[Tensor] add ColoTensor TP1Dcol Embedding (#899)

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Ziyue Jiang 2022-04-28 17:45:06 +08:00 committed by GitHub
parent e46e423c00
commit 2c0d19d755
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9 changed files with 173 additions and 27 deletions

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@ -2,3 +2,4 @@ from .linear import colo_linear
from .element_wise import *
from .layernorm import colo_layernorm
from .loss import colo_cross_entropy
from .embedding import colo_embedding

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@ -0,0 +1,56 @@
import torch
from colossalai.tensor.op_wrapper import colo_op_impl
from colossalai.context import ParallelMode
from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input, \
gather_forward_split_backward, reduce_grad
from colossalai.nn.layer.utils import divide
from colossalai.core import global_context as gpc
from packaging import version
from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
def colo_embedding_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, args, kwargs) -> ColoTensor:
# embedding_1Dcol split the weight(lookup table)
# Gather splitted lookup table
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol_Embedding)
if not input_tensor.is_gathered():
input_tensor.gather()
output_parallel = torch.nn.functional.embedding(input_tensor.torch_tensor(), weight.torch_tensor(),
*args, **kwargs)
output = ColoTensor.init_from_torch_tensor(output_parallel)
out_parallel_action_list = [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]
output_spec = TensorSpec(out_parallel_action_list)
output.set_spec(output_spec, shard=False)
output.set_shard_pattern(ShardPattern.Col)
output.gather()
return output
@colo_op_impl(torch.nn.functional.embedding)
def colo_embedding(types, args, kwargs, pg):
"""Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding``.
This method looks up an embedding table.
"""
input_tensor = args[0]
weight = args[1]
args = args[2:]
if not isinstance(input_tensor, ColoTensor):
input_tensor = ColoTensor.init_from_torch_tensor(input_tensor)
if not isinstance(weight, ColoTensor):
weight = ColoTensor.init_from_torch_tensor(weight)
# Handle differen parallel actions.
if not weight.has_spec(): # No Model Parallel Applied
input_tensor = input_tensor.torch_tensor()
weight = weight.torch_tensor()
output = torch.nn.functional.embedding(input_tensor, weight, *args, **kwargs)
return ColoTensor.init_from_torch_tensor(output)
elif weight.shard_spec.num_action == 1: # Single Model Parallel Applied
compute_patterns = weight.shard_spec.compute_patterns
if ComputePattern.TP1DCol_Embedding in compute_patterns:
return colo_embedding_1Dcol(input_tensor, weight, args, kwargs)
else:
raise NotImplementedError
else:
raise NotImplementedError

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@ -27,7 +27,7 @@ def colo_layernorm(types, args=(), kwargs=None, pg=None):
eps = kwargs['eps']
if isinstance(input_tensor, ColoTensor):
if input_tensor.is_activation() and not input_tensor.is_gathered():
if not input_tensor.is_gathered():
input_tensor.gather()
input_tensor = input_tensor.torch_tensor()
if isinstance(weight, ColoTensor):

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@ -9,8 +9,8 @@ from packaging import version
from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias:ColoTensor) -> ColoTensor:
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow_Linear)
# Input:S[1] x Weight:S[0] = Output:P
# All-Reduce(Output) + bias = res
# Input:S[1]
@ -47,7 +47,7 @@ def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTe
# Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
# All-Gather(Output)
# Input:B
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol_Linear)
if input_tensor.is_gathered():
# Not splited yet.
assert input_tensor.shape[-1] == weight.size(-1), \
@ -108,9 +108,9 @@ def colo_linear(types, args, kwargs, pg):
return ColoTensor.init_from_torch_tensor(torch.nn.functional.linear(input_tensor, weight, bias))
elif weight.shard_spec.num_action == 1: # Single Model Parallel Applied
compute_patterns = weight.shard_spec.compute_patterns
if ComputePattern.TP1DRow in compute_patterns:
if ComputePattern.TP1DRow_Linear in compute_patterns:
return colo_linear_1Drow(input_tensor, weight, bias)
elif ComputePattern.TP1DCol in compute_patterns:
elif ComputePattern.TP1DCol_Linear in compute_patterns:
return colo_linear_1Dcol(input_tensor, weight, bias)
else:
raise NotImplementedError

