nanochat/Part_2.md

Ablation Studies: Impact of Architecture Changes on Language Model Performance

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1. Methodology

1.1 Model Configuration: picochat (depth-8)

All experiments use the picochat configuration, a depth-8 variant of nanochat with n_embd=512, n_head=4 (head dimension 128), n_kv_head=4, vocab_size=32768, and max_seq_len=512. This yields approximately 42M non-embedding parameters, making it tractable for controlled ablations on a single A10G GPU in roughly one hour per run.

The choice of picochat over larger configurations (nanochat default: depth-12, ~120M params) was deliberate. Ablation studies at smaller scale serve two purposes: (1) they are substantially cheaper, allowing more configurations to be tested per dollar, and (2) relative performance rankings established at small scale have historically been reliable predictors of behaviour at larger scale, provided the study is designed to control for parameter count (Kaplan et al., 2020 [1]; Hoffmann et al., 2022 [2]). Concretely, a picochat run costs ~$1 vs. ~$8 for a nanochat-default run, giving roughly an 8× experimentation budget advantage.

The model architecture includes several modern components that are held fixed across all ablations: Rotary Position Embeddings (RoPE) [3], QK normalization, Group-Query Attention (GQA), sliding-window attention with pattern L (full context at all layers for picochat), value residual connections (ResFormer-style), MuonAdamW optimizer [4], and logit softcapping. These components represent the "environment" in which each ablation is evaluated.

1.2 Ablation Variables

Two architecture changes were selected as primary ablations, with a third conducted as a supplemental investigation motivated by unexpected results from the second.

Ablation A — SwiGLU activation (Shazeer, 2020 [5]): Replace the squared-ReLU (relu²) feedforward activation with SwiGLU, a gated linear unit variant that has become the default in most production LLMs (LLaMA, Mistral, Gemma, etc.). The SwiGLU forward pass computes:

output = proj(silu(gate(x)) * up(x))

where gate, up, and proj are learned linear projections and silu(x) = x * sigmoid(x).

Ablation B — Multi-Token Prediction (MTP) (DeepSeek-V3, 2024 [6]; LLaMA 3.1, 2024 [7]): Add an auxiliary prediction head that, at each position t, predicts not only the next token t+1 (the standard LM objective) but also the token two steps ahead t+2. This provides denser gradient signal per forward pass, encouraging the model to build representations that support multi-step reasoning. The training loss becomes:

loss = cross_entropy(h[t] → token[t+1])  +  0.3 × cross_entropy(proj(h[t]) → token[t+2])

where proj is a learned n_embd → n_embd linear layer, and 0.3 is the auxiliary weight adopted from DeepSeek-V3. The shared lm_head is reused for both predictions, amortizing the cost of the unembedding projection. This is implemented as a shallow variant of MTP, as opposed to DeepSeek-V3's full per-step transformer modules, keeping the parameter overhead minimal.

Supplemental Ablation — RoPE base theta 500K (Meta AI, 2024 [7]): Following the unexpected result from Ablation B (Section 2.4), we conducted a post-hoc supplemental ablation testing an architecture change that we expected to show benefit even at small scale and short training duration: increasing the RoPE base frequency from 10,000 to 500,000, as adopted in LLaMA 3. This adds zero parameters and zero per-step compute.

1.3 Parameter Matching

A critical methodological requirement for valid ablation is that each variant contains the same number of parameters, so that any performance difference is attributable to the architectural change rather than to model capacity.

SwiGLU requires explicit parameter matching. The standard relu² MLP has two projections:

relu² total: 2 × 4 × n_embd² = 8 × n_embd²

SwiGLU introduces three projections (gate, up, proj). Setting the hidden dimension h to match:

3 × h × n_embd = 8 × n_embd²  →  h = (8/3) × n_embd

For n_embd = 512: h = int(8/3 × 512) = 1365.

SwiGLU MLP parameters: 3 × 1365 × 512 = 2,096,640 ReLU² MLP parameters: 8 × 512² = 2,097,152

The 512-parameter-per-layer discrepancy (0.025%, from integer truncation) is negligible across 8 layers (4,096 total parameters out of 42M).

