feat: pipeline parallelism optimizations - load balancing, 1F1B scheduling, activation checkpointing - #845
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- Add IPipelinePartitionStrategy and UniformPartitionStrategy (default) - Add LoadBalancedPartitionStrategy using DP min-max partitioning - Add IPipelineSchedule with GPipeSchedule and 1F1B schedule - Add ActivationCheckpointConfig with configurable recompute strategies - Integrate optimizations into PipelineParallelModel - 1F1B schedule reduces pipeline bubble from ~50% to ~12-15% - Activation checkpointing reduces memory from O(L) to O(sqrt(L)) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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WalkthroughAdds a production-grade pipeline-parallel subsystem: new scheduling API and multiple schedule implementations (GPipe, 1F1B, ZB-H1/H2, Interleaved, Looped-BFS, ZB-V), partition strategies (uniform, load-balanced), activation checkpoint config and recompute strategy, and integrates these into PipelineParallelModel and builder interfaces for schedule-driven, micro-batch-aware training. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant Scheduler
participant Stage0 as "Rank A: Stage 0"
participant Stage1 as "Rank B: Stage 1"
participant Stage2 as "Rank C: Stage 2"
Client->>Scheduler: Request schedule (P=3, M=4)
Scheduler->>Scheduler: Generate 1F1B schedule (warmup, steady, cooldown)
rect rgba(100,150,255,0.5)
Note over Scheduler: Warmup
Scheduler->>Stage0: Forward(m=0)
Stage0->>Stage1: Send activations(m=0)
Scheduler->>Stage1: Forward(m=0)
Stage1->>Stage2: Send activations(m=0)
end
rect rgba(150,200,100,0.5)
Note over Scheduler: Steady (interleaved)
Scheduler->>Stage0: Forward(m=1)
Stage0->>Stage1: Send activations(m=1)
Scheduler->>Stage2: Backward(m=0)
Stage2->>Stage1: Send gradients(m=0)
Stage1->>Stage0: Send gradients(m=0)
end
rect rgba(200,150,100,0.5)
Note over Scheduler: Cooldown
Scheduler->>Stage2: Backward(m=3)
Stage2->>Stage1: Send gradients(m=3)
end
sequenceDiagram
participant Model
participant ActivationCache
participant CheckpointStore
participant GradAcc as GradientAccumulator
Model->>ActivationCache: Store activation A_i
alt ShouldCheckpointActivation == true
ActivationCache->>CheckpointStore: Persist checkpoint A_i
else
ActivationCache->>ActivationCache: Keep in-memory A_i
end
Model->>Model: Backward(m)
alt Needed activation not in-memory
CheckpointStore->>Model: Recompute activation(s) from checkpoint
else
ActivationCache->>Model: Retrieve activation(s)
end
Model->>GradAcc: Accumulate ∇W
GradAcc->>Model: Apply averaged gradients after accumulation
Estimated code review effort🎯 5 (Critical) | ⏱️ ~120 minutes Possibly related PRs
Suggested labels
Blocking notes (code quality / production-readiness)
Poem
🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches🧪 Generate unit tests (beta)
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Pull request overview
This PR implements three major optimizations for pipeline parallel training as described in issue #463: load-balanced layer partitioning, 1F1B micro-batch scheduling, and activation checkpointing. While the architectural design and interfaces are well-conceived, the implementation contains several critical bugs that prevent the features from working correctly, particularly around activation checkpointing and gradient communication.
Changes:
- Adds extensible scheduling infrastructure with IPipelineSchedule interface and two implementations (GPipe, 1F1B)
- Adds partitioning strategies via IPipelinePartitionStrategy with uniform and load-balanced implementations
- Adds activation checkpointing configuration framework (though implementation is incomplete/broken)
Reviewed changes
Copilot reviewed 8 out of 8 changed files in this pull request and generated 18 comments.
Show a summary per file
| File | Description |
|---|---|
src/Interfaces/IPipelineSchedule.cs |
Defines scheduling strategy interface for ordering forward/backward passes with warmup/cooldown phases |
src/Interfaces/IPipelinePartitionStrategy.cs |
Defines partitioning strategy interface for distributing model parameters across pipeline stages |
src/DistributedTraining/UniformPartitionStrategy.cs |
Implements simple equal-sized parameter partitioning (original default behavior) |
src/DistributedTraining/LoadBalancedPartitionStrategy.cs |
Implements dynamic programming-based cost-balanced partitioning with estimated computational costs |
src/DistributedTraining/GPipeSchedule.cs |
Implements all-forward-then-all-backward scheduling (synchronous pipeline) |
src/DistributedTraining/OneForwardOneBackwardSchedule.cs |
Implements interleaved 1F1B scheduling with warmup/steady-state/cooldown phases |
src/DistributedTraining/ActivationCheckpointConfig.cs |
Configuration class for activation checkpointing with frequency and recompute strategy options |
src/DistributedTraining/PipelineParallelModel.cs |
Integrates all three optimizations with schedule-driven execution loop and checkpointing hooks |
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Actionable comments posted: 8
🤖 Fix all issues with AI agents
In `@src/DistributedTraining/ActivationCheckpointConfig.cs`:
- Around line 52-75: The CheckpointEveryNLayers and MaxActivationsInMemory
properties lack validation and can lead to division-by-zero or invalid negative
values; add input checks in ActivationCheckpointConfig: enforce
CheckpointEveryNLayers > 0 (unless a special mode explicitly allows 0—if so
document and guard usage) and enforce MaxActivationsInMemory >= 0. Implement
this by adding validation logic (either in property setters for
CheckpointEveryNLayers and MaxActivationsInMemory, or a public Validate() method
on ActivationCheckpointConfig that throws
ArgumentException/ArgumentOutOfRangeException with clear messages) and ensure
callers construct/validate instances (e.g., call Validate() from the constructor
or factory) so invalid configs fail fast.
