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tensorplay.amp.autocast_mode

Classes

class autocast [source]

autocast(device_type: str, dtype: Optional[tensorplay.DType] = None, enabled: bool = True, cache_enabled: Optional[bool] = None)

Instances of autocast serve as context managers or decorators that allow regions of your script to run in mixed precision.

In these regions, ops run in an op-specific dtype chosen by autocast to improve performance while maintaining accuracy. See the Autocast Op Reference<autocast-op-reference> for details.

When entering an autocast-enabled region, Tensors may be any type. You should not call half() or bfloat16() on your model(s) or inputs when using autocasting.

autocast should wrap only the forward pass(es) of your network, including the loss computation(s). Backward passes under autocast are not recommended. Backward ops run in the same type that autocast used for corresponding forward ops.

Example for CUDA Devices

# Creates model and optimizer in default precision
model = Net().cuda()
optimizer = optim.SGD(model.parameters(), ...)

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

    # Enables autocasting for the forward pass (model + loss)
    with tensorplay.autocast(device_type="cuda"):
        output = model(input)
        loss = loss_fn(output, target)

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

See the Automatic Mixed Precision examples<amp-examples> for usage (along with gradient scaling) in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).

autocast can also be used as a decorator, e.g., on the forward method of your model

class AutocastModel(nn.Module):

@tensorplay.autocast(device_type="cuda")
def forward(self, input):

Floating-point Tensors produced in an autocast-enabled region may be float16. After returning to an autocast-disabled region, using them with floating-point Tensors of different dtypes may cause type mismatch errors. If so, cast the Tensor(s) produced in the autocast region back to float32 (or other dtype if desired). If a Tensor from the autocast region is already float32, the cast is a no-op, and incurs no additional overhead. CUDA Example

# Creates some tensors in default dtype (here assumed to be float32)
a_float32 = tensorplay.rand((8, 8), device="cuda")
b_float32 = tensorplay.rand((8, 8), device="cuda")
c_float32 = tensorplay.rand((8, 8), device="cuda")
d_float32 = tensorplay.rand((8, 8), device="cuda")

with tensorplay.autocast(device_type="cuda"):
    # tensorplay.mm is on autocast's list of ops that should run in float16.
    # Inputs are float32, but the op runs in float16 and produces float16 output.
    # No manual casts are required.
    e_float16 = tensorplay.mm(a_float32, b_float32)
    # Also handles mixed input types
    f_float16 = tensorplay.mm(d_float32, e_float16)

# After exiting autocast, calls f_float16.float() to use with d_float32
g_float32 = tensorplay.mm(d_float32, f_float16.float())

CPU Training Example

# Creates model and optimizer in default precision
model = Net()
optimizer = optim.SGD(model.parameters(), ...)

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

        # Runs the forward pass with autocasting.
        with tensorplay.autocast(device_type="cpu", dtype=tensorplay.bfloat16):
            output = model(input)
            loss = loss_fn(output, target)

        loss.backward()
        optimizer.step()

CPU Inference Example

# Creates model in default precision
model = Net().eval()

with tensorplay.autocast(device_type="cpu", dtype=tensorplay.bfloat16):
    for input in data:
        # Runs the forward pass with autocasting.
        output = model(input)

CPU Inference Example with Jit Trace

class TestModel(nn.Module):
    def __init__(self, input_size, num_classes):
        super().__init__()
        self.fc1 = nn.Linear(input_size, num_classes)
    def forward(self, x):
        return self.fc1(x)

input_size = 2
num_classes = 2
model = TestModel(input_size, num_classes).eval()

# For now, we suggest to disable the Jit Autocast Pass,
# As the issue: https://github.com/pytensorplay/pytensorplay/issues/75956
tensorplay._C._jit_set_autocast_mode(False)

with tensorplay.cpu.amp.autocast(cache_enabled=False):
    model = tensorplay.jit.trace(model, tensorplay.randn(1, input_size))
model = tensorplay.jit.freeze(model)
# Models Run
for _ in range(3):
    model(tensorplay.randn(1, input_size))

Type mismatch errors in an autocast-enabled region are a bug; if this is what you observe, please file an issue.

autocast(enabled=False) subregions can be nested in autocast-enabled regions. Locally disabling autocast can be useful, for example, if you want to force a subregion to run in a particular dtype. Disabling autocast gives you explicit control over the execution type. In the subregion, inputs from the surrounding region should be cast to dtype before use

