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# mypy: allow-untyped-defs
# Copyright (c) Meta Platforms, Inc. and affiliates
# implement matrix related ops for distributed tensor
from typing import cast, Dict, List, Tuple
import torch
import torch.distributed as dist
import torch.distributed.tensor._api as dtensor
aten = torch.ops.aten
def _requires_data_exchange(padding):
# TODO: whether there requires data exchange is currently determined by padding
return padding[1] != 0
def _is_supported(input_size, kernel_size, stride, padding, dilation):
if dilation[1] != 1:
raise RuntimeError("Dilation must be 1 for tensor parallel convolution.")
if padding[1] != 0:
if stride[1] != 1:
raise RuntimeError(
"Stride must be 1 when there is padding for tensor parallel convolution."
)
if kernel_size[3] // 2 > input_size[3]:
raise RuntimeError(
"kernel_size[3] // 2 should be less than or equal to input_size[3] for tensor parallel convolution."
)
else:
if not (input_size[3] % stride[1] == 0 and stride[1] == kernel_size[3]):
raise RuntimeError(
"It requires that input_size[3] is divisible by stride[1] and stride[1] equals kernel_size[3] "
"when there is padding for tensor parallel convolution."
)
return True
def _ring_send_recv_construct(in_tensor, d1, d2, left, right, rank, size):
# dist comms and reconstruct local input tensor
send_to_right = in_tensor[:, :, :, -d1:].contiguous()
send_to_left = in_tensor[:, :, :, :d2].contiguous()
recv_from_right = torch.zeros_like(send_to_left)
recv_from_left = torch.zeros_like(send_to_right)
send_op_right = dist.P2POp(dist.isend, send_to_right, right)
send_op_left = dist.P2POp(dist.isend, send_to_left, left)
recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
reqs = dist.batch_isend_irecv(
[send_op_right, send_op_left, recv_op_left, recv_op_right]
)
for req in reqs:
req.wait()
if rank == 0:
in_tensor = torch.cat([in_tensor, recv_from_right], dim=-1)
elif rank == size - 1:
in_tensor = torch.cat([recv_from_left, in_tensor], dim=-1)
else:
in_tensor = torch.cat([recv_from_left, in_tensor, recv_from_right], dim=-1)
return in_tensor
def _ring_send_recv_aggregate(grad_in_tensor, d1, d2, left, right, rank, size):
# dist comms and aggregate gradients for edge pixels
send_to_right = grad_in_tensor[:, :, :, -d2:].contiguous()
send_to_left = grad_in_tensor[:, :, :, :d1].contiguous()
recv_from_right = torch.zeros_like(send_to_left)
recv_from_left = torch.zeros_like(send_to_right)
send_op_right = dist.P2POp(dist.isend, send_to_right, right)
send_op_left = dist.P2POp(dist.isend, send_to_left, left)
recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
reqs = dist.batch_isend_irecv(
[send_op_right, send_op_left, recv_op_left, recv_op_right]
)
for req in reqs:
req.wait()
if rank == 0:
grad_in_tensor = grad_in_tensor[:, :, :, :-d2]
grad_in_tensor[:, :, :, -d1:] = torch.add(
grad_in_tensor[:, :, :, -d1:], recv_from_right
)
elif rank == size - 1:
grad_in_tensor = grad_in_tensor[:, :, :, d1:]
grad_in_tensor[:, :, :, :d2] = torch.add(
grad_in_tensor[:, :, :, :d2], recv_from_left
)
else:
grad_in_tensor = grad_in_tensor[:, :, :, d1:-d2]
grad_in_tensor[:, :, :, -d1:] = torch.add(
grad_in_tensor[:, :, :, -d1:], recv_from_right
)
grad_in_tensor[:, :, :, :d2] = torch.add(
grad_in_tensor[:, :, :, :d2], recv_from_left
)
def tp_convolution(
op_call: torch._ops.OpOverload,
local_tensor_args: Tuple[object, ...],
local_tensor_kwargs: Dict[str, object],
) -> object:
assert op_call == aten.convolution.default
assert len(local_tensor_args) == 9
rank = dist.get_rank()
size = dist.get_world_size()
in_tensor = cast(torch.Tensor, local_tensor_args[0])
weight = cast(torch.Tensor, local_tensor_args[1])
stride, padding, dilation = local_tensor_args[3:6]
assert _is_supported(in_tensor.shape, weight.shape, stride, padding, dilation)
assert isinstance(padding, List)
if not _requires_data_exchange(padding):
local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
return local_results
else:
# step 0 compute the overlap pixels of the input tensor
d = weight.shape[3] - 1
d1 = d // 2
d2 = d - d1
assert d1 + d2 == d
right = (rank + 1) % size
left = (rank - 1 + size) % size
# step1 reconstruct local input tensor
in_tensor = _ring_send_recv_construct(
in_tensor, d1, d2, left, right, rank, size
