cross attention, rope, kv cache, gqa, gelu by lucidrains · Pull Request #696 · mratsim/Arraymancer

lucidrains · 2026-10-06T14:29:33Z

Summary by CodeRabbit

New Features
- Added GELU and QuickGELU activations, rotary position embeddings, and RMSNorm support.
- Expanded attention with grouped-query and cross-attention, additive bias, key masks, and incremental KV caching.
- Updated the Shakespeare transformer example with configurable position encodings, gated feed-forward layers, cached decoding, and temperature and top-k sampling. Learned-position mode requires a maximum sequence length and limits generation accordingly.
API Changes
- Renamed the layer normalization functions to layernorm and rmsnorm.
Bug Fixes
- Improved embedding gradient handling across input shapes.
Tests
- Added coverage for activations, rotary embeddings, attention variants, masks, and cached decoding.

…rity with llama style archs

coderabbitai · 2026-10-06T14:31:50Z

No actionable comments were generated in the recent review.

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Commits

Reviewing files that changed from the base of the PR and between 07c4319 and 344252c.

Files selected for processing: 1

src/arraymancer/nn_primitives/nnp_embedding.nim

Files skipped from review as they are similar to previous changes: 1

src/arraymancer/nn_primitives/nnp_embedding.nim

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Walkthrough

The changes add GELU and rotary embedding APIs, extend multi-head attention with grouped key/value heads, masks, cross-attention, and KV caches, and update the Shakespeare transformer example to use these capabilities. Tensor iteration, embedding gradients, and transpose behavior also change.

Changes: Transformer attention and generation

Layer / File(s)	Summary
GELU and rotary embedding APIs `src/arraymancer/nn_primitives/nnp_activation.nim`, `src/arraymancer/nn/activation/gelu.nim`, `src/arraymancer/nn/layers/rotary.nim`, `src/arraymancer/nn.nim`, `tests/nn/test_activations.nim`, `tests/nn/test_rotary.nim`, `tests/tests_cpu.nim`	Added GELU and QuickGELU tensor operations and autograd support. Added rotary embedding operations and public exports. Added tests for forward results and gradients.
Grouped and cache-aware attention `src/arraymancer/nn/layers/attention.nim`, `src/arraymancer/nn_primitives/nnp_attention.nim`, `tests/nn/test_attention.nim`	Added separate context dimensions, grouped key/value heads, cross-attention, additive and boolean masks, rotary frequencies, and KV caches. Added offset causal masks and tests for cached outputs, masks, and gradients.
Transformer model and generation `examples/ex08_transformer_shakespeare.nim`, `src/arraymancer/nn/layers/layernorm.nim`, `src/arraymancer/nn/layers/rmsnorm.nim`, `tests/nn/test_layernorm.nim`, `tests/nn/test_rmsnorm.nim`, `arraymancer.nimble`	Updated the example model to use RMSNorm, gated GELU, configurable rotary or learned positions, and per-block caches. Added top-k sampling and optional cached generation. Added CLI options for cache use, top-k, and position mode. The example reports cache settings, elapsed time, and generated character count. The test task compiles the example. The exported normalization procedures are renamed to `layernorm` and `rmsnorm`.
Tensor iteration and shape operations `src/arraymancer/tensor/private/p_accessors.nim`, `src/arraymancer/tensor/shapeshifting.nim`, `src/arraymancer/tensor/operators_blas_l2l3.nim`, `src/arraymancer/nn_primitives/nnp_embedding.nim`, `src/arraymancer/nn_primitives/nnp_rmsnorm.nim`, `tests/nn/test_attention.nim`, `tests/tensor/test_shapeshifting.nim`	Added strided offset iteration and used it in mixed-contiguity iteration. Routed `transpose2d` through dimension-specific transpose. Reshaped embedding gradients consistently across input ranks. Added tests for broadcasted operations and transpose behavior.

Priority: Not assessed

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant generate
  participant TransformerLM
  participant MultiHeadAttention
  participant sampleLast
  generate->>TransformerLM: forward token with per-block caches and position frequencies
  TransformerLM->>MultiHeadAttention: forward hidden states with past KVCache
  MultiHeadAttention-->>TransformerLM: attention output and updated KVCache
  TransformerLM-->>generate: logits and updated per-block caches
  generate->>sampleLast: select last-position logits with temperature and top-k
  sampleLast-->>generate: sampled token

Merge Risk: Minimal · up to 34425

No established issue in the selected change blocks merging after normal checks.

