core.common.gp_utils

core.common.gp_utils#

Copyright (c) Meta, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Attributes#

`_GRAPH_PARALLEL_GROUP`
`_DATA_PARALLEL_GROUP`

Classes#

`CopyToModelParallelRegion`	Base class to create custom autograd.Function.
`ReduceFromModelParallelRegion`	Base class to create custom autograd.Function.
`ScatterToModelParallelRegion`	Base class to create custom autograd.Function.
`GatherFromModelParallelRegion`	Base class to create custom autograd.Function.

Functions#

`ensure_div`(→ None)
`divide_and_check_no_remainder`(→ int)
`setup_gp`(→ None)
`cleanup_gp`(→ None)
`initialized`(→ bool)
`get_dp_group`()
`get_gp_group`()
`get_dp_rank`(→ int)
`get_gp_rank`(→ int)
`get_dp_world_size`(→ int)
`get_gp_world_size`(→ int)
`pad_tensor`(→ torch.Tensor)
`trim_tensor`(tensor[, sizes, dim])
`_split_tensor`(tensor, num_parts[, dim, contiguous_chunks])
`_reduce`(→ torch.Tensor)
`_split`(→ torch.Tensor)
`_gather`(→ torch.Tensor)
`_gather_with_padding`(→ torch.Tensor)
`copy_to_model_parallel_region`(→ torch.Tensor)
`reduce_from_model_parallel_region`(→ torch.Tensor)
`scatter_to_model_parallel_region`(→ torch.Tensor)
`gather_from_model_parallel_region`(→ torch.Tensor)

Module Contents#

core.common.gp_utils._GRAPH_PARALLEL_GROUP = None#

core.common.gp_utils._DATA_PARALLEL_GROUP = None#

core.common.gp_utils.ensure_div(a: int, b: int) → None#

core.common.gp_utils.divide_and_check_no_remainder(a: int, b: int) → int#

core.common.gp_utils.setup_gp(config) → None#

core.common.gp_utils.cleanup_gp() → None#

core.common.gp_utils.initialized() → bool#

core.common.gp_utils.get_dp_group()#

core.common.gp_utils.get_gp_group()#

core.common.gp_utils.get_dp_rank() → int#

core.common.gp_utils.get_gp_rank() → int#

core.common.gp_utils.get_dp_world_size() → int#

core.common.gp_utils.get_gp_world_size() → int#

core.common.gp_utils.pad_tensor(tensor: torch.Tensor, dim: int = -1, target_size: int | None = None) → torch.Tensor#

core.common.gp_utils.trim_tensor(tensor: torch.Tensor, sizes: torch.Tensor | None = None, dim: int = 0)#

core.common.gp_utils._split_tensor(tensor: torch.Tensor, num_parts: int, dim: int = -1, contiguous_chunks: bool = False)#

core.common.gp_utils._reduce(ctx: Any, input: torch.Tensor) → torch.Tensor#

core.common.gp_utils._split(input: torch.Tensor, dim: int = -1) → torch.Tensor#

core.common.gp_utils._gather(input: torch.Tensor, dim: int = -1) → torch.Tensor#

core.common.gp_utils._gather_with_padding(input: torch.Tensor, dim: int = -1) → torch.Tensor#

class core.common.gp_utils.CopyToModelParallelRegion(*args, **kwargs)#

Bases: torch.autograd.Function

Base class to create custom autograd.Function.

To create a custom autograd.Function, subclass this class and implement the forward() and backward() static methods. Then, to use your custom op in the forward pass, call the class method apply. Do not call forward() directly.

To ensure correctness and best performance, make sure you are calling the correct methods on ctx and validating your backward function using torch.autograd.gradcheck().

See extending-autograd for more details on how to use this class.

Examples:

>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
>>> class Exp(Function):
>>>     @staticmethod
>>>     def forward(ctx, i):
>>>         result = i.exp()
>>>         ctx.save_for_backward(result)
>>>         return result
>>>
>>>     @staticmethod
>>>     def backward(ctx, grad_output):
>>>         result, = ctx.saved_tensors
>>>         return grad_output * result
>>>
>>> # Use it by calling the apply method:
>>> # xdoctest: +SKIP
>>> output = Exp.apply(input)

static forward(ctx, input: torch.Tensor) → torch.Tensor#

Define the forward of the custom autograd Function.