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@ -142,14 +142,19 @@ class ColoTensor(object):
if self._shard_pattern is not ShardPattern.NA: # reshard
self.gather()
# Model Parameters
if ComputePattern.TP1DRow in self._shard_spec.compute_patterns:
parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
self._shard_1d(parallel_action=parallel_action, dim=-1)
self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
elif ComputePattern.TP1DCol in self._shard_spec.compute_patterns:
parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
self._shard_1d(parallel_action=parallel_action, dim=0)
self._shard_pattern = ShardPattern.Row
if self._shard_spec.num_action == 1:
parallel_action = self._shard_spec.get_action_by_compute_pattern(
self._shard_spec.compute_patterns[0])
if parallel_action.compute_pattern in [ComputePattern.TP1DRow_Linear, \
ComputePattern.TP1DCol_Embedding]:
self._shard_1d(parallel_action=parallel_action, dim=-1)
self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
elif parallel_action.compute_pattern in [ComputePattern.TP1DCol_Linear, \
ComputePattern.TP1DRow_Embedding]:
self._shard_1d(parallel_action=parallel_action, dim=0)
self._shard_pattern = ShardPattern.Row
else:
raise NotImplementedError
def gather(self):
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
class ComputePattern(Enum):
TP1DRow = 1
TP1DCol = 2
ZeRO = 3
DP = 4
TP1DRow_Linear = 1
TP1DCol_Linear = 2
TP1DRow_Embedding = 3
TP1DCol_Embedding = 4
ZeRO = 5
DP = 6
class ShardPattern(Enum):
@ -43,14 +45,14 @@ class TensorSpec(object):
# using ZeRO with DP-degree = 4 and 1DRowTP with TP-degree = 2.
# parallel_action_list = [
# ParallelAction(10, ComputePattern.ZeRO, gpc.get_group(ParallelMode.DATA)),
# ParallelAction(1, ComputePattern.TP1DRow, gpc.get_group(ParallelMode.PARALLEL_1D))
# ParallelAction(1, ComputePattern.TP1DRow_Linear, gpc.get_group(ParallelMode.PARALLEL_1D))
# ]
# When the ColoTensor is initialized,
# we first splitting tensor according to ParallelAction of ZeRO,
# then splitting tensor according to ParallelAction of TP1DRow.
# then splitting tensor according to ParallelAction of TP1DRow_Linear.
# During Linear computation
# Before Linear Op, we gather the tensors according to ZeRO.
# We perform Linear Op according to compute pattern of TP1DRow.
# We perform Linear Op according to compute pattern of TP1DRow_Linear.
# After Linear Op, we split the tensors according to ZeRO.
def __init__(self, parallel_action_list: List[ParallelAction] = [], shard_pattern: ShardPattern = ShardPattern.NA):

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@ -0,0 +1,82 @@
import torch
from colossalai.context.parallel_mode import ParallelMode
from colossalai.tensor import ColoTensor
from functools import partial
import colossalai
import pytest
import torch
import torch.multiprocessing as mp
from colossalai.testing import parameterize, rerun_if_address_is_in_use
from colossalai.utils.cuda import get_current_device
from colossalai.utils import free_port
from colossalai.core import global_context as gpc
from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
from _utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
def run_embedding_tp1d_col_test():
device = get_current_device()
dtype = torch.float32
DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
num_embeddings = 12
embedding_dim = 32
local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
layer_master = torch.nn.Embedding(num_embeddings, embedding_dim)
layer = torch.nn.Embedding(num_embeddings, embedding_dim)
A_master = torch.tensor((0,3,6,9), device=device)
A = broadcast_tensor_chunk(A_master, chunk_size=1)
W_shape = (num_embeddings, embedding_dim)
W_master = torch.randn(W_shape, dtype=dtype, device=device)
W = broadcast_tensor_chunk(W_master, chunk_size=1)
W.requires_grad = True
# replace the torch nn.Parameters with ColoTensor
sharded_weight = ColoTensor.init_from_torch_tensor(W)
parallel_action_list = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Embedding,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec = TensorSpec(parallel_action_list)
sharded_weight.set_spec(spec) # reshard
replace_parameter_add_grad(layer, sharded_weight)
out = layer(A)
replace_parameter_add_grad(layer_master, W_master)
C_master = layer_master(A_master)
C = C_master.clone()
check_equal(out, C)
grad_shape = C_master.shape
grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
grad = broadcast_tensor_chunk(grad_master, chunk_size=1)
out.backward(grad)
grad_master = grad_master.clone()
C_master.backward(grad_master)
W_grad = W_master.grad
W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[local_rank]
check_equal(W_grad, layer.weight.grad)
def run_dist(rank, world_size, port):
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_embedding_tp1d_col_test()
@pytest.mark.dist
@parameterize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_embedding_1d(world_size):
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_embedding_1d()

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@ -47,7 +47,7 @@ def run_linear_tp1d_col_test():
sharded_weight = ColoTensor.init_from_torch_tensor(W)
sharded_bias = ColoTensor.init_from_torch_tensor(B)
parallel_action_list = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec = TensorSpec(parallel_action_list)
sharded_weight.set_spec(spec) # reshard
@ -110,7 +110,7 @@ def run_linear_tp1d_row_test():
# replace the torch nn.Parameters with ColoTensor
sharded_weight = ColoTensor.init_from_torch_tensor(W)
parallel_action_list = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec = TensorSpec(parallel_action_list)
sharded_weight.set_spec(spec=spec) # reshard
@ -145,7 +145,7 @@ def run_linear_tp1d_row_test():
def run_dist(rank, world_size, port):
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
#run_linear_tp1d_row_test()
run_linear_tp1d_row_test()
run_linear_tp1d_col_test()
@pytest.mark.dist

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@ -38,12 +38,12 @@ def run_1d_col_tp():
model = model_builder(checkpoint=True)
parallel_action_list_row = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_row = TensorSpec(parallel_action_list_row)
parallel_action_list_col = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_col = TensorSpec(parallel_action_list_col)
@ -168,7 +168,7 @@ def run_1d_row_tp():
model = model_builder(checkpoint=True)
parallel_action_list = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec = TensorSpec(parallel_action_list)