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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
batched collective operations for overhead amortization and better
bandwidth utilization
"""
import math
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from deepspeed.comm import ProcessGroup, all_to_all_single
from deepspeed.accelerator import get_accelerator
from deepspeed.utils import instrument_w_nvtx
from deepspeed.ops import op_builder
from deepspeed.utils import logger
def _torch_reduce_scatter_fn(input_tensor: Tensor, output_tensor: Tensor, group=None, async_op=False, prof=False):
return instrument_w_nvtx(dist.reduce_scatter_fn)(output_tensor, input_tensor, group=group, async_op=False)
quantizer_module = None
@instrument_w_nvtx
@torch.no_grad()
def all_to_all_quant_reduce(tensors: List[Tensor], groups: {}) -> List[Tensor]:
global quantizer_module
if quantizer_module is None:
quantizer_module = op_builder.QuantizerBuilder().load()
local_world_size = get_accelerator().device_count()
global_world_size = dist.get_world_size()
num_nodes = global_world_size // local_world_size
this_rank = dist.get_rank()
intra_idx = int(this_rank / local_world_size)
inter_idx = this_rank % local_world_size
output_lst: List[Tensor] = [None] * len(tensors)
for idx, tensor in enumerate(tensors):
if tensor.dim() == 1:
output_lst[idx] = reduce_scatter_coalesced([tensor])[0]
elif tensor.numel() % (2 * global_world_size) != 0:
# Due to the constraint of 2-stage all-to-all, the input tensor must be divisible by 2 * global_world_size
# Otherwise, all-to-all cannot be performed because of shape mismatch.
# See more at https://github.com/microsoft/DeepSpeed/pull/5056
logger.warning(
f"qgZ falls back to reduce_scatter because tensor size = {tensor.numel()} is not divisible by (2 * global_world_size) = {2 * global_world_size}. Please consider allocating a new world to enable qgZ"
)
output_lst[idx] = reduce_scatter_coalesced([tensor])[0]
else:
intra_quant_group = max(tensor.shape[0], tensor.shape[1], global_world_size)
inter_quant_group = intra_quant_group // local_world_size
intra_quant_int4, intra_q_scales = quantizer_module.swizzle_quant(tensor, intra_quant_group, 4,
quantizer_module.Symmetric, 1, num_nodes,
local_world_size)
local_output = torch.empty_like(intra_quant_int4)
scale_output = torch.empty_like(intra_q_scales)
all_to_all_single(local_output, intra_quant_int4, group=groups[f'local_{intra_idx}'])
all_to_all_single(scale_output, intra_q_scales, group=groups[f'local_{intra_idx}'])
global_input_tensor, global_scales = quantizer_module.quantized_reduction(
local_output, scale_output, intra_quant_group, inter_quant_group, 4, quantizer_module.Symmetric,
local_world_size)
global_output = torch.empty_like(global_input_tensor)
global_scale_output = torch.empty_like(global_scales)
all_to_all_single(global_output, global_input_tensor, group=groups[f'global_{inter_idx}'])
all_to_all_single(global_scale_output, global_scales, group=groups[f'global_{inter_idx}'])
final_output = quantizer_module.dequantize(global_output, global_scale_output, global_scale_output.numel(),
4, quantizer_module.Symmetric)
assert final_output.numel(
) % num_nodes == 0, f"final_output.numel()={final_output.numel()} is not divisible by num_nodes={num_nodes}"
output_lst[idx] = (sum(list(final_output.chunk(num_nodes))) / num_nodes).view(-1)
return output_lst
@instrument_w_nvtx
@torch.no_grad()
def reduce_scatter_coalesced(
tensors: List[Tensor],
group: ProcessGroup = None,
) -> List[Tensor]:
"""simultaneously reduce-scatter a list of tensors - this can be done more
efficiently than individual reduce scatter calls
TODO. see if PyTorch team wants a c++ version of this for ProcessGroupNCCL
"""
this_rank = dist.get_rank(group)
world_sz = dist.get_world_size(group)
partition_lst_for_each_tensor = [None] * len(tensors)
for tensor_idx, tensor in enumerate(tensors):
flattened_tensor = tensor.view(-1)
chunk_sz = math.ceil(tensor.numel() / world_sz)
partition_lst_for_each_tensor[tensor_idx] = [
flattened_tensor[rank * chunk_sz:rank * chunk_sz + chunk_sz] for rank in range(0, world_sz)
]
padded_partition_sz_for_each_tensor = tuple(math.ceil(t.numel() / world_sz) for t in tensors)
if len(tensors) == 1 and tensors[0].numel() % world_sz == 0:
# if there's only one tensor being reduced and we don't need to pad
# we have an opportunity to avoid a memory allocation
tensor_partition_flat_buffer = tensors[0].view(-1)
else:
# interleave tensor partitions such that the correct reduced partitions of each tensor
# end up at each rank
tensor_partitions_lst_with_padding = []
for rank in range(world_sz):
for tensor_idx in range(len(tensors)):
# add tensor content
tensor_chunk = partition_lst_for_each_tensor[tensor_idx][rank]
tensor_partitions_lst_with_padding.append(tensor_chunk)
# add padding if necessary
padding_sz = padded_partition_sz_for_each_tensor[tensor_idx] - tensor_chunk.numel()
if padding_sz > 0:
tensor_partitions_lst_with_padding.append(
torch.empty(padding_sz, dtype=tensor_chunk.dtype, device=tensor_chunk.device))
tensor_partition_flat_buffer = instrument_w_nvtx(torch.cat)(tensor_partitions_lst_with_padding)
tensor_partition_flat_buffer.div_(world_sz) # pre-divide
tensor_partition_buffer_for_each_rank: List[Tensor] = torch.chunk(tensor_partition_flat_buffer, world_sz)
# batched reduce-scatter call
_torch_reduce_scatter_fn(tensor_partition_flat_buffer,
tensor_partition_buffer_for_each_rank[this_rank],
group=group)
# reverse procedure of the interleaving done previously, done on the
# result of the batched reduce-scatter
output_lst: List[Tensor] = [None] * len(tensors)
offset = 0
for tensor_idx in range(len(tensors)):
output_lst[tensor_idx] = tensor_partition_buffer_for_each_rank[this_rank].narrow(
0, offset, partition_lst_for_each_tensor[tensor_idx][this_rank].numel())
offset += padded_partition_sz_for_each_tensor[tensor_idx]
return output_lst
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