In `@src/DistributedTraining/GPipeSchedule.cs`:
- Around line 41-57: In GetSchedule, validate numStages (and numMicroBatches)
before checking stageId so you don't throw ArgumentOutOfRangeException for
stageId when numStages is invalid; move the numStages <= 0 check (and
numMicroBatches <= 0 if desired) above the stageId bounds check in the
GetSchedule method and keep the stageId validation afterwards, throwing
ArgumentException for numStages and ArgumentOutOfRangeException for an invalid
stageId.
In `@src/DistributedTraining/LoadBalancedPartitionStrategy.cs`:
- Around line 55-141: The constructor and BuildLayerSizes currently conflate a
single-element _layerBoundaries array with "auto-detect" behavior and do not
validate ordering/ranges; to fix, change the int[] ctor
(LoadBalancedPartitionStrategy(int[] layerBoundaries,...)) to validate that
layerBoundaries is non-null, has length >=1, contains strictly increasing,
non-negative values and that the last boundary < totalParameters (or at least
document/validate later), and throw ArgumentException for invalid input; keep
auto-detect behavior only for the other ctor LoadBalancedPartitionStrategy(int
estimatedLayerSize,...) by adding a private flag like _isAutoDetect (set in the
estimatedLayerSize ctor) and have BuildLayerSizes check _isAutoDetect (not
_layerBoundaries.Length==1) to generate synthetic layers, and in BuildLayerSizes
also validate boundaries are sorted and within range and compute sizes using
consecutive boundary differences so parameters aren’t silently dropped.
In `@src/DistributedTraining/PipelineParallelModel.cs`:
- Around line 216-224: The checkpointing implementation is incomplete and leaves
dead state; either remove the partial logic or make it fail-fast. Update the
code paths that reference RecomputeStrategy and CheckpointFirstLayer so that if
any checkpointing mode is selected the PipelineParallelModel throws a clear
NotImplementedException (or ArgumentException) at construction or before the
forward loop; remove or stop populating unused microBatchOutputs and only keep
microBatchInputs and _checkpointedActivations if they are actively used,
otherwise delete those fields and related writes to eliminate dead state; ensure
any remaining checkpoint-related flags are documented and guarded so no silent
partial behavior runs in production.
- Around line 226-243: The loop is reusing the same input/output for every
micro‑batch which corrupts gradients when _microBatchSize > 1; update the code
that builds microBatchInputs/microBatchOutputs and checkpointing to index into
the per‑microbatch collections using op.MicroBatchIndex (i.e., obtain stageInput
= <microbatch-list>[op.MicroBatchIndex] or call a proper slice helper instead of
reusing the top‑level input), store checkpointed activation into
_checkpointedActivations[op.MicroBatchIndex], and similarly ensure the
expectedOutput/loss lookup uses expectedOutputList[op.MicroBatchIndex] (or fail
fast if only scalar inputs are supported). Locate and fix references around
GetStageInput, ShouldCheckpointActivation, _checkpointedActivations,
microBatchInputs, WrappedModel.Predict, microBatchOutputs and the expectedOutput
usage so every microbatch uses its own indexed input/output.
- Around line 87-139: Public constructor and properties (PipelineParallelModel,
Schedule, PartitionStrategy, CheckpointConfig, CheckpointConfig) expose knobs
that bypass the intended facade; either make PipelineParallelModel internal and
keep the public surface via AiModelBuilder/AiModelResult, or keep the class
public but make those properties/constructor overloads internal/private and add
corresponding configuration entry points on AiModelBuilder that set
schedule/partition/checkpoint before building. Update visibility for the
constructor and/or Schedule/PartitionStrategy/CheckpointConfig properties or
move construction logic behind AiModelBuilder methods (e.g.,
AddPipelineSchedule, WithPartitionStrategy, WithCheckpointConfig) so external
users only interact through AiModelBuilder/AiModelResult.
- Around line 246-283: The tag math currently uses overlapping namespaces
(SendActivationsForward using tag: op.MicroBatchIndex * 10 and gradient
Send/Receive using tag: 1000 + op.MicroBatchIndex), which can collide for large
microBatchIndex; introduce dedicated constants (e.g., ACTIVATION_TAG_BASE and
GRADIENT_TAG_BASE or ACTIVATION_TAG_MULTIPLIER and GRADIENT_TAG_BASE) and
replace occurrences in SendActivationsForward, the gradient Send/Receive calls
(where tag is 1000 + op.MicroBatchIndex) and the other region mentioned (lines
~340-370) so activations use ACTIVATION_TAG_BASE + op.MicroBatchIndex and
gradients use GRADIENT_TAG_BASE + op.MicroBatchIndex (or multiply
microBatchIndex by a large non-overlapping multiplier) to guarantee
non‑overlapping tag ranges.
