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# Parallel state and collective helpers for the MiniMax H3 visual VAE.
import os
import math
import torch
import torch.nn.functional as F
import torch.distributed as dist
from torch.autograd import Function
from torch.distributed import group, ReduceOp
def get_group_rank(group_size):
global_rank = int(os.environ["RANK"])
group_rank = global_rank % group_size
return group_rank
_parallel_state = {}
# The torch.autograd.Function subclasses below keep their backward() methods
# to satisfy the autograd.Function contract; only the forward paths are
# exercised in this inference-only bundle.
def get_parallel_state():
return _parallel_state
class _AllGather(Function):
@staticmethod
def forward(ctx, group, tensor):
tensor = tensor.contiguous()
ctx.group = group
group_size = dist.get_world_size(group=group)
out_tensor_list = [torch.empty_like(tensor) for _ in range(group_size)]
dist.all_gather(out_tensor_list, tensor, group=group)
return tuple(out_tensor_list)
@staticmethod
def backward(ctx, *grad_outputs):
rank = dist.get_rank(group=ctx.group)
gx = torch.empty_like(grad_outputs[rank])
gx = gx.contiguous()
grad_outputs = tuple(t.contiguous() for t in grad_outputs)
dist.reduce_scatter(gx, list(grad_outputs), op=ReduceOp.SUM, group=ctx.group)
return (None, gx)
@torch.compiler.disable
def all_gather(tensor, group=group.WORLD):
return _AllGather.apply(group, tensor)
class _AllGatherVarShape(Function):
@staticmethod
def forward(ctx, group, tensor):
tensor = tensor.contiguous()
ctx.group = group
ctx.original_shape = tensor.shape
shape_info = torch.tensor(
list(tensor.shape), dtype=torch.long, device=tensor.device
)
shape_list = [
torch.empty_like(shape_info)
for _ in range(dist.get_world_size(group=group))
]
dist.all_gather(shape_list, shape_info, group=group)
all_shapes = [tuple(shape_tensor.tolist()) for shape_tensor in shape_list]
ctx.all_shapes = all_shapes
flat_tensor = tensor.flatten()
max_size = max(math.prod(s) for s in all_shapes)
if flat_tensor.numel() < max_size:
padded = torch.zeros(max_size, dtype=tensor.dtype, device=tensor.device)
padded[: flat_tensor.numel()] = flat_tensor
flat_tensor = padded
gathered_flat = [torch.empty_like(flat_tensor) for _ in range(len(all_shapes))]
dist.all_gather(gathered_flat, flat_tensor, group=group)
return tuple(
t[: math.prod(shape)].reshape(shape)
for t, shape in zip(gathered_flat, all_shapes)
)
@staticmethod
def backward(ctx, *grad_outputs):
rank = dist.get_rank(group=ctx.group)
grad_input = grad_outputs[rank]
if grad_input is None:
return None, torch.zeros(
ctx.original_shape, device=next(iter(grad_outputs)).device
)
max_size = max(math.prod(shape) for shape in ctx.all_shapes)
padded_grads = []
for grad, shape in zip(grad_outputs, ctx.all_shapes):
if grad is not None:
flat_grad = grad.flatten()
else:
flat_grad = torch.zeros(
math.prod(shape),
dtype=grad_input.dtype,
device=grad_input.device,
)
if flat_grad.numel() < max_size:
padded = torch.zeros(
max_size, dtype=flat_grad.dtype, device=flat_grad.device
)
padded[: flat_grad.numel()] = flat_grad
padded_grads.append(padded)
else:
padded_grads.append(flat_grad)
result_grad = torch.empty_like(padded_grads[0])
dist.reduce_scatter(result_grad, padded_grads, op=ReduceOp.SUM, group=ctx.group)
original_size = math.prod(ctx.original_shape)
return None, result_grad[:original_size].reshape(ctx.original_shape)
@torch.compiler.disable
def all_gather_var_shape(tensor, group=group.WORLD):
return _AllGatherVarShape.apply(group, tensor)
class _AllReduce(Function):
