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Performance & Quantization

← Back to the Soup README

QAT, FP8, the Quant Menu (I + II), KV-cache, NVFP4, save formats, Cut Cross-Entropy, gradient checkpointing, kernel auto-composition, activation offloading, and multi-GPU / DeepSpeed / FSDP.

Contents:

Quantization-Aware Training (QAT)

Train with simulated quantization for significantly better post-quantization quality compared to standard QLoRA:

# Install QAT support
pip install "soup-cli[qat]"

base: meta-llama/Llama-3.1-8B-Instruct
task: sft

data:
  train: ./data/train.jsonl
  format: alpaca

training:
  epochs: 3
  lr: 2e-5
  quantization: 4bit
  quantization_aware: true  # Enable QAT
  lora:
    r: 64
    alpha: 16

output: ./output

When to use QAT vs post-training quantization:

  • QAT (quantization_aware: true): Better quality when you plan to deploy with aggressive quantization (int8/int4). ~5-10% slower training, but the model learns to compensate for quantization noise.
  • Post-training quantization (default): Faster training, good enough for most use cases. Quantize after training with soup export --quant q4_k_m.

QAT works with all training tasks (SFT, DPO, GRPO, PPO, KTO, ORPO, SimPO, IPO, Pretrain) and vision modality. Not compatible with the unsloth backend. After QAT training, export to GGUF normally with soup export.

FP8 Training (Hopper+)

For H100 / H200 / B100 / B200 GPUs, train with float8 matmuls for ~2x speedup vs bf16 at comparable quality. This extends QAT infrastructure via torchao.float8:

pip install "soup-cli[qat]"   # torchao >= 0.5.0 includes torchao.float8

training:
  quantization_aware: fp8   # ← string 'fp8', not bool true
  quantization: none        # FP8 converts linears directly; no bnb 4bit needed

FP8 Scaling Recipes (v0.28.1)

Choose a scaling recipe to trade off speed vs accuracy:

training:
  quantization_aware: fp8
  fp8_recipe: rowwise      # tensorwise | rowwise | rowwise_with_gw_hp
Recipe Kernel Scaling Trade-off
tensorwise (default) cuBLAS Single scale per tensor Fastest, good accuracy
rowwise CUTLASS Per-row scale, e4m3, power-of-2 scales Slower, more accurate
rowwise_with_gw_hp CUTLASS Rowwise + grad_weight in high precision Slowest, most accurate

Omitting fp8_recipe defaults to tensorwise (identical to v0.28.0 behavior).

Bool true stays on the int8 QAT path for backward compatibility. FP8 requires CUDA + Hopper+ (compute capability ≥ 9.0) and is rejected on unsloth/mlx backends. Wired across every transformer-backend trainer (SFT, DPO, GRPO, KTO, ORPO, SimPO, IPO, PPO, Reward-Model, Embedding, Pretrain).

Cut Cross-Entropy (Large-Vocab Models)

Models with 128k+ vocabularies (Llama 3.1, Qwen2) materialise a huge (batch, seq, vocab) logits tensor that dominates VRAM. Cut Cross-Entropy computes the loss in chunks instead:

pip install "soup-cli[cce]"    # or: pip install cut-cross-entropy

training:
  use_cut_ce: true   # Patches the CE kernel before model load

Architecture detection matches on the model name's last path component (meta-llama/Llama-3.1-8B → llama patcher) so org prefixes don't trigger the wrong recipe. Saves 8-24 GB VRAM at common batch × seq shapes. Not compatible with unsloth (own CE kernel) or mlx. Wired across every transformer-backend trainer (SFT, DPO, GRPO, KTO, ORPO, SimPO, IPO, PPO, Reward-Model, Embedding, Pretrain) — note that PPO has its own forward loop so cut_ce no-ops gracefully there.

Gradient Checkpointing Tiers

Instead of a boolean, gradient_checkpointing now accepts a tier that trades compute for memory more precisely:

training:
  # One of: false | true | "selective" | "medium" | "full" | "auto"
  gradient_checkpointing: auto
  • full / true — every transformer block (~30% slowdown, biggest save).
  • medium — every other block (balance).
  • selective — attention only (~10% slowdown, modest save).
  • auto — pick based on detected VRAM: < 24 GB → full, 24-80 GB → medium, > 80 GB → selective.

