mirror of https://github.com/hpcaitech/ColossalAI
[Tensor] add ColoTensor TP1Dcol Embedding (#899)
parent
e46e423c00
commit
2c0d19d755
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@ -2,3 +2,4 @@ from .linear import colo_linear
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from .element_wise import *
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from .layernorm import colo_layernorm
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from .loss import colo_cross_entropy
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from .embedding import colo_embedding
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@ -0,0 +1,56 @@
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import torch
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from colossalai.tensor.op_wrapper import colo_op_impl
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from colossalai.context import ParallelMode
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from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input, \
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gather_forward_split_backward, reduce_grad
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from colossalai.nn.layer.utils import divide
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from colossalai.core import global_context as gpc
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from packaging import version
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from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
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def colo_embedding_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, args, kwargs) -> ColoTensor:
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# embedding_1Dcol split the weight(lookup table)
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# Gather splitted lookup table
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol_Embedding)
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if not input_tensor.is_gathered():
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input_tensor.gather()
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output_parallel = torch.nn.functional.embedding(input_tensor.torch_tensor(), weight.torch_tensor(),
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*args, **kwargs)
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output = ColoTensor.init_from_torch_tensor(output_parallel)
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out_parallel_action_list = [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]
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output_spec = TensorSpec(out_parallel_action_list)
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output.set_spec(output_spec, shard=False)
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output.set_shard_pattern(ShardPattern.Col)
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output.gather()
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return output
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@colo_op_impl(torch.nn.functional.embedding)
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def colo_embedding(types, args, kwargs, pg):
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"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding``.
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This method looks up an embedding table.
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"""
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input_tensor = args[0]
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weight = args[1]
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args = args[2:]
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if not isinstance(input_tensor, ColoTensor):
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input_tensor = ColoTensor.init_from_torch_tensor(input_tensor)
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if not isinstance(weight, ColoTensor):
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weight = ColoTensor.init_from_torch_tensor(weight)
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# Handle differen parallel actions.
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if not weight.has_spec(): # No Model Parallel Applied
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input_tensor = input_tensor.torch_tensor()
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weight = weight.torch_tensor()
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output = torch.nn.functional.embedding(input_tensor, weight, *args, **kwargs)
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return ColoTensor.init_from_torch_tensor(output)
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elif weight.shard_spec.num_action == 1: # Single Model Parallel Applied
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compute_patterns = weight.shard_spec.compute_patterns
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if ComputePattern.TP1DCol_Embedding in compute_patterns:
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return colo_embedding_1Dcol(input_tensor, weight, args, kwargs)
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else:
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raise NotImplementedError
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else:
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raise NotImplementedError
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@ -27,7 +27,7 @@ def colo_layernorm(types, args=(), kwargs=None, pg=None):
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eps = kwargs['eps']
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if isinstance(input_tensor, ColoTensor):
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if input_tensor.is_activation() and not input_tensor.is_gathered():
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if not input_tensor.is_gathered():
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input_tensor.gather()
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input_tensor = input_tensor.torch_tensor()
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if isinstance(weight, ColoTensor):
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@ -9,8 +9,8 @@ from packaging import version
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from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
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def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
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def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias:ColoTensor) -> ColoTensor:
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow_Linear)
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# Input:S[1] x Weight:S[0] = Output:P
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# All-Reduce(Output) + bias = res
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# Input:S[1]
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@ -47,7 +47,7 @@ def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTe
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# Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
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# All-Gather(Output)
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# Input:B
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
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parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol_Linear)
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if input_tensor.is_gathered():
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# Not splited yet.
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assert input_tensor.shape[-1] == weight.size(-1), \
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@ -108,9 +108,9 @@ def colo_linear(types, args, kwargs, pg):
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return ColoTensor.init_from_torch_tensor(torch.nn.functional.linear(input_tensor, weight, bias))
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elif weight.shard_spec.num_action == 1: # Single Model Parallel Applied
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compute_patterns = weight.shard_spec.compute_patterns
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if ComputePattern.TP1DRow in compute_patterns:
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if ComputePattern.TP1DRow_Linear in compute_patterns:
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return colo_linear_1Drow(input_tensor, weight, bias)
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elif ComputePattern.TP1DCol in compute_patterns:
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elif ComputePattern.TP1DCol_Linear in compute_patterns:
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return colo_linear_1Dcol(input_tensor, weight, bias)
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else:
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raise NotImplementedError
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@ -142,14 +142,19 @@ class ColoTensor(object):
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if self._shard_pattern is not ShardPattern.NA: # reshard
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self.gather()
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# Model Parameters
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if ComputePattern.TP1DRow in self._shard_spec.compute_patterns:
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parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
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self._shard_1d(parallel_action=parallel_action, dim=-1)
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self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
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elif ComputePattern.TP1DCol in self._shard_spec.compute_patterns:
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parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
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self._shard_1d(parallel_action=parallel_action, dim=0)
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self._shard_pattern = ShardPattern.Row
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if self._shard_spec.num_action == 1:
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parallel_action = self._shard_spec.get_action_by_compute_pattern(
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self._shard_spec.compute_patterns[0])
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if parallel_action.compute_pattern in [ComputePattern.TP1DRow_Linear, \
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ComputePattern.TP1DCol_Embedding]:
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self._shard_1d(parallel_action=parallel_action, dim=-1)
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self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
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elif parallel_action.compute_pattern in [ComputePattern.TP1DCol_Linear, \
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ComputePattern.TP1DRow_Embedding]:
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self._shard_1d(parallel_action=parallel_action, dim=0)
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self._shard_pattern = ShardPattern.Row
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else:
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raise NotImplementedError
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def gather(self):
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assert self.is_activation(), 'Currently we only support gather Activation ColoTensor.'