Architecture risk: Low · up to 73318

The change affects 4 systems.

Changed systems: src, tests, arraymancer.nimble, examples

Architecture concerns
No architecture-level concerns identified.

Review details

Systems and components

observed — src (service) was modified; 6 changed files map to changed impact.
observed — tests (service) was modified; 4 changed files map to changed impact.
observed — arraymancer.nimble (service) was modified; 1 changed file maps to changed impact.
observed — examples (service) was modified; 1 changed file maps to changed impact.

Before / after behavior

observed — Modified behavior in src/arraymancer/nn.nim: The neural-network barrel module now imports GELU and rotary modules and adds gelu and rotary to its exports.
observed — Modified behavior in src/arraymancer/nn/layers/rotary.nim: Adds the file license header and imports tensor, autograd, and math modules.
observed — Modified behavior in src/arraymancer/nn/layers/rotary.nim: Adds exported RotaryEmbedding and RotaryFreqs objects, containing embedding dimensions, base and inverse frequencies, plus cosine and sine frequency tensors.
observed — Modified behavior in src/arraymancer/nn/layers/rotary.nim: Adds init, which defaults rotary_dim to head_dim and base to 10000; it asserts that head_dim is positive and even and that the resolved rotary dimension is positive, even, and no larger than head_dim. It initializes inverse frequencies to zero, then assigns powers of base for the rotary pairs.

Pre-merge checks: 5

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title identifies the main changes: cross attention, rotary embeddings, KV caching, grouped-query attention, and GELU support. It is concise and directly related to the changeset, although it is a …
Docstring Coverage		No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Title check

Explanation

The title identifies the main changes: cross attention, rotary embeddings, KV caching, grouped-query attention, and GELU support. It is concise and directly related to the changeset, although it is a feature list rather than a sentence.

Full details: Docstring Coverage

Explanation

No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 0 functions across 0 files. (1 skipped: 1 unsupported.)

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_{A rabbit checks the cached keys,
Then hops through rotary breeze.
GELU shapes each hidden row,
Top-k picks where tokens go.
With RMSNorm in the light,
New words spring into sight.}

_{Comment @coderabbitai help to get the list of available commands.}

greptile-apps · 2026-10-06T14:37:30Z

Confidence Score: 5/5

[Medium impact] The PR appears safe to merge, with a non-blocking performance issue in to_heads.

Findings

Attention repeats projection work ▶

Summary

Adds GELU and QuickGELU, rotary embeddings, grouped-query and cross-attention, attention bias, and cached decoding.

Attention layers can use separate contexts, grouped heads, masks, biases, and cached keys.
Attention can rotate query and key vectors to represent token positions.
Neural network layers can use GELU and QuickGELU activations.
The Shakespeare example trains and generates text with cached attention and new sampling options.
Tensor operations iterate over strided operands when their shapes are broadcast.
Normalization helpers use the shorter names layernorm and rmsnorm.
Tensor helper edits leave their observable behavior unchanged.

Diagram

%%{init: {'theme': 'neutral'}}%%
flowchart LR
  X[Input] --> Q[Query projection]
  C[Input or context] --> KV[Key and value projections]
  KV --> R[Rotate fresh keys]
  P[Cached keys and values] --> A[Attention]
  R --> A
  Q --> A
  M[Masks and bias] --> A
  A --> O[Output projection]
  R --> N[Updated cache]

_{Reviews (2) · Last reviewed commit: "ask for camelcase" · Reviewed by Greptile}

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🪄 Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @examples/ex08_transformer_shakespeare.nim:
- Around line 221-233: Before the training loop in the sample-every argument
handling flow, validate sampleEvery and replace nonpositive values with a
positive fallback based on steps, ensuring step modulo sampleEvery cannot divide
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📒 Files selected for processing (11)

examples/ex08_transformer_shakespeare.nim
src/arraymancer/nn.nim
src/arraymancer/nn/activation/gelu.nim
src/arraymancer/nn/layers/attention.nim
src/arraymancer/nn/layers/rotary.nim
src/arraymancer/nn_primitives/nnp_activation.nim
src/arraymancer/nn_primitives/nnp_attention.nim
tests/nn/test_activations.nim
tests/nn/test_attention.nim
tests/nn/test_rotary.nim
tests/tests_cpu.nim

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…ed by openai

…wise it is always full window, if set then sliding

…ild the full encoder/decoder architecture from the original paper

coderabbitai

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🪄 Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @examples/ex08_transformer_shakespeare.nim:
- Line 171: Update the remaining-capacity calculation in the `maxNew` expression
to allow sampling one final token when the prompt reaches the learned-position
limit. Add one to the remaining capacity before applying the existing zero lower
bound and requested-length cap.