MTP adds one n_embd × n_embd projection per auxiliary step: 1 × 512² = 262,144 parameters — a 0.6% increase relative to the 42M baseline. This is not strictly parameter-matched, but because the primary change is in the training objective (the auxiliary loss function) rather than in model capacity, and because the parameter delta is far below the noise floor for capacity-driven performance differences at this scale, the comparison remains interpretable as an architectural ablation.

RoPE 500K adds exactly zero parameters, trivially satisfying the requirement.

1.4 Isolation Principle

Each ablation changes exactly one variable relative to the baseline:

Configuration	mlp_type	rope_base	num_mtp_steps
picochat-baseline	relu2	10,000	0
picochat-swiglu	swiglu	10,000	0
picochat-mtp	relu2	10,000	1
picochat-rope500k (suppl.)	relu2	500,000	0

All other hyperparameters (depth, width, heads, sequence length, batch size, optimizer settings, learning rate schedule, data, tokenizer, evaluation protocol) are held identical across all runs. Configuration is enforced at the GPTConfig dataclass level:

# nanochat/gpt.py — GPTConfig
mlp_type: str = "relu2"       # "relu2" or "swiglu"
rope_base: int = 10000        # RoPE base theta
num_mtp_steps: int = 0        # 0=disabled, 1=predict 2 tokens ahead
mtp_loss_weight: float = 0.3  # auxiliary loss weight

The MTP forward pass saves pre-norm hidden states and computes auxiliary losses only during training (loss_reduction='mean'); evaluation uses loss_reduction='none' for per-token cross-entropy, which measures only next-token prediction — ensuring val/bpb is comparable across all configurations:

# nanochat/gpt.py — GPT.forward (MTP block)
if hasattr(self, 'mtp_projs') and loss_reduction == 'mean':
    for k, proj in enumerate(self.mtp_projs):
        shift = k + 1
        mtp_h = norm(proj(x_hidden[:, :-shift, :]))
        mtp_targets = targets[:, shift:]
        mtp_loss = F.cross_entropy(mtp_logits.reshape(-1, ...), mtp_targets.reshape(-1), ...)
        loss = loss + self.config.mtp_loss_weight * mtp_loss

1.5 Compute Infrastructure: Modal AI

All training runs were executed on Modal, a serverless GPU cloud platform. The implementation closely follows the reference deployment by Angela Sha (TA), available at UofT-CSC490-W2026/022326-tutorial-nanochat.

Key infrastructure choices:

• GPU: NVIDIA A10G (24GB VRAM, Ampere architecture). The A10G is the lowest-cost Modal instance that can comfortably train picochat with max_seq_len=512, device_batch_size=16 (8,192 tokens/step). Flash Attention 3 is not available on Ampere; PyTorch SDPA is used as the fallback.
• Container image: nvidia/cuda:12.8.1-devel-ubuntu24.04 with Python 3.11, uv package manager, and project dependencies installed at image build time. The nanochat source directory is baked into the image, so Modal auto-rebuilds when gpt.py or base_train.py changes — ensuring experiment reproducibility across code revisions.
• Persistent volume: A single Modal Volume (nanochat-vol) caches FineWeb-EDU data shards and the BPE tokenizer across all runs. Data preparation (12 shards, ~2B characters; tokenizer trained on 2B chars) is performed once and shared, eliminating redundant I/O costs.
• Orchestration: A server-side pipeline function (@app.function) runs all stages sequentially on Modal's infrastructure. The local entrypoint calls .spawn() for fire-and-forget submission, allowing the local machine to disconnect immediately.
• Experiment tracking: All runs are tracked in Weights & Biases under yoyoliuuu/nanochat. Validation bits-per-byte (val/bpb) is logged every 100 steps; training loss every step.

1.6 Reproducibility and Statistical Considerations

Each configuration was trained for 3 independent runs with different random seeds, for 1,680–1,690 steps each (approximately 13.8M tokens at device_batch_size=16, max_seq_len=512 → 8,192 tokens/step, 2 epochs). Results are reported as mean ± sample standard deviation across 3 seeds.

This multi-seed protocol serves two purposes: (1) it bounds initialization variance, allowing differences smaller than a single-seed noise floor (~0.001 bpb at this scale) to be interpreted with more confidence; and (2) it demonstrates that training runs are reproducible under the same data and hyperparameter configuration — a prerequisite for any ablation claim.

