torchrec.distributed

Torchrec Distributed

Torchrec distributed provides the necessary modules and operations to enable model parallelism.

These include:

• model parallelism through DistributedModelParallel.

• collective operations for comms, including All-to-All and Reduce-Scatter.

  • collective operations wrappers for sparse features, KJT, and various embedding types.

• sharded implementations of various modules including ShardedEmbeddingBag for nn.EmbeddingBag, ShardedEmbeddingBagCollection for EmbeddingBagCollection

  • embedding sharders that define sharding for any sharded module implementation.

  • support for various compute kernels, which are optimized for compute device (CPU/GPU) and may include batching together embedding tables and/or optimizer fusion.

• pipelined training through TrainPipelineSparseDist that overlaps dataloading device transfer (copy to GPU), inter*device communications (input_dist), and computation (forward, backward) for increased performance.

• quantization support for reduced precision training and inference.

torchrec.distributed.collective_utils

This file contains utilities for constructing collective based control flows.

torchrec.distributed.collective_utils.invoke_on_rank_and_broadcast_result(pg: ProcessGroup, rank: int, func: Callable[[...], T], *args: Any, **kwargs: Any) → T

Invokes a function on the designated rank and broadcasts the result to all members within the group.

Example:

id = invoke_on_rank_and_broadcast_result(pg, 0, allocate_id)

torchrec.distributed.collective_utils.is_leader(pg: Optional[ProcessGroup], leader_rank: int = 0) → bool

Checks if the current processs is the leader.

Parameters:

• pg (Optional[dist.ProcessGroup]) – the process’s rank within the pg is used to determine if the process is the leader. pg being None implies that the process is the only member in the group (e.g. a single process program).

• leader_rank (int) – the definition of leader (defaults to 0). The caller can override it with a context-specific definition.

torchrec.distributed.collective_utils.run_on_leader(pg: ProcessGroup, rank: int)

torchrec.distributed.comm

torchrec.distributed.comm.get_group_rank(world_size: Optional[int] = None, rank: Optional[int] = None) → int

Gets the group rank of the worker group. Also available with GROUP_RANK environment varible A number between 0 and get_num_groups() (See https://pytorch.org/docs/stable/elastic/run.html)

torchrec.distributed.comm.get_local_rank(world_size: Optional[int] = None, rank: Optional[int] = None) → int

Gets the local rank of the local processes (see https://pytorch.org/docs/stable/elastic/run.html) This is usually the rank of the worker on its node

torchrec.distributed.comm.get_local_size(world_size: Optional[int] = None) → int

torchrec.distributed.comm.get_num_groups(world_size: Optional[int] = None) → int

Gets the number of worker groups. Usually equivalent to max_nnodes (See https://pytorch.org/docs/stable/elastic/run.html)

torchrec.distributed.comm.intra_and_cross_node_pg(device: Optional[device] = None) → Tuple[Optional[ProcessGroup], Optional[ProcessGroup]]

Creates sub process groups (intra and cross node)

torchrec.distributed.comm_ops

class torchrec.distributed.comm_ops.All2AllDenseInfo(output_splits: List[int], batch_size: int, input_shape: List[int], input_splits: List[int])

Bases: object

The data class that collects the attributes when calling the alltoall_dense operation.

batch_size: int

input_shape: List[int]

input_splits: List[int]

output_splits: List[int]

class torchrec.distributed.comm_ops.All2AllPooledInfo(batch_size_per_rank: List[int], dim_sum_per_rank: List[int], dim_sum_per_rank_tensor: Optional[Tensor], cumsum_dim_sum_per_rank_tensor: Optional[Tensor], codecs: Optional[QuantizedCommCodecs] = None)

Bases: object

The data class that collects the attributes when calling the alltoall_pooled operation.

batch_size_per_rank

batch size in each rank

Type:

List[int]

dim_sum_per_rank

number of features (sum of dimensions) of the embedding in each rank.

Type:

List[int]

dim_sum_per_rank_tensor

the tensor version of dim_sum_per_rank, this is only used by the fast kernel of _recat_pooled_embedding_grad_out.

Type:

Optional[Tensor]

cumsum_dim_sum_per_rank_tensor

cumulative sum of dim_sum_per_rank, this is only used by the fast kernel of _recat_pooled_embedding_grad_out.

Type:

Optional[Tensor]

B_local

local batch size before scattering.

Type:

int

batch_size_per_rank: List[int]

codecs: Optional[QuantizedCommCodecs] = None

cumsum_dim_sum_per_rank_tensor: Optional[Tensor]

dim_sum_per_rank: List[int]

dim_sum_per_rank_tensor: Optional[Tensor]

class torchrec.distributed.comm_ops.All2AllSequenceInfo(embedding_dim: int, lengths_after_sparse_data_all2all: Tensor, forward_recat_tensor: Optional[Tensor], backward_recat_tensor: Tensor, input_splits: List[int], output_splits: List[int], variable_batch_size: bool = False, codecs: Optional[QuantizedCommCodecs] = None, permuted_lengths_after_sparse_data_all2all: Optional[Tensor] = None)

Bases: object

The data class that collects the attributes when calling the alltoall_sequence operation.

embedding_dim

embedding dimension.

Type:

int

lengths_after_sparse_data_all2all

lengths of sparse features after AlltoAll.

Type:

Tensor

forward_recat_tensor

recat tensor for forward.

Type:

Optional[Tensor]

backward_recat_tensor

recat tensor for backward.

Type:

Tensor

input_splits

input splits.

Type:

List[int]

output_splits

output splits.

Type:

List[int]

variable_batch_size

whether variable batch size is enabled

Type:

bool

permuted_lengths_after_sparse_data_all2all

lengths of sparse features before AlltoAll.

Type:

Optional[Tensor]

backward_recat_tensor: Tensor

codecs: Optional[QuantizedCommCodecs] = None

embedding_dim: int

forward_recat_tensor: Optional[Tensor]

input_splits: List[int]

lengths_after_sparse_data_all2all: Tensor

output_splits: List[int]

permuted_lengths_after_sparse_data_all2all: Optional[Tensor] = None

variable_batch_size: bool = False

class torchrec.distributed.comm_ops.All2AllVInfo(dims_sum_per_rank: ~typing.List[int], B_global: int, B_local: int, B_local_list: ~typing.List[int], D_local_list: ~typing.List[int], input_split_sizes: ~typing.List[int] = <factory>, output_split_sizes: ~typing.List[int] = <factory>, codecs: ~typing.Optional[~torchrec.distributed.types.QuantizedCommCodecs] = None)

Bases: object

The data class that collects the attributes when calling the alltoallv operation.

dim_sum_per_rank

number of features (sum of dimensions) of the embedding in each rank.

Type:

List[int]

B_global

global batch size for each rank.

Type:

int

B_local

local batch size before scattering.

Type:

int

B_local_list

(List[int]): local batch sizes for each embedding table locally (in my current rank).

Type:

List[int]

D_local_list

embedding dimension of each embedding table locally (in my current rank).

Type:

List[int]

input_split_sizes

The input split sizes for each rank, this remembers how to split the input when doing the all_to_all_single operation.

Type:

List[int]

output_split_sizes

The output split sizes for each rank, this remembers how to fill the output when doing the all_to_all_single operation.

Type:

List[int]

B_global: int

B_local: int

B_local_list: List[int]

D_local_list: List[int]

codecs: Optional[QuantizedCommCodecs] = None

dims_sum_per_rank: List[int]

input_split_sizes: List[int]

output_split_sizes: List[int]

class torchrec.distributed.comm_ops.All2All_Pooled_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *unused) → Tuple[None, None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], a2ai: All2AllPooledInfo, input_embeddings: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.All2All_Pooled_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, grad_output: Tensor) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_tensor: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.All2All_Seq_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *unused) → Tuple[None, None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], a2ai: All2AllSequenceInfo, sharded_input_embeddings: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.All2All_Seq_Req_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, sharded_grad_output: Tensor) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_tensor: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.All2Allv_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *grad_output)

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], a2ai: All2AllVInfo, inputs: List[Tensor]) → Tensor

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.

class torchrec.distributed.comm_ops.All2Allv_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, *grad_outputs) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_tensor: Tensor) → Tuple[Tensor]

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.

class torchrec.distributed.comm_ops.AllGatherBaseInfo(input_size: Size, codecs: Optional[QuantizedCommCodecs] = None)

Bases: object

The data class that collects the attributes when calling the all_gatther_base_pooled operation.

input_size

the size of the input tensor.

Type:

int

codecs: Optional[QuantizedCommCodecs] = None

input_size: Size

class torchrec.distributed.comm_ops.AllGatherBase_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *unused: Tensor) → Tuple[Optional[Tensor], ...]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], agi: AllGatherBaseInfo, input: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.AllGatherBase_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, grad_outputs: Tensor) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_tensor: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.ReduceScatterBaseInfo(input_sizes: Size, codecs: Optional[QuantizedCommCodecs] = None)

Bases: object

The data class that collects the attributes when calling the reduce_scatter_base_pooled operation.

input_sizes

the sizes of the input flatten tensor.

Type:

torch.Size

codecs: Optional[QuantizedCommCodecs] = None

input_sizes: Size

class torchrec.distributed.comm_ops.ReduceScatterBase_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *unused: Tensor) → Tuple[Optional[Tensor], ...]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], rsi: ReduceScatterBaseInfo, inputs: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.ReduceScatterBase_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, grad_output: Tensor) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_Tensor: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.ReduceScatterInfo(input_sizes: List[Size], codecs: Optional[QuantizedCommCodecs] = None)

Bases: object

The data class that collects the attributes when calling the reduce_scatter_pooled operation.

input_sizes

the sizes of the input tensors. This remembers the sizes of the input tensors when running the backward pass and producing the gradient.

Type:

List[torch.Size]

codecs: Optional[QuantizedCommCodecs] = None

input_sizes: List[Size]

class torchrec.distributed.comm_ops.ReduceScatterVInfo(input_sizes: List[Size], input_splits: List[int], equal_splits: bool, total_input_size: List[int], codecs: Optional[QuantizedCommCodecs])

Bases: object

The data class that collects the attributes when calling the reduce_scatter_v_pooled operation.

input_sizes

the sizes of the input tensors. This remembers the sizes of the input tensors when running the backward pass and producing the gradient.

Type:

List[torch.Size]

input_splits

the splits of the input tensors along dim0.