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📒 Files selected for processing (3)

examples/ex08_transformer_shakespeare.nim
src/arraymancer/nn/layers/attention.nim
tests/nn/test_attention.nim

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tests/nn/test_attention.nim

🚧 Files skipped from review as they are similar to previous changes (1)

src/arraymancer/nn/layers/attention.nim

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mratsim · 2026-10-30T14:10:14Z

Whoops it seems like I missed this PR, thank you

lucidrains · 2026-10-30T14:38:38Z

Whoops it seems like I missed this PR, thank you

yes, this would allow for the classic encoder / decoder from the original paper, as well as llama (rotary embeddings)!

coderabbitai

Actionable comments posted: 1

🪄 Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @src/arraymancer/nn_primitives/nnp_embedding.nim:
- Line 78: Preserve the rank-2 fast path in the embedding backward pass: update
the code around `dOutput2d` to reuse `dOutput` directly when its rank is 2, and
reshape only higher-rank gradients.

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Commits

Reviewing files that changed from the base of the PR and between 73318c2 and 762bf45.

Files selected for processing: 19

examples/ex08_transformer_shakespeare.nim
src/arraymancer/nn/activation/gelu.nim
src/arraymancer/nn/layers/attention.nim
src/arraymancer/nn/layers/layernorm.nim
src/arraymancer/nn/layers/rmsnorm.nim
src/arraymancer/nn/layers/rotary.nim
src/arraymancer/nn_primitives/nnp_activation.nim
src/arraymancer/nn_primitives/nnp_attention.nim
src/arraymancer/nn_primitives/nnp_embedding.nim
src/arraymancer/nn_primitives/nnp_rmsnorm.nim
src/arraymancer/tensor/operators_blas_l2l3.nim
src/arraymancer/tensor/private/p_accessors.nim
src/arraymancer/tensor/shapeshifting.nim
tests/nn/test_activations.nim
tests/nn/test_attention.nim
tests/nn/test_layernorm.nim
tests/nn/test_rmsnorm.nim
tests/nn/test_rotary.nim
tests/tensor/test_shapeshifting.nim

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lucidrains added 2 commits October 6, 2026 07:19

add rope, gelu, gqa, for example wire up geglu + kv cache - breaks pa…

9374587

…rity with llama style archs

rotary should be moved out of attention

4e6de3a

greptile-apps Bot reviewed Oct 6, 2026

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coderabbitai Bot reviewed Oct 6, 2026

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Comment thread examples/ex08_transformer_shakespeare.nim Resolved

lucidrains added 5 commits October 6, 2026 07:43

another round of critique, add the quick gelu approximation populariz…

d00e448

…ed by openai

cleanup

10d4001

in ex08, only enforce max seq len if abs pos emb is being used, other…

a4004ad

…wise it is always full window, if set then sliding

remove sliding, cap generation for abs pos emb

7b08e0d

cleanup ex08, also validate that self/cross attention is enough to bu…

73318c2

…ild the full encoder/decoder architecture from the original paper

coderabbitai Bot reviewed Oct 8, 2026

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Comment thread examples/ex08_transformer_shakespeare.nim Resolved

lucidrains added 3 commits October 7, 2026 18:30

address feedback from a transformer

6c3250a

make sure attention bias can be sent in, either learned or not

a0636e2

ask for camelcase

762bf45

mratsim closed this Oct 30, 2026

mratsim reopened this Oct 30, 2026

greptile-apps Bot reviewed Oct 30, 2026

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Comment thread src/arraymancer/nn/layers/attention.nim Resolved

coderabbitai Bot reviewed Oct 30, 2026

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Comment thread src/arraymancer/nn_primitives/nnp_embedding.nim Resolved

lucidrains added 2 commits October 30, 2026 09:39

address coderabbit to_heads feedback

07c4319

skip reshape for rank 2 for doutput2d

344252c

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cross attention, rope, kv cache, gqa, gelu - #696

cross attention, rope, kv cache, gqa, gelu#696
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Confidence Score: 5/5

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