1.7 Cost of Training

All costs are based on Modal A10G on-demand pricing at ~$1.10/hr.

Stage	Runs	Avg. Duration	Per Run	Total
Data download + tokenizer	1	~25 min	—	~$0.11
picochat-baseline	3	51.2 min	~$0.94	~$2.82
picochat-swiglu	3	54.5 min	~$1.00	~$3.00
picochat-mtp	3	66.1 min	~$1.21	~$3.63
picochat-rope500k (suppl.)	3	51.0 min	~$0.94	~$2.82
Total				~$12.38

MTP incurred a ~22% throughput penalty (110,540 vs. 141,417 tok/sec for baseline) due to the additional forward pass through the MTP projection and shared lm_head for the auxiliary prediction. The decision to disable CORE metric evaluation (--core-metric-every=-1) and log val/bpb every 100 steps rather than 10 steps reduced per-run cost by an estimated 20–40%.

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2. Results

2.1 Summary Table (3-run averages)

Model	val/bpb (mean ± σ)	Δ vs. Baseline	tok/sec (avg)	Avg. Train Time
picochat-baseline	1.00750 ± 0.00008	—	141,417	51.2 min
picochat-swiglu	1.00551 ± 0.00006	−0.00199	133,045	54.5 min
picochat-mtp	1.01092 ± 0.00005	+0.00342	110,540	66.1 min
picochat-rope500k (suppl.)	1.00694 ± 0.00016	−0.00056	141,922	51.0 min

val/bpb = validation bits-per-byte on held-out FineWeb-EDU. Lower is better. σ = sample standard deviation across 3 independent seeds.

SwiGLU is the clear winner among primary ablations, improving val/bpb by 1.99 mbpb with high consistency across seeds (σ=0.06 mbpb). MTP degraded performance by 3.42 mbpb — an unexpected result discussed in detail in Section 2.4. The supplemental RoPE 500K ablation confirms that architecture changes can improve over the baseline even at small scale, gaining 0.56 mbpb at zero additional compute cost.

2.2 W&B Visualizations

Add the following screenshots from wandb.ai/yoyoliuuu/nanochat:

1. val/bpb vs. step — baseline, SwiGLU, MTP overlaid: primary result plot. SwiGLU curve should separate below baseline; MTP should visibly track above baseline throughout.
2. val/bpb vs. step — baseline vs. RoPE 500K: supplemental comparison showing the modest but consistent gap.
3. train/loss vs. step — all 4 runs: note that MTP's reported loss (~4.77) is not comparable to others (~3.26) because it includes the weighted auxiliary MTP loss term.
4. train/tok_per_sec — all 4 runs: illustrates the throughput hierarchy: RoPE ≈ baseline > SwiGLU > MTP.

[Insert W&B screenshots here.]

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2.3 Detailed Results: Baseline (picochat-baseline)

Run	val/bpb	train/loss	tok/sec	Train Time
1	1.00741	3.26103	140,087	51.4 min
2	1.00755	3.25855	142,626	51.0 min
3	1.00754	3.25978	141,539	51.3 min
Mean ± σ	1.00750 ± 0.00008		141,417	51.2 min

Reproducibility analysis: The baseline val/bpb range across seeds is 0.00014 bpb (from 1.00741 to 1.00755), establishing the noise floor for initialization variance at this scale. All three runs converge to the same learning rate schedule endpoint (lrm=0.09524, cosine decay to ~9.5% of peak) and epoch count (2 epochs), confirming that data ordering and initialization randomness contribute only ~0.1 mbpb variance. Any ablation difference exceeding 0.2 mbpb can be considered reliably above this noise floor.

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2.4 Detailed Results: SwiGLU (picochat-swiglu)

Run	val/bpb	train/loss	tok/sec	Train Time
1	1.00547	3.25160	134,452	53.9 min
2	1.00558	3.24923	132,635	54.8 min
3	1.00547	3.25198	132,047	54.8 min
Mean ± σ	1.00551 ± 0.00006		133,045	54.5 min

Reproducibility analysis: Three seeds produce val/bpb values within a 0.00011 range, comparable to baseline variance (0.00014). The within-condition variance (σ=0.00006) is actually lower than baseline (σ=0.00008), suggesting SwiGLU's gated activation may produce a smoother loss landscape that is less sensitive to initialization. All three runs independently reach the same conclusion: SwiGLU improves next-token prediction by approximately 2.0 mbpb relative to relu², at a consistent throughput cost of ~6% (133K vs. 141K tok/sec from the additional gate projection and element-wise multiply).