Type:

List[int]

total_input_size

(List[int]): total input size

Type:

List[int]

codecs: Optional[QuantizedCommCodecs]

equal_splits: bool

input_sizes: List[Size]

input_splits: List[int]

total_input_size: List[int]

class torchrec.distributed.comm_ops.ReduceScatterV_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *unused: Tensor) → Tuple[Optional[Tensor], ...]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], rsi: ReduceScatterVInfo, input: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.ReduceScatterV_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, grad_output: Tensor) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_tensor: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.ReduceScatter_Req(*args, **kwargs)

Bases: Function

static backward(ctx, *unused: Tensor) → Tuple[Optional[Tensor], ...]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], rsi: ReduceScatterInfo, *inputs: Any) → Tensor

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.

class torchrec.distributed.comm_ops.ReduceScatter_Wait(*args, **kwargs)

Bases: Function

static backward(ctx, grad_output: Tensor) → Tuple[None, None, Tensor]

Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).

This function is to be overridden by all subclasses.

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 computated w.r.t. the output.

static forward(ctx, pg: ProcessGroup, myreq: Request[Tensor], *dummy_tensor: Tensor) → Tensor

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.

class torchrec.distributed.comm_ops.Request(pg: ProcessGroup, device: device)

Bases: Awaitable[W]

Defines a collective operation request for a process group on a tensor.

Parameters:

pg (dist.ProcessGroup) – The process group the request is for.

torchrec.distributed.comm_ops.all_gather_base_pooled(input: Tensor, group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[Tensor]

All-gathers tensors from all processes in a group to form a flattened pooled embeddings tensor. Input tensor is of size output tensor size divided by world size.

Parameters:

• input (Tensor) – tensor to gather, .

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting tensor.

Return type:

Awaitable[Tensor]

Warning

all_gather_base_pooled is experimental and subject to change.

torchrec.distributed.comm_ops.alltoall_pooled(a2a_pooled_embs_tensor: Tensor, batch_size_per_rank: List[int], dim_sum_per_rank: List[int], dim_sum_per_rank_tensor: Optional[Tensor] = None, cumsum_dim_sum_per_rank_tensor: Optional[Tensor] = None, group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[Tensor]

Performs AlltoAll operation for a single pooled embedding tensor. Each process splits the input pooled embeddings tensor based on the world size, and then scatters the split list to all processes in the group. Then concatenates the received tensors from all processes in the group and returns a single output tensor.

Parameters:

• a2a_pooled_embs_tensor (Tensor) – input pooled embeddings. Must be pooled together before passing into this function. Its shape is B x D_local_sum, where D_local_sum is the dimension sum of all the local embedding tables.

• batch_size_per_rank (List[int]) – batch size in each rank.

• dim_sum_per_rank (List[int]) – number of features (sum of dimensions) of the embedding in each rank.

• dim_sum_per_rank_tensor (Optional[Tensor]) – the tensor version of dim_sum_per_rank, this is only used by the fast kernel of _recat_pooled_embedding_grad_out.

• cumsum_dim_sum_per_rank_tensor (Optional[Tensor]) – cumulative sum of dim_sum_per_rank, this is only used by the fast kernel of _recat_pooled_embedding_grad_out.

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

• codecs – Optional[QuantizedCommCodecs]: Quantized communication codecs

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting tensor.

Return type:

Awaitable[List[Tensor]]

Warning

alltoall_pooled is experimental and subject to change.

torchrec.distributed.comm_ops.alltoall_sequence(a2a_sequence_embs_tensor: Tensor, forward_recat_tensor: Tensor, backward_recat_tensor: Tensor, lengths_after_sparse_data_all2all: Tensor, input_splits: List[int], output_splits: List[int], variable_batch_size: bool = False, group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[Tensor]

Performs AlltoAll operation for sequence embeddings. Each process splits the input tensor based on the world size, and then scatters the split list to all processes in the group. Then concatenates the received tensors from all processes in the group and returns a single output tensor.

Note

AlltoAll operator for Sequence embedding tensors. Does not support mixed dimensions.

Parameters:

• a2a_sequence_embs_tensor (Tensor) – input embeddings.

• forward_recat_tensor (Tensor) – recat tensor for forward.

• backward_recat_tensor (Tensor) – recat tensor for backward.

• lengths_after_sparse_data_all2all (Tensor) – lengths of sparse features after AlltoAll.

• input_splits (Tensor) – input splits.

• output_splits (Tensor) – output splits.

• variable_batch_size (bool) – whether variable batch size is enabled

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

• codecs – Optional[QuantizedCommCodecs]: Quantized communication codecs

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting tensor.

Return type:

Awaitable[List[Tensor]]

Warning

alltoall_sequence is experimental and subject to change.

torchrec.distributed.comm_ops.alltoallv(inputs: List[Tensor], out_split: Optional[List[int]] = None, per_rank_split_lengths: Optional[List[int]] = None, group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[List[Tensor]]

Performs alltoallv operation for a list of input embeddings. Each process scatters the list to all processes in the group.

Parameters:

• inputs (List[Tensor]) – list of tensors to scatter, one per rank. The tensors in the list usually have different lengths.

• out_split (Optional[List[int]]) – output split sizes (or dim_sum_per_rank), if not specified, we will use per_rank_split_lengths to construct a output split with the assumption that all the embs have the same dimension.

• per_rank_split_lengths (Optional[List[int]]) – split lengths per rank. If not specified, the out_split must be specified.

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting list of tensors.

Return type:

Awaitable[List[Tensor]]

Warning

alltoallv is experimental and subject to change.

torchrec.distributed.comm_ops.reduce_scatter_base_pooled(inputs: Tensor, group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[Tensor]

Reduces then scatters a flattened pooled embeddings tensor to all processes in a group. Input tensor is of size output tensor size times world size.

Parameters:

• inputs (Tensor) – flattened tensor to scatter, .

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting tensor.

Return type:

Awaitable[Tensor]

Warning

reduce_scatter_base_pooled is experimental and subject to change.

torchrec.distributed.comm_ops.reduce_scatter_pooled(inputs: List[Tensor], group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[Tensor]

Performs reduce-scatter operation for a pooled embeddings tensor split into world size number of chunks. The result of the reduce operation gets scattered to all processes in the group.

Parameters:

• inputs (List[Tensor]) – list of tensors to scatter, one per rank.

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting tensor.

Return type:

Awaitable[Tensor]

Warning

reduce_scatter_pooled is experimental and subject to change.

torchrec.distributed.comm_ops.reduce_scatter_v_pooled(input: Tensor, input_splits: List[int], group: Optional[ProcessGroup] = None, codecs: Optional[QuantizedCommCodecs] = None) → Awaitable[Tensor]

Performs reduce-scatter-v operation for a pooled embeddings tensor split unevenly into world size number of chunks. The result of the reduce operation gets scattered to all processes in the group according to input_splits.

Parameters:

• input (Tensor) – tensors to scatter, one per rank.

• input_splits (List[int]) – input splits.

• group (Optional[dist.ProcessGroup]) – The process group to work on. If None, the default process group will be used.

Returns:

async work handle (Awaitable), which can be wait() later to get the resulting tensor.

Return type:

Awaitable[Tensor]

Warning

reduce_scatter_v_pooled is experimental and subject to change.

torchrec.distributed.comm_ops.set_gradient_division(val: bool) → None

torchrec.distributed.dist_data

class torchrec.distributed.dist_data.EmbeddingsAllToOne(device: device, world_size: int, cat_dim: int)

Bases: Module

Merges the pooled/sequence embedding tensor on each device into single tensor.

Parameters:

• device (torch.device) – device on which buffer will be allocated.

• world_size (int) – number of devices in the topology.

• cat_dim (int) – which dimension you would like to concatenate on. For pooled embedding it is 1; for sequence embedding it is 0.

forward(tensors: List[Tensor]) → Awaitable[Tensor]

Performs AlltoOne operation on pooled/sequence embeddings tensors.

Parameters:

tensors (List[torch.Tensor]) – list of embedding tensors.

Returns:

awaitable of the merged embeddings.

Return type:

Awaitable[torch.Tensor]

training: bool

class torchrec.distributed.dist_data.KJTAllToAll(pg: ProcessGroup, splits: List[int], device: Optional[device] = None, stagger: int = 1)

Bases: Module

Redistributes KeyedJaggedTensor to a ProcessGroup according to splits.

Implementation utilizes AlltoAll collective as part of torch.distributed.

The input provides the necessary tensors and input splits to distribute. The first collective call in KJTAllToAllSplitsAwaitable will transmit output splits (to allocate correct space for tensors) and batch size per rank. The following collective calls in KJTAllToAllTensorsAwaitable will transmit the actual tensors asynchronously.

Parameters:

• pg (dist.ProcessGroup) – ProcessGroup for AlltoAll communication.

• splits (List[int]) – List of len(pg.size()) which indicates how many features to send to each pg.rank(). It is assumed the KeyedJaggedTensor is ordered by destination rank. Same for all ranks.

• device (Optional[torch.device]) – device on which buffers will be allocated.

• stagger (int) – stagger value to apply to recat tensor, see _get_recat function for more detail.

Example:

keys=['A','B','C']
splits=[2,1]
kjtA2A = KJTAllToAll(pg, splits, device)
awaitable = kjtA2A(rank0_input)

# where:
# rank0_input is KeyedJaggedTensor holding

#         0           1           2
# 'A'    [A.V0]       None        [A.V1, A.V2]
# 'B'    None         [B.V0]      [B.V1]
# 'C'    [C.V0]       [C.V1]      None

# rank1_input is KeyedJaggedTensor holding

#         0           1           2
# 'A'     [A.V3]      [A.V4]      None
# 'B'     None        [B.V2]      [B.V3, B.V4]
# 'C'     [C.V2]      [C.V3]      None

rank0_output = awaitable.wait()

# where:
# rank0_output is KeyedJaggedTensor holding

#         0           1           2           3           4           5
# 'A'     [A.V0]      None      [A.V1, A.V2]  [A.V3]      [A.V4]      None
# 'B'     None        [B.V0]    [B.V1]        None        [B.V2]      [B.V3, B.V4]

# rank1_output is KeyedJaggedTensor holding
#         0           1           2           3           4           5
# 'C'     [C.V0]      [C.V1]      None        [C.V2]      [C.V3]      None

forward(input: KeyedJaggedTensor) → Awaitable[KJTAllToAllTensorsAwaitable]

Sends input to relevant ProcessGroup ranks.

The first wait will get the output splits for the provided tensors and issue tensors AlltoAll. The second wait will get the tensors.

Parameters:

input (KeyedJaggedTensor) – KeyedJaggedTensor of values to distribute.

Returns:

awaitable of a KJTAllToAllTensorsAwaitable.

Return type:

Awaitable[KJTAllToAllTensorsAwaitable]

training: bool

class torchrec.distributed.dist_data.KJTAllToAllSplitsAwaitable(pg: ProcessGroup, input: KeyedJaggedTensor, splits: List[int], labels: List[str], tensor_splits: List[List[int]], input_tensors: List[Tensor], keys: List[str], device: device, stagger: int)

Bases: Awaitable[KJTAllToAllTensorsAwaitable]

Awaitable for KJT tensors splits AlltoAll.

Parameters:

• pg (dist.ProcessGroup) – ProcessGroup for AlltoAll communication.

