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import torch
import torch.distributed as dist
from torch import nn
def _quantize_per_tensor_cuda(x, scale, zero_point):
y = torch.round(x / scale) + zero_point
y = torch.clamp(y, 0, 255).to(torch.uint8)
return y
def _dequantize_per_tensor_cuda(y, scale, zero_point):
x = scale * (y.to(torch.float32) - zero_point)
return x
def _quantize_per_channel_cuda(x, scale, zero_point):
y = torch.zeros(x.size(), device=x.device)
for i in range(x.size()[0]):
y[i, :] = torch.round(x[i, :] / scale[i]) + zero_point[i]
y = torch.clamp(y, 0, 255).to(torch.uint8)
return y
def _dequantize_per_channel_cuda(y, scale, zero_point):
y = y.to(torch.float32).cuda(y.device)
x = torch.zeros_like(y, device=y.device)
for i in range(x.size()[0]):
x[i, :] = scale[i] * (y[i, :] - zero_point[i])
return x
def _get_allgather_out_list(all_gather_in_list, world_size):
out_list = [
torch.zeros_like(
all_gather_in_list,
device=all_gather_in_list.device,
dtype=all_gather_in_list.dtype,
)
for _ in range(world_size)
]
return out_list
def quantization_pertensor_hook(
process_group: dist.ProcessGroup, bucket: dist.GradBucket
) -> torch.futures.Future[torch.Tensor]:
"""
Applies the ``torch.quantize_per_tensor`` logic to DDP using ``allgather``
protocol. Workers first allgather the scale and zero point of their own
``GradBucket`` prior to the quantization. After all workers have that information,
the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's
own gradient tensor, and uses ``allgather`` to communicate these accross all workers.
The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes and
aggregates each quantized gradient tensor locally and returns the mean.
.. warning ::
This is experimental, and uses ``allgather`` protocol which is considerably slower than
``allreduce`` protocol. It works only with flattened grads.
Example::
>>> # xdoctest: +SKIP
>>> ddp_model.register_comm_hook(process_group, quantization_pertensor_hook)
"""
group_to_use = process_group if process_group is not None else dist.group.WORLD
rank = process_group.rank() if process_group is not None else dist.get_rank()
world_size = group_to_use.size()
tensor = bucket.buffer()
myObserver = torch.quantization.MinMaxObserver().cuda(tensor.device)
myObserver(tensor)
s, z = myObserver.calculate_qparams()
s_and_z = torch.FloatTensor([s, z]).cuda(tensor.device)
all_ranks_s_and_z = _get_allgather_out_list(s_and_z, world_size)
# First, allgather scale and zeros.
fut = dist.all_gather(
all_ranks_s_and_z, s_and_z, group=group_to_use, async_op=True
).get_future()
def quantize_and_allgather(fut):
# Store scale and zeros accross all workers.
all_ranks_s_and_z = fut.wait()[0]
# All workers quantize their own ``GradBucket`` tensors.
quantized_tensor = _quantize_per_tensor_cuda(
tensor, all_ranks_s_and_z[rank][0], all_ranks_s_and_z[rank][1]
)
# Allgather quantized tensors.
fut = dist.all_gather(
_get_allgather_out_list(quantized_tensor, world_size),
quantized_tensor,
group=group_to_use,
async_op=True,
).get_future()
return fut.wait()
def dequantize_and_aggregate(fut):
all_ranks_quantized_tensor = fut.wait()[0]
aggregated_dequantized_tensor = torch.zeros_like(
all_ranks_quantized_tensor[0], device=tensor.device, dtype=torch.float32
)
# Using previously allgathered scales and zeros, dequantize gradient tensors
# locally and then aggregate them.
for r, quantized_tensor in enumerate(all_ranks_quantized_tensor):
aggregated_dequantized_tensor += _dequantize_per_tensor_cuda(
quantized_tensor, all_ranks_s_and_z[r][0], all_ranks_s_and_z[r][1]
)
return aggregated_dequantized_tensor / world_size
return fut.then(quantize_and_allgather).then(dequantize_and_aggregate)
def quantization_perchannel_hook(
process_group: dist.ProcessGroup, bucket: dist.GradBucket, bucket_size=512
) -> torch.futures.Future[torch.Tensor]:
"""
Applies the ``torch.quantize_per_channel`` logic to DDP using ``allgather``
protocol. Compared to pertensor, the main motivation of perchannel is
for considerably large tensors such as a tensor that contains 6 million
elements quantizing per a bucket size of 512 (or 128) elements may significantly
increase the resolution.
It first splits ``GradBucket`` tensor into multiple chunks (channels) of ``bucket_size``
elements. Then, workers allgather the scales and zero points of their own
``GradBucket`` prior to the quantization. After all workers have that information,
the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's
own gradient tensor, and uses ``allgather`` to communicate these accross all workers.
The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes, flattens, and
aggregates each quantized gradient tensor locally and returns the mean.
.. warning ::
This is experimental, and uses ``allgather`` protocol which is considerably slower than
``allreduce`` protocol. It works only with flattened grads.
Example::
>>> # xdoctest: +SKIP
>>> ddp_model.register_comm_hook(process_group, quantization_perchannel_hook)
"""
group_to_use = process_group if process_group is not None else dist.group.WORLD
rank = process_group.rank() if process_group is not None else dist.get_rank()
world_size = group_to_use.size()
tensor = bucket.buffer()
tensor_in_channels = (
nn.functional.pad(
input=tensor,
pad=(0, bucket_size - len(tensor) % bucket_size),
mode="constant",
value=0,
)
.view(-1, bucket_size)
.cuda(tensor.device)
)
myPerChannelObserver = torch.quantization.PerChannelMinMaxObserver().cuda(
tensor.device
)
myPerChannelObserver(tensor_in_channels)
s_ch, z_ch = myPerChannelObserver.calculate_qparams()
s_and_z = torch.stack((s_ch, z_ch)).cuda(tensor.device)
all_ranks_s_and_z = _get_allgather_out_list(s_and_z, world_size)
# First, allgather scale and zeros.
fut = dist.all_gather(
all_ranks_s_and_z, s_and_z, group=group_to_use, async_op=True
).get_future()
def quantize_and_allgather(fut):
# Store scale and zeros accross all workers.
all_ranks_s_and_z = fut.wait()[0]
# All workers quantize their corresponding ``GradBucket`` tensors.
quantized_tensor = _quantize_per_channel_cuda(
tensor_in_channels,
all_ranks_s_and_z[rank, 0, :],
all_ranks_s_and_z[rank, 1, :],
)
# Allgather quantized tensors.
fut = dist.all_gather(
_get_allgather_out_list(quantized_tensor, world_size),
quantized_tensor,
group=group_to_use,
async_op=True,
).get_future()
return fut.wait()
def dequantize_and_aggregate(fut):
all_ranks_quantized_tensor = fut.wait()[0]
aggregated_dequantized_tensor = torch.zeros_like(
all_ranks_quantized_tensor[0], device=tensor.device, dtype=torch.float32
)
# Using previously allgathered scales and zeros, dequantize gradient tensors
# locally and then aggregate them.
for r, quantized_tensor in enumerate(all_ranks_quantized_tensor):
aggregated_dequantized_tensor += _dequantize_per_channel_cuda(
quantized_tensor, all_ranks_s_and_z[r][0], all_ranks_s_and_z[r][1]
)
return (
torch.flatten(aggregated_dequantized_tensor).cuda(tensor.device)[
: tensor.size()[0]
]
/ world_size
)
return fut.then(quantize_and_allgather).then(dequantize_and_aggregate)
|