)
# step2 feed local input tensor to op_call
local_tensor_args_list = list(local_tensor_args)
local_tensor_args_list[0] = in_tensor
local_tensor_args = cast(Tuple[object, ...], local_tensor_args_list)
local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
# step3 remove extra outputs from the results
padding_w = padding[1]
w = local_results.size(3)
if rank == 0:
local_results = local_results[:, :, :, : w - padding_w]
elif rank == size - 1:
local_results = local_results[:, :, :, padding_w:]
else:
local_results = local_results[:, :, :, padding_w : w - padding_w]
return local_results
def tp_convolution_backward(
op_call: torch._ops.OpOverload,
local_tensor_args: Tuple[object, ...],
local_tensor_kwargs: Dict[str, object],
) -> object:
assert op_call == aten.convolution_backward.default
assert len(local_tensor_args) == 11
rank = dist.get_rank()
size = dist.get_world_size()
grad_out_tensor = cast(torch.Tensor, local_tensor_args[0])
in_tensor = cast(torch.Tensor, local_tensor_args[1])
weight = cast(torch.Tensor, local_tensor_args[2])
stride, padding, dilation = local_tensor_args[4:7]
assert _is_supported(in_tensor.shape, weight.shape, stride, padding, dilation)
assert isinstance(padding, List)
if not _requires_data_exchange(padding):
local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
return local_results
else:
# step 0 compute the overlap pixels of the input tensor
d = weight.shape[3] - 1
d1 = d // 2
d2 = d - d1
assert d1 + d2 == d
right = (rank + 1) % size
left = (rank - 1 + size) % size
# step1 reconstruct local input tensor
in_tensor = _ring_send_recv_construct(
in_tensor, d1, d2, left, right, rank, size
)
# step2 reconstruct local gradient output tensor
padding_w = padding[1]
if rank == 0:
grad_out_tensor = torch.nn.functional.pad(
grad_out_tensor, (0, padding_w), "constant", 0
)
elif rank == size - 1:
grad_out_tensor = torch.nn.functional.pad(
grad_out_tensor, (padding_w, 0), "constant", 0
)
else:
grad_out_tensor = torch.nn.functional.pad(
grad_out_tensor, (padding_w, padding_w), "constant", 0
)
# step3 feed local input tensor to op_call
local_tensor_args_list = list(local_tensor_args)
local_tensor_args_list[0] = grad_out_tensor
local_tensor_args_list[1] = in_tensor
local_tensor_args = cast(Tuple[object, ...], local_tensor_args_list)
local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
# step4 aggregate gradients for edge pixels
grad_in_tensor = local_results[0]
grad_in_tensor = _ring_send_recv_aggregate(
grad_in_tensor, d1, d2, left, right, rank, size
)
local_results = list(local_results)
local_results[0] = grad_in_tensor
local_results = cast(Tuple[object, ...], local_results)
return local_results
def convolution_handler(
op_call: torch._ops.OpOverload,
args: Tuple[object, ...],
kwargs: Dict[str, object],
) -> object:
# extract local tensor and sharding infos to a OpInfo
op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
# sharding propagation
dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
output_sharding = op_info.output_sharding
assert output_sharding is not None, "output sharding should not be None"
# local propagation
local_results = tp_convolution(
op_call, tuple(op_info.local_args), op_info.local_kwargs
)
return dtensor.DTensor._op_dispatcher.wrap(
local_results, output_sharding.output_spec
)
def convolution_backward_handler(
op_call: torch._ops.OpOverload,
args: Tuple[object, ...],
kwargs: Dict[str, object],
) -> object:
# Redistribute grad_output tensor to the same placement as input tensor
args = list(args)
assert isinstance(args[0], dtensor.DTensor) and isinstance(args[1], dtensor.DTensor)
args[0] = args[0].redistribute(args[1].device_mesh, args[1].placements)
args = tuple(args)
# extract local tensor and sharding infos to a OpInfo
op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
# sharding propagation
dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
output_sharding = op_info.output_sharding
assert output_sharding is not None, "output sharding should not be None"
# local propagation
local_results = tp_convolution_backward(
op_call, tuple(op_info.local_args), op_info.local_kwargs
)
return dtensor.DTensor._op_dispatcher.wrap(
local_results, output_sharding.output_spec
)
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