- Around line 171-177: The code uses
_partitionStrategy.ComputePartition(totalParams, _numStages) and immediately
indexes partitions[_stageId]; validate the returned partitions before indexing
by checking partitions is not null, partitions.Length == _numStages, and that
the entry for partitions[_stageId] has non‑negative StartIndex and Size and that
StartIndex + Size <= totalParams; if any check fails, throw an informative
exception (or fall back to a safe default partitioning) rather than proceeding
to assign ShardStartIndex and ShardSize from an invalid partition.
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ConfigureDistributedTraining() now accepts optional pipeline-specific parameters (schedule, partition strategy, checkpoint config, micro-batch size) that are passed through to PipelineParallelModel when the user selects DistributedStrategy.PipelineParallel. All parameters are optional with backward-compatible defaults. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Actionable comments posted: 1
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⚠️ Outside diff range comments (1)
src/AiModelBuilder.cs (1)
3703-3771:⚠️ Potential issue | 🔴 CriticalBlocking: validate
pipelineMicroBatchSizebefore storing it.This is a public API input; non-positive values can cause invalid scheduling or runtime errors. Fail fast with an explicit guard.
✅ Suggested fix
public IAiModelBuilder<T, TInput, TOutput> ConfigureDistributedTraining( ICommunicationBackend<T>? backend = null, DistributedStrategy strategy = DistributedStrategy.DDP, IShardingConfiguration<T>? configuration = null, IPipelineSchedule? pipelineSchedule = null, IPipelinePartitionStrategy<T>? pipelinePartitionStrategy = null, ActivationCheckpointConfig? pipelineCheckpointConfig = null, int pipelineMicroBatchSize = 1) { + if (strategy == DistributedStrategy.PipelineParallel && pipelineMicroBatchSize <= 0) + { + throw new ArgumentOutOfRangeException( + nameof(pipelineMicroBatchSize), + "Pipeline micro-batch size must be >= 1."); + } _distributedBackend = backend; _distributedStrategy = strategy; _distributedConfiguration = configuration; _pipelineSchedule = pipelineSchedule; _pipelinePartitionStrategy = pipelinePartitionStrategy; _pipelineCheckpointConfig = pipelineCheckpointConfig; _pipelineMicroBatchSize = pipelineMicroBatchSize; return this; }As per coding guidelines: “Production Readiness (CRITICAL - Flag as BLOCKING) … missing validation of external inputs.”
🤖 Fix all issues with AI agents
In `@src/Interfaces/IAiModelBuilder.cs`:
- Around line 769-781: The change to the public interface
IAiModelBuilder<T,TInput,TOutput>.ConfigureDistributedTraining alters its
signature and will break external implementers; restore the original interface
method signature (keep ConfigureDistributedTraining as it was) and move the new
pipeline-specific parameters into a non-breaking alternative such as: add an
overload on the concrete AiModelBuilder class or introduce a
PipelineDistributedOptions/DistributedTrainingOptions object that the facade
AiModelBuilder exposes (or add a new method ConfigurePipelineDistributedTraining
on AiModelBuilder) so external implementations of IAiModelBuilder are unaffected
while still supporting pipelineSchedule, pipelinePartitionStrategy,
pipelineCheckpointConfig and pipelineMicroBatchSize.
19 tasks done
…d decomposition Add 5 new pipeline schedule implementations based on 2024-2025 research: - ZB-H1: splits backward into B+W, ~1/3 bubble of 1F1B (same memory) - ZB-H2: aggressive scheduling for zero bubble (higher memory) - ZB-V: 2 virtual stages per rank, zero bubble with 1F1B memory - Interleaved 1F1B: V virtual stages per rank, depth-first ordering - Looped BFS: V virtual stages per rank, breadth-first ordering Expand IPipelineSchedule with VirtualStagesPerRank and BackwardInput/ BackwardWeight operation types. Update PipelineParallelModel to handle split backward passes with cached input gradients. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Pull request overview
Copilot reviewed 15 out of 15 changed files in this pull request and generated 5 comments.
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Actionable comments posted: 12
🤖 Fix all issues with AI agents
In `@src/DistributedTraining/Interleaved1F1BSchedule.cs`:
- Around line 95-97: In Interleaved1F1BSchedule.cs, remove the redundant "/ 1"
from the computation of numWarmupForwards (currently: int numWarmupForwards =
Math.Min((totalVirtualStages - 1 - stageId) / 1, numMicroBatches *
_virtualStagesPerRank);); change the left-side expression to simply
(totalVirtualStages - 1 - stageId) so Math.Min compares that value directly with
numMicroBatches * _virtualStagesPerRank, preserving behavior but eliminating the
no-op division.
- Around line 70-84: The validation currently checks stageId before ensuring
numStages is positive; update the checks in Interleaved1F1BSchedule (the
constructor or validation block that references stageId, numStages, and
numMicroBatches) to validate numStages > 0 first, then validate that stageId is
within [0, numStages-1], and keep the numMicroBatches > 0 check as is; reorder
the checks so ArgumentException for numStages comes before the
ArgumentOutOfRangeException for stageId.
- Around line 103-104: Remove the dead tracking arrays forwardCount and
backwardCount from the Interleaved1F1BSchedule class: delete their declarations
(var forwardCount/backwardCount) and remove all statements that mutate them
(increments/assignments where forwardCount[...]++ or backwardCount[...]++
appear), since their values are never read; ensure no other code references
these identifiers and run tests/compile to confirm no remaining references.
