SolitaryThinker
b2d55a7ba0
Replace the re-export stubs with real vendored copies of the fastvideo modeling
+ layer + supporting infra, for the kept diffusion models (wan21, wan_causal,
ltx2, flux2, matrixgame2). v2 now imports ZERO `fastvideo.*` — it is
self-contained. (bagel/qwen_omni load from vllm_omni, an external pkg, not
fastvideo; cosmos3's load_id was already dangling — both out of scope here.)
Vendored (cp + `sed fastvideo. -> v2.`):
- models/ the 5 models' nn.Module dits/vaes/encoders/audio/upsamplers + the
component loader/ + the lazy class registry (other families' rows are
dormant/lazy — only the 5 resolve).
- layers/ attention/ platforms/ distributed/ configs/ logging_utils/ hooks/
third_party/pynvml + top-level forward_context/fastvideo_args/envs/logger/
utils/version — copied verbatim (layers et al. 'as is').
- api/ slimmed to schema + results (the VideoGenerator's config dataclasses);
the fastvideo parser/presets/overrides (which pull the pipeline runtime) are
intentionally NOT vendored — v2 has its own runtime/loop.
Decoupling surgery (cut the loader's coupling to the fastvideo runtime):
- configs/pipeline_registry.py (vendored from fastvideo/registry.py, renamed to
avoid colliding with v2/registry.py): dropped the _register_presets() auto-call
and matrixgame3 (removed model); config-class resolution preserved.
- configs/pipelines/__init__.py: dropped the registry back-edge (fixes an import
cycle) — base.py imports the registry lazily where used.
- torch_backend: load_component now uses v2.models.loader.
The only remaining external 'fastvideo*' refs are `fastvideo_kernel` (the
separate optional CUDA-kernel pkg for sparse/MoBA attention) — guarded; the dense
TORCH_SDPA path v2 uses never imports it.
Vendored subtrees added to the pre-commit exclude (faithful copies, mirroring the
existing fastvideo/models exclusion — not re-linted, to stay re-syncable).
Verified: grep finds zero fastvideo-package imports in v2/; Wan2.1 T2V on H100 is
BIT-IDENTICAL to the fastvideo-backed path (same .npy SHA256, byte-for-byte); CPU
mini 156 (154 passed + 2 env-skipped on x86, torch present). Backup: branch v2_backup.
278 lines
12 KiB
Python
278 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/distributed/device_communicators/base_device_communicator.py
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from typing import Any
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import torch
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import torch.distributed as dist
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from torch import Tensor
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from torch.distributed import ProcessGroup, ReduceOp
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class DistributedAutograd:
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"""Collection of autograd functions for distributed operations.
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This class provides custom autograd functions for distributed operations like all_reduce,
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all_gather, and all_to_all. Each operation is implemented as a static inner class with
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proper forward and backward implementations.
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"""
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class AllReduce(torch.autograd.Function):
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"""Differentiable all_reduce operation.
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The gradient of all_reduce is another all_reduce operation since the operation
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combines values from all ranks equally.
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"""
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@staticmethod
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def forward(ctx: Any, group: ProcessGroup, input_: Tensor, op: dist.ReduceOp | None = None) -> Tensor:
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ctx.group = group
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ctx.op = op
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output = input_.clone()
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dist.all_reduce(output, group=group, op=op)
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return output
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@staticmethod
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def backward(ctx: Any, grad_output: Tensor) -> tuple[None, Tensor, None]:
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grad_output = grad_output.clone()
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dist.all_reduce(grad_output, group=ctx.group, op=ctx.op)
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return None, grad_output, None
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class AllGather(torch.autograd.Function):
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"""Differentiable all_gather operation.
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The operation gathers tensors from all ranks and concatenates them along a specified dimension.
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The backward pass uses reduce_scatter to efficiently distribute gradients back to source ranks.
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"""
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@staticmethod
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def forward(ctx: Any, group: ProcessGroup, input_: Tensor, world_size: int, dim: int) -> Tensor:
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ctx.group = group
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ctx.world_size = world_size
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ctx.dim = dim
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ctx.input_shape = input_.shape
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# NCCL all_gather_into_tensor requires contiguous tensors.