Legacy boolean configs continue to work unchanged.

Kernel Auto-Composition

Let Soup benchmark available kernel combinations and pick the fastest for your GPU on the first training steps:

training:
  kernel_auto_compose: true

Enumerates baseline / Liger / FlashAttention / Cut-Cross-Entropy combos, benchmarks each briefly on the trainer's actual model (forward-only under torch.no_grad() so live gradients aren't polluted), and adopts the fastest. Falls back to baseline on CPU and backs off for unsloth/mlx backends (both manage kernels internally). Wired across every transformer-backend trainer (SFT, DPO, GRPO, KTO, ORPO, SimPO, IPO, PPO, Reward-Model, Embedding, Pretrain).

Cross-Document Attention Masking

When packing: true packs multiple short documents into one sequence, the default causal mask allows attention to bleed across doc boundaries. Enable block-diagonal masking to prevent this:

training:
  packing: true
  packing_cross_doc_attn_mask: true

The mask builder is numpy-vectorised (np.tril per block) to stay fast at large max_length. Misconfiguring it without packing: true is rejected at config-load time.

Quant Menu — 9 Quantization Formats

Pick the right quantization format for your base model and hardware. Soup loads the appropriate quantization_config and trains LoRA on top:

# Train LoRA on top of a pre-quantized GPTQ checkpoint:
base: TheBloke/Llama-2-7B-Chat-GPTQ
training:
  quantization: gptq        # or: awq, hqq:4bit, aqlm, eetq, mxfp4, fp8

# FSDP + QLoRA — set quant_storage:
training:
  quantization: 4bit
  bnb_4bit_quant_storage: bfloat16
Format Bits Use case Optional dep
4bit 4 Default. Best general LoRA training. bitsandbytes
8bit 8 Larger memory budget, more accurate gradients. bitsandbytes
none 16/32 Full fine-tuning or DPO/PPO without quant. —
gptq 2/3/4/8 Train LoRA on top of an existing GPTQ checkpoint. gptqmodel
awq 4 Train LoRA on top of an existing AWQ checkpoint. autoawq
hqq:Nbit 1, 2, 3, 4, 5, 6, 8 Wide bit range; compose with LoRA. hqq
aqlm 2 Extreme compression. aqlm
eetq 8 Fast 8-bit kernel for SM75+. eetq
mxfp4 4 Newer 4-bit type with better activation distribution. bitsandbytes ≥ 0.45
fp8 — Train fp16/bf16 on top of FP8-released checkpoints. transformers ≥ 4.45

Compatibility matrix. soup train runs check_quant_distributed_compat() at startup. HQQ / EETQ / AQLM hard-fail with FSDP and ZeRO-3 (sourced from LlamaFactory's matrix at quantization.py:199/211); BNB 4-bit + FSDP without bnb_4bit_quant_storage emits a yellow warning.

Pre-quantized + QAT. gptq / awq / hqq:* / aqlm / eetq / mxfp4 / fp8 all carry their own scale; combining with quantization_aware (int8 QAT or 'fp8') is rejected at config-load.

Multi-trainer support. Quant Menu is wired across all 12 transformer-backend trainers (SFT / DPO / GRPO / KTO / ORPO / SimPO / IPO / PPO / RewardModel / Pretrain / Embedding / BCO). PPO's reward model also loads with the same Quant Menu config as the policy when tcfg is passed in, so a GPTQ-policy + GPTQ-reward run does not silently OOM in fp16. MLX backend is rejected with a distinct error message; vision and audio modality now thread the same unified Quant Menu loader (the modality: text gate was dropped in v0.71.19), so the full menu — gptq / awq / hqq:* / aqlm / eetq / mxfp4 / fp8 — applies to multi-modal SFT too (a given vision/audio checkpoint still needs a class + kernel that supports the chosen format, e.g. autoawq for awq).