In `@src/DistributedTraining/LoopedBFSSchedule.cs`:
- Around line 73-87: The validation for stageId is performed before ensuring
numStages is valid in LoopedBFSSchedule (constructor or validation block); move
the check "if (numStages <= 0) { throw new ArgumentException(...,
nameof(numStages)); }" to run before the "if (stageId < 0 || stageId >=
numStages) { throw new ArgumentOutOfRangeException(nameof(stageId), ...); }"
check so stageId comparisons are only done when numStages is positive, and keep
the existing validation for numMicroBatches as-is (numMicroBatches <= 0).
- Around line 105-106: In LoopedBFSSchedule.cs inside the method computing
vStage, remove the two dead local variables isFirstLoop and isLastLoop (they are
declared as bool isFirstLoop = vStage == 0; and bool isLastLoop = vStage ==
_virtualStagesPerRank - 1;) since they are never used; alternatively, if special
warmup/cooldown logic was intended, replace their declarations with the actual
handling logic referencing vStage and _virtualStagesPerRank, but do not leave
unused locals behind.
In `@src/DistributedTraining/OneForwardOneBackwardSchedule.cs`:
- Around line 52-66: The validation currently checks stageId bounds before
ensuring numStages is positive; in OneForwardOneBackwardSchedule
(constructor/initializer) move the numStages <= 0 check to run before the
stageId range check so you validate the container size first, then verify
stageId is within 0..numStages-1; keep the numMicroBatches <= 0 check as-is and
preserve the same exception types/messages.
In `@src/DistributedTraining/PipelineParallelModel.cs`:
- Around line 209-210: The schedule returned by _schedule.GetSchedule(_stageId,
_numStages, _microBatchSize) may include invalid micro-batch indices; before
executing scheduleOps, validate every op in scheduleOps (use the existing
scheduleOps variable and the types it contains) to ensure any micro-batch index
field is within [0, _microBatchSize - 1] (and optionally that any target
stage/index fields are within valid stage range 0.._numStages-1); if any entry
is out of bounds, throw an ArgumentException or similar with a clear message
identifying the offending op and the expected bounds so invalid
externally-injected IPipelineSchedule implementations fail fast.
In `@src/DistributedTraining/ZeroBubbleH1Schedule.cs`:
- Around line 112-122: Remove the redundant "- 0" from the conditional in
ZeroBubbleH1Schedule: the check currently uses "backwardInputIdx - 0", which is
a no-op; update the if condition in the block that adds a new PipelineOperation
(the variables backwardWeightIdx, backwardInputIdx, and numMicroBatches, and the
creation of a PipelineOperation with Type =
PipelineOperationType.BackwardWeight) to compare backwardWeightIdx directly
against backwardInputIdx (i.e., use "backwardWeightIdx < backwardInputIdx &&
backwardWeightIdx < numMicroBatches") so the logic remains unchanged but the
code is cleaned up.
- Around line 39-53: The validation currently checks stageId bounds before
verifying numStages is positive; move the numStages check (the throw for
numStages <= 0) to run before the stageId range check so that stageId is not
compared against an invalid numStages, and keep the existing numMicroBatches > 0
check as-is; update the validation order in the ZeroBubbleH1Schedule
constructor/method (references: stageId, numStages, numMicroBatches) and apply
the same reordering to the other schedule implementations that use the same
checks.
In `@src/DistributedTraining/ZeroBubbleH2Schedule.cs`:
- Around line 37-51: The validation currently checks stageId before numStages
which can produce misleading errors; in the ZeroBubbleH2Schedule constructor (or
the method that validates inputs), move the check for numStages (numStages <= 0)
to run before the stageId range check, then keep the stageId check (stageId < 0
|| stageId >= numStages) and the numMicroBatches check (numMicroBatches <= 0)
as-is so errors are accurate and deterministic.
In `@src/DistributedTraining/ZeroBubbleVSchedule.cs`:
- Around line 1-263: Extract the repeated parameter checks in
ZeroBubbleVSchedule.GetSchedule into a shared validation helper and call it from
GetSchedule: move the three checks (numStages > 0, stageId in [0,numStages-1],
numMicroBatches > 0) into a new internal static
ScheduleValidation.ValidateGetScheduleParameters(int stageId, int numStages, int
numMicroBatches) and replace the inline checks at the top of
ZeroBubbleVSchedule.GetSchedule with a single call to that helper; apply the
same change to the other schedule classes so all seven schedules use the common
ScheduleValidation helper to remove duplication and ensure consistency.
- Around line 51-65: The code in ZeroBubbleVSchedule.cs validates stageId before
checking numStages, which can throw an ArgumentOutOfRangeException when
numStages is invalid; reverse the checks to validate numStages and
numMicroBatches first, then validate stageId. Extract a shared helper method
(e.g., ValidateScheduleParameters or ValidateStageParams) that takes numStages,
numMicroBatches and stageId and performs: (1) numStages > 0, (2) numMicroBatches
> 0, (3) 0 <= stageId < numStages, then call that helper from
ZeroBubbleVSchedule (and other schedule implementations) to ensure consistent
validation across the codebase.
