A from-scratch JAX reimplementation of DeepSeek-V4.1-Flash ideas, scaled into a model small enough to train, profile, and optimize on a Kaggle TPU v5e-8.
Rather than only shrinking layer counts, this project keeps the architectural pieces that make V4.1 interesting—CED/CSA2 cache sharing, Full/Reindex/Reuse retrieval, partial + inverse RoPE, grouped low-rank output projection, Single-Pass mHC, routed MoE, Engram memory, DSpark speculative decoding, and the hybrid optimizer—and then asks a systems question:
How much of a modern frontier-model architecture can be reproduced, trained, and optimized on a free 8-chip TPU?
Status — active work in progress. The readable reference architecture, packed-LM/indexer training path, mixed-precision sharding baseline, and a v5e-native training backend are implemented. The native path now replaces dense attention with TPU SplashAttention and removes per-token expert-matrix gathers with memory-bounded expert-parallel dispatch. Real v5e profiling and training runs are currently being validated, so performance numbers are intentionally not claimed yet.
This is not a checkpoint-compatible miniature of the 552B production model, and it does not claim to reproduce DeepSeek's private training recipe. docs/implementation_scope.md separates released behavior, faithful semantic references, educational approximations, and intentionally deferred systems work.
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| Area | What this repo implements |
|---|---|
| Model architecture | 7-layer nano CED/CSA2 backbone with SWA, Full, Reindex and Reuse states |
| Attention | MLA-style shared latent K/V, partial + inverse RoPE, attention sink, grouped low-rank wo_a, dense reference path + TPU-native Splash path |
| Sparse retrieval | Cross-layer index-K reuse, dynamic multi-head indexer scoring, hierarchical candidate blocks, late selective distillation |
| Residual / FFN | Single-Pass mHC + routed/shared-expert MoE + clipped SwiGLU |
| TPU MoE | Tokamax 0.0.12 ragged-dot forward/backward, resident expert shards, dropless dispatch and reduce-scatter |
| Memory | Engram-style hashed n-gram memory with packed-sequence-safe hashing |
| Speculation | One-stage DSpark reference with separate MoE, Markov correction and confidence prediction |
| Optimizer | Inspectable AdamW / Muon / head-wise Muon / Sinkhorn-balanced parameter rules |
| TPU systems | v5e-8 2×4 mesh, direct-to-shard BF16 init, FP32 control paths, native shard_map hot kernels, block remat, compiler diagnostics |
| Notebook | Checked-in Kaggle smoke notebook that fetches the current repo and exercises the native backend |
Tech: Python · JAX · XLA/GSPMD · TPU v5e · SplashAttention/Pallas · shard_map · MoE · sparse attention · mixed precision · speculative decoding
Run the pretraining-only Kaggle CPU notebook to pre-tokenize FineWeb-Edu with the frozen nano tokenizer into 8K rows. It targets 3B nonpadding training tokens plus a separate 10M-token validation set, writes compact shards with bounded memory, and resumes completed source files. It runs independently of mid-training and SFT/agent-trace preparation. See the data guide for storage, restart, and training-loader details.
Use the pretraining notebook on a fresh TPU v5e-8 session with the prepared corpus and tokenizer attached. It runs the validated four-row, tile1024, quarter-buffer preset for 2.4B non-padding tokens, reserving 600M of this experiment's 3B budget for mid-training. It includes held-out validation, full resumable checkpoints and an eight-hour session deadline. See the run guide for the exact schedule and resume procedure.
Large-model architecture papers often make individual ideas look simple in isolation, while the hard part is how they interact in an executable system. This repo reconstructs those interactions explicitly and makes them small enough to inspect:
- preserve cross-layer KV/index lifetimes instead of replacing CSA2 with ordinary attention;
- preserve mHC's multiple residual streams rather than collapsing it into a normal residual connection;
- keep optimizer behavior parameter-family-specific rather than hiding everything behind one generic transform;
- maintain a readable dense reference while adding separate hardware-specific execution paths;
- expose architecture, quantization, rematerialization and sharding choices as configuration rather than hard-coding one experiment;
- use actual compiler/HBM/collective behavior to decide which kernels deserve optimization.