• input (KeyedJaggedTensor) – input KJT.

• splits (List[int]) – list of len(pg.size()) which indicates how many features to send to each pg.rank(). It is assumed the KeyedJaggedTensor is ordered by destination rank. Same for all ranks.

• tensor_splits (Dict[str, List[int]]) – tensor splits provided by input KJT.

• input_tensors (List[torch.Tensor]) – provided KJT tensors (ie. lengths, values) to redistribute according to splits.

• keys (List[str]) – KJT keys after AlltoAll.

• device (torch.device) – device on which buffers will be allocated.

• stagger (int) – stagger value to apply to recat tensor.

class torchrec.distributed.dist_data.KJTAllToAllTensorsAwaitable(pg: ProcessGroup, input: KeyedJaggedTensor, splits: List[int], input_splits: List[List[int]], output_splits: List[List[int]], input_tensors: List[Tensor], labels: List[str], batch_size_per_rank: List[int], keys: List[str], device: device, stagger: int)

Bases: Awaitable[KeyedJaggedTensor]

Awaitable for KJT tensors AlltoAll.

Parameters:

• pg (dist.ProcessGroup) – ProcessGroup for AlltoAll communication.

• input (KeyedJaggedTensor) – input KJT.

• splits (List[int]) – list of len(pg.size()) which indicates how many features to send to each pg.rank(). It is assumed the KeyedJaggedTensor is ordered by destination rank. Same for all ranks.

• input_splits (List[List[int]]) – input splits (number of values each rank will get) for each tensor in AlltoAll.

• output_splits (List[List[int]]) – output splits (number of values per rank in output) for each tensor in AlltoAll.

• input_tensors (List[torch.Tensor]) – provided KJT tensors (ie. lengths, values) to redistribute according to splits.

• labels (List[str]) – labels for each provided tensor.

• batch_size_per_rank (List[int]) – batch size per rank, to support variable batch size.

• keys (List[str]) – KJT keys after AlltoAll.

• device (torch.device) – device on which buffers will be allocated.

• stagger (int) – stagger value to apply to recat tensor.

class torchrec.distributed.dist_data.KJTOneToAll(splits: List[int], world_size: int)

Bases: Module

Redistributes KeyedJaggedTensor to all devices.

Implementation utilizes OnetoAll function, which essentially P2P copies the feature to the devices.

Parameters:

• splits (List[int]) – lengths of features to split the KeyJaggedTensor features into before copying them.

• world_size (int) – number of devices in the topology.

forward(kjt: KeyedJaggedTensor) → Awaitable[KJTList]

Splits features first and then sends the slices to the corresponding devices.

Parameters:

kjt (KeyedJaggedTensor) – the input features.

Returns:

awaitable of KeyedJaggedTensor splits.

Return type:

Awaitable[List[KeyedJaggedTensor]]

training: bool

class torchrec.distributed.dist_data.PooledEmbeddingsAllGather(pg: ProcessGroup, codecs: Optional[QuantizedCommCodecs] = None)

Bases: Module

The module class that wraps all-gather communication primitive for pooled embedding communication

We have a local input tensor with a layout of [batch_size, dimension]. We need to gather input tensors from all ranks into a flatten output tensor.

The class returns the async Awaitable handle for pooled embeddings tensor. The all-gather is only available for NCCL backend.

Parameters:

pg (dist.ProcessGroup) – The process group that the all-gather communication happens within.

Example:

init_distributed(rank=rank, size=2, backend="nccl")
pg = dist.new_group(backend="nccl")
input = torch.randn(2, 2)
m = PooledEmbeddingsAllGather(pg)
output = m(input)
tensor = output.wait()

forward(local_emb: Tensor) → PooledEmbeddingsAwaitable

Performs reduce scatter operation on pooled embeddings tensor.

Parameters:

local_emb (torch.Tensor) – tensor of shape [num_buckets x batch_size, dimension].

Returns:

awaitable of pooled embeddings of tensor of shape [batch_size, dimension].

Return type:

PooledEmbeddingsAwaitable

training: bool

class torchrec.distributed.dist_data.PooledEmbeddingsAllToAll(pg: ProcessGroup, dim_sum_per_rank: List[int], device: Optional[device] = None, callbacks: Optional[List[Callable[[Tensor], Tensor]]] = None, codecs: Optional[QuantizedCommCodecs] = None)

Bases: Module

Shards batches and collects keys of tensor with a ProcessGroup according to dim_sum_per_rank.

Implementation utilizes alltoall_pooled operation.

Parameters:

• pg (dist.ProcessGroup) – ProcessGroup for AlltoAll communication.

• dim_sum_per_rank (List[int]) – number of features (sum of dimensions) of the embedding in each rank.

• device (Optional[torch.device]) – device on which buffers will be allocated.

• callbacks (Optional[List[Callable[[torch.Tensor], torch.Tensor]]]) – callback functions.

Example:

dim_sum_per_rank = [2, 1]
a2a = PooledEmbeddingsAllToAll(pg, dim_sum_per_rank, device)

t0 = torch.rand((6, 2))
t1 = torch.rand((6, 1))
rank0_output = a2a(t0).wait()
rank1_output = a2a(t1).wait()
print(rank0_output.size())
    # torch.Size([3, 3])
print(rank1_output.size())
    # torch.Size([3, 3])

property callbacks: List[Callable[[Tensor], Tensor]]

forward(local_embs: Tensor, batch_size_per_rank: Optional[List[int]] = None) → PooledEmbeddingsAwaitable

Performs AlltoAll pooled operation on pooled embeddings tensor.

Parameters:

• local_embs (torch.Tensor) – tensor of values to distribute.

• batch_size_per_rank (Optional[List[int]]) – batch size per rank, to support variable batch size.

Returns:

awaitable of pooled embeddings.

Return type:

PooledEmbeddingsAwaitable

training: bool

class torchrec.distributed.dist_data.PooledEmbeddingsAwaitable(tensor_awaitable: Awaitable[Tensor])

Bases: Awaitable[Tensor]

Awaitable for pooled embeddings after collective operation.

Parameters:

tensor_awaitable (Awaitable[torch.Tensor]) – awaitable of concatenated tensors from all the processes in the group after collective.

property callbacks: List[Callable[[Tensor], Tensor]]

class torchrec.distributed.dist_data.PooledEmbeddingsReduceScatter(pg: ProcessGroup, codecs: Optional[QuantizedCommCodecs] = None)

Bases: Module

The module class that wraps reduce-scatter communication primitives for pooled embedding communication in row-wise and twrw sharding.

For pooled embeddings, we have a local model-parallel output tensor with a layout of [num_buckets x batch_size, dimension]. We need to sum over num_buckets dimension across batches. We split tensor along the first dimension into unequal chunks (tensor slices of different buckets) according to input_splits and reduce them into the output tensor and scatter the results for corresponding ranks.

The class returns the async Awaitable handle for pooled embeddings tensor. The reduce-scatter-v is only available for NCCL backend.

Parameters:

• pg (dist.ProcessGroup) – The process group that the reduce-scatter communication happens within.

• codecs – Quantization codec

forward(local_embs: Tensor, input_splits: Optional[List[int]] = None) → PooledEmbeddingsAwaitable

Performs reduce scatter operation on pooled embeddings tensor.

Parameters:

• local_embs (torch.Tensor) – tensor of shape [num_buckets x batch_size, dimension].

• input_splits (Optional[List[int]]) – list of splits for local_embs dim0.

Returns:

awaitable of pooled embeddings of tensor of shape [batch_size, dimension].

Return type:

PooledEmbeddingsAwaitable

training: bool

class torchrec.distributed.dist_data.SeqEmbeddingsAllToOne(device: device, world_size: int)

Bases: Module

Merges the pooled/sequence embedding tensor on each device into single tensor.

Parameters:

• device (torch.device) – device on which buffer will be allocated

• world_size (int) – number of devices in the topology.

• cat_dim (int) – which dimension you like to concate on. For pooled embedding it is 1; for sequence embedding it is 0.

forward(tensors: List[Tensor]) → Awaitable[List[Tensor]]

Performs AlltoOne operation on pooled embeddings tensors.

Parameters:

tensors (List[torch.Tensor]) – list of pooled embedding tensors.

Returns:

awaitable of the merged pooled embeddings.

Return type:

Awaitable[torch.Tensor]

training: bool

class torchrec.distributed.dist_data.SequenceEmbeddingsAllToAll(pg: ProcessGroup, features_per_rank: List[int], device: Optional[device] = None, codecs: Optional[QuantizedCommCodecs] = None)

Bases: Module

Redistributes sequence embedding to a ProcessGroup according to splits.

Parameters:

• pg (dist.ProcessGroup) – the process group that the AlltoAll communication happens within.

• features_per_rank (List[int]) – list of number of features per rank.

• device (Optional[torch.device]) – device on which buffers will be allocated.

Example:

init_distributed(rank=rank, size=2, backend="nccl")
pg = dist.new_group(backend="nccl")
features_per_rank = [4, 4]
m = SequenceEmbeddingsAllToAll(pg, features_per_rank)
local_embs = torch.rand((6, 2))
sharding_ctx: SequenceShardingContext
output = m(
    local_embs=local_embs,
    lengths=sharding_ctx.lengths_after_input_dist,
    input_splits=sharding_ctx.input_splits,
    output_splits=sharding_ctx.output_splits,
    unbucketize_permute_tensor=None,
)
tensor = output.wait()

forward(local_embs: Tensor, lengths: Tensor, input_splits: List[int], output_splits: List[int], unbucketize_permute_tensor: Optional[Tensor] = None, batch_size_per_rank: Optional[List[int]] = None, sparse_features_recat: Optional[Tensor] = None) → SequenceEmbeddingsAwaitable

Performs AlltoAll operation on sequence embeddings tensor.

Parameters:

• local_embs (torch.Tensor) – input embeddings tensor.

• lengths (torch.Tensor) – lengths of sparse features after AlltoAll.

• input_splits (List[int]) – input splits of AlltoAll.

• output_splits (List[int]) – output splits of AlltoAll.

• unbucketize_permute_tensor (Optional[torch.Tensor]) – stores the permute order of the KJT bucketize (for row-wise sharding only).

• batch_size_per_rank – (Optional[List[int]]): batch size per rank.

• sparse_features_recat (Optional[torch.Tensor]) – recat tensor used for sparse feature input dist.

Returns:

awaitable of sequence embeddings.

Return type:

SequenceEmbeddingsAwaitable

training: bool

class torchrec.distributed.dist_data.SequenceEmbeddingsAwaitable(tensor_awaitable: Awaitable[Tensor], unbucketize_permute_tensor: Optional[Tensor], embedding_dim: int)

Bases: Awaitable[Tensor]

Awaitable for sequence embeddings after collective operation.