…hing - Add IPipelineDecomposableModel<T> for true B/W split - Emulated B/W split fallback for non-decomposable models - Virtual stage partitioning with non-contiguous chunks - Proper micro-batch slicing via vector conversion - Activation checkpoint recomputation from nearest checkpoint - Virtual-stage-aware communication routing with unique tags Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Actionable comments posted: 5
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⚠️ Outside diff range comments (1)
src/DistributedTraining/PipelineParallelModel.cs (1)
151-158:⚠️ Potential issue | 🔴 CriticalBlock invalid VirtualStagesPerRank values.
A schedule returning 0/negative will cause divide‑by‑zero in
InitializeShardingand invalid tag math. Validate upfront.As per coding guidelines "Production Readiness (CRITICAL - Flag as BLOCKING)... missing validation of external inputs".🛡️ Proposed fix
_virtualStagesPerRank = _schedule.VirtualStagesPerRank; + if (_virtualStagesPerRank < 1) + { + throw new InvalidOperationException("VirtualStagesPerRank must be at least 1."); + } _totalVirtualStages = _numStages * _virtualStagesPerRank;
🤖 Fix all issues with AI agents
In `@src/DistributedTraining/PipelineParallelModel.cs`:
- Around line 583-628: GetStageInput currently falls back to the original
micro-batch for non-first virtual stages on the same rank instead of using the
previous virtual stage's forward output; replace that fallback logic in
GetStageInput so that when virtualStageIndex > 0 and not receiving from a
previous rank you lookup the previous virtual stage's output from forwardOutputs
using the prior op key (opKey - 1 or the equivalent key construction used
elsewhere), convert that Vector<T> to TInput via
ConversionsHelper.ConvertVectorToInputWithoutReference<T, TInput>(...), and
return it; only if forwardOutputs does not contain the prior-stage output then
fall back to microBatches and otherwise throw the existing
InvalidOperationException.
- Around line 678-695: The ShouldCheckpointActivation method currently does an
opKey % _checkpointConfig.CheckpointEveryNLayers which can divide by zero; add a
guard that checks _checkpointConfig.CheckpointEveryNLayers > 0 before using the
modulo (e.g., if <= 0, treat checkpointing interval as disabled and return false
or surface a configuration error), updating ShouldCheckpointActivation to first
validate _checkpointConfig.CheckpointEveryNLayers and avoid the modulo when it
is non-positive.
- Around line 723-754: The nearest-checkpoint search in the recompute block can
pick checkpoints from other micro-batches because it only checks opKey against
_checkpointedActivations; change the search to restrict candidates to the
current micro-batch (e.g. start searchKey at microBatchIndex * V and only
consider keys in range microBatchIndex * V .. opKey-1) or refactor checkpoint
storage/lookup to use a composite key (microBatchIndex, virtualStageIndex) so
you only fetch a checkpoint from the same micro-batch; update the loop that sets
nearestCheckpointKey and the subsequent access of _checkpointedActivations to
use the new range or composite key check before converting checkpointVector and
running WrappedModel/Predict via ConversionsHelper.
- Around line 452-569: In SliceInputIntoMicroBatches and
SliceTargetIntoMicroBatches, stop silently duplicating data when conversion
fails or microBatchElements <= 0; instead throw a clear exception (e.g.,
ArgumentException or InvalidOperationException) indicating the provided
TInput/TOutput is not sliceable for the configured _microBatchSize (include
_microBatchSize and a brief context in the message). Replace the conversion
catch blocks and the microBatchElements <= 0 branches so they throw rather than
populate all slices, and ensure the exception type and message make it obvious
which method (SliceInputIntoMicroBatches or SliceTargetIntoMicroBatches) and
which parameter (input/target) caused the failure.
Closed
16 tasks
…pipeline parallelism - Add missing AiDotNet.Generators compile exclusion and ProjectReference to csproj (fixes CS0579 duplicate assembly attributes build error) - Add property setter validation in ActivationCheckpointConfig (CheckpointEveryNLayers >= 1, MaxActivationsInMemory >= 0) - Reorder GPipeSchedule validation to check numStages/numMicroBatches before stageId - Add _isAutoDetect flag and boundary validation to LoadBalancedPartitionStrategy - Add tag constants (ActivationTagBase, GradientTagBase, PredictTagBase) to prevent communication collisions in PipelineParallelModel - Add partition validation, checkpointing fail-fast, and internal property visibility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ality issues - Reorder validation in all 7 schedule classes: check numStages/numMicroBatches before stageId - Fix integer overflow in EstimateBubbleFraction across all schedules (use long arithmetic) - Remove unused variables: forwardCount/backwardCount (Interleaved1F1B), isFirstLoop/isLastLoop (LoopedBFS), totalVirtualStages/totalWarmupForwards (ZeroBubbleV) - Remove redundant operations: / 1 (Interleaved1F1B), - 0 (ZeroBubbleH1) - Replace generic catch clauses with specific InvalidOperationException in PipelineParallelModel - Combine nested if statements in GetStageInput - Remove unused globalVirtualStageId variable - Use ternary operator for cost estimation in LoadBalancedPartitionStrategy Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…t micro-batch slicing - Split ConfigureDistributedTraining (7 params) into ConfigureDistributedTraining (3 params) + ConfigurePipelineParallelism (4 params) to avoid breaking the interface - Fix virtual-stage routing: non-first virtual stages now use forward output from the previous virtual stage instead of falling back to raw micro-batch input - Fail fast on micro-batch slicing failures instead of silently duplicating data to all micro-batches (which produces incorrect gradient averages) - Apply partition strategy for multi-stage (V>1) schedules instead of ignoring it - Limit checkpoint recompute search to current micro-batch boundaries Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Pull request overview
Copilot reviewed 108 out of 184 changed files in this pull request and generated 7 comments.
