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127 lines
6.1 KiB
127 lines
6.1 KiB
import torch.nn.functional as F
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from typing import Optional
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from torch import Tensor
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from colossalai.tensor.op_wrapper import colo_op_impl
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from colossalai.tensor import ComputePattern, ComputePattern, ComputeSpec, ColoTensor, distspec, ColoTensorSpec, \
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ShardSpec, ReplicaSpec
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from ._utils import GeneralTensor, convert_to_colo_tensor
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def colo_embedding_bag_1Dcol(input_tensor: ColoTensor,
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weight: ColoTensor,
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offsets: Optional[Tensor] = None,
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max_norm: Optional[float] = None,
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norm_type: float = 2,
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scale_grad_by_freq: bool = False,
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mode: str = "mean",
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sparse: bool = False,
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per_sample_weights: Optional[Tensor] = None,
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include_last_offset: bool = False,
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padding_idx: Optional[int] = None) -> ColoTensor:
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# embedding_bag_1Dcol split the weight(lookup table) to (num_embeddings, embedding_dim/P)
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# Gather splitted lookup table
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pg = weight.get_process_group()
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input_tensor = input_tensor.redistribute(ReplicaSpec())
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output_parallel = F.embedding_bag(input_tensor,
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weight,
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offsets=offsets,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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mode=mode,
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sparse=sparse,
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per_sample_weights=per_sample_weights,
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include_last_offset=include_last_offset,
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padding_idx=padding_idx)
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output_spec = ColoTensorSpec(pg, ShardSpec([-1], [weight.get_tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
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if weight.compute_spec.output_replicate:
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return output.to_replicate()
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else:
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return output
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def colo_embedding_bag_1d(tp_mode: str,
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input_tensor: ColoTensor,
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weight: ColoTensor,
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offsets: Optional[Tensor] = None,
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max_norm: Optional[float] = None,
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norm_type: float = 2,
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scale_grad_by_freq: bool = False,
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mode: str = "mean",
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sparse: bool = False,
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per_sample_weights: Optional[Tensor] = None,
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include_last_offset: bool = False,
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padding_idx: Optional[int] = None) -> ColoTensor:
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assert tp_mode in ('col',)
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funcs = {'col': colo_embedding_bag_1Dcol}
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return funcs[tp_mode](input_tensor,
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weight,
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offsets=offsets,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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mode=mode,
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sparse=sparse,
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per_sample_weights=per_sample_weights,
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include_last_offset=include_last_offset,
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padding_idx=padding_idx)
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@colo_op_impl(F.embedding_bag)
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def colo_embedding_bag(input_tensor: GeneralTensor,
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weight: GeneralTensor,
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offsets: Optional[Tensor] = None,
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max_norm: Optional[float] = None,
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norm_type: float = 2,
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scale_grad_by_freq: bool = False,
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mode: str = "mean",
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sparse: bool = False,
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per_sample_weights: Optional[Tensor] = None,
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include_last_offset: bool = False,
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padding_idx: Optional[int] = None):
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"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding_bag``.
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This method looks up an embedding table.
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"""
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assert isinstance(weight, ColoTensor)
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input_tensor = convert_to_colo_tensor(input_tensor, weight.get_process_group())
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# Handle differen parallel actions.
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if not weight.has_compute_spec(): # No Model Parallel Applied
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assert weight.is_replicate(), 'Invalid weight spec for native embedding op'
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return ColoTensor.from_torch_tensor(
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tensor=F.embedding_bag(input_tensor,
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weight,
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offsets=offsets,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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mode=mode,
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sparse=sparse,
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per_sample_weights=per_sample_weights,
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include_last_offset=include_last_offset,
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padding_idx=padding_idx),
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spec=ColoTensorSpec(weight.get_process_group()))
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elif weight.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied
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if weight.is_shard_1dcol():
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tp_mode = 'col'
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else:
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raise NotImplementedError
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return colo_embedding_bag_1d(tp_mode,
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input_tensor,
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weight,
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offsets=offsets,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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mode=mode,
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sparse=sparse,
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per_sample_weights=per_sample_weights,
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include_last_offset=include_last_offset,
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padding_idx=padding_idx)
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else:
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raise NotImplementedError
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