Parameters:

• tensor_awaitable (Awaitable[torch.Tensor]) – awaitable of concatenated tensors from all the processes in the group after collective.

• unbucketize_permute_tensor (Optional[torch.Tensor]) – stores the permute order of KJT bucketize (for row-wise sharding only).

• embedding_dim (int) – embedding dimension.

class torchrec.distributed.dist_data.SplitsAllToAllAwaitable(input_tensors: List[Tensor], num_workers: int, device: device, pg: ProcessGroup)

Bases: Awaitable[List[List[int]]]

Awaitable for splits AlltoAll.

Parameters:

• input_tensors (List[torch.Tensor]) – tensor of splits to redistribute.

• num_workers (int) – total ranks.

• device (torch.device) – device on which buffers are allocated.

• pg (dist.ProcessGroup) – ProcessGroup for AlltoAll communication.

torchrec.distributed.embedding

class torchrec.distributed.embedding.EmbeddingCollectionAwaitable(*args, **kwargs)

Bases: LazyAwaitable[Dict[str, JaggedTensor]]

class torchrec.distributed.embedding.EmbeddingCollectionContext(sharding_contexts: List[torchrec.distributed.sharding.sequence_sharding.SequenceShardingContext] = <factory>)

Bases: Multistreamable

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

sharding_contexts: List[SequenceShardingContext]

class torchrec.distributed.embedding.EmbeddingCollectionSharder(fused_params: Optional[Dict[str, Any]] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None, variable_batch_size: bool = False)

Bases: BaseEmbeddingSharder[EmbeddingCollection]

This implementation uses non-fused EmbeddingCollection

property module_type: Type[EmbeddingCollection]

shard(module: EmbeddingCollection, params: Dict[str, ParameterSharding], env: ShardingEnv, device: Optional[device] = None) → ShardedEmbeddingCollection

Does the actual sharding. It will allocate parameters on the requested locations as specified by corresponding ParameterSharding.

Default implementation is data-parallel replication.

Parameters:

• module (M) – module to shard.

• params (ModuleShardingPlan) – dict of fully qualified parameter names (module path + parameter name, ‘.’-separated) to its sharding spec.

• env (ShardingEnv) – sharding environment that has the process group.

• device (torch.device) – compute device.

Returns:

sharded module implementation.

Return type:

ShardedModule[Any, Any, Any]

shardable_parameters(module: EmbeddingCollection) → Dict[str, Parameter]

List of parameters that can be sharded.

sharding_types(compute_device_type: str) → List[str]

List of supported sharding types. See ShardingType for well-known examples.

class torchrec.distributed.embedding.ShardedEmbeddingCollection(module: EmbeddingCollection, table_name_to_parameter_sharding: Dict[str, ParameterSharding], env: ShardingEnv, fused_params: Optional[Dict[str, Any]] = None, device: Optional[device] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None)

Bases: ShardedEmbeddingModule[KJTList, List[Tensor], Dict[str, JaggedTensor], EmbeddingCollectionContext], FusedOptimizerModule

Sharded implementation of EmbeddingCollection. This is part of the public API to allow for manual data dist pipelining.

compute(ctx: EmbeddingCollectionContext, dist_input: KJTList) → List[Tensor]

compute_and_output_dist(ctx: EmbeddingCollectionContext, input: KJTList) → LazyAwaitable[Dict[str, JaggedTensor]]

In case of multiple output distributions it makes sense to override this method and initiate the output distibution as soon as the corresponding compute completes.

create_context() → EmbeddingCollectionContext

property fused_optimizer: KeyedOptimizer

input_dist(ctx: EmbeddingCollectionContext, features: KeyedJaggedTensor) → Awaitable[Awaitable[KJTList]]

output_dist(ctx: EmbeddingCollectionContext, output: List[Tensor]) → LazyAwaitable[Dict[str, JaggedTensor]]

training: bool

torchrec.distributed.embedding.create_embedding_sharding(sharding_type: str, sharding_infos: List[EmbeddingShardingInfo], env: ShardingEnv, device: Optional[device] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None) → EmbeddingSharding[SequenceShardingContext, KeyedJaggedTensor, Tensor, Tensor]

torchrec.distributed.embedding.create_sharding_infos_by_sharding(module: EmbeddingCollectionInterface, table_name_to_parameter_sharding: Dict[str, ParameterSharding], fused_params: Optional[Dict[str, Any]]) → Dict[str, List[EmbeddingShardingInfo]]

torchrec.distributed.embedding_lookup

class torchrec.distributed.embedding_lookup.GroupedEmbeddingsLookup(grouped_configs: List[GroupedEmbeddingConfig], pg: Optional[ProcessGroup] = None, device: Optional[device] = None)

Bases: BaseEmbeddingLookup[KeyedJaggedTensor, Tensor]

Lookup modules for Sequence embeddings (i.e Embeddings)

forward(sparse_features: KeyedJaggedTensor) → Tensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

load_state_dict(state_dict: OrderedDict[str, Union[torch.Tensor, ShardedTensor]], strict: bool = True) → _IncompatibleKeys

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns:

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

named_parameters_by_table() → Iterator[Tuple[str, TableBatchedEmbeddingSlice]]

Like named_parameters(), but yields table_name and embedding_weights which are wrapped in TableBatchedEmbeddingSlice. For a single table with multiple shards (i.e CW) these are combined into one table/weight. Used in composability.

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

class torchrec.distributed.embedding_lookup.GroupedPooledEmbeddingsLookup(grouped_configs: List[GroupedEmbeddingConfig], device: Optional[device] = None, pg: Optional[ProcessGroup] = None, feature_processor: Optional[BaseGroupedFeatureProcessor] = None)

Bases: BaseEmbeddingLookup[KeyedJaggedTensor, Tensor]

Lookup modules for Pooled embeddings (i.e EmbeddingBags)

forward(sparse_features: KeyedJaggedTensor) → Tensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

load_state_dict(state_dict: OrderedDict[str, Union[ShardedTensor, torch.Tensor]], strict: bool = True) → _IncompatibleKeys

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns:

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

named_parameters_by_table() → Iterator[Tuple[str, TableBatchedEmbeddingSlice]]

Like named_parameters(), but yields table_name and embedding_weights which are wrapped in TableBatchedEmbeddingSlice. For a single table with multiple shards (i.e CW) these are combined into one table/weight. Used in composability.

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

class torchrec.distributed.embedding_lookup.InferGroupedEmbeddingsLookup(grouped_configs_per_rank: List[List[GroupedEmbeddingConfig]], world_size: int, fused_params: Optional[Dict[str, Any]] = None)

Bases: InferGroupedLookupMixin, BaseEmbeddingLookup[KJTList, List[Tensor]]

training: bool

class torchrec.distributed.embedding_lookup.InferGroupedLookupMixin

Bases: ABC

forward(sparse_features: KJTList) → List[Tensor]

load_state_dict(state_dict: OrderedDict[str, torch.Tensor], strict: bool = True) → _IncompatibleKeys

named_buffers(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, Tensor]]

named_parameters(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, Parameter]]

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

class torchrec.distributed.embedding_lookup.InferGroupedPooledEmbeddingsLookup(grouped_configs_per_rank: List[List[GroupedEmbeddingConfig]], world_size: int, fused_params: Optional[Dict[str, Any]] = None)

Bases: InferGroupedLookupMixin, BaseEmbeddingLookup[KJTList, List[Tensor]]

training: bool

class torchrec.distributed.embedding_lookup.MetaInferGroupedEmbeddingsLookup(grouped_configs: List[GroupedEmbeddingConfig], device: Optional[device] = None, fused_params: Optional[Dict[str, Any]] = None)

Bases: BaseEmbeddingLookup[KeyedJaggedTensor, Tensor]

meta embedding lookup module for inference since inference lookup has references for multiple TBE ops over all gpu workers. inference grouped embedding lookup module contains meta modules allocated over gpu workers.

forward(sparse_features: KeyedJaggedTensor) → Tensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

load_state_dict(state_dict: OrderedDict[str, Union[ShardedTensor, torch.Tensor]], strict: bool = True) → _IncompatibleKeys

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns:

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

class torchrec.distributed.embedding_lookup.MetaInferGroupedPooledEmbeddingsLookup(grouped_configs: List[GroupedEmbeddingConfig], device: Optional[device] = None, feature_processor: Optional[BaseGroupedFeatureProcessor] = None, fused_params: Optional[Dict[str, Any]] = None)

Bases: BaseEmbeddingLookup[KeyedJaggedTensor, Tensor]

meta embedding bag lookup module for inference since inference lookup has references for multiple TBE ops over all gpu workers. inference grouped embedding bag lookup module contains meta modules allocated over gpu workers.

forward(sparse_features: KeyedJaggedTensor) → Tensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

load_state_dict(state_dict: OrderedDict[str, Union[ShardedTensor, torch.Tensor]], strict: bool = True) → _IncompatibleKeys

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns:

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

torchrec.distributed.embedding_lookup.embeddings_cat_empty_rank_handle(embeddings: List[Tensor], dummy_embs_tensor: Tensor, dim: int = 0) → Tensor

torchrec.distributed.embedding_lookup.fx_wrap_tensor_view2d(x: Tensor, dim0: int, dim1: int) → Tensor

torchrec.distributed.embedding_sharding

class torchrec.distributed.embedding_sharding.BaseEmbeddingDist

Bases: ABC, Module, Generic[C, T, W]

Converts output of EmbeddingLookup from model-parallel to data-parallel.

abstract forward(local_embs: T, sharding_ctx: Optional[C] = None) → Awaitable[W]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class torchrec.distributed.embedding_sharding.BaseSparseFeaturesDist

Bases: ABC, Module, Generic[F]

Converts input from data-parallel to model-parallel.

abstract forward(sparse_features: KeyedJaggedTensor) → Awaitable[Awaitable[F]]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class torchrec.distributed.embedding_sharding.EmbeddingSharding(qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None)

Bases: ABC, Generic[C, F, T, W], FeatureShardingMixIn

Used to implement different sharding types for EmbeddingBagCollection, e.g. table_wise.

abstract create_input_dist(device: Optional[device] = None) → BaseSparseFeaturesDist[F]

abstract create_lookup(device: Optional[device] = None, fused_params: Optional[Dict[str, Any]] = None, feature_processor: Optional[BaseGroupedFeatureProcessor] = None) → BaseEmbeddingLookup[F, T]

abstract create_output_dist(device: Optional[device] = None) → BaseEmbeddingDist[C, T, W]

abstract embedding_dims() → List[int]

abstract embedding_names() → List[str]

abstract embedding_names_per_rank() → List[List[str]]

abstract embedding_shard_metadata() → List[Optional[ShardMetadata]]

property qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]]

class torchrec.distributed.embedding_sharding.EmbeddingShardingContext(batch_size_per_rank: List[int] = <factory>)

Bases: Multistreamable

batch_size_per_rank: List[int]

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

class torchrec.distributed.embedding_sharding.EmbeddingShardingInfo(embedding_config: torchrec.modules.embedding_configs.EmbeddingTableConfig, param_sharding: torchrec.distributed.types.ParameterSharding, param: torch.Tensor, fused_params: Union[Dict[str, Any], NoneType] = None)

Bases: object

embedding_config: EmbeddingTableConfig

fused_params: Optional[Dict[str, Any]] = None

param: Tensor

param_sharding: ParameterSharding

class torchrec.distributed.embedding_sharding.KJTListAwaitable(awaitables: List[Awaitable[KeyedJaggedTensor]])

Bases: Awaitable[KJTList]

Awaitable of KJTList.