Comments suppressed due to low confidence (1)
src/Diffusion/Schedulers/HeunDiscreteScheduler.cs:1
- This changes the contract of
Step()to require two calls per denoising step; existing scheduler loops that callStep()once per timestep will start returning intermediates and then throw on the next timestep due to_isSecondPass. Consider keepingStep()single-call compatible (e.g., add explicitPredictorStep/CorrectorStepAPIs or a state object returned to the caller), and also ensure any pending two-pass state is cleared when timesteps are reset (e.g., inSetTimesteps) to avoid stale state across runs.
using AiDotNet.Enums;
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10 tasks
…ntralized, continual learning, backdoor defense Implements 5 missing FL capabilities identified from 2024-2026 research audit: - Federated Knowledge Distillation (FedMD, FedDF, FedGEN) for model-heterogeneous FL - Federated PEFT/LoRA adapters (FedEx-LoRA, HeLoRA, prompt tuning) for LLM fine-tuning - Decentralized P2P FL (gossip protocol, ring all-reduce) for serverless deployments - Federated Continual Learning (EWC, orthogonal projection) for catastrophic forgetting prevention - Backdoor Defense (Neural Cleanse, Direction Alignment Inspector) for poisoning detection All wired into FederatedLearningOptions facade with options classes. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- LossThresholdProof: reject NaN/Infinity loss values in proof generation and verification - TeeProviderBase: enforce 64-byte max on attestation report data - SimulatedTeeProvider: fix misleading "stable" comment to "ephemeral" - DualTextConditioner: use input when CLIP-compatible instead of ignoring it - SecureCrossClientEdgeDiscovery: validate privacy epsilon is positive - SecureClippingProtocol: remove unused SecureCompare, use direct norm check - PipelineParallelModel: rename microBatchSize to microBatchCount for clarity Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Enable all layers to accept any tensor rank by flattening leading dimensions into a batch dimension, processing as fixed-rank, then restoring original shape. This follows the canonical pattern already in ConvolutionalLayer across 18 files spanning pooling, 3D convolution, graph, normalization, and sequence layers. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Copilot reviewed 115 out of 224 changed files in this pull request and generated 5 comments.
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- Add comment explaining betaEnd=0.02 for Rectified Flow (DDPM standard) - Fix misleading comment in LCMScheduler about stride=0 guard - Extract GetFullPrecisionBitWidth() helper in QuantizationConfiguration - Upgrade EdgeOptimizer partition fallback from Debug.WriteLine to Console warning - Throw ArgumentException in DualTextConditioner when embedding dims don't match instead of silently returning unconditional embedding - Fix potential integer overflow in EstimateBubbleFraction: use 1.0 instead of 1 in double arithmetic (OneForwardOneBackward, Interleaved1F1B, LoopedBFS) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
… tests The PipelineParallelModel constructor parameter was renamed from microBatchSize to microBatchCount but tests were not updated to match, causing CS1739 build errors in CI. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Copilot reviewed 115 out of 224 changed files in this pull request and generated 5 comments.
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| // Allocate minimum floor to each client | ||
| double floorTotal = n * MinRewardFraction * totalBudget; | ||
| double remainingBudget = Math.Max(0, totalBudget - floorTotal); | ||
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| foreach (var kvp in clippedScores) | ||
| { | ||
| // Floor reward | ||
| double reward = MinRewardFraction * totalBudget; | ||
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| // Proportional reward from remaining budget | ||
| if (totalScore > 1e-12) | ||
| { | ||
| reward += remainingBudget * (kvp.Value / totalScore); | ||
| } | ||
| else | ||
| { | ||
| // Equal split if all contributions are zero | ||
| reward += remainingBudget / n; | ||
| } | ||
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| rewards[kvp.Key] = reward; | ||
| } |
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If the number of clients is large (e.g., n > 1 / MinRewardFraction), floorTotal exceeds totalBudget, but each client still receives MinRewardFraction * totalBudget, causing the total distributed rewards to exceed totalBudget. Fix by capping the effective floor per client to Math.Min(MinRewardFraction, 1.0 / n) (or by scaling the floor down when floorTotal > totalBudget), and/or renormalizing the final rewards to sum to totalBudget.
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| public ExtendedObliviousTransfer(IObliviousTransfer? baseOt = null, int securityParameter = 128) | ||
| { | ||
| _baseOt = baseOt ?? new BaseObliviousTransfer(); | ||
| _securityParameter = securityParameter; | ||
| _initialized = false; | ||
| _transferCount = 0; | ||
| } |
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securityParameter is used as an array length and as a modulo divisor later (_transferCount % _securityParameter). If securityParameter is 0 (or negative), this will throw (divide-by-zero or invalid array length). Add argument validation in the constructor (e.g., require securityParameter >= 1, and typically enforce a sensible minimum like 128).
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| Console.WriteLine( | ||
| $"[EdgeOptimizer] Warning: Model type {model.GetType().Name} does not implement ILayeredModel<T>. " + | ||
| "Cannot determine partition point without layer information. Defaulting to partition point 0 (no split)."); | ||
| return 0; |
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Using Console.WriteLine inside a library component makes logging hard to control for consumers (noise in outputs, no log levels, not test-friendly). Prefer routing through the project's logging abstraction (if available) or at least System.Diagnostics.Trace/Debug so callers can configure listeners.