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@ -4,10 +4,12 @@ from colossalai.context.parallel_mode import ParallelMode
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class ComputePattern(Enum):
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TP1DRow = 1
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TP1DCol = 2
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ZeRO = 3
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DP = 4
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TP1DRow_Linear = 1
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TP1DCol_Linear = 2
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TP1DRow_Embedding = 3
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TP1DCol_Embedding = 4
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ZeRO = 5
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DP = 6
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class ShardPattern(Enum):
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@ -43,14 +45,14 @@ class TensorSpec(object):
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# using ZeRO with DP-degree = 4 and 1DRowTP with TP-degree = 2.
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# parallel_action_list = [
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# ParallelAction(10, ComputePattern.ZeRO, gpc.get_group(ParallelMode.DATA)),
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# ParallelAction(1, ComputePattern.TP1DRow, gpc.get_group(ParallelMode.PARALLEL_1D))
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# ParallelAction(1, ComputePattern.TP1DRow_Linear, gpc.get_group(ParallelMode.PARALLEL_1D))
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# ]
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# When the ColoTensor is initialized,
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# we first splitting tensor according to ParallelAction of ZeRO,
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# then splitting tensor according to ParallelAction of TP1DRow.
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# then splitting tensor according to ParallelAction of TP1DRow_Linear.
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# During Linear computation
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# Before Linear Op, we gather the tensors according to ZeRO.
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# We perform Linear Op according to compute pattern of TP1DRow.
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# We perform Linear Op according to compute pattern of TP1DRow_Linear.
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# After Linear Op, we split the tensors according to ZeRO.
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def __init__(self, parallel_action_list: List[ParallelAction] = [], shard_pattern: ShardPattern = ShardPattern.NA):
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@ -0,0 +1,82 @@
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import torch
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.tensor import ColoTensor
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.core import global_context as gpc
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from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
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from _utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
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def run_embedding_tp1d_col_test():
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device = get_current_device()
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dtype = torch.float32
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DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
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num_embeddings = 12
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embedding_dim = 32
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local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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layer_master = torch.nn.Embedding(num_embeddings, embedding_dim)
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layer = torch.nn.Embedding(num_embeddings, embedding_dim)
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A_master = torch.tensor((0,3,6,9), device=device)
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A = broadcast_tensor_chunk(A_master, chunk_size=1)
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W_shape = (num_embeddings, embedding_dim)
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W_master = torch.randn(W_shape, dtype=dtype, device=device)
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W = broadcast_tensor_chunk(W_master, chunk_size=1)
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W.requires_grad = True
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# replace the torch nn.Parameters with ColoTensor
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sharded_weight = ColoTensor.init_from_torch_tensor(W)
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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Embedding,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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sharded_weight.set_spec(spec) # reshard
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replace_parameter_add_grad(layer, sharded_weight)
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out = layer(A)
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replace_parameter_add_grad(layer_master, W_master)
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C_master = layer_master(A_master)
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C = C_master.clone()
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check_equal(out, C)
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grad_shape = C_master.shape
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grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
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grad = broadcast_tensor_chunk(grad_master, chunk_size=1)
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out.backward(grad)
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grad_master = grad_master.clone()
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C_master.backward(grad_master)
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W_grad = W_master.grad
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W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[local_rank]
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check_equal(W_grad, layer.weight.grad)
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def run_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_embedding_tp1d_col_test()
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@pytest.mark.dist
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@parameterize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_embedding_1d(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_embedding_1d()
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@ -47,7 +47,7 @@ def run_linear_tp1d_col_test():
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sharded_weight = ColoTensor.init_from_torch_tensor(W)
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sharded_bias = ColoTensor.init_from_torch_tensor(B)
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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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sharded_weight.set_spec(spec) # reshard
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@ -110,7 +110,7 @@ def run_linear_tp1d_row_test():
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# replace the torch nn.Parameters with ColoTensor
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sharded_weight = ColoTensor.init_from_torch_tensor(W)
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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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sharded_weight.set_spec(spec=spec) # reshard
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@ -145,7 +145,7 @@ def run_linear_tp1d_row_test():
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def run_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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#run_linear_tp1d_row_test()
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run_linear_tp1d_row_test()
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run_linear_tp1d_col_test()
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@pytest.mark.dist
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@ -38,12 +38,12 @@ def run_1d_col_tp():
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model = model_builder(checkpoint=True)
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parallel_action_list_row = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_row = TensorSpec(parallel_action_list_row)
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parallel_action_list_col = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_col = TensorSpec(parallel_action_list_col)
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@ -168,7 +168,7 @@ def run_1d_row_tp():
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model = model_builder(checkpoint=True)
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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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