This function is to be overridden by all subclasses. There are two ways to define forward:

Usage 1 (Combined forward and ctx):

@staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
    pass

It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See combining-forward-context for more details

Usage 2 (Separate forward and ctx):

@staticmethod
def forward(*args: Any, **kwargs: Any) -> Any:
    pass

@staticmethod
def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
    pass

The forward no longer accepts a ctx argument.
Instead, you must also override the torch.autograd.Function.setup_context() staticmethod to handle setting up the ctx object. output is the output of the forward, inputs are a Tuple of inputs to the forward.
See extending-autograd for more details

The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with ctx.save_for_backward() if they are intended to be used in backward (equivalently, vjp) or ctx.save_for_forward() if they are intended to be used for in jvp.

static backward(ctx, grad_output: torch.Tensor) → torch.Tensor#

Define a formula for differentiating the operation with backward mode automatic differentiation.

This function is to be overridden by all subclasses. (Defining this function is equivalent to defining the vjp function.)

It must accept a context ctx as the first argument, followed by as many outputs as the forward() returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs to forward(). Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.

The context can be used to retrieve tensors saved during the forward pass. It also has an attribute ctx.needs_input_grad as a tuple of booleans representing whether each input needs gradient. E.g., backward() will have ctx.needs_input_grad[0] = True if the first input to forward() needs gradient computed w.r.t. the output.

class core.common.gp_utils.ReduceFromModelParallelRegion(*args, **kwargs)#

Bases: torch.autograd.Function

Base class to create custom autograd.Function.

To create a custom autograd.Function, subclass this class and implement the forward() and backward() static methods. Then, to use your custom op in the forward pass, call the class method apply. Do not call forward() directly.

To ensure correctness and best performance, make sure you are calling the correct methods on ctx and validating your backward function using torch.autograd.gradcheck().

See extending-autograd for more details on how to use this class.

Examples:

>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
>>> class Exp(Function):
>>>     @staticmethod
>>>     def forward(ctx, i):
>>>         result = i.exp()
>>>         ctx.save_for_backward(result)
>>>         return result
>>>
>>>     @staticmethod
>>>     def backward(ctx, grad_output):
>>>         result, = ctx.saved_tensors
>>>         return grad_output * result
>>>
>>> # Use it by calling the apply method:
>>> # xdoctest: +SKIP
>>> output = Exp.apply(input)

static forward(ctx, input: torch.Tensor) → torch.Tensor#

Define the forward of the custom autograd Function.

This function is to be overridden by all subclasses. There are two ways to define forward:

Usage 1 (Combined forward and ctx):

@staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
    pass

It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See combining-forward-context for more details

Usage 2 (Separate forward and ctx):

@staticmethod
def forward(*args: Any, **kwargs: Any) -> Any:
    pass

@staticmethod
def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
    pass

The forward no longer accepts a ctx argument.
Instead, you must also override the torch.autograd.Function.setup_context() staticmethod to handle setting up the ctx object. output is the output of the forward, inputs are a Tuple of inputs to the forward.
See extending-autograd for more details

The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with ctx.save_for_backward() if they are intended to be used in backward (equivalently, vjp) or ctx.save_for_forward() if they are intended to be used for in jvp.

static backward(ctx, grad_output: torch.Tensor) → torch.Tensor#

Define a formula for differentiating the operation with backward mode automatic differentiation.

This function is to be overridden by all subclasses. (Defining this function is equivalent to defining the vjp function.)

It must accept a context ctx as the first argument, followed by as many outputs as the forward() returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs to forward(). Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.

The context can be used to retrieve tensors saved during the forward pass. It also has an attribute ctx.needs_input_grad as a tuple of booleans representing whether each input needs gradient. E.g., backward() will have ctx.needs_input_grad[0] = True if the first input to forward() needs gradient computed w.r.t. the output.

class core.common.gp_utils.ScatterToModelParallelRegion(*args, **kwargs)#

Bases: torch.autograd.Function

Base class to create custom autograd.Function.

To create a custom autograd.Function, subclass this class and implement the forward() and backward() static methods. Then, to use your custom op in the forward pass, call the class method apply. Do not call forward() directly.

To ensure correctness and best performance, make sure you are calling the correct methods on ctx and validating your backward function using torch.autograd.gradcheck().

See extending-autograd for more details on how to use this class.

Examples:

>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
>>> class Exp(Function):
>>>     @staticmethod
>>>     def forward(ctx, i):
>>>         result = i.exp()
>>>         ctx.save_for_backward(result)
>>>         return result
>>>
>>>     @staticmethod
>>>     def backward(ctx, grad_output):
>>>         result, = ctx.saved_tensors
>>>         return grad_output * result
>>>
>>> # Use it by calling the apply method:
>>> # xdoctest: +SKIP
>>> output = Exp.apply(input)

static forward(ctx, input: torch.Tensor, dim: int = -1) → torch.Tensor#

Define the forward of the custom autograd Function.