The goal is therefore as much ML systems / accelerator engineering as model reproduction.
- End-to-end readable JAX backbone and causal-LM loss
- CED handoff and CSA2
SWA / Full / Reindex / Reusestate machine - Partial/inverse RoPE, attention sink and grouped low-rank output projection
- Single-Pass mHC, MoE and Engram reference implementations
- One-stage DSpark forward / Markov / confidence reference
- Hybrid optimizer implementation and training-phase parameter freezing
- Packed-sequence-safe late indexer distillation without a full
T×Ktraining-time score matrix - v5e-aware GSPMD placement, BF16 payload / FP32 control policy and direct-to-shard initialization
- TPU-native expert-parallel MoE path that avoids per-token whole-expert weight gathers
- TPU-native SplashAttention path for local + compressed-global CSA2 attention
- Native block rematerialization for attention + MoE training
- 8-device CPU/SPMD regression tests for native MoE and Splash forward/backward behavior
- Checked-in Kaggle v5e smoke notebook that defaults to the native backend
- First recorded native Kaggle v5e-8 HBM / compile / step-time profile
- Replace gather/reduce-scatter MoE communication with ragged all-to-all / ring-style dispatch if profiling justifies it
- Packed MXFP4 storage/dequant kernels for main KV and index caches
- Real training dataset + pretrained tokenizer pipeline
- Training curves, retrieval diagnostics and DSpark acceptance measurements
The GitHub Actions workflow is intentionally manual-only; most correctness tests are run locally, while TPU-specific validation belongs in Kaggle rather than a CPU CI runner.
DeepSeek's released inference code contains a useful five-layer tiny topology anchor:
L0 SWA | L1 Full(r=2) -> L2 Reuse | L3 Full(r=1) -> L4 Reuse
That anchor never gets deep enough to demonstrate the decoder's separate Reindex stage, so this project adds exactly one two-layer retrieval group:
context / causal encoder
L0 SWA only
L1 Full(r=2, KV+index source) -> L2 Reuse
CED handoff
final context representation -> generation KV source
generation / causal decoder
L3 Full(r=1, KV+index source, candidate source) -> L4 Reuse
L5 Reindex(r=1, same KV/index-K) -> L6 Reuse
speculation
DSpark: 1 block-parallel Transformer layer + separate MoE + Markov + confidence
L5 deliberately does not create a new KV bank. It changes the retrieval decision over the index-K owned by L3. L3 also publishes hierarchical candidate blocks, and L5 rescans that candidate set before L6 reuses its new selection.
DSpark consumes the last three backbone layer features (L4/L5/L6) by default, echoing the production model's final-target-layer pattern while keeping the draft network itself to one educational Transformer stage.
All dimensions, group counts, RoPE settings, compression ratios, expert counts, DSpark rank/block size, optimizer constants, rematerialization policy and sharding intentions live in configuration dataclasses rather than being buried in kernels.
The readable JAX reference includes:
- CED / CSA2: SWA / Full / Reindex / Reuse, shared compressed-KV lifetimes, r=1/r=2 learned compression, fixed-128 local overlap and exact local/global shared-softmax semantics.
- MLA-style attention: Q low-rank bottleneck, one latent K/V shared by Q heads, learned attention sink, partial RoPE on the last channels, compressed positional regime, inverse RoPE on attention output, and grouped low-rank
wo_a(G=2by default). - Sparse indexer: Q from main
qr, K from pre-RoPE compressed latent, partial RoPE, query-dependent head weights, ReLU head scores, cross-layer K reuse and released-style hierarchical candidate-block selection. - Single-Pass mHC: multi-stream residuals, state-conditioned pre/post/combine coefficients, Sinkhorn stream mixing and cross-sublayer timing; mHC epsilon is separate from language RMSNorm epsilon.
- MoE: sqrt-softplus router, selection-only correction bias, routed + shared expert, clipped SwiGLU; DSpark has an independently configured expert count.