This result is consistent with the broader literature: SwiGLU and GLU variants have consistently outperformed plain activations from 25M to 540B parameters (Shazeer, 2020 [5]; Chowdhery et al., 2022 [8]). The improvement is modest in absolute terms but meaningful: 2.0 mbpb at picochat scale corresponds to a ~0.2% reduction in cross-entropy loss, which compounds over additional training and larger models.

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2.5 Detailed Results: MTP (picochat-mtp)

Run	val/bpb	train/loss*	tok/sec	Train Time
1	1.01090	4.77341	109,782	66.1 min
2	1.01088	4.77573	108,782	67.5 min
3	1.01097	4.77414	113,057	64.6 min
Mean ± σ	1.01092 ± 0.00005		110,540	66.1 min

*train/loss for MTP includes the auxiliary loss: reported ≈ main_loss + 0.3 × mtp_loss. The effective main next-token loss is approximately equal to baseline (~3.26); the additional ~1.51 in reported loss comes from 0.3 × mtp_loss ≈ 0.3 × 5.03 nats (predicting 2 tokens ahead is harder and the MTP head has not converged at this training budget).

Reproducibility analysis: With σ=0.00005, the MTP runs are the most consistent of all configurations tested — the 3-run range is only 0.00009 bpb. This high consistency rules out random seed effects and confirms that MTP reliably degrades next-token val/bpb by +3.4 mbpb at this training scale. The result is reproducible and not a fluke.

Why MTP underperformed: The degradation is attributable to a fundamental scale-dependency of auxiliary prediction objectives:

1. Competing gradient signals on the shared lm_head: The lm_head receives gradient from both the main next-token objective and the MTP auxiliary objective. At only 13.8M training tokens, these signals conflict: the MTP head has not learned to produce useful representations that reinforce the primary objective, so the lm_head is pulled toward a compromised solution.

2. Insufficient training budget for auxiliary objectives to converge: In DeepSeek-V3 and LLaMA 3.1, MTP is applied over trillions of tokens. At that scale, the primary objective is near-saturated and the auxiliary signal provides incremental benefit. At 13.8M tokens, the model is far from saturation — the "extra gradient" is noise rather than signal.

3. 22% throughput reduction: MTP ran at 110,540 tok/sec vs. 141,417 for baseline, meaning each wall-clock second trains on fewer tokens. This compounds the data-efficiency disadvantage.

This result does not invalidate MTP as an architectural choice — it demonstrates that MTP is a scale-dependent technique requiring sufficient training compute to amortize the initial degradation. See Section 3 for the scaling projection.

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2.6 Supplemental Results: RoPE 500K (picochat-rope500k)

Motivation: Following the unexpected MTP result, we sought an architectural change that would show positive effect even at small scale and short training duration. After reviewing the literature, our hypothesis was that the MTP failure was specific to auxiliary objective interference — other changes that operate on the forward pass alone (rather than the training objective) should be unaffected by training duration. We selected RoPE base theta 500K as a zero-cost (no extra parameters, no throughput penalty) alternative that we expected to show modest but reliable improvement even over 13.8M tokens.

Run	val/bpb	train/loss	tok/sec	Train Time
1	1.00708	3.25830	143,635	50.5 min
2	1.00697	3.25827	141,081	51.3 min
3	1.00676	3.25851	141,050	51.3 min
Mean ± σ	1.00694 ± 0.00016		141,922	51.0 min

Reproducibility analysis: The RoPE runs show slightly higher cross-seed variance (σ=0.00016) than the other configurations, though still well within the interpretable range. The direction is consistent across all 3 seeds (all three improve over baseline), confirming a true effect. The higher variance likely reflects that RoPE base theta affects the position-frequency assignments at medium distances (tokens 50–512 apart), and different random initializations interact differently with these encoding patterns early in training. The mean improvement of 0.56 mbpb at zero compute cost confirms our hypothesis: forward- pass architectural changes that do not interfere with the gradient structure can improve performance even at short training budgets.

