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AMP (Automatic Mixed Precision) with Pytorch/XLA

Pytorch/XLA's AMP extends Pytorch's AMP package with support for automatic mixed precision on XLA:GPU and XLA:TPU devices. AMP is used to accelerate training and inference by executing certain operations in float32 and other operations in a lower precision datatype (float16 or bfloat16 depending on hardware support). This document describes how to use AMP on XLA devices and best practices.

AMP for XLA:TPU

AMP on TPUs automatically casts operations to run in either float32 or bfloat16 because TPUs natively support bfloat16. A simple TPU AMP example is below:

# Creates model and optimizer in default precision
model = Net().to(xm.xla_device())
# Pytorch/XLA provides sync-free optimizers for improved performance
optimizer = syncfree.SGD(model.parameters(), ...)

for input, target in data:
    optimizer.zero_grad()

    # Enables autocasting for the forward pass
    with autocast(xm.xla_device()):
        output = model(input)
        loss = loss_fn(output, target)

    # Exits the context manager before backward()
    loss.backward()
    xm.optimizer_step.(optimizer)

autocast(xm.xla_device()) aliases torch.amp.autocast('xla') when the XLA Device is a TPU. Alternatively, if a script is only used with TPUs, then torch.amp.autocast('xla') can be directly used.

Please file an issue or submit a pull request if there is an operator that should be autocasted that is not included.

Best Practices

  1. autocast should wrap only the forward pass(es) and loss computation(s) of the network. Backward ops run in the same type that autocast used for the corresponding forward ops.
  2. Do not set XLA_USE_BF16 flag when using AMP on TPUs. This will override the per-operator precision settings provided by AMP and cause all operators to execute in bfloat16.
  3. Since TPU's use bfloat16 mixed precision, gradient scaling is not necessary.
  4. Pytorch/XLA provides modified version of optimizers that avoid the additional sync between device and host.

Supported Operators

AMP on TPUs operates like Pytorch's AMP. Rules for how autocasting is applied is summarized below:

Only out-of-place ops and Tensor methods are eligible to be autocasted. In-place variants and calls that explicitly supply an out=... Tensor are allowed in autocast-enabled regions, but won’t go through autocasting. For example, in an autocast-enabled region a.addmm(b, c) can autocast, but a.addmm_(b, c) and a.addmm(b, c, out=d) cannot. For best performance and stability, prefer out-of-place ops in autocast-enabled regions.

Ops that run in float64 or non-floating-point dtypes are not eligible, and will run in these types whether or not autocast is enabled. Additionally, Ops called with an explicit dtype=... argument are not eligible, and will produce output that respects the dtype argument.

Ops not listed below do not go through autocasting. They run in the type defined by their inputs. Autocasting may still change the type in which unlisted ops run if they’re downstream from autocasted ops.

Ops that autocast to bfloat16:

__matmul__, addbmm, addmm, addmv, addr, baddbmm, bmm, conv1d, conv2d, conv3d, conv_transpose1d, conv_transpose2d, conv_transpose3d, linear, matmul, mm, relu, prelu, max_pool2d

Ops that autocast to float32:

batch_norm, log_softmax, binary_cross_entropy, binary_cross_entropy_with_logits, prod, cdist, trace, chloesky ,inverse, reflection_pad, replication_pad, mse_loss, cosine_embbeding_loss, nll_loss, multilabel_margin_loss, qr, svd, triangular_solve, linalg_svd, linalg_inv_ex

Ops that autocast to widest input type:

stack, cat, index_copy

AMP for XLA:GPU

AMP on XLA:GPU devices reuse Pytorch's AMP rules. See Pytorch's AMP documentation for CUDA specific behavior. A simple CUDA AMP example is below:

# Creates model and optimizer in default precision
model = Net().to(xm.xla_device())
# Pytorch/XLA provides sync-free optimizers for improved performance
optimizer = syncfree.SGD(model.parameters(), ...)
scaler = GradScaler()

for input, target in data:
    optimizer.zero_grad()

    # Enables autocasting for the forward pass
    with autocast(xm.xla_device()):
        output = model(input)
        loss = loss_fn(output, target)

    # Exits the context manager before backward pass
    scaler.scale(loss).backward()
    gradients = xm._fetch_gradients(optimizer)
    xm.all_reduce('sum', gradients, scale=1.0 / xm.xrt_world_size())
    scaler.step(optimizer)
    scaler.update()

autocast(xm.xla_device()) aliases torch.cuda.amp.autocast() when the XLA Device is a CUDA device. Alternatively, if a script is only used with CUDA devices, then torch.cuda.amp.autocast can be directly used.

Best Practices

  1. autocast should wrap only the forward pass(es) and loss computation(s) of the network. Backward ops run in the same type that autocast used for the corresponding forward ops.
  2. Do not set XLA_USE_F16 flag when using AMP on Cuda devices. This will override the per-operator precision settings provided by AMP and cause all operators to execute in float16.
  3. Use gradient scaling to prevent float16 gradients from underflowing.
  4. Pytorch/XLA provides modified version of optimizers that avoid the additional sync between device and host.

Examples

Our mnist training script and imagenet training script demonstrate how AMP is used on both TPUs and GPUs.