Activation Offloading (Small-VRAM Large-Batch)

Offload saved activations to RAM or disk during the backward pass to fit bigger effective batch sizes on smaller GPUs:

training:
  activation_offloading: cpu    # or "disk"

cpu moves saved tensors to RAM (fast, bounded by system RAM); disk writes them to a scratch dir under the training output directory (slower, bounded by free disk). Scratch paths are containment-checked vs the current working directory, torch.load(weights_only=True) prevents arbitrary Python deserialization on reload, and the context manager best-effort cleans up scratch files on normal exit and on crash.

Not compatible with unsloth (own memory manager) or mlx. Wired across every transformer-backend trainer (SFT, DPO, GRPO, KTO, ORPO, SimPO, IPO, PPO, Reward-Model, Embedding, Pretrain).

Correctness First (v0.36.0)

Four silent-failure modes Soup had → loud failures.

Assistant-only loss masking

By default, Soup masks every non-assistant token with -100 so the SFT loss reflects only what the model should generate. Toggle via data.train_on_responses_only (default true):

data:
  train: data.jsonl
  train_on_responses_only: true   # default
  # OR per-message control:
  # train_on_messages_with_train_field: true

When the tokenizer ships a chat template with {% generation %} markers, the mask is exact. Without those markers, Soup falls back to an incremental tokenize-delta walk and documents the looseness.

--trust-remote-code opt-in (every command, every trainer)

Every command that loads a model now requires --trust-remote-code to execute custom Python from a model repo (auto_map in config.json). First-party orgs (Meta, Mistral, Qwen, Google, etc.) suppress the warning panel; everything else prints a REMOTE CODE WARNING panel before loading. Unknown-org local checkpoints with auto_map raise a friendly ValueError at construction time instead of silently exec'ing inside from_pretrained.

Coverage:

  • soup train (every task — SFT, DPO, GRPO, KTO, ORPO, SimPO, IPO, PPO, Reward Model, Pretrain, Embedding, BCO, and the unified Preference dispatcher)
  • soup chat, soup serve, soup data download, soup eval auto
  • soup diff, soup export, soup merge, soup infer, soup data generate
soup train --config soup.yaml --trust-remote-code
soup infer --model my-org/custom-arch-model --input prompts.jsonl --trust-remote-code
soup export --model ./adapter --format gguf --trust-remote-code

Chat-template hardening

Tokenizers without a chat template now raise a ValueError with a fix suggestion instead of silently building garbage f"{role}: {content}" strings.

data:
  train: data.jsonl
  chat_template: chatml   # or: llama3, qwen2.5, mistral, gemma3, phi4, deepseek-r1, or a raw Jinja string

Raw Jinja strings are validated: null bytes / >64KB / filesystem-touching directives ({% include %}, {% import %}, {% from %}, {% macro %}, {% extends %}) are rejected at config-load.

OOM-probe auto batch size

training:
  batch_size: auto                  # unchanged
  auto_batch_size_strategy: probe   # NEW: 'static' | 'probe' | 'auto' (default)

Replaces the static memory formula with a real try-halve-then-double-to-ceiling loop. Picked size is cached at ~/.soup/batch_cache.json keyed on (model, max_length, quantization, lora_r, gpu_name, gpu_memory_gb) so repeat runs short-circuit.

Multi-GPU / DeepSpeed / FSDP

Train on multiple GPUs with DeepSpeed or PyTorch FSDP2:

# DeepSpeed ZeRO Stage 2 (recommended for most cases)
soup train --config soup.yaml --deepspeed zero2

# DeepSpeed ZeRO Stage 3 (for very large models)
soup train --config soup.yaml --deepspeed zero3

# DeepSpeed ZeRO Stage 2 with CPU offload (memory-constrained)
soup train --config soup.yaml --deepspeed zero2_offload

# DeepSpeed ZeRO++ — quantized weights + gradients, hierarchical partitioning
soup train --config soup.yaml --deepspeed zero++

# FSDP2 Full Shard (native PyTorch, like ZeRO-3)
soup train --config soup.yaml --fsdp full_shard

# FSDP2 Shard Grad Op (like ZeRO-2)
soup train --config soup.yaml --fsdp shard_grad

# FSDP2 Full Shard with CPU offload
soup train --config soup.yaml --fsdp full_offload

--gpus flag — topology-aware launch

# Auto-detect GPU count; print the exact accelerate command
soup train --config soup.yaml --gpus auto

# Explicit GPU count
soup train --config soup.yaml --gpus 4

soup detects NVLink / PCIe interconnect and prints the correct accelerate launch command. Copy-paste to start distributed training (auto-reexec ships in v0.27.1).