@staticmethod
def forward(ctx, _input, op, group):
ctx.group = group
ctx.op = op
_input = _input.clone()
dist.all_reduce(_input, op=op, group=group)
return _input
@staticmethod
def backward(ctx, grad_output):
grad_output = grad_output.clone()
dist.all_reduce(grad_output, op=ctx.op, group=ctx.group)
return grad_output, None, None
@torch.compiler.disable
def all_reduce(input_, op, group):
return _AllReduce.apply(input_, op, group)
class _AlltoAllSingle(Function):
@staticmethod
def forward(ctx, group, input):
ctx.group = group
world_size = dist.get_world_size(group=group)
if world_size == 1:
return input
input = input.contiguous()
output = torch.empty_like(input)
dist.all_to_all_single(
output,
input,
group=group,
)
return output
@staticmethod
def backward(ctx, grad_output):
return (None, _AlltoAllSingle.apply(ctx.group, grad_output))
@torch.compiler.disable
def all_to_all_single(input, group=group.WORLD):
return _AlltoAllSingle.apply(group, input)
@torch.compiler.disable
def get_subseq(input, sp_size=None):
if sp_size is None:
state = get_parallel_state()
if not state.get("sp_enabled", False):
return input
sp_size = state["sp_size"]
sp_rank = state["sp_rank"]
else:
sp_rank = get_group_rank(sp_size)
if sp_size == 1:
return input
if input.shape[1] % sp_size != 0:
raise ValueError(
f"Input shape {input.shape} is not divisible by sp_size {sp_size}"
)
return torch.chunk(input, sp_size, dim=1)[sp_rank]
@torch.compiler.disable
def gather_subseq(input, sp_size=None, local_process_group=None):
if sp_size is None:
state = get_parallel_state()
if not state.get("sp_enabled", False):
return input
sp_size = state["sp_size"]
local_process_group = state["sp_process_group"]
if sp_size == 1:
return input
output = all_gather(input, group=local_process_group)
output = torch.cat(output, dim=1)
return output
@torch.compiler.disable
def all_to_all_4D(
input: torch.tensor,
scatter_idx: int = 2,
gather_idx: int = 1,
group=None,
):
assert (
input.dim() == 4
), f"input must be 4D tensor, got {input.dim()} and shape {input.shape}"
if group is None:
seq_world_size = 1
else:
seq_world_size = dist.get_world_size(group)
if seq_world_size == 1:
return input
if scatter_idx == 2 and gather_idx == 1:
bs, shard_seqlen, hc, hs = input.shape
seqlen = shard_seqlen * seq_world_size
shard_hc = hc // seq_world_size
input_t = (
input.reshape(bs, shard_seqlen, seq_world_size, shard_hc, hs)
.transpose(0, 2)
.contiguous()
)
output = all_to_all_single(input_t, group=group)
output = output.reshape(seqlen, bs, shard_hc, hs)
output = output.transpose(0, 1).contiguous().reshape(bs, seqlen, shard_hc, hs)
return output
elif scatter_idx == 1 and gather_idx == 2:
bs, seqlen, shard_hc, hs = input.shape
hc = shard_hc * seq_world_size
shard_seqlen = seqlen // seq_world_size
input_t = (
input.reshape(bs, seq_world_size, shard_seqlen, shard_hc, hs)
.transpose(0, 3)
.transpose(0, 1)
.contiguous()
.reshape(seq_world_size, shard_hc, shard_seqlen, bs, hs)
)
output = all_to_all_single(input_t, group=group)
output = output.reshape(hc, shard_seqlen, bs, hs)
output = output.transpose(0, 2).contiguous().reshape(bs, shard_seqlen, hc, hs)
return output
else:
raise RuntimeError("scatter_idx must be 1 or 2 and gather_idx must be 1 or 2")
@torch.compiler.disable
def exchange_borders(
input_, padding, pad_mode, sp_rank, sp_size, group, dim=-1, async_op=False
):
if async_op and input_.requires_grad:
raise ValueError("async_op is not supported backward, check previous commits")
slice_indices = [slice(None)] * input_.ndim
slice_indices[dim] = slice(None, padding)
