Motivation

DeepSpeed owns the gradient-accumulation boundary via an internal micro-step counter:
backward() bumps micro_steps, and step() only reduces gradients + runs the optimizer when

(self.micro_steps + 1) % self.gradient_accumulation_steps() == 0

This enables symmetric forward/backward/step on every micro-batch, which is ideal for the
standard training loop.

It breaks down for callers that want to own the boundary themselves — notably HTTP/RPC-driven RL
backends (e.g. Tinker-protocol servers) where one logical optimizer step arrives as N backward()
calls followed by exactly one step(), with N client-controlled and not known at config time.
FSDP-based backends handle this natively (they call optimizer.step() when they choose); DeepSpeed's
only lever today is set_gradient_accumulation_boundary(bool), which must be toggled around every
backward, is easy to get wrong, and couples the caller to engine internals.

Proposal

Add a boolean ds_config flag managed_gradient_accumulation (default True, i.e. current behavior).
When False ("unmanaged" mode):

• micro-step tracking is disabled; there is no counter-driven boundary,
• is_gradient_accumulation_boundary() is True only while step() executes,
• backward() accumulates; step() finalizes accumulated grads and applies exactly one optimizer
  update — unconditionally, every call,
• the caller is responsible for calling step() at the true boundary.

This gives a first-class, config-level API for "backward and step are separate primitives, the client
owns the boundary" without per-backward toggling.

Per-stage semantics

• ZeRO 0/1 / DDP: backward() accumulates gradients locally (no reduction); step() performs the
  all-reduce then the optimizer update.
• ZeRO 2/3: keep reducing/partitioning on every backward() (their natural behavior), but defer
  the averaged_gradients finalization + optimizer update to step() via a
  finalize_gradient_accumulation_boundary() hook.
• ZeRO offload (CPU/NVMe optimizer): supported; the boundary-time grad-norm computation and
  fp32/optimizer-buffer copies move into that finalize hook invoked from step().

Gradient scaling (open question)

DeepSpeed scales loss/grads by the configured gradient_accumulation_steps:

gas_scaled_loss = loss / self.gradient_accumulation_steps() if scale_wrt_gas else loss

In unmanaged mode the number of backward() calls per step may differ from the configured GAS (and
may vary per step). Callers who accumulate a variable count should either set
gradient_accumulation_steps accordingly or use backward(loss, scale_wrt_gas=False) and average
client-side. Open question: should unmanaged mode default to scale_wrt_gas=False, or expose a
per-step "effective accumulation count"?

Non-goals / open questions

• Pipeline parallelism (PipelineModule.train_batch() owns the whole schedule) is out of scope
  for v1; managed_gradient_accumulation=False would be rejected there.
• Naming: managed_gradient_accumulation vs external_grad_accum_boundary / manual_optimizer_step.
• Scaling knob: should there be a first-class control for variable accumulation counts (see above)?
• Interactions with ZenFlow, eigenvalue computation, and gradient clipping edge cases.

Reference implementation

A working prototype exists covering ZeRO 0-3 including CPU optimizer offload, with unit tests asserting
numerical parity against managed mode (N backwards + 1 step == managed GAS=N). Happy to contribute as
stacked PRs (ZeRO 0/1 -> 2 -> 3 -> offload) if there's interest.