FSDP2 + torch.compile

Stack torch.compile on top of any FSDP preset for +20-30% throughput:

# soup.yaml
training:
  use_fsdp2_compile: true

Requires --fsdp, CUDA, and backend: transformers.

Pipeline parallelism config (wiring only in v0.27.0)

training:
  parallelism: pipeline
  pipeline_stages: 4

Config validation ships in v0.27.0; live execution ships in v0.27.1. See recipes/deepseek-v3-pipeline for a full scaffold.

Performance + Long-Context

Optimize training throughput and extend context windows:

# soup.yaml — performance options
training:
  use_liger: true            # Liger Kernel fused ops (20-60% memory savings)
  use_flash_attn: true       # FlashAttention v2/v3 auto-detection
  gradient_checkpointing: true  # Required for long sequences

  # Long-context (128k+ tokens)
  rope_scaling_type: dynamic  # RoPE scaling: linear, dynamic, yarn, longrope
  # use_ring_attention: true  # Sequence parallelism across GPUs

data:
  max_length: 131072          # Up to 1M tokens supported

Install optional performance packages:

pip install "soup-cli[liger]"     # Liger Kernel fused operations
pip install flash-attn --no-build-isolation  # FlashAttention
pip install "soup-cli[ring-attn]" # Ring FlashAttention (sequence parallelism)

Live CUDA Batch-Size Probe

Set auto_batch_size_strategy: probe in training: and Soup will run a real OOM-probe before training:

training:
  batch_size: auto
  auto_batch_size_strategy: probe

For each candidate size B, the probe runs ONE forward + backward + step on a synthetic batch of B sequences of length max_length. On torch.cuda.OutOfMemoryError it halves; otherwise it doubles up to 4 × static_estimate. The picked size is cached per (model, max_length, quantization, lora_r, gpu) tuple in ~/.soup/batch_cache.json so subsequent runs skip the probe.

CPU sessions and auto_batch_size_strategy: static skip the probe. Synthetic batch tensors are freed before the backward pass so peak VRAM reflects the realistic training step. SFT-only this release — non-SFT trainers fall back to the static estimate.

FSDP Shard Consolidation

# Preview the plan (which shards, total size) without writing
soup merge-sharded-fsdp-weights ./fsdp-checkpoint -o ./merged.safetensors --plan-only

# Consolidate for real
soup merge-sharded-fsdp-weights ./fsdp-checkpoint -o ./merged.safetensors

Consolidates pytorch_model_fsdp_*.bin shard files into a single .safetensors. Each shard is loaded one at a time (streaming, not all-at-once) with torch.load(weights_only=True), tensor shapes validated (a duplicate key with a conflicting shape is rejected; a same-shape duplicate keeps the first and warns), and the merged dict written atomically. cwd-containment + symlink rejection apply to the output path and every shard; per-shard 16 GiB cap; _MAX_SHARDS=1024. --plan-only prints the plan and exits 0. Live torch-side consolidation shipped in v0.71.14.