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if not input_.is_contiguous():
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input_ = input_.contiguous()
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input_size = input_.size()
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output_size = (input_size[0] * world_size, ) + input_size[1:]
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output_tensor = torch.empty(output_size, dtype=input_.dtype, device=input_.device)
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dist.all_gather_into_tensor(output_tensor, input_, group=group)
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output_tensor = output_tensor.reshape((world_size, ) + input_size)
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output_tensor = output_tensor.movedim(0, dim)
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output_tensor = output_tensor.reshape(input_size[:dim] + (world_size * input_size[dim], ) +
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input_size[dim + 1:])
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return output_tensor
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@staticmethod
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def backward(ctx: Any, grad_output: Tensor) -> tuple[None, Tensor, None, None]:
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# Split the gradient tensor along the gathered dimension
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dim_size = grad_output.size(ctx.dim) // ctx.world_size
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grad_chunks = grad_output.reshape(grad_output.shape[:ctx.dim] + (ctx.world_size, dim_size) +
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grad_output.shape[ctx.dim + 1:])
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grad_chunks = grad_chunks.movedim(ctx.dim, 0)
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# Each rank only needs its corresponding gradient
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grad_input = torch.empty(ctx.input_shape, dtype=grad_output.dtype, device=grad_output.device)
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dist.reduce_scatter_tensor(grad_input, grad_chunks.contiguous(), group=ctx.group)
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return None, grad_input, None, None
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class Slice(torch.autograd.Function):
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"""Differentiable slice (shard) operation.
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Forward selects this rank's chunk along `dim`.
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Backward all-gathers local gradients to reconstruct full input grad.
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"""
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@staticmethod
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def forward(ctx: Any, group: ProcessGroup, input_: Tensor, world_size: int, dim: int,
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scale_grad: bool) -> Tensor:
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ctx.group = group
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ctx.world_size = world_size
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ctx.dim = dim
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ctx.scale_grad = scale_grad
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ctx.rank = dist.get_rank(group)
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if world_size == 1:
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return input_.contiguous()
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shard_size = input_.size(dim) // world_size
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output = input_.movedim(dim, 0)
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output = output[ctx.rank * shard_size:(ctx.rank + 1) * shard_size]
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return output.movedim(0, dim).contiguous()
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@staticmethod
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def backward(ctx: Any, grad_output: Tensor) -> tuple[None, Tensor, None, None, None]:
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if ctx.world_size == 1:
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return None, grad_output, None, None, None
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grad_output = grad_output.movedim(ctx.dim, 0).contiguous()
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shard_size = grad_output.shape[0]
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grad_input_shape = (shard_size * ctx.world_size, ) + tuple(grad_output.shape[1:])
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grad_input = torch.empty(grad_input_shape, dtype=grad_output.dtype, device=grad_output.device)
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dist.all_gather_into_tensor(grad_input, grad_output, group=ctx.group)
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if ctx.scale_grad:
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grad_input = grad_input / ctx.world_size
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return None, grad_input.movedim(0, ctx.dim), None, None, None
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class AllToAll4D(torch.autograd.Function):
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"""Differentiable all_to_all operation specialized for 4D tensors.
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This operation is particularly useful for attention operations where we need to
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redistribute data across ranks for efficient parallel processing.
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The operation supports two modes:
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1. scatter_dim=2, gather_dim=1: Used for redistributing attention heads
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2. scatter_dim=1, gather_dim=2: Used for redistributing sequence dimensions
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"""
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@staticmethod
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def forward(ctx: Any, group: ProcessGroup, input_: Tensor, world_size: int, scatter_dim: int,
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gather_dim: int) -> Tensor:
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ctx.group = group
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ctx.world_size = world_size
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ctx.scatter_dim = scatter_dim
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ctx.gather_dim = gather_dim
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if world_size == 1:
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return input_
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assert input_.dim() == 4, f"input must be 4D tensor, got {input_.dim()} and shape {input_.shape}"
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if scatter_dim == 2 and gather_dim == 1:
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bs, shard_seqlen, hn, hd = input_.shape
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seqlen = shard_seqlen * world_size
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shard_hn = hn // world_size
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input_ = input_.transpose(0, 2).contiguous() # hn, shard_seqlen, bs, hd
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output = torch.empty_like(input_)
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dist.all_to_all_single(output, input_, group=group) # hn, shard_seqlen, bs, hd
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output = torch.cat(output.split(shard_hn), dim=1) # sharded hn, seqlen, bs, hd
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output = output.transpose(0, 2).contiguous() # bs, seqlen, sharded_hn, hd
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return output
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elif scatter_dim == 1 and gather_dim == 2:
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bs, seqlen, shard_hn, hd = input_.shape
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hn = shard_hn * world_size
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shard_seqlen = seqlen // world_size
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input_ = input_.transpose(0, 2).contiguous() # shard_hn, seqlen, bs, hd
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input_ = input_.reshape(shard_hn, world_size, shard_seqlen, bs,
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hd).transpose(0, 1).reshape(shard_hn * world_size, shard_seqlen, bs,
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hd).contiguous()
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output = torch.empty_like(input_)
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dist.all_to_all_single(output, input_, group=group)
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output = output.transpose(0, 2).contiguous() # bs, seqlen, sharded_hn, hd
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return output
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else:
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raise RuntimeError(
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f"Invalid scatter_dim={scatter_dim}, gather_dim={gather_dim}. "
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f"Only (scatter_dim=2, gather_dim=1) and (scatter_dim=1, gather_dim=2) are supported.")