- Engram: segment-safe causal n-gram hashing, conditional hashed memory and per-stream gated injection.
- Low-precision reference: software E2M1/E4M3/UE8M0 fake quantization, compressed-KV FP4 QAT path, indexer MXFP4-style path and optional SWA FP8 path. Packed MXFP4/Pallas storage is a later kernel milestone.
- Hybrid optimizer reference: inspectable AdamW / Muon / head-wise Muon / Sinkhorn-balanced parameter partitioning and explicit LR schedule.
- DSpark: projected selected-layer target context,
[anchor, noise, ...]draft blocks, block-parallel attention mask, one draft Transformer layer, separate MoE, vanilla low-rank Markov correction, sequential Markov sampling helper and confidence head. - Activation rematerialization: configurable
none,attention, andblockpolicies wired withjax.checkpoint; the native v5e training compiler uses block remat by default.
The ordinary LM forward intentionally runs with no full indexer scoring: it does not build index-K or materialize a T × K retrieval matrix. compute_indexer=True exists only as a diagnostic/evaluation path for checking Full/Reindex/Reuse semantics.
Late in pre/mid-training, training.py runs a separate selective auxiliary objective:
for each index source (L1, L3, L5):
sample up to query_budget eligible positions across the batch
-> build shared index-K once
-> score only selected student Q rows
-> reconstruct teacher mass from served layers' main Q/K + complete LSE
-> CE over legal candidates
The default follows the proposed local_window + top_k eligibility rule: 128 + 512 = 640 raw-token query position. A ratio-aware ablation, local_window + r * top_k, waits until roughly 1152 raw positions for encoder r=2, when a 512-entry retrieval limit actually becomes selective.
IndexerTrainingConfig(query_budget=128, query_seed=0, apply_candidate_mask=False)
is the default: sample without replacement, refresh using the optimizer step, and
train L5 over full legal compressed history. The budget caps positions across the
global batch per retriever, not positions per document. Equal eligibility rules
share the selected positions; three retrievers with 128 positions each mean 384
query/group evaluations. Set apply_candidate_mask=True to test L3-restricted L5
distillation. Evaluation/inference keeps the hierarchical candidate mask. See
query selection and ablations for shapes and cost limits.
Nano retrievers serve only two layers by default, so all_served and Full + last use the same teacher set. Teacher attention is always stop-gradient. By default student inputs from the backbone are detached too, so the auxiliary loss trains the indexer-specific parameters without perturbing the backbone. This is an educational/stability choice, not a claimed DeepSeek recipe, and is configurable.
The first real Kaggle v5e smoke run successfully initialized the 8-chip topology, sharded parameters and optimizer state, but the readable reference training graph exhausted notebook memory while compiling the full step.
That failure exposed two reference-code patterns that are semantically convenient but poor accelerator execution strategies:
- Dense attention materialization. The readable CSA2 path constructs explicit query-key score/mask tensors. This is useful for tests but scales poorly with sequence length.
- Per-token expert-matrix gathers. The reference MoE indexes whole expert matrices with each token's selected expert IDs. For top-2 routing this can create enormous logical intermediates even though the actual parameter tree is small.
The solution in this repo is not to delete the reference implementation. Instead, the public API keeps both:
from nano_dsv41f import (
compile_pretrain_step, # v5e-native default
compile_pretrain_step_reference, # readable dense ablation/reference
)The native backend lives in tpu_native.py and specializes only the operations where the dense expression is structurally wrong for v5e.
The default backbone has 8 routed experts on 8 TPU chips. The native path also supports multiple resident experts per chip; the combined smoke uses 16 experts. Expert counts must divide evenly over the expert mesh.
Conceptually:
context-sharded token activations
↓
FP32 router / top-k
↓
all-gather token activations and routing metadata
↓
sort assignments into contiguous local expert groups
↓
Tokamax ragged gate/up projection
↓
clipped SwiGLU + Tokamax ragged down projection
↓
FP32 weighted scatter + reduce-scatter
↓
original context shard
The critical property is that expert matrices remain resident. The native path never evaluates expert_weights[per_token_indices], so whole [D,F] matrices are no longer duplicated once per token assignment.
tokamax==0.0.12 now owns the routed expert GEMMs and their input/weight backward
kernels. TPU execution explicitly selects Mosaic, so unsupported kernels fail
instead of silently using a dense fallback. CPU tests use Tokamax's XLA path.