Parameters:

awaitables (List[Awaitable[KeyedJaggedTensor]]) – list of Awaitable of sparse features.

class torchrec.distributed.embedding_sharding.KJTListSplitsAwaitable(awaitables: List[Awaitable[Awaitable[KeyedJaggedTensor]]], ctx: C)

Bases: Awaitable[Awaitable[KJTList]], Generic[C]

Awaitable of Awaitable of KJTList.

Parameters:

• awaitables (List[Awaitable[Awaitable[KeyedJaggedTensor]]]) – result from calling forward on KJTAllToAll with sparse features to redistribute.

• ctx (C) – sharding context to save the metadata from the input dist to for the embedding AlltoAll.

class torchrec.distributed.embedding_sharding.ListOfKJTListAwaitable(awaitables: List[Awaitable[KJTList]])

Bases: Awaitable[ListOfKJTList]

This module handles the tables-wise sharding input features distribution for inference.

Parameters:

awaitables (List[Awaitable[KJTList]]) – list of Awaitable of KJTList.

class torchrec.distributed.embedding_sharding.ListOfKJTListSplitsAwaitable(awaitables: List[Awaitable[Awaitable[KJTList]]])

Bases: Awaitable[Awaitable[ListOfKJTList]]

Awaitable of Awaitable of ListOfKJTList.

Parameters:

awaitables (List[Awaitable[Awaitable[KJTList]]]) – list of Awaitable of Awaitable of sparse features list.

torchrec.distributed.embedding_sharding.bucketize_kjt_before_all2all(kjt: KeyedJaggedTensor, num_buckets: int, block_sizes: Tensor, output_permute: bool = False, bucketize_pos: bool = False) → Tuple[KeyedJaggedTensor, Optional[Tensor]]

Bucketizes the values in KeyedJaggedTensor into num_buckets buckets, lengths are readjusted based on the bucketization results.

Note: This function should be used only for row-wise sharding before calling KJTAllToAll.

Parameters:

• num_buckets (int) – number of buckets to bucketize the values into.

• block_sizes – (torch.Tensor): bucket sizes for the keyed dimension.

• output_permute (bool) – output the memory location mapping from the unbucketized values to bucketized values or not.

• bucketize_pos (bool) – output the changed position of the bucketized values or not.

Returns:

the bucketized KeyedJaggedTensor and the optional permute mapping from the unbucketized values to bucketized value.

Return type:

Tuple[KeyedJaggedTensor, Optional[torch.Tensor]]

torchrec.distributed.embedding_sharding.group_tables(tables_per_rank: List[List[ShardedEmbeddingTable]]) → List[List[GroupedEmbeddingConfig]]

Groups tables by DataType, PoolingType, and EmbeddingComputeKernel.

Parameters:

tables_per_rank (List[List[ShardedEmbeddingTable]]) – list of sharded embedding tables per rank with consistent weightedness.

Returns:

per rank list of GroupedEmbeddingConfig for features.

Return type:

List[List[GroupedEmbeddingConfig]]

torchrec.distributed.embedding_types

class torchrec.distributed.embedding_types.BaseEmbeddingLookup

Bases: ABC, Module, Generic[F, T]

Interface implemented by different embedding implementations: e.g. one, which relies on nn.EmbeddingBag or table-batched one, etc.

abstract forward(sparse_features: F) → T

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class torchrec.distributed.embedding_types.BaseEmbeddingSharder(fused_params: Optional[Dict[str, Any]] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None, variable_batch_size: bool = False)

Bases: ModuleSharder[M]

compute_kernels(sharding_type: str, compute_device_type: str) → List[str]

List of supported compute kernels for a given sharding type and compute device.

property fused_params: Optional[Dict[str, Any]]

sharding_types(compute_device_type: str) → List[str]

List of supported sharding types. See ShardingType for well-known examples.

storage_usage(tensor: Tensor, compute_device_type: str, compute_kernel: str) → Dict[str, int]

List of system resources and corresponding usage given a compute device and compute kernel

class torchrec.distributed.embedding_types.BaseGroupedFeatureProcessor

Bases: Module

Abstract base class for grouped feature processor

abstract forward(features: KeyedJaggedTensor) → KeyedJaggedTensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class torchrec.distributed.embedding_types.BaseQuantEmbeddingSharder(fused_params: Optional[Dict[str, Any]] = None, shardable_params: Optional[List[str]] = None)

Bases: ModuleSharder[M]

compute_kernels(sharding_type: str, compute_device_type: str) → List[str]

List of supported compute kernels for a given sharding type and compute device.

property fused_params: Optional[Dict[str, Any]]

shardable_parameters(module: M) → Dict[str, Parameter]

List of parameters that can be sharded.

sharding_types(compute_device_type: str) → List[str]

List of supported sharding types. See ShardingType for well-known examples.

storage_usage(tensor: Tensor, compute_device_type: str, compute_kernel: str) → Dict[str, int]

List of system resources and corresponding usage given a compute device and compute kernel

class torchrec.distributed.embedding_types.EmbeddingAttributes(compute_kernel: torchrec.distributed.embedding_types.EmbeddingComputeKernel = <EmbeddingComputeKernel.DENSE: 'dense'>)

Bases: object

compute_kernel: EmbeddingComputeKernel = 'dense'

class torchrec.distributed.embedding_types.EmbeddingComputeKernel(value)

Bases: Enum

An enumeration.

DENSE = 'dense'

FUSED = 'fused'

FUSED_UVM = 'fused_uvm'

FUSED_UVM_CACHING = 'fused_uvm_caching'

QUANT = 'quant'

QUANT_UVM = 'quant_uvm'

QUANT_UVM_CACHING = 'quant_uvm_caching'

class torchrec.distributed.embedding_types.FeatureShardingMixIn

Bases: object

Feature Sharding Interface to provide sharding-aware feature metadata.

feature_names() → List[str]

feature_names_per_rank() → List[List[str]]

features_per_rank() → List[int]

class torchrec.distributed.embedding_types.GroupedEmbeddingConfig(data_type: torchrec.modules.embedding_configs.DataType, pooling: torchrec.modules.embedding_configs.PoolingType, is_weighted: bool, has_feature_processor: bool, compute_kernel: torchrec.distributed.embedding_types.EmbeddingComputeKernel, embedding_tables: List[torchrec.distributed.embedding_types.ShardedEmbeddingTable], fused_params: Union[Dict[str, Any], NoneType] = None)

Bases: object

compute_kernel: EmbeddingComputeKernel

data_type: DataType

dim_sum() → int

embedding_dims() → List[int]

embedding_names() → List[str]

embedding_shard_metadata() → List[Optional[ShardMetadata]]

embedding_tables: List[ShardedEmbeddingTable]

feature_hash_sizes() → List[int]

feature_names() → List[str]

fused_params: Optional[Dict[str, Any]] = None

has_feature_processor: bool

is_weighted: bool

num_features() → int

pooling: PoolingType

class torchrec.distributed.embedding_types.KJTList(features: List[KeyedJaggedTensor])

Bases: Multistreamable

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

class torchrec.distributed.embedding_types.ListOfKJTList(features: List[KJTList])

Bases: Multistreamable

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

class torchrec.distributed.embedding_types.ModuleShardingMixIn

Bases: object

The interface to access a sharded module’s sharding scheme.

property shardings: Dict[str, FeatureShardingMixIn]

class torchrec.distributed.embedding_types.OptimType(value)

Bases: Enum

An enumeration.

ADAGRAD = 'ADAGRAD'

ADAM = 'ADAM'

LAMB = 'LAMB'

LARS_SGD = 'LARS_SGD'

PARTIAL_ROWWISE_ADAM = 'PARTIAL_ROWWISE_ADAM'

PARTIAL_ROWWISE_LAMB = 'PARTIAL_ROWWISE_LAMB'

ROWWISE_ADAGRAD = 'ROWWISE_ADAGRAD'

SGD = 'SGD'

SHAMPOO = 'SHAMPOO'

class torchrec.distributed.embedding_types.ShardedConfig(local_rows: int = 0, local_cols: int = 0)

Bases: object

local_cols: int = 0

local_rows: int = 0

class torchrec.distributed.embedding_types.ShardedEmbeddingModule(qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None)

Bases: ShardedModule[CompIn, DistOut, Out, ShrdCtx], ModuleShardingMixIn

All model-parallel embedding modules implement this interface. Inputs and outputs are data-parallel.

Args::

qcomm_codecs_registry (Optional[Dict[str, QuantizedCommCodecs]]) : Mapping of CommOp name to QuantizedCommCodecs

extra_repr() → str

Pretty prints representation of the module’s lookup modules, input_dists and output_dists

training: bool

class torchrec.distributed.embedding_types.ShardedEmbeddingTable(num_embeddings: int, embedding_dim: int, name: str = '', data_type: torchrec.modules.embedding_configs.DataType = <DataType.FP32: 'FP32'>, feature_names: List[str] = <factory>, weight_init_max: Union[float, NoneType] = None, weight_init_min: Union[float, NoneType] = None, need_pos: bool = False, pooling: torchrec.modules.embedding_configs.PoolingType = <PoolingType.SUM: 'SUM'>, is_weighted: bool = False, has_feature_processor: bool = False, embedding_names: List[str] = <factory>, compute_kernel: torchrec.distributed.embedding_types.EmbeddingComputeKernel = <EmbeddingComputeKernel.DENSE: 'dense'>, local_rows: int = 0, local_cols: int = 0, local_metadata: Union[torch.distributed._shard.metadata.ShardMetadata, NoneType] = None, global_metadata: Union[torch.distributed._shard.sharded_tensor.metadata.ShardedTensorMetadata, NoneType] = None, fused_params: Union[Dict[str, Any], NoneType] = None)

Bases: ShardedMetaConfig, EmbeddingAttributes, EmbeddingTableConfig

fused_params: Optional[Dict[str, Any]] = None

class torchrec.distributed.embedding_types.ShardedMetaConfig(local_rows: int = 0, local_cols: int = 0, local_metadata: Union[torch.distributed._shard.metadata.ShardMetadata, NoneType] = None, global_metadata: Union[torch.distributed._shard.sharded_tensor.metadata.ShardedTensorMetadata, NoneType] = None)