BitNet 1.58-Bit Fine-Tuning (BETA, live in v0.71.20)

training.quantization: bitnet_1.58 routes to a live BitNetTrainerWrapper (an SFT subclass) for ternary-weight training. It is gated on the upstream onebitllms package — when absent, training fails fast with a friendly RuntimeError naming it (onebitllms is CUDA/Linux-only). The export targets run a real llama.cpp TQ1_0 ternary GGUF export (reusing the v0.53.1 convert→quantize pipeline) instead of a stub:

soup export --model ./output --format bitnet   # → TQ1_0 ternary GGUF
soup export --model ./output --format tq1_0     # same flavour, explicit name

The export requires a built llama.cpp toolchain (the convert/quantize binaries raise a friendly FileNotFoundError when missing). A ready-made falcon-e-bitnet-sft recipe is shipped:

soup recipes use falcon-e-bitnet-sft
soup train --config soup.yaml

Restricted to task ∈ {sft, pretrain, dpo} on backend ∈ {transformers, unsloth} with text modality; the cross-validator rejects MLX and vision/audio configurations loudly at config load.

MoE Expert Quantization + Router-Only Training (live in v0.71.20)

For fused-MoE models trained with moe_lora: true, two live toggles:

  • training.moe_expert_quant: nf4 | int8_rowwise — quantizes just the fused-MoE expert nn.Linear layers with bitsandbytes (Linear4bit for nf4, Linear8bitLt for int8_rowwise), leaving attention + the gating router in full precision. The swap runs before get_peft_model (QLoRA-on-experts), so PEFT attaches its adapters to the quantized base. The source weights are genuinely carried into the quantized layer (validated dequant error 0.0155 vs source on an RTX 3050). CUDA + bitsandbytes are required — a friendly RuntimeError fires on CPU / without bnb.
  • training.train_router_only: true — freeze every expert parameter and train only the gating router (applied after LoRA, on the final parameter set).

Both reject silently-no-op combinations: setting either flag without moe_lora=true fails at config load with an actionable message.

Unsloth Dynamic 2.0 GGUF Ladder (v0.53.0)

soup export --format gguf-ud --calibration-data <calib.jsonl> is the planned dispatch surface for the 14-entry UD ladder (UD-Q8_K_XL … UD-IQ1_M). v0.53.0 ships the closed-allowlist validators, MappingProxyType-wrapped metadata, and a calibration-data path shape check; live llama.cpp imatrix invocation lands in v0.53.1. The IQ + Apple/ARM-friendly GGUF flavours (IQ4_NL, Q4_0_4_4, Q5_K_M, etc.) ship as separate frozensets so future export-CLI dispatch can pick by family.

KV Cache Types (v0.53.0)

training.kv_cache_type: q8_0 | bf16 | f16 | fp8 controls the inference-time KV cache element type. fp8 is Hopper-only; the MLX backend is rejected at config load.

The live serve runtime shipped in v0.71.14 for the transformers backend:

soup serve --model ./output --kv-cache-type bf16     # cache stored in the model compute dtype
soup serve --model ./output --kv-cache-type q8_0     # 8-bit quantized KV cache (needs `hqq`)
  • bf16 / f16 resolve the model compute dtype for the default DynamicCache (no extra dependency).
  • q8_0 wires the transformers quantized KV cache (cache_implementation="quantized", hqq backend). If no quant backend (hqq / optimum-quanto) is installed, the CLI exits 2 with an install hint rather than crashing.
  • fp8 is rejected on pre-Hopper GPUs (compute capability < 9.0) with a friendly runtime error naming vLLM as the path on Ampere/Ada.
  • vLLM / SGLang serve wiring is still tracked under #140 (infra-blocked).

FP8 Attention + NVFP4 + Native unsloth_bnb_4bit

Three TrainingConfig bools extend the v0.28.0 FP8 menu. fp8_attention and nvfp4 are LIVE torchao converters as of v0.71.21 (hardware-gated):

  • fp8_attention: true — requires quantization_aware: fp8 AND a non-MLX backend. Converts the attention projections (q/k/v/o and fused variants) to torchao float8 training on Hopper+ GPUs. Missing torchao or a pre-Hopper GPU degrades to a clear advisory; a conversion-phase failure raises an honest "model may be PARTIALLY converted" error instead of training on a half-converted model.
  • nvfp4: true — Blackwell-only FP4 training via torchao NVFP4Config + quantize_. Gated to non-MLX + modality: text; the SM ≥ 10 runtime check fires at trainer construction.
  • unsloth_bnb_4bit: true — promotes "Unsloth Dynamic 4-bit" from an implicit backend=unsloth + quantization=4bit combo to a named flag. Mutual rejection of inconsistent combos at config load.