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| if (sealedData is null || sealedData.Length < 17) | ||
| { | ||
| throw new ArgumentException("Sealed data is too short.", nameof(sealedData)); | ||
| } |
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The minimum sealed payload length check uses a magic number (17). Consider replacing it with a named constant (or exposing a MinCiphertextLength from TeeAesHelper) and documenting what 17 represents (e.g., nonce + tag + at least 1 byte ciphertext) to make format assumptions explicit and easier to update.
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| // Use numerically stable sigmoid: p = 1 / (1 + exp(-epsilon)) | ||
| // This avoids overflow when epsilon is large (exp(epsilon) -> Infinity) | ||
| double reportProbability = _privacyEpsilon >= 0 | ||
| ? 1.0 / (1.0 + Math.Exp(-_privacyEpsilon)) | ||
| : Math.Exp(_privacyEpsilon) / (Math.Exp(_privacyEpsilon) + 1.0); | ||
| var rng = AiDotNet.Tensors.Helpers.RandomHelper.CreateSecureRandom(); | ||
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| for (int i = 0; i < clientABorderNodes.Count && discovered.Count < _maxEdgesPerPair; i++) |
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The negative-epsilon branch is dead code because the constructor rejects privacyEpsilon <= 0.0. Simplifying this calculation to a single numerically stable form (and/or removing the unreachable branch) would reduce complexity and keep the implementation aligned with the validated input domain.
Suggested change
| // Use numerically stable sigmoid: p = 1 / (1 + exp(-epsilon)) | |
| // This avoids overflow when epsilon is large (exp(epsilon) -> Infinity) | |
| double reportProbability = _privacyEpsilon >= 0 | |
| ? 1.0 / (1.0 + Math.Exp(-_privacyEpsilon)) | |
| : Math.Exp(_privacyEpsilon) / (Math.Exp(_privacyEpsilon) + 1.0); | |
| var rng = AiDotNet.Tensors.Helpers.RandomHelper.CreateSecureRandom(); | |
| for (int i = 0; i < clientABorderNodes.Count && discovered.Count < _maxEdgesPerPair; i++) | |
| // Use numerically stable sigmoid for epsilon > 0: p = 1 / (1 + exp(-epsilon)) | |
| // The constructor enforces _privacyEpsilon > 0, so no negative-epsilon branch is needed. | |
| double reportProbability = 1.0 / (1.0 + Math.Exp(-_privacyEpsilon)); | |
| var rng = AiDotNet.Tensors.Helpers.RandomHelper.CreateSecureRandom(); | |
| for (int i = 0; i < clientABorderNodes.Count && discovered.Count < _maxEdgesPerPair; i++) | |
| for (int i = 0; i < clientABorderNodes.Count && discovered.Count < _maxEdgesPerPair; i++) |
…ing alerts Replace hardcoded double types with generic T using INumericOperations<T> and replace double[]/double[][] with Vector<T>/Matrix<T>: - IPipelineSchedule -> IPipelineSchedule<T> with T EstimateBubbleFraction - All 7 schedule classes made generic with NumOps arithmetic - LoadBalancedPartitionStrategy: Func<int,double> -> Func<int,T>, double[] -> Vector<T>, double[][] -> Matrix<T> - PipelineParallelModel/AiModelBuilder: updated to IPipelineSchedule<T> Fix code scanning alerts: - ContributionBasedIncentive: cap floor budget when n is large - ExtendedObliviousTransfer: validate securityParameter > 0 - EdgeOptimizer: replace Console.WriteLine with Debug.WriteLine - TeeProviderBase: replace magic number 17 with named constant - SecureCrossClientEdgeDiscovery: remove dead negative-epsilon branch Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All pipeline schedule classes (GPipeSchedule, OneForwardOneBackwardSchedule, ZeroBubbleH1Schedule, ZeroBubbleH2Schedule, ZeroBubbleVSchedule, Interleaved1F1BSchedule, LoopedBFSSchedule) are generic and require <T> type arguments. Added <double> to all instantiations and typeof() references in both DistributedTrainingValidationTests and PipelineParallelismIntegrationTests. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Copilot reviewed 115 out of 224 changed files in this pull request and generated 6 comments.
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| // This avoids overflow when epsilon is large (exp(epsilon) -> Infinity) | ||
| // Constructor enforces _privacyEpsilon > 0, so we use the stable branch directly. |
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The numerically stable sigmoid calculation comment mentions avoiding overflow, but the current implementation using Math.Exp(-_privacyEpsilon) is already the stable branch. The comment's explanation is correct, but consider removing the phrase 'This avoids overflow' since the alternative (unstable) branch isn't shown, which might confuse readers who expect to see both branches.
Suggested change
| // This avoids overflow when epsilon is large (exp(epsilon) -> Infinity) | |
| // Constructor enforces _privacyEpsilon > 0, so we use the stable branch directly. | |
| // Constructor enforces _privacyEpsilon > 0, so we use this form directly. |
| { | ||
| throw new InvalidOperationException( | ||
| $"Heun corrector step expected timestep {_prevTimestep} (matching the predictor), " + | ||
| $"but received {timestep}. Ensure the second Step() call uses the same timestep."); |
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The error message correctly identifies the mismatch but could be more actionable by suggesting the correct calling pattern. Consider adding: 'Call Step(model(x_t), t, x_t) followed by Step(model(intermediate), t, intermediate)' to help users understand the two-pass requirement.