first_tensor = input_[tuple(slice_indices)].contiguous()
slice_indices[dim] = slice(-padding, None)
last_tensor = input_[tuple(slice_indices)].contiguous()
if async_op:
first_borders = [torch.empty_like(first_tensor) for _ in range(sp_size)]
last_borders = [torch.empty_like(last_tensor) for _ in range(sp_size)]
handle_first = dist.all_gather(
first_borders, first_tensor, group=group, async_op=True
)
handle_last = dist.all_gather(
last_borders, last_tensor, group=group, async_op=True
)
else:
first_borders = all_gather(first_tensor, group=group)
last_borders = all_gather(last_tensor, group=group)
if dim < 0:
pad_dim = -1 - dim
else:
pad_dim = input_.ndim - 1 - dim
pad_size = [0] * ((input_.ndim - 2) * 2)
pad_size[pad_dim * 2] = padding
pad_size[pad_dim * 2 + 1] = padding
output = F.pad(input_, pad_size, mode=pad_mode)
slice_indices = [slice(None)] * input_.ndim
slice_indices[dim] = slice(-padding, None)
if async_op:
handle_first.wait()
if sp_rank < sp_size - 1:
output[tuple(slice_indices)] = first_borders[sp_rank + 1]
else:
output[tuple(slice_indices)] += first_borders[0] * 0.0
slice_indices = [slice(None)] * input_.ndim
slice_indices[dim] = slice(None, padding)
if async_op:
handle_last.wait()
if sp_rank > 0:
output[tuple(slice_indices)] = last_borders[sp_rank - 1]
else:
output[tuple(slice_indices)] += last_borders[sp_size - 1] * 0.0
return output
@torch.compiler.disable
def exchange_strides(
input_, pad_mode, sp_rank, sp_size, group, dim=-1, async_op=False
):
if async_op and input_.requires_grad:
raise ValueError("async_op is not supported backward, check previous commits")
if dim not in [-1, -2]:
raise ValueError("dim must be -1 (W) or -2 (H) for exchange_strides")
if dim == -1:
if input_.ndim == 5:
input_ = F.pad(input_, (0, 0, 0, 1, 0, 0), mode=pad_mode)
elif input_.ndim == 4:
input_ = F.pad(input_, (0, 0, 0, 1), mode=pad_mode)
else:
raise ValueError(f"Input must have 4 or 5 dimensions, got {input_.ndim}")
left_border = input_[..., :1].contiguous()
if async_op:
left_borders = [torch.empty_like(left_border) for _ in range(sp_size)]
handle = dist.all_gather(
left_borders, left_border, group=group, async_op=True
)
else:
left_borders = all_gather(left_border, group=group)
if input_.ndim == 5:
output = F.pad(input_, (0, 1, 0, 0, 0, 0), mode=pad_mode)
elif input_.ndim == 4:
output = F.pad(input_, (0, 1, 0, 0), mode=pad_mode)
else:
raise ValueError(f"Input must have 4 or 5 dimensions, got {input_.ndim}")
if async_op:
handle.wait()
if sp_rank != sp_size - 1:
output[..., -1:] = left_borders[sp_rank + 1]
else:
output[..., -1:] += left_borders[0] * 0.0
else:
if input_.ndim == 5:
input_ = F.pad(input_, (0, 1, 0, 0, 0, 0), mode=pad_mode)
elif input_.ndim == 4:
input_ = F.pad(input_, (0, 1, 0, 0), mode=pad_mode)
else:
raise ValueError(f"Input must have 4 or 5 dimensions, got {input_.ndim}")
top_border = input_[..., :1, :].contiguous()
if async_op:
top_borders = [torch.empty_like(top_border) for _ in range(sp_size)]
handle = dist.all_gather(
top_borders, top_border, group=group, async_op=True
)
else:
top_borders = all_gather(top_border, group=group)
if input_.ndim == 5:
output = F.pad(input_, (0, 0, 0, 1, 0, 0), mode=pad_mode)
elif input_.ndim == 4:
output = F.pad(input_, (0, 0, 0, 1), mode=pad_mode)
else:
raise ValueError(f"Input must have 4 or 5 dimensions, got {input_.ndim}")
if async_op:
handle.wait()
if sp_rank != sp_size - 1:
output[..., -1:, :] = top_borders[sp_rank + 1]
else:
output[..., -1:, :] += top_borders[0] * 0.0
return output
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