Cross-validator ordering picks the most actionable error: quantization_aware='fp8' prerequisite fires before the MLX rejection on fp8_attention, so a YAML missing both surfaces the deeper issue first.

LF / Axolotl Quant Parity (v0.53.0)

  • bnb_4bit_use_double_quant: true — requires quantization: 4bit. Activates BNB's double-quantization. Combinations with the Quant Menu formats (gptq / awq / hqq:Nbit / aqlm / eetq / mxfp4 / fp8) are rejected at config load.
  • llm_int8: true — an explicit 8-bit assertion. Unlike v0.41.0 load_in_8bit (which rewrites quantization to 8bit), llm_int8 enforces that the user has ALSO set quantization: 8bit. Mismatch raises with an actionable message.
  • quantize_ref_model: true / quantize_reward_model: true — extend the v0.40.5 Quant Menu wiring to the reference / reward models inside preference and RLHF training. quantize_ref_model accepts any task with a reference policy (dpo / ipo / simpo / orpo / bco / kto / preference / grpo / ppo); quantize_reward_model accepts ppo / reward_model.

Advanced Save Formats (v0.53.0)

soup merge --save-format 4bit and --save-format 4bit_forced will write a single BNB-4bit-quantized merged checkpoint without the wasteful dequant → merge → requant cycle (unsloth merged_4bit recipe). v0.53.0 ships the closed allowlist + spec metadata; the live writer lands in v0.53.1.

soup export --format torchao --quant-config <yaml> is the planned PTQ export surface for torchao.quantize_ + save_pretrained. Four schemes are allowlisted: Int4WeightOnly, Int8DynActInt4, Float8DynActFloat8, NVFP4. CASE-SENSITIVE — these are PyTorch class names and torchao.quantize_ looks them up by exact name. Diverges from --save-format (lowercase-normalised) on purpose; documented at both validators.

Quant Menu II + Export Pipeline (v0.53.1)

v0.53.1 lifts the v0.53.0 schema-only stubs to live wiring:

# Single-stage BNB-4bit merged checkpoint (no dequant/merge/requant)
soup merge -a ./adapter -o ./merged_4bit --save-format 4bit

# TorchAO PTQ export — closed per-scheme kwarg allowlist
cat > q.yaml <<EOF
scheme: Int4WeightOnly
group_size: 32
EOF
soup export --model ./merged --format torchao --quant-config ./q.yaml --output ./out

# Unsloth Dynamic 2.0 / IQ / Apple-ARM GGUF via llama.cpp imatrix
soup export --model ./merged --format gguf-ud \
    --gguf-flavour UD-Q4_K_XL \
    --calibration-data ./calib.jsonl \
    --output ./out/model.UD-Q4_K_XL.gguf

# Deploy autopilot with live Quant-Lobotomy measurement
soup deploy autopilot --target rtx-4090-24gb \
    --base meta-llama/Llama-3.2-1B \
    --measure --tasks ./eval_tasks.jsonl \
    --measure-candidates 4bit,gptq,awq

Autopilot also detects pre-quantized bases automatically — TheBloke/Llama-2-7B-Chat-GPTQ is recommended gptq instead of stacking 4-bit on top. Detection runs against the base-model name regex AND any local config.json's quantization_config.quant_method. Out-of-cwd model paths are silently skipped (soft-probe semantics).

The advanced GGUF pipeline uses POSIX O_NOFOLLOW to defeat the TOCTOU race between the dispatch-time symlink check and the actual open of the calibration data — a crafted environment cannot race-swap the calibration file between validate and read.

soup deploy autopilot --measure caches results at ~/.soup/deploy_autopilot_cache.json keyed on (base, profile, eval-tasks). Repeat invocations short-circuit; pass SOUP_DEPLOY_AUTOPILOT_CACHE=<path> to redirect (constrained to home / cwd / tempdir). The recommended candidate uses soft-fallback: first OK by insertion order, else the candidate with the smallest delta (least drop relative to its own baseline).