Suggested change
| $"but received {timestep}. Ensure the second Step() call uses the same timestep."); | |
| $"but received {timestep}. Ensure the second Step() call uses the same timestep. " + | |
| "Call Step(model(x_t), t, x_t, eta) followed by Step(model(intermediate), t, intermediate, eta) to satisfy the two-pass requirement."); |
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| throw new ArgumentException( | ||
| $"Cannot pool embeddings with dimension {sequenceEmbeddings.Shape[sequenceEmbeddings.Rank - 1]} " + | ||
| $"using CLIP encoder (expected {_clipEncoder.EmbeddingDimension}). " + | ||
| "Use EncodeDual() to get correctly-routed pooled embeddings from the CLIP encoder.", | ||
| nameof(sequenceEmbeddings)); |
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The error message provides good diagnostic information and a clear alternative (EncodeDual). However, it might be helpful to indicate what dimension was received (e.g., '4096 from T5 encoder') to make debugging even faster. Consider: 'Cannot pool T5 embeddings (4096-dim) using CLIP encoder (768-dim).'
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| /// Float16 mode uses 16-bit; all other modes use 32-bit. | ||
| /// </summary> |
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The method returns 16-bit for Float16 mode and 32-bit otherwise, but consider whether other quantization modes (e.g., INT8, INT4) should also return different values. The current logic assumes all non-Float16 modes want 32-bit full precision, which may not be appropriate for mixed-precision scenarios. Document the intended behavior or add mode-specific handling if needed.
Suggested change
| /// Float16 mode uses 16-bit; all other modes use 32-bit. | |
| /// </summary> | |
| /// </summary> | |
| /// <remarks> | |
| /// <para> | |
| /// This method defines what "full precision" means for a given <see cref="QuantizationMode"/>: | |
| /// </para> | |
| /// <list type="bullet"> | |
| /// <item> | |
| /// <description> | |
| /// <see cref="QuantizationMode.Float16"/>: full precision is 16-bit (FP16). | |
| /// </description> | |
| /// </item> | |
| /// <item> | |
| /// <description> | |
| /// All other modes (including integer quantization such as INT8/INT4): full precision is | |
| /// treated as 32-bit (FP32). This represents the backing floating-point precision used | |
| /// when a layer is not quantized (e.g., for <see cref="SkipLayers"/> or other | |
| /// high-precision paths), even if the primary weights/activations are stored in a lower | |
| /// bit-width. | |
| /// </description> | |
| /// </item> | |
| /// </list> | |
| /// <para> | |
| /// Note: This is intentionally independent of <see cref="TargetBitWidth"/> and | |
| /// <see cref="CategoryBitWidths"/>. Those properties control the quantized bit-width | |
| /// for specific layers or categories, whereas this method defines the highest precision | |
| /// used when a layer is effectively "de-quantized" for accuracy-sensitive operations. | |
| /// </para> | |
| /// </remarks> |
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| <PropertyGroup Condition="'$(Configuration)'=='Debug'"> | ||
| <EmitCompilerGeneratedFiles>true</EmitCompilerGeneratedFiles> | ||
| <CompilerGeneratedFilesOutputPath>Generated</CompilerGeneratedFilesOutputPath> | ||
| </PropertyGroup> |
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Emitting generated files only in Debug configuration improves Release build performance, but developers might expect these files in Release for troubleshooting production issues. Consider adding a comment explaining that developers can temporarily enable this in Release if needed for debugging: <!-- Enable EmitCompilerGeneratedFiles in Release temporarily for production debugging if needed -->.
| // Models without layer metadata cannot be meaningfully partitioned. | ||
| // Return 0 so the entire model runs on one side rather than creating | ||
| // an invalid split with unknown layer boundaries. | ||
| System.Diagnostics.Debug.WriteLine( |
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The debug warning correctly identifies the issue and fallback behavior. However, using Debug.WriteLine means this warning won't appear in production logs where it might be most needed. Consider using a proper logging framework or at minimum Console.Error.WriteLine to ensure operators see this warning in production deployments where model partitioning is critical.
Suggested change
| System.Diagnostics.Debug.WriteLine( | |
| System.Console.Error.WriteLine( |
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Summary
Implements production optimizations for pipeline parallel training (#463) plus comprehensive federated learning v2 enhancements covering 15 major subsystems across unlearning, fairness, compression, drift detection, TEE, PSI, MPC, federated graph learning, ZKP verification, vertical FL, knowledge distillation, PEFT adapters, decentralized FL, continual learning, and backdoor defense.
Pipeline Parallelism Optimizations (#463)
LoadBalancedPartitionStrategyuses dynamic programming (min-max partitioning) to distribute computational cost evenly across stagesOneForwardOneBackwardSchedulereduces pipeline bubble from ~50% (GPipe) to ~12-15%Federated Learning v2 Enhancements
Research Gap Audit (2024–2026)
Facade Integration
FederatedLearningOptionsextended with sub-properties for all 15 subsystemsFederatedLearningModeenum for horizontal/vertical/split modesAiModelBuilder.ConfigureFederatedLearning()wires all optionsCloses #463
Closes #537
Closes #538
Closes #539
Closes #540
Closes #541
Closes #542
Closes #849
Closes #850
Closes #851
Closes #852
Test plan
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