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@staticmethod
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def backward(ctx: Any, grad_output: Tensor) -> tuple[None, Tensor, None, None, None]:
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if ctx.world_size == 1:
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return None, grad_output, None, None, None
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# For backward pass, we swap scatter_dim and gather_dim
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output = DistributedAutograd.AllToAll4D.apply(ctx.group, grad_output, ctx.world_size, ctx.gather_dim,
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ctx.scatter_dim)
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return None, output, None, None, None
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class DeviceCommunicatorBase:
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"""
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Base class for device-specific communicator with autograd support.
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It can use the `cpu_group` to initialize the communicator.
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If the device has PyTorch integration (PyTorch can recognize its
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communication backend), the `device_group` will also be given.
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"""
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def __init__(self,
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cpu_group: ProcessGroup,
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device: torch.device | None = None,
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device_group: ProcessGroup | None = None,
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unique_name: str = ""):
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self.device = device or torch.device("cpu")
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self.cpu_group = cpu_group
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self.device_group = device_group
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self.unique_name = unique_name
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self.rank = dist.get_rank(cpu_group)
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self.world_size = dist.get_world_size(cpu_group)
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self.ranks = dist.get_process_group_ranks(cpu_group)
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self.global_rank = dist.get_rank()
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self.global_world_size = dist.get_world_size()
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self.rank_in_group = dist.get_group_rank(self.cpu_group, self.global_rank)
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def all_reduce(self, input_: torch.Tensor, op: dist.ReduceOp | None = ReduceOp.SUM) -> torch.Tensor:
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"""Performs an all_reduce operation with gradient support."""
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return DistributedAutograd.AllReduce.apply(self.device_group, input_, op)
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def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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"""Performs an all_gather operation with gradient support."""
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if dim < 0:
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dim += input_.dim()
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return DistributedAutograd.AllGather.apply(self.device_group, input_, self.world_size, dim)
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def slice(self, input_: torch.Tensor, dim: int = -1, *, scale_grad: bool) -> torch.Tensor:
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"""Performs rank-local slicing with all-gather backward."""
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if dim < 0:
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dim += input_.dim()
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return DistributedAutograd.Slice.apply(self.device_group, input_, self.world_size, dim, scale_grad)
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def all_to_all_4D(self, input_: torch.Tensor, scatter_dim: int = 2, gather_dim: int = 1) -> torch.Tensor:
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"""Performs a 4D all-to-all operation with gradient support."""
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return DistributedAutograd.AllToAll4D.apply(self.device_group, input_, self.world_size, scatter_dim, gather_dim)
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def gather(self, input_: torch.Tensor, dst: int = 0, dim: int = -1) -> torch.Tensor | None:
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"""
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NOTE: We assume that the input tensor is on the same device across
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all the ranks.
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NOTE: `dst` is the local rank of the destination rank.
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"""
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world_size = self.world_size
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assert -input_.dim() <= dim < input_.dim(), (f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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# Allocate output tensor.
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gather_list = [torch.empty_like(input_) for _ in range(world_size)] if self.rank_in_group == dst else None
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# Gather.
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torch.distributed.gather(input_, gather_list, dst=self.ranks[dst], group=self.device_group)
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output_tensor = torch.cat(gather_list, dim=dim) if self.rank_in_group == dst else None
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return output_tensor
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def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
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"""Sends a tensor to the destination rank in a non-blocking way"""
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"""NOTE: `dst` is the local rank of the destination rank."""
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if dst is None:
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dst = (self.rank_in_group + 1) % self.world_size
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torch.distributed.send(tensor, self.ranks[dst], self.device_group)
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def recv(self, size: torch.Size, dtype: torch.dtype, src: int | None = None) -> torch.Tensor:
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"""Receives a tensor from the source rank."""
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"""NOTE: `src` is the local rank of the source rank."""
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if src is None:
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src = (self.rank_in_group - 1) % self.world_size
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tensor = torch.empty(size, dtype=dtype, device=self.device)
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torch.distributed.recv(tensor, self.ranks[src], self.device_group)
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return tensor
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def destroy(self) -> None:
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pass
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