Transformer and inference internals

What you will be able to do

1. Trace tokenization, attention, residual flow, and decoding.
2. Relate context and KV cache to memory/latency.
3. Measure decoding trade-offs.

Prerequisites: Builder-level interface validation, Python, testing, metadata logging and state-transition tracing. Hardware benchmarks and model accounts are not required.

This curriculum runs self-study on VetsLearn. Where the text calls for an instructor, peer or formal review, use the stated acceptance criteria for self-review. Reflection questions support reflection; they do not replace independent assessment. A peer review is optional unless your actual organization requires it.

Part 1 Technical instruction

Transformers operate on token representations. Causal masking restricts a token from attending to future positions. Attention cost grows with sequence length; KV caching avoids recomputing earlier keys and values during autoregressive decoding but consumes memory. Batching improves throughput while potentially increasing queue latency. Quantization reduces memory and compute at possible quality cost. Decoding parameters alter the output distribution; they are not truth controls. Profile the actual workload because theoretical FLOPs do not include tokenization, queuing, transfer, or application overhead.

Mechanism and implementation

Causal attention mixes value vectors using query/key similarity with a future-token mask. Transformer variants, hybrids and other architectures exist; undisclosed internals must not be guessed. Context windows, serving state, persistent memory and supported reasoning controls are distinct. KV reuse consumes memory.

Constructed offline example

Save as topic_example.py and run python topic_example.py with an installed Python 3.11+ interpreter. This standard-library construction uses synthetic values; its checks are not provider measurements.

def causal_pairs(length):
    return [(query, key) for query in range(length) for key in range(query + 1)]
assert len(causal_pairs(4)) == 10
assert all(key <= query for query, key in causal_pairs(4))
layers, kv_heads, head_dim, bytes_each, tokens = 2, 2, 4, 2, 10
estimated = 2 * layers * kv_heads * head_dim * bytes_each * tokens
assert estimated == 640
print("constructed KV bytes:", estimated)

Engineering decisions

• Choose context length from evidence, not the maximum advertised value.
• Balance batch throughput with tail latency.
• Document weight provenance, license, checksum, and runtime code before loading.

Worked example

A local simulator estimates prefill, decode, and cache cost as context and output length change. The learner identifies the knee where a larger context breaches the latency objective and proposes retrieval or summarization instead.

Diagnose the failure

No future key is visible; doubling tokens doubles this estimate. It is not a hardware benchmark. Longer reasoning and low temperature guarantee neither truth nor perfect recall.

Part 2 Guided practice

Draw the four-token triangular mask. Double tokens in the simplified KV estimate while holding other variables fixed. Independently identify what compaction can lose and which pending actions must persist separately.

Answer criteria and feedback: No future key is visible; doubling tokens doubles this estimate. It is not a hardware benchmark. Longer reasoning and low temperature guarantee neither truth nor perfect recall.

Continue with the downloadable pack below. If the pack or interpreter is unavailable, trace this self-contained example by hand in an input/expected-status/controlling-rule table and label execution unverified. Do not invent passing output. No account, credential, paid call, purchase or actual deployment is required.

Part 3 Remediation

Hold all but one variable constant. If results conflict, inspect units and workload mix before drawing an architectural conclusion. After repair, reassess with the alternate scenario in scenario_alt.json. A critical authorization, data-handling, evidence-integrity, or prohibited-action failure cannot be averaged into successful practice; it requires targeted remediation.

Part 4 Sources and currency

• https://arxiv.org/abs/1706.03762
• https://pytorch.org/docs/stable/index.html

Use the workflow decision reference for model/deployment/harness distinctions and controlled comparisons. Source currency must be checked before each cohort. Provider/product behavior is illustrative where mocked; resolve current interfaces only from official documentation in an authorized environment.