Bases: ShardedConfig

global_metadata: Optional[ShardedTensorMetadata] = None

local_metadata: Optional[ShardMetadata] = None

torchrec.distributed.embedding_types.compute_kernel_to_embedding_location(compute_kernel: EmbeddingComputeKernel) → EmbeddingLocation

torchrec.distributed.embeddingbag

class torchrec.distributed.embeddingbag.EmbeddingAwaitable(*args, **kwargs)

Bases: LazyAwaitable[Tensor]

class torchrec.distributed.embeddingbag.EmbeddingBagCollectionAwaitable(*args, **kwargs)

Bases: LazyAwaitable[KeyedTensor]

class torchrec.distributed.embeddingbag.EmbeddingBagCollectionContext(sharding_contexts: List[Union[torchrec.distributed.embedding_sharding.EmbeddingShardingContext, NoneType]] = <factory>)

Bases: Multistreamable

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

sharding_contexts: List[Optional[EmbeddingShardingContext]]

class torchrec.distributed.embeddingbag.EmbeddingBagCollectionSharder(fused_params: Optional[Dict[str, Any]] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None, variable_batch_size: bool = False)

Bases: BaseEmbeddingSharder[EmbeddingBagCollection]

This implementation uses non-fused EmbeddingBagCollection

property module_type: Type[EmbeddingBagCollection]

shard(module: EmbeddingBagCollection, params: Dict[str, ParameterSharding], env: ShardingEnv, device: Optional[device] = None) → ShardedEmbeddingBagCollection

Does the actual sharding. It will allocate parameters on the requested locations as specified by corresponding ParameterSharding.

Default implementation is data-parallel replication.

Parameters:

• module (M) – module to shard.

• params (ModuleShardingPlan) – dict of fully qualified parameter names (module path + parameter name, ‘.’-separated) to its sharding spec.

• env (ShardingEnv) – sharding environment that has the process group.

• device (torch.device) – compute device.

Returns:

sharded module implementation.

Return type:

ShardedModule[Any, Any, Any]

shardable_parameters(module: EmbeddingBagCollection) → Dict[str, Parameter]

List of parameters that can be sharded.

class torchrec.distributed.embeddingbag.EmbeddingBagSharder(fused_params: Optional[Dict[str, Any]] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None, variable_batch_size: bool = False)

Bases: BaseEmbeddingSharder[EmbeddingBag]

This implementation uses non-fused nn.EmbeddingBag

property module_type: Type[EmbeddingBag]

shard(module: EmbeddingBag, params: Dict[str, ParameterSharding], env: ShardingEnv, device: Optional[device] = None) → ShardedEmbeddingBag

Does the actual sharding. It will allocate parameters on the requested locations as specified by corresponding ParameterSharding.

Default implementation is data-parallel replication.

Parameters:

• module (M) – module to shard.

• params (ModuleShardingPlan) – dict of fully qualified parameter names (module path + parameter name, ‘.’-separated) to its sharding spec.

• env (ShardingEnv) – sharding environment that has the process group.

• device (torch.device) – compute device.

Returns:

sharded module implementation.

Return type:

ShardedModule[Any, Any, Any]

shardable_parameters(module: EmbeddingBag) → Dict[str, Parameter]

List of parameters that can be sharded.

class torchrec.distributed.embeddingbag.ShardedEmbeddingBag(module: EmbeddingBag, table_name_to_parameter_sharding: Dict[str, ParameterSharding], env: ShardingEnv, fused_params: Optional[Dict[str, Any]] = None, device: Optional[device] = None)

Bases: ShardedEmbeddingModule[KeyedJaggedTensor, Tensor, Tensor, NullShardedModuleContext], FusedOptimizerModule

Sharded implementation of nn.EmbeddingBag. This is part of the public API to allow for manual data dist pipelining.

compute(ctx: NullShardedModuleContext, dist_input: KeyedJaggedTensor) → Tensor

create_context() → NullShardedModuleContext

property fused_optimizer: KeyedOptimizer

input_dist(ctx: NullShardedModuleContext, input: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None) → Awaitable[Awaitable[KeyedJaggedTensor]]

load_state_dict(state_dict: OrderedDict[str, torch.Tensor], strict: bool = True) → _IncompatibleKeys

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns:

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_modules(memo: Optional[Set[Module]] = None, prefix: str = '', remove_duplicate: bool = True) → Iterator[Tuple[str, Module]]

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Parameters:

• memo – a memo to store the set of modules already added to the result

• prefix – a prefix that will be added to the name of the module

• remove_duplicate – whether to remove the duplicated module instances in the result or not

Yields:

(str, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
...     print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

output_dist(ctx: NullShardedModuleContext, output: Tensor) → LazyAwaitable[Tensor]

sharded_parameter_names(prefix: str = '') → Iterator[str]

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

class torchrec.distributed.embeddingbag.ShardedEmbeddingBagCollection(module: EmbeddingBagCollectionInterface, table_name_to_parameter_sharding: Dict[str, ParameterSharding], env: ShardingEnv, fused_params: Optional[Dict[str, Any]] = None, device: Optional[device] = None, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None)

Bases: ShardedEmbeddingModule[KJTList, List[Tensor], KeyedTensor, EmbeddingBagCollectionContext], FusedOptimizerModule

Sharded implementation of EmbeddingBagCollection. This is part of the public API to allow for manual data dist pipelining.

compute(ctx: EmbeddingBagCollectionContext, dist_input: KJTList) → List[Tensor]

compute_and_output_dist(ctx: EmbeddingBagCollectionContext, input: KJTList) → LazyAwaitable[KeyedTensor]

In case of multiple output distributions it makes sense to override this method and initiate the output distibution as soon as the corresponding compute completes.

create_context() → EmbeddingBagCollectionContext

property fused_optimizer: KeyedOptimizer

input_dist(ctx: EmbeddingBagCollectionContext, features: KeyedJaggedTensor) → Awaitable[Awaitable[KJTList]]

output_dist(ctx: EmbeddingBagCollectionContext, output: List[Tensor]) → LazyAwaitable[KeyedTensor]

training: bool

torchrec.distributed.embeddingbag.create_embedding_bag_sharding(sharding_type: str, sharding_infos: List[EmbeddingShardingInfo], env: ShardingEnv, device: Optional[device] = None, permute_embeddings: bool = False, need_pos: bool = False, qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None) → EmbeddingSharding[EmbeddingShardingContext, KeyedJaggedTensor, Tensor, Tensor]

torchrec.distributed.embeddingbag.create_sharding_infos_by_sharding(module: EmbeddingBagCollectionInterface, table_name_to_parameter_sharding: Dict[str, ParameterSharding], prefix: str, fused_params: Optional[Dict[str, Any]], suffix: Optional[str] = 'weight') → Dict[str, List[EmbeddingShardingInfo]]

torchrec.distributed.embeddingbag.replace_placement_with_meta_device(sharding_infos: List[EmbeddingShardingInfo]) → None

Placement device and tensor device could be unmatched in some scenarios, e.g. passing meta device to DMP and passing cuda to EmbeddingShardingPlanner. We need to make device consistent after getting sharding planner.

torchrec.distributed.grouped_position_weighted

class torchrec.distributed.grouped_position_weighted.GroupedPositionWeightedModule(max_feature_lengths: Dict[str, int], device: Optional[device] = None)

Bases: BaseGroupedFeatureProcessor

forward(features: KeyedJaggedTensor) → KeyedJaggedTensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

torchrec.distributed.model_parallel

class torchrec.distributed.model_parallel.DataParallelWrapper

Bases: ABC

Interface implemented by custom data parallel wrappers.

abstract wrap(dmp: DistributedModelParallel, env: ShardingEnv, device: device) → None

class torchrec.distributed.model_parallel.DefaultDataParallelWrapper(bucket_cap_mb: int = 25)

Bases: DataParallelWrapper

Default data parallel wrapper, which applies data parallel to all unsharded modules.

wrap(dmp: DistributedModelParallel, env: ShardingEnv, device: device) → None

class torchrec.distributed.model_parallel.DistributedModelParallel(module: Module, env: Optional[ShardingEnv] = None, device: Optional[device] = None, plan: Optional[ShardingPlan] = None, sharders: Optional[List[ModuleSharder[Module]]] = None, init_data_parallel: bool = True, init_parameters: bool = True, data_parallel_wrapper: Optional[DataParallelWrapper] = None)

Bases: Module, FusedOptimizerModule

Entry point to model parallelism.

Parameters:

• module (nn.Module) – module to wrap.

• env (Optional[ShardingEnv]) – sharding environment that has the process group.

• device (Optional[torch.device]) – compute device, defaults to cpu.

• plan (Optional[ShardingPlan]) – plan to use when sharding, defaults to EmbeddingShardingPlanner.collective_plan().

• sharders (Optional[List[ModuleSharder[nn.Module]]]) – ModuleSharders available to shard with, defaults to EmbeddingBagCollectionSharder().

• init_data_parallel (bool) – data-parallel modules can be lazy, i.e. they delay parameter initialization until the first forward pass. Pass True to delay initialization of data parallel modules. Do first forward pass and then call DistributedModelParallel.init_data_parallel().

• init_parameters (bool) – initialize parameters for modules still on meta device.

• data_parallel_wrapper (Optional[DataParallelWrapper]) – custom wrapper for data parallel modules.

Example:

@torch.no_grad()
def init_weights(m):
    if isinstance(m, nn.Linear):
        m.weight.fill_(1.0)
    elif isinstance(m, EmbeddingBagCollection):
        for param in m.parameters():
            init.kaiming_normal_(param)

m = MyModel(device='meta')
m = DistributedModelParallel(m)
m.apply(init_weights)

bare_named_parameters(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, Parameter]]

copy(device: device) → DistributedModelParallel

Recursively copy submodules to new device by calling per-module customized copy process, since some modules needs to use the original references (like ShardedModule for inference).

forward(*args, **kwargs) → Any

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

property fused_optimizer: KeyedOptimizer

init_data_parallel() → None

See init_data_parallel c-tor argument for usage. It’s safe to call this method multiple times.

load_state_dict(state_dict: OrderedDict[str, torch.Tensor], prefix: str = '', strict: bool = True) → _IncompatibleKeys

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns:

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

property module: Module

Property to directly access sharded module, which will not be wrapped in DDP, FSDP, DMP, or any other parallelism wrappers.

named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Tensor]]

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool, optional) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.

• remove_duplicate (bool, optional) – whether to remove the duplicated buffers in the result. Defaults to True.

Yields:

(str, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) → Iterator[Tuple[str, Parameter]]

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

• remove_duplicate (bool, optional) – whether to remove the duplicated parameters in the result. Defaults to True.

