FP8 strategy recipe to leverage these characteristics effectively.

For half-precision data types (FP16 and BF16), global loss-scaling techniques such as static loss-scaling or dynamic loss-scaling handle convergence issues that arise from information loss due to rounding gradients in half-precision. However, the dynamic range of FP8 is even narrower, and the global loss scaling techniques are not sufficient. At this point, we need a finer-grained per-tensor scaling technique. Delayed scaling is a strategy that selects a scaling factor based on the maximum absolute values observed in a number of tensors form previous iterations. There's a trade-off in this strategy; it uses the full performance benefits of FP8 computation but requires memory for keeping the maximum value history of tensors. To learn more about the delayed scaling strategy in general, see the paper FP8 Formats for Deep Learning.

In practice, using FP8 is helpful in all training scenarios on P5 instances. We strongly recommend enabling FP8 whenever possible for enhancing training performance.

SMP v2 supports Transformer Engine out of the box. Therefore, when running FP8 training with SMP v2 on P5 instances of SageMaker (ml.p5.48xlarge), the only thing you need to do is to import torch.sagemaker in your training script and keep using the native Transformer Engine Python package. To learn more about using Transformer Engine for FP8 training in general, see Using FP8 with Transformer Engine in the NVIDIA Transformer Engine documentation. The following code snippet shows how the code lines for importing the SMP library and setting up FP8 in your training script should look.

import torch.sagemaker as tsm
import transformer_engine.pytorch as te
from transformer_engine.common.recipe import DelayedScaling, Format

# Initialize the SMP torch.sagemaker API.
tsm.init()

# Define a transformer model and wrap it with the torch.sagemaker.transform API.
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_config(ModelConfig)
model = tsm.transform(model)

# Enable E4M3 during forward pass, E5M2 during backward pass.
fp8_format = Format.HYBRID

# Create an FP8 recipe.
fp8_recipe = DelayedScaling(fp8_format=fp8_format, amax_history_len=32, amax_compute_algo="max")

# Enable FP8 autocasting.
with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe, fp8_group=tsm.state.world_process_group):
    out = model(inp)

loss = out.sum()
loss.backward()

To find a practical example of FP8 training with SMP v2 on P5 instances, see the example notebook at Accelerate SageMaker PyTorch FSDP Training of Llama-v2 (or GPT-NeoX) with FP8 on P5 instances.

Mixed precision training with half-precision data types using PyTorch FSDP

SMP v2 supports PyTorch FSDP MixedPrecision for training jobs on P4 and P5 instances. PyTorch FSDP provides various configurations for mixed precision for both performance improvement and memory reduction.

The standard way to configure a model for mixed precision is to create the model in float32, and then allow FSDP to cast the parameters to float16 or bfloat16 on the fly by passing a MixedPrecision policy, as shown in the following code snippet. For more information about options to change the dtype for parameters, reduction, or buffers for mixed precision in PyTorch, see PyTorch FSDP MixedPrecision API in the PyTorch documentation.

# Native PyTorch API
from torch.distributed.fsdp import MixedPrecision

dtype = torch.bfloat16
mixed_precision_policy = MixedPrecision(
    param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype
)

model = FSDP(
    model,
    ...,
    mixed_precision=mixed_precision_policy
)

Note that certain models (such as the Hugging Face Transformers Llama model) expect buffers as float32. To use float32, replace torch.bfloat16 with torch.float32 in the line defining the dtype object.