# Creates some tensors in default dtype (here assumed to be float32)
a_float32 = tensorplay.rand((8, 8), device="cuda")
b_float32 = tensorplay.rand((8, 8), device="cuda")
c_float32 = tensorplay.rand((8, 8), device="cuda")
d_float32 = tensorplay.rand((8, 8), device="cuda")

with tensorplay.autocast(device_type="cuda"):
    e_float16 = tensorplay.mm(a_float32, b_float32)
    with tensorplay.autocast(device_type="cuda", enabled=False):
        # Calls e_float16.float() to ensure float32 execution
        # (necessary because e_float16 was created in an autocasted region)
        f_float32 = tensorplay.mm(c_float32, e_float16.float())

    # No manual casts are required when re-entering the autocast-enabled region.
    # tensorplay.mm again runs in float16 and produces float16 output, regardless of input types.
    g_float16 = tensorplay.mm(d_float32, f_float32)

The autocast state is thread-local. If you want it enabled in a new thread, the context manager or decorator must be invoked in that thread. This affects tensorplay.nn.DataParallel and tensorplay.nn.parallel.DistributedDataParallel when used with more than one GPU per process (see Working with Multiple GPUs<amp-multigpu>).

Args

• device_type (str, required): Device type to use. Possible values are: 'cuda', 'cpu', 'mtia', 'maia', 'xpu', and 'hpu'. The type is the same as the type attribute of a tensorplay.device. Thus, you may obtain the device type of a tensor using Tensor.device.type.
• enabled (bool, optional): Whether autocasting should be enabled in the region.
• Default: True
• dtype (tensorplay_dtype, optional): Data type for ops run in autocast. It uses the default value (tensorplay.float16 for CUDA and tensorplay.bfloat16 for CPU), given by ~tensorplay.get_autocast_dtype, if dtype is None.
• Default: None
• cache_enabled (bool, optional): Whether the weight cache inside autocast should be enabled.
• Default: True

Methods

__init__(self, device_type: str, dtype: Optional[tensorplay.DType] = None, enabled: bool = True, cache_enabled: Optional[bool] = None) [source]

Initialize self. See help(type(self)) for accurate signature.

---

Functions

autocast_decorator() [source]

autocast_decorator(autocast_instance, func)

custom_bwd() [source]

custom_bwd(bwd=None, *, device_type: str)

Create a helper decorator for backward methods of custom autograd functions.

Autograd functions are subclasses of tensorplay.autograd.Function. Ensures that backward executes with the same autocast state as forward. See the example page<amp-custom-examples> for more detail.

Args

• device_type (str): Device type to use. 'cuda', 'cpu', 'mtia', 'maia', 'xpu' and so on. The type is the same as the type attribute of a tensorplay.device. Thus, you may obtain the device type of a tensor using Tensor.device.type.

custom_fwd() [source]

custom_fwd(fwd=None, *, device_type: str, cast_inputs: Optional[tensorplay.DType] = None)

Create a helper decorator for forward methods of custom autograd functions.

Autograd functions are subclasses of tensorplay.autograd.Function. See the example page<amp-custom-examples> for more detail.

Args

• device_type (str): Device type to use. 'cuda', 'cpu', 'mtia', 'maia', 'xpu' and so on. The type is the same as the type attribute of a tensorplay.device. Thus, you may obtain the device type of a tensor using Tensor.device.type.
• cast_inputs (tensorplay.dtype` or None, optional, default=None`): If not None, when forwardruns in an autocast-enabled region, casts incoming floating-point Tensors to the target dtype (non-floating-point Tensors are not affected), then executesforwardwith autocast disabled. IfNone, forward``'s internal ops execute with the current autocast state.

INFO

If the decorated forward is called outside an autocast-enabled region, custom_fwd<custom_fwd> is a no-op and cast_inputs has no effect.

is_autocast_available() [source]

is_autocast_available(device_type: str) -> bool

Return a bool indicating if autocast is available on device_type.

Args

• device_type (str): Device type to use. Possible values are: 'cuda', 'cpu', 'mtia', 'maia', 'xpu', and so on. The type is the same as the type attribute of a tensorplay.device. Thus, you may obtain the device type of a tensor using Tensor.device.type.