Dynamic group sizes cover every assignment, including extreme routing imbalance;
unused rows are masked before and after each dot. The old custom tile loop is removed.
Communication remains all-gather plus reduce-scatter. Packed buffers use a static worst-case bound, while ragged GEMM work follows group sizes. This change does not implement ragged all-to-all. The shared expert stays a local dense GEMM. See Tokamax integration and validation for runtime pins, controls, memory tradeoffs, and physical TPU benchmark instructions.
The always-on shared expert remains replicated and is computed locally on each context shard.
The training backend now puts SplashAttention inside the actual CSA2 backbone, rather than treating it as a standalone demo.
Each layer forms one sparse attention problem over:
[ local latent KV | compressed global latent KV ]
with a static structural mask plus packed-sequence SegmentIds:
local branch:
same segment
causal
within the most recent 128 tokens
global branch:
same segment
compressed group position <= query position - 128
Local and compressed-global keys are disjoint, so one sparse Splash softmax is mathematically equivalent to the reference path's exact local/global shared-softmax merge. This avoids materializing either dense attention matrix and avoids running two differentiable softmax branches.
The learned denominator-only attention sink is included in the Splash normalization. For late indexer distillation, the native backend can additionally request a stop-gradient LSE/teacher pass so the selective teacher objective still has the complete attention normalizer it needs.
The surrounding model remains on the topology-aware Auto mesh. Only the hot native primitives enter a flattened 8-device manual shard_map view; this keeps explicit sequence/expert communication local to the operation instead of contaminating unrelated model contractions with manual-axis semantics.
The target is a single-host 2×4 v5e-8 mesh.
- the outer model uses a topology-aware Auto mesh so XLA can legally reshard ordinary JAX operations;
- token/context positions are distributed over all eight chips;
- attention parameters remain comparatively simple while Splash owns sequence-parallel attention execution;
- backbone routed experts stay resident with one or more experts per chip;
- large matrix/table payload parameters initialize directly into final shards as BF16;
- norm, bias, sink, router/control vectors and optimizer accumulators remain FP32;
- block rematerialization is the native training default to trade extra compute for lower activation HBM;
- base-LM and late-indexer training compile as separate static executables;
- parameters and optimizer state are donated across training steps;
- compiler diagnostics report estimated memory, cost-analysis fields and StableHLO collective counts when compilation succeeds.
This is now a real accelerator-specific execution path, but it is still under v5e validation. The 8-device CPU/SPMD tests exercise native MoE and Splash forward/backward structure; they are correctness regressions, not TPU performance evidence.
For full 8192-token pretraining stress tests, use
notebooks/nano_dsv41f_pretrain_stress.ipynb.
It compares the seven-layer baseline at attention CP8/DP1 and CP2/DP4, then a
48-expert/128-width/top-4 candidate, all at the same global batch. An optional
bounded search measures the observed batch-capacity boundary in DP-sized increments. Native attention now
honors these layouts; MoE remains EP8. It records base and late-indexer compile times,
synchronized optimizer-step timings, compiler/device memory, and incremental failure
reports. See the stress-test recipe and interpretation.
Regenerate this notebook with python scripts/build_stress_notebook.py.
For the 4/8/24-row throughput investigation with real packed corpus inputs, use
notebooks/nano_dsv41f_pretrain_profile.ipynb.
It checks the attached tokenizer/corpus, records per-layer routing and dispatch loads,
and exports separate warmed-up TPU traces after unprofiled timing.
See the measured 4/8/24-row results and testable hypotheses
for the next attention-batching experiment.
Run that controlled experiment with
notebooks/nano_dsv41f_attention_hypothesis.ipynb.
It freezes real model attention inputs, checks gradients, compares local vmap
with sequential attention, and offers a tile-pressure probe and full-model A/B.