This function is to be overridden by all subclasses. There are two ways to define forward:

Usage 1 (Combined forward and ctx):

@staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
    pass

It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See combining-forward-context for more details

Usage 2 (Separate forward and ctx):

@staticmethod
def forward(*args: Any, **kwargs: Any) -> Any:
    pass

@staticmethod
def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
    pass

The forward no longer accepts a ctx argument.
Instead, you must also override the torch.autograd.Function.setup_context() staticmethod to handle setting up the ctx object. output is the output of the forward, inputs are a Tuple of inputs to the forward.
See extending-autograd for more details

The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with ctx.save_for_backward() if they are intended to be used in backward (equivalently, vjp) or ctx.save_for_forward() if they are intended to be used for in jvp.

static backward(ctx, grad_output: torch.Tensor)#

Define a formula for differentiating the operation with backward mode automatic differentiation.

This function is to be overridden by all subclasses. (Defining this function is equivalent to defining the vjp function.)

It must accept a context ctx as the first argument, followed by as many outputs as the forward() returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs to forward(). Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.

The context can be used to retrieve tensors saved during the forward pass. It also has an attribute ctx.needs_input_grad as a tuple of booleans representing whether each input needs gradient. E.g., backward() will have ctx.needs_input_grad[0] = True if the first input to forward() needs gradient computed w.r.t. the output.

class core.common.gp_utils.GatherFromModelParallelRegion(*args, **kwargs)#

Bases: torch.autograd.Function

Base class to create custom autograd.Function.

To create a custom autograd.Function, subclass this class and implement the forward() and backward() static methods. Then, to use your custom op in the forward pass, call the class method apply. Do not call forward() directly.

To ensure correctness and best performance, make sure you are calling the correct methods on ctx and validating your backward function using torch.autograd.gradcheck().

See extending-autograd for more details on how to use this class.

Examples:

>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
>>> class Exp(Function):
>>>     @staticmethod
>>>     def forward(ctx, i):
>>>         result = i.exp()
>>>         ctx.save_for_backward(result)
>>>         return result
>>>
>>>     @staticmethod
>>>     def backward(ctx, grad_output):
>>>         result, = ctx.saved_tensors
>>>         return grad_output * result
>>>
>>> # Use it by calling the apply method:
>>> # xdoctest: +SKIP
>>> output = Exp.apply(input)

static forward(ctx, input: torch.Tensor, dim: int = -1) → torch.Tensor#

Define the forward of the custom autograd Function.

This function is to be overridden by all subclasses. There are two ways to define forward:

Usage 1 (Combined forward and ctx):

@staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
    pass

It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
See combining-forward-context for more details

Usage 2 (Separate forward and ctx):

@staticmethod
def forward(*args: Any, **kwargs: Any) -> Any:
    pass

@staticmethod
def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
    pass

The forward no longer accepts a ctx argument.
Instead, you must also override the torch.autograd.Function.setup_context() staticmethod to handle setting up the ctx object. output is the output of the forward, inputs are a Tuple of inputs to the forward.
See extending-autograd for more details

The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with ctx.save_for_backward() if they are intended to be used in backward (equivalently, vjp) or ctx.save_for_forward() if they are intended to be used for in jvp.

static backward(ctx, grad_output: torch.Tensor)#

Define a formula for differentiating the operation with backward mode automatic differentiation.

This function is to be overridden by all subclasses. (Defining this function is equivalent to defining the vjp function.)

It must accept a context ctx as the first argument, followed by as many outputs as the forward() returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs to forward(). Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.

The context can be used to retrieve tensors saved during the forward pass. It also has an attribute ctx.needs_input_grad as a tuple of booleans representing whether each input needs gradient. E.g., backward() will have ctx.needs_input_grad[0] = True if the first input to forward() needs gradient computed w.r.t. the output.

core.common.gp_utils.copy_to_model_parallel_region(input: torch.Tensor) → torch.Tensor#

core.common.gp_utils.reduce_from_model_parallel_region(input: torch.Tensor) → torch.Tensor#

core.common.gp_utils.scatter_to_model_parallel_region(input: torch.Tensor, dim: int = -1) → torch.Tensor#

core.common.gp_utils.gather_from_model_parallel_region(input: torch.Tensor, dim: int = -1) → torch.Tensor#