Yields:

(str, Parameter) – Tuple containing the name and parameter

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

property plan: ShardingPlan

sparse_grad_parameter_names(destination: Optional[List[str]] = None, prefix: str = '') → List[str]

state_dict(destination: Optional[Dict[str, Any]] = None, prefix: str = '', keep_vars: bool = False) → Dict[str, Any]

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

training: bool

torchrec.distributed.model_parallel.get_module(module: Module) → Module

Unwraps DMP module.

Does not unwrap data parallel wrappers (i.e. DDP/FSDP), so overriding implementations by the wrappers can be used.

torchrec.distributed.model_parallel.get_unwrapped_module(module: Module) → Module

Unwraps module wrapped by DMP, DDP, or FSDP.

torchrec.distributed.quant_embeddingbag

class torchrec.distributed.quant_embeddingbag.QuantEmbeddingBagCollectionSharder(fused_params: Optional[Dict[str, Any]] = None, shardable_params: Optional[List[str]] = None)

Bases: BaseQuantEmbeddingSharder[EmbeddingBagCollection]

property module_type: Type[EmbeddingBagCollection]

shard(module: EmbeddingBagCollection, params: Dict[str, ParameterSharding], env: ShardingEnv, device: Optional[device] = None) → ShardedQuantEmbeddingBagCollection

Does the actual sharding. It will allocate parameters on the requested locations as specified by corresponding ParameterSharding.

Default implementation is data-parallel replication.

Parameters:

• module (M) – module to shard.

• params (ModuleShardingPlan) – dict of fully qualified parameter names (module path + parameter name, ‘.’-separated) to its sharding spec.

• env (ShardingEnv) – sharding environment that has the process group.

• device (torch.device) – compute device.

Returns:

sharded module implementation.

Return type:

ShardedModule[Any, Any, Any]

class torchrec.distributed.quant_embeddingbag.ShardedQuantEmbeddingBagCollection(module: EmbeddingBagCollectionInterface, table_name_to_parameter_sharding: Dict[str, ParameterSharding], env: ShardingEnv, fused_params: Optional[Dict[str, Any]] = None)

Bases: ShardedEmbeddingModule[ListOfKJTList, List[List[Tensor]], KeyedTensor, NullShardedModuleContext]

Sharded implementation of EmbeddingBagCollection. This is part of the public API to allow for manual data dist pipelining.

compute(ctx: NullShardedModuleContext, dist_input: ListOfKJTList) → List[List[Tensor]]

compute_and_output_dist(ctx: NullShardedModuleContext, input: ListOfKJTList) → LazyAwaitable[KeyedTensor]

In case of multiple output distributions it makes sense to override this method and initiate the output distibution as soon as the corresponding compute completes.

copy(device: device) → Module

create_context() → NullShardedModuleContext

input_dist(ctx: NullShardedModuleContext, features: KeyedJaggedTensor) → Awaitable[Awaitable[ListOfKJTList]]

output_dist(ctx: NullShardedModuleContext, output: List[List[Tensor]]) → LazyAwaitable[KeyedTensor]

property shardings: Dict[str, FeatureShardingMixIn]

training: bool

torchrec.distributed.quant_embeddingbag.create_infer_embedding_bag_sharding(sharding_type: str, sharding_infos: List[EmbeddingShardingInfo], env: ShardingEnv) → EmbeddingSharding[NullShardingContext, KJTList, List[Tensor], Tensor]

torchrec.distributed.train_pipeline

class torchrec.distributed.train_pipeline.ArgInfo(input_attrs: List[str], is_getitems: List[bool], name: Union[str, NoneType])

Bases: object

input_attrs: List[str]

is_getitems: List[bool]

name: Optional[str]

class torchrec.distributed.train_pipeline.PipelinedForward(name: str, args: List[ArgInfo], module: ShardedModule, context: TrainPipelineContext, dist_stream: Optional[Stream])

Bases: object

property args: List[ArgInfo]

property name: str

class torchrec.distributed.train_pipeline.Tracer(leaf_modules: Optional[List[str]] = None)

Bases: Tracer

graph: Graph

is_leaf_module(m: Module, module_qualified_name: str) → bool

A method to specify whether a given nn.Module is a “leaf” module.

Leaf modules are the atomic units that appear in the IR, referenced by call_module calls. By default, Modules in the PyTorch standard library namespace (torch.nn) are leaf modules. All other modules are traced through and their constituent ops are recorded, unless specified otherwise via this parameter.

Parameters:

• m (Module) – The module being queried about

• module_qualified_name (str) – The path to root of this module. For example, if you have a module hierarchy where submodule foo contains submodule bar, which contains submodule baz, that module will appear with the qualified name foo.bar.baz here.

Note

Backwards-compatibility for this API is guaranteed.

module_stack: OrderedDict[str, str]

node_name_to_scope: Dict[str, Tuple[str, type]]

proxy_buffer_attributes: bool = False

scope: Scope

class torchrec.distributed.train_pipeline.TrainPipeline(*args, **kwds)

Bases: ABC, Generic[In, Out]

abstract progress(dataloader_iter: Iterator[In]) → Out

class torchrec.distributed.train_pipeline.TrainPipelineBase(model: Module, optimizer: Optimizer, device: device)

Bases: TrainPipeline[In, Out]

This class runs training iterations using a pipeline of two stages, each as a CUDA stream, namely, the current (default) stream and self._memcpy_stream. For each iteration, self._memcpy_stream moves the input from host (CPU) memory to GPU memory, and the default stream runs forward, backward, and optimization.

progress(dataloader_iter: Iterator[In]) → Out

class torchrec.distributed.train_pipeline.TrainPipelineContext(input_dist_requests: Dict[str, torchrec.distributed.types.Awaitable[Any]] = <factory>, module_contexts: Dict[str, torchrec.streamable.Multistreamable] = <factory>, feature_processor_forwards: List[Any] = <factory>)

Bases: object

feature_processor_forwards: List[Any]

input_dist_requests: Dict[str, Awaitable[Any]]

module_contexts: Dict[str, Multistreamable]

class torchrec.distributed.train_pipeline.TrainPipelineSparseDist(model: Module, optimizer: Optimizer, device: device)

Bases: TrainPipeline[In, Out]

This pipeline overlaps device transfer, and ShardedModule.input_dist() with forward and backward. This helps hide the all2all latency while preserving the training forward / backward ordering.

stage 3: forward, backward - uses default CUDA stream stage 2: ShardedModule.input_dist() - uses data_dist CUDA stream stage 1: device transfer - uses memcpy CUDA stream

ShardedModule.input_dist() is only done for top-level modules in the call graph. To be considered a top-level module, a module can only depend on ‘getattr’ calls on input.

Input model must be symbolically traceable with the exception of ShardedModule and DistributedDataParallel modules.

progress(dataloader_iter: Iterator[In]) → Out

synced_pipeline_id: Dict[int, int] = {}

torchrec.distributed.types

class torchrec.distributed.types.Awaitable

Bases: ABC, Generic[W]

property callbacks: List[Callable[[W], W]]

wait() → W

class torchrec.distributed.types.CommOp(value)

Bases: Enum

An enumeration.

POOLED_EMBEDDINGS_ALL_TO_ALL = 'pooled_embeddings_all_to_all'

POOLED_EMBEDDINGS_REDUCE_SCATTER = 'pooled_embeddings_reduce_scatter'

SEQUENCE_EMBEDDINGS_ALL_TO_ALL = 'sequence_embeddings_all_to_all'

class torchrec.distributed.types.ComputeKernel(value)

Bases: Enum

An enumeration.

DEFAULT = 'default'

class torchrec.distributed.types.GenericMeta

Bases: type

class torchrec.distributed.types.LazyAwaitable(*args, **kwargs)

Bases: Awaitable[W]

The LazyAwaitable type which exposes a wait() API, concrete types can control how to initialize and how the wait() behavior should be in order to achieve specific async operation.

This base LazyAwaitable type is a “lazy” async type, which means it will delay wait() as late as possible, see details in __torch_function__ below. This could help the model automatically enable computation and communication overlap, model author doesn’t need to manually call wait() if the results is used by a pytorch function, or by other python operations (NOTE: need to implement corresponding magic methods like __getattr__ below)

Some caveats:

• This works with Pytorch functions, but not any generic method, if you would like to do arbitary python operations, you need to implement the corresponding magic methods

• In the case that one function have two or more arguments are LazyAwaitable, the lazy wait mechanism can’t ensure perfect computation/communication overlap (i.e. quickly waited the first one but long wait on the second)

class torchrec.distributed.types.LazyNoWait(*args, **kwargs)

Bases: LazyAwaitable[W]

class torchrec.distributed.types.ModuleSharder(qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None)

Bases: ABC, Generic[M]

ModuleSharder is per each module, which supports sharding, e.g. EmbeddingBagCollection.

Args::

qcomm_codecs_registry (Optional[Dict[str, QuantizedCommCodecs]]) : Mapping of CommOp name to QuantizedCommCodecs

compute_kernels(sharding_type: str, compute_device_type: str) → List[str]

List of supported compute kernels for a given sharding type and compute device.

abstract property module_type: Type[M]

property qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]]

abstract classmethod shard(module: M, params: Dict[str, ParameterSharding], env: ShardingEnv, device: Optional[device] = None) → ShardedModule[Any, Any, Any, Any]

Does the actual sharding. It will allocate parameters on the requested locations as specified by corresponding ParameterSharding.

Default implementation is data-parallel replication.

Parameters:

• module (M) – module to shard.

• params (ModuleShardingPlan) – dict of fully qualified parameter names (module path + parameter name, ‘.’-separated) to its sharding spec.

• env (ShardingEnv) – sharding environment that has the process group.

• device (torch.device) – compute device.

Returns:

sharded module implementation.

Return type:

ShardedModule[Any, Any, Any]

shardable_parameters(module: M) → Dict[str, Parameter]

List of parameters that can be sharded.

sharding_types(compute_device_type: str) → List[str]

List of supported sharding types. See ShardingType for well-known examples.

storage_usage(tensor: Tensor, compute_device_type: str, compute_kernel: str) → Dict[str, int]

List of system resources and corresponding usage given a compute device and compute kernel.

torchrec.distributed.types.ModuleShardingPlan

Map of ParameterSharding per parameter (usually a table). This describes the sharding plan for a torchrec module (e.g. EmbeddingBagCollection)

alias of Dict[str, ParameterSharding]

class torchrec.distributed.types.NoOpQuantizedCommCodec(*args, **kwds)

Bases: Generic[QuantizationContext]

Default No-Op implementation of QuantizedCommCodec

calc_quantized_size(input_len: int, ctx: Optional[QuantizationContext] = None) → int

create_context() → Optional[QuantizationContext]

decode(input_grad: Tensor, ctx: Optional[QuantizationContext] = None) → Tensor

encode(input_tensor: Tensor, ctx: Optional[QuantizationContext] = None) → Tensor

quantized_dtype() → dtype

class torchrec.distributed.types.NoWait(obj: W)

Bases: Awaitable[W]

class torchrec.distributed.types.NullShardedModuleContext

Bases: Multistreamable

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

class torchrec.distributed.types.NullShardingContext

Bases: Multistreamable

record_stream(stream: Stream) → None

See https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html

class torchrec.distributed.types.ParameterSharding(sharding_type: str, compute_kernel: str, ranks: Optional[List[int]] = None, sharding_spec: Optional[ShardingSpec] = None)

Bases: object

Describes the sharding of the parameter.

sharding_type (str): how this parameter is sharded. See ShardingType for well-known

types.

compute_kernel (str): compute kernel to be used by this parameter. ranks (Optional[List[int]]): rank of each shard. sharding_spec (Optional[ShardingSpec]): list of ShardMetadata for each shard.