See controls, environment variables and interpretation.
A lightweight notebook is checked in at notebooks/nano_dsv41f_kaggle.ipynb. From a blank Kaggle session, select TPU, enable Internet, and run top-to-bottom. The notebook fetches the requested Git ref and prints the exact commit SHA for reproducibility.
Regenerate both maintained notebook entry points with:
python scripts/build_notebook.pyBoth notebooks install requirements-tpu.txt (JAX/jaxlib 0.10.2, libtpu 0.0.42.1)
before importing JAX. They run the model in fresh Python processes. The combined notebook
runs only the 20-step packed smoke; the main notebook first runs operator parity/timing.
A real Splash forward/backward preflight fails before model initialization if the loaded
TPU client is stale. The smoke uses T=2048, 16 experts, three odd-length documents,
10 base steps and 10 late-indexer steps. JSON reports include the commit, runtime build,
compiler memory, collectives, all losses and synchronized timings (first two steps of
each phase excluded from the timing median).
For an unmerged branch, set os.environ["NANO_DSV41F_REF"] before the bootstrap cell.
See the operator pass and fidelity audit for exact commands,
validation scope, changes to mHC checkpoint semantics and remaining reproduction gaps.
Once this smoke test is stable, the next layer is a pretrained ~32K tokenizer plus packed real data. Token efficiency is deliberately secondary to keeping embeddings/softmax and HBM reasonable for the nano model.
src/nano_dsv41f/
config.py architecture/training/sharding hyperparameters
rope.py partial/inverse RoPE + compressed/YaRN frequency reference
quantization.py software FP4/FP8 fake-QAT reference
layers.py small JAX primitives + dtype-preserving linear/RMSNorm
packing.py packed-example/query-selection utilities
attention.py standalone mask/LSE helpers
compression.py learned r=1/r=2 compression with FP32 control math
csa2.py readable dense CSA2 + Full/Reindex/Reuse semantics
indexer.py fixed-shape teacher selection + distillation math
indexer_scorer.py released-style indexer + hierarchy reference
training.py packed causal LM + selective late-stage indexer objective
mhc.py Single-Pass mHC mechanics + BF16 payload boundaries
moe.py readable routed/shared-expert MoE reference
engram.py conditional hashed memory, FP32 gate/BF16 payload
dspark.py one-stage released-style DSpark reference
optimizer.py hybrid optimizer rules and update kernels
model.py end-to-end semantic backbone + DSpark wiring
tpu.py v5e Auto mesh, PartitionSpecs, direct-to-shard init
tpu_native.py active v5e Splash + expert-parallel training backend
tpu_moe.py Tokamax ragged expert products and assignment packing
precision.py BF16 payload / FP32 control parameter policy
profiling.py AOT memory/cost/collective diagnostics
splash.py low-level Splash helpers/reference experiments
scripts/
build_notebook.py generate both Kaggle notebooks from one bootstrap
The remaining milestones are deliberately driven by real hardware/data evidence:
- finish the current Kaggle v5e-8 run and record native compile memory, StableHLO collectives and steady-state step time;
- compare native vs reference memory behavior on the smallest sequence lengths where both compile;
- profile Tokamax ragged GEMMs and dispatch; consider ragged all-to-all / ring-of-experts if communication dominates;
- implement packed MXFP4 main-KV/index storage with on-chip software dequantization instead of materializing dequantized caches in HBM;
- decide whether vocab-parallel cross-entropy is worthwhile once the 32K LM head is measured on hardware;
- attach a pretrained tokenizer + packed corpus and publish training curves / retrieval diagnostics;
- add DSpark training/verification metrics once the backbone run is stable.
See docs/implementation_scope.md for the detailed fidelity matrix.
Primary references are the DeepSeek-V4.1-Flash technical report/released configuration and inference implementation, vLLM's V4.1 implementation for deployment details, JAX's TPU/Pallas/SplashAttention implementation, and DeepSeek's released DeepSpec DSpark code.
This project is unaffiliated with DeepSeek.