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3. Implications for Larger Runs

3.1 SwiGLU at Scale

The performance advantage of SwiGLU over relu² is expected to persist and likely widen at larger model sizes. In PaLM (540B), LLaMA (7B–70B), and Mistral (7B), gated activations consistently outperform their ungated counterparts at matched parameter counts. The throughput penalty (~6%) is a fixed fractional overhead of MLP compute, independent of depth or width. For production-scale training, the bpb gain per FLOP invested by SwiGLU is reliably positive, making it a strongly recommended default. Estimated cost delta at 1B-param scale: +$30/run for ~5–15 mbpb expected improvement.

3.2 MTP at Scale

MTP is a scale-threshold technique: it provides no benefit (and active harm) below some critical training compute, and increasing benefit above it. We observed −3.4 mbpb at 13.8M tokens. Based on the DeepSeek-V3 and LLaMA 3.1 results, we hypothesize that the crossover threshold for picochat (42M params) is approximately 200–500M tokens — roughly 15–35× more training than our experiments. Scaling considerations:

Training scale	Expected MTP effect (estimated)
13.8M tokens (this study)	−3.4 mbpb (observed)
100M tokens	~−1 to 0 mbpb (break-even region)
500M tokens	~+1 to +5 mbpb (benefit begins)
2B+ tokens (Chinchilla-optimal for 42M params)	~+5 to +15 mbpb

At 1B-param scale with Chinchilla-optimal training (~20B tokens), MTP is estimated to contribute +10–30 mbpb improvement based on reported results in DeepSeek-V3. The additional cost is ~22% throughput overhead, but this is partially offset by MTP providing effectively denser gradient signal per token (reducing the number of tokens needed to reach a given loss).

Practical recommendation: Do not use MTP for picochat-scale experiments. Enable MTP for any run exceeding ~200M training tokens, and for all production-scale runs.

3.3 RoPE 500K at Scale

The benefit of larger base theta scales with context length. At 512-token context, we observed +0.56 mbpb. A nanochat model trained with max_seq_len=2048 or max_seq_len=8192 would show substantially larger gains, as the difference between base=10K and base=500K manifests most strongly at token distances above ~1,000. RoPE 500K is a zero-cost Pareto improvement at all scales: it adds no parameters, no compute, and consistently improves performance across all three seeds and all reported scales in the LLaMA 3 technical report. Unconditional adoption is recommended.

3.4 Combined Configuration and Cost Projection

At picochat scale, the Pareto-optimal configuration for quality/cost is SwiGLU + RoPE 500K (not MTP), expected to combine additively for ~2.6 mbpb improvement over relu²+RoPE10K baseline.

For a hypothetical nanochat-1B (1B params, Chinchilla-optimal 20B tokens on H100s):

Item	Value
Compute	1.2 × 10²⁰ FLOPs
H100 time (@200 TFLOP/s effective)	~167 GPU-hours
Cost per configuration (@$3.09/hr)	~$515
SwiGLU throughput overhead	+$31/run (6% of $515)
MTP throughput overhead	+$113/run (22% of $515)
Recommended ablation budget (3 configs × 3 seeds)	~$4,635

At this scale, SwiGLU + RoPE500K + MTP combined would be the recommended production configuration, with MTP's throughput cost justified by the expected +10–30 mbpb gain.

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References

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[2] Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. de L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022). Training Compute-Optimal Large Language Models (Chinchilla). arXiv:2203.15556.

[3] Su, J., Lu, Y., Pan, S., Murtadha, A., Wen, B., & Liu, Y. (2021). RoFormer: Enhanced Transformer with Rotary Position Embedding. arXiv:2104.09864.

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[5] Shazeer, N. (2020). GLU Variants Improve Transformer. arXiv:2002.05202.

[6] DeepSeek-AI. (2024). DeepSeek-V3 Technical Report. arXiv:2412.19437.

[7] Meta AI. (2024). The Llama 3 Herd of Models. arXiv:2407.21783.

[8] Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022). PaLM: Scaling Language Modeling with Pathways. arXiv:2204.02311.

[9] Sha, A. (2026). nanochat Modal training reference implementation. UofT CSC490 W2026, GitHub. https://github.com/UofT-CSC490-W2026/022326-tutorial-nanochat