Note

ShardingType.TABLE_WISE - rank where this embedding is placed ShardingType.COLUMN_WISE - rank where the embedding shards are placed, seen as individual tables ShardingType.TABLE_ROW_WISE - first rank when this embedding is placed ShardingType.ROW_WISE, ShardingType.DATA_PARALLEL - unused

compute_kernel: str

ranks: Optional[List[int]] = None

sharding_spec: Optional[ShardingSpec] = None

sharding_type: str

class torchrec.distributed.types.ParameterStorage(value)

Bases: Enum

Well-known physical resources, which can be used as constraints by ShardingPlanner.

DDR = 'ddr'

HBM = 'hbm'

class torchrec.distributed.types.QuantizedCommCodec(*args, **kwds)

Bases: Generic[QuantizationContext]

Provide an implementation to quantized, or apply mixed precision, to the tensors used in collective calls (pooled_all_to_all, reduce_scatter, etc). The dtype is the dtype of the tensor called from encode.

This makes the assumption that the input tensor has type torch.float32

>>>
    quantized_tensor = quantized_comm_codec.encode(input_tensor)
    quantized_tensor.dtype == quantized_comm_codec.quantized_dtype
    collective_call(output_tensors, input_tensors=tensor)
    output_tensor = decode(output_tensors)

torch.assert_close(input_tensors, output_tensor)

calc_quantized_size(input_len: int, ctx: Optional[QuantizationContext] = None) → int

Given the length of input tensor, returns the length of tensor after quantization. Used by INT8 codecs where the quantized tensor have some additional parameters. For other cases, the quantized tensor should have the same length with input.

create_context() → Optional[QuantizationContext]

Create a context object that can be used to carry session-based parameters between encoder and decoder.

decode(input_grad: Tensor, ctx: Optional[QuantizationContext] = None) → Tensor

encode(input_tensor: Tensor, ctx: Optional[QuantizationContext] = None) → Tensor

quantized_dtype() → dtype

tensor.dtype of the resultant encode(input_tensor)

class torchrec.distributed.types.QuantizedCommCodecs(forward: ~torchrec.distributed.types.QuantizedCommCodec = <torchrec.distributed.types.NoOpQuantizedCommCodec object>, backward: ~torchrec.distributed.types.QuantizedCommCodec = <torchrec.distributed.types.NoOpQuantizedCommCodec object>)

Bases: object

The quantization codecs to use for the forward and backward pass respectively of a comm op (e.g. pooled_all_to_all, reduce_scatter, sequence_all_to_all).

backward: QuantizedCommCodec = <torchrec.distributed.types.NoOpQuantizedCommCodec object>

forward: QuantizedCommCodec = <torchrec.distributed.types.NoOpQuantizedCommCodec object>

class torchrec.distributed.types.ShardableModule

Bases: ABC, Module

abstract shard(env: ShardingEnv, device: Optional[device] = None) → ShardedModule[Any, Any, Any, Any]

training: bool

class torchrec.distributed.types.ShardedModule(qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]] = None)

Bases: ABC, Module, Generic[CompIn, DistOut, Out, ShrdCtx], ModuleNoCopyMixin

All model-parallel modules implement this interface. Inputs and outputs are data-parallel.

Args::

qcomm_codecs_registry (Optional[Dict[str, QuantizedCommCodecs]]) : Mapping of CommOp name to QuantizedCommCodecs

Note

‘input_dist’ / ‘output_dist’ are responsible of transforming inputs / outputs from data-parallel to model parallel and vise-versa.

abstract compute(ctx: ShrdCtx, dist_input: CompIn) → DistOut

compute_and_output_dist(ctx: ShrdCtx, input: CompIn) → LazyAwaitable[Out]

In case of multiple output distributions it makes sense to override this method and initiate the output distibution as soon as the corresponding compute completes.

abstract create_context() → ShrdCtx

forward(*input, **kwargs) → LazyAwaitable[Out]

Executes the input dist, compute, and output dist steps.

Parameters:

• *input – input.

• **kwargs – keyword arguments.

Returns:

awaitable of output from output dist.

Return type:

LazyAwaitable[Out]

abstract input_dist(ctx: ShrdCtx, *input, **kwargs) → Awaitable[Awaitable[CompIn]]

abstract output_dist(ctx: ShrdCtx, output: DistOut) → LazyAwaitable[Out]

property qcomm_codecs_registry: Optional[Dict[str, QuantizedCommCodecs]]

sharded_parameter_names(prefix: str = '') → Iterator[str]

training: bool

class torchrec.distributed.types.ShardingEnv(world_size: int, rank: int, pg: Optional[ProcessGroup] = None)

Bases: object

Provides an abstraction over torch.distributed.ProcessGroup, which practically enables DistributedModelParallel to be used during inference.

classmethod from_local(world_size: int, rank: int) → ShardingEnv

Creates a local host-based sharding environment.

Note

Typically used during single host inference.

classmethod from_process_group(pg: ProcessGroup) → ShardingEnv

Creates ProcessGroup-based sharding environment.

Note

Typically used during training.

class torchrec.distributed.types.ShardingPlan(plan: Dict[str, Dict[str, ParameterSharding]])

Bases: object

Representation of sharding plan. This uses the FQN of the larger wrapped model (i.e the model that is wrapped using DistributedModelParallel) ModuleShardingPlan should be used when TorchRec composability is desired.

plan

dict keyed by module path of dict of parameter sharding specs keyed by parameter name.

Type:

Dict[str, ModuleShardingPlan]

get_plan_for_module(module_path: str) → Optional[Dict[str, ParameterSharding]]

Parameters:

module_path (str) –

Returns:

dict of parameter sharding specs keyed by parameter name. None if sharding specs do not exist for given module_path.

Return type:

Optional[ModuleShardingPlan]

plan: Dict[str, Dict[str, ParameterSharding]]

class torchrec.distributed.types.ShardingPlanner

Bases: ABC

Plans sharding. This plan can be saved and re-used to ensure sharding stability.

abstract collective_plan(module: Module, sharders: List[ModuleSharder[Module]]) → ShardingPlan

Calls self.plan(…) on rank 0 and broadcasts.

Parameters:

• module (nn.Module) – module that sharding is planned for.

• sharders (List[ModuleSharder[nn.Module]]) – provided sharders for module.

Returns:

the computed sharding plan.

Return type:

ShardingPlan

abstract plan(module: Module, sharders: List[ModuleSharder[Module]]) → ShardingPlan

Plans sharding for provided module and given sharders.

Parameters:

• module (nn.Module) – module that sharding is planned for.

• sharders (List[ModuleSharder[nn.Module]]) – provided sharders for module.

Returns:

the computed sharding plan.

Return type:

ShardingPlan

class torchrec.distributed.types.ShardingType(value)

Bases: Enum

Well-known sharding types, used by inter-module optimizations.

COLUMN_WISE = 'column_wise'

DATA_PARALLEL = 'data_parallel'

ROW_WISE = 'row_wise'

TABLE_COLUMN_WISE = 'table_column_wise'

TABLE_ROW_WISE = 'table_row_wise'

TABLE_WISE = 'table_wise'

torchrec.distributed.types.scope(method)

torchrec.distributed.utils

class torchrec.distributed.utils.CopyableMixin

Bases: Module

Allows copying of module to a target device.

Example:

class MyModule(CopyableMixin):
    ...

Parameters:

device – torch.device to copy to

Returns

nn.Module on new device

copy(device: device) → Module

training: bool

torchrec.distributed.utils.add_prefix_to_state_dict(state_dict: Dict[str, Any], prefix: str) → None

Adds prefix to all keys in state dict, in place.

Parameters:

• state_dict (Dict[str, Any]) – input state dict to update.

• prefix (str) – name to filter from state dict keys.

Returns:

None.

torchrec.distributed.utils.append_prefix(prefix: str, name: str) → str

Appends provided prefix to provided name.

torchrec.distributed.utils.copy_to_device(module: Module, current_device: device, to_device: device) → Module

torchrec.distributed.utils.filter_state_dict(state_dict: OrderedDict[str, torch.Tensor], name: str) → OrderedDict[str, torch.Tensor]

Filters state dict for keys that start with provided name. Strips provided name from beginning of key in the resulting state dict.

Parameters:

• state_dict (OrderedDict[str, torch.Tensor]) – input state dict to filter.

• name (str) – name to filter from state dict keys.

Returns:

filtered state dict.

Return type:

OrderedDict[str, torch.Tensor]

torchrec.distributed.utils.get_unsharded_module_names(model: Module) → List[str]

Retrieves names of top level modules that do not contain any sharded sub-modules.

Parameters:

model (torch.nn.Module) – model to retrieve unsharded module names from.

Returns:

list of names of modules that don’t have sharded sub-modules.

Return type:

List[str]

torchrec.distributed.utils.init_parameters(module: Module, device: device) → None

torchrec.distributed.utils.merge_fused_params(fused_params: Optional[Dict[str, Any]] = None, param_fused_params: Optional[Dict[str, Any]] = None) → Dict[str, Any]

Configure the fused_params including cache_precision if the value is not preset.

Values set in table_level_fused_params take precidence over the global fused_params

Parameters:

• fused_params (Optional[Dict[str, Any]]) – the original fused_params

• grouped_fused_params –

Returns:

a non-null configured fused_params dictionary to be used to configure the embedding lookup kernel

Return type:

[Dict[str, Any]]

torchrec.distributed.utils.none_throws(optional: Optional[_T], message: str = 'Unexpected `None`') → _T

Convert an optional to its value. Raises an AssertionError if the value is None

torchrec.distributed.utils.optimizer_type_to_emb_opt_type(optimizer_class: Type[Optimizer]) → Optional[EmbOptimType]

class torchrec.distributed.utils.sharded_model_copy(device: Optional[Union[str, int, device]])

Bases: object

Allows copying of DistributedModelParallel module to a target device.

Example:

# Copying model to CPU.

m = DistributedModelParallel(m)
with sharded_model_copy("cpu"):
    m_cpu = copy.deepcopy(m)