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feat(v0.2.9): Unified LLM Interface with ModelSpec Abstraction#80

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feature/v0.2.9
Dec 16, 2025
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feat(v0.2.9): Unified LLM Interface with ModelSpec Abstraction#80
m96-chan merged 14 commits intomainfrom
feature/v0.2.9

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@m96-chan m96-chan commented Dec 16, 2025

Summary

  • Unified LLM Interface: Single CausalTransformerModel supports GPT-2, LLaMA 2/3, and Qwen3
  • ModelSpec Abstraction: Architecture differences (norm type, activation, RoPE, QK-norm) handled declaratively
  • Auto-detection: detect_model_spec() identifies model type from tensor names
  • Hybrid Attention: CPU decode (seq_len=1) + GPU prefill for optimal performance
  • KV-Cache Generation: Efficient autoregressive text generation with model.generate()
  • Sharded Model Support: Load large models split across multiple safetensors files
  • FP16/FP32 Selection: Choose dtype at load time

New APIs

API Description
load_model_from_safetensors(path, dtype, spec) Unified model loader
detect_model_spec(tensor_names) Auto-detect GPT2/LLaMA/Qwen3
model.generate(ids, max_new_tokens, ...) KV-cache generation
gpk.sdpa_causal(q, k, v) Scaled Dot-Product Attention
gpk.rope_inplace(x, freqs) Rotary Position Embedding
gpk.silu(x) SiLU activation
gpk.rmsnorm(x, w, eps) RMS LayerNorm

Tested Models

Model Size Status
GPT-2 124M ✅ 8.7 tok/s
TinyLlama-1.1B 1.1B ✅ 1.8 tok/s (FP16)
Qwen3-8B 8B ✅ 0.2 tok/s (FP16)

Breaking Changes

None. Legacy aliases (GPT2Model, LlamaModel, etc.) still work.

Test plan

  • pytest tests/test_llm_unified.py -v (9 tests pass)
  • GPT-2 E2E demo with generation
  • TinyLlama-1.1B E2E demo with FP16
  • Qwen3-8B E2E demo with HuggingFace tokenizers

🤖 Generated with Claude Code

m96-chan and others added 14 commits December 16, 2025 08:40
@m96-chan @claude
refactor: organize root directory structure
28bff03 
- Create scripts/ directory for development tools
- Move benchmark*.py to scripts/
- Move build_cuda*.bat, compile_dump.bat to scripts/
- Move dump_*.cu debug tools to scripts/
- Move demo_scheduler_log.py to examples/
- Delete redundant TechStack.md (info in CLAUDE.md)
- Update README.md Project Structure section

Root directory now contains only essential project files.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
feat(v0.2.9): General LLM Execution - Attention layer and E2E inference
b84bce5 
## New Features
- **Softmax GPU kernel**: Row-wise softmax with numerical stability
- **CausalSelfAttention**: Multi-head causal self-attention for GPT-2
- **Full TransformerBlock**: ln_1 -> attention -> residual -> ln_2 -> mlp -> residual

## Changes
- `softmax()` operation added to ops API (native GPU + CPU fallback)
- `CausalSelfAttention` class with QKV projection and causal masking
- `TransformerBlock` updated to support attention (backward compatible)
- `load_gpt2_from_safetensors()` now loads attention weights by default

## API
- `gpk.softmax(input)` - Row-wise softmax
- `gpk.llm.CausalSelfAttention` - Attention module
- `load_gpt2_from_safetensors(path, load_attention=True)` - Full model loading

## Architecture Support
- GPT-2 E2E inference now possible
- GPT-2/GPT-Neo/LLaMA-style architectures supported

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
feat(v0.2.9): Unified Transformer + Hybrid Attention
bf8e123 
- Unify GPT-2 and LLaMA into common Transformer abstraction
  - TransformerConfig: vocab_size, hidden_size, num_layers, num_heads,
    num_kv_heads, norm_type, activation, use_rope, causal
  - CausalTransformerModel with generate() method
  - Attention, MLP, Norm, TransformerBlock classes
  - Legacy aliases preserved for backward compatibility

- Hybrid Attention execution
  - GPU SDPA for prefill (seq_len > 1)
  - CPU numpy for decode (seq_len = 1) to minimize kernel overhead

- New GPU tensor ops (CUDA kernels)
  - concat_axis0, repeat_interleave_axis1, transpose_3d_021, reshape_copy
  - Required for GQA KV head expansion

- Add E2E demo (examples/demo_llm_e2e.py)

Benchmark (RTX 3090 Ti):
  GPT-2 (124M): 11.2 tok/s decode, 89.6 ms/token
  TinyLlama (1.1B): 5.3 tok/s decode, 188.2 ms/token

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
feat(llm): Add ShardedSafeTensorsFile and Qwen3 support
a5bd739 
- ShardedSafeTensorsFile: lazy-load sharded safetensors models
  - load_safetensors() auto-detects .index.json
  - Opens shards on-demand, not all at once

- QK Norm support in Attention class
  - For Qwen3 style models with Q/K normalization
  - Reshape 3D->2D for norm, then back to 3D

- Qwen3Config and load_qwen3_from_safetensors()
  - head_dim=128, rope_theta=1e6
  - Auto-detect config from tensor shapes

Note: Qwen3-8B requires ~32GB VRAM at FP32, needs FP16 support

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
feat(llm): Add FP16 inference support for Qwen3-8B
74b8324 
- Add dtype parameter to load_qwen3_from_safetensors (float32/float16)
- Fix RoPE to handle FP16: convert to FP32 for computation, back to FP16
- Fix SDPA dtype preservation for KV cache
- Fix _forward_cpu QK Norm dtype preservation
- Fix o_proj output dtype in _forward_cpu
- Add FP16 fallback for reshape_copy (native only supports FP32)
- Add FP16 fallback for transpose_3d_021 (native only supports FP32)
- Fix unused variable kv_len in sdpa_causal

Tested: Qwen3-8B (16.4GB) FP16 inference fits in 24GB VRAM
- Forward pass: 2297ms for 1 token
- Generation: 16 tokens in 73.9s (0.2 tok/s with CPU fallbacks)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
feat(ops): Add native FP16/BF16 kernels for transpose_3d_021 and resh…
d22e8ee 
…ape_copy

- Add transpose_021_f16_kernel and transpose_021_bf16_kernel
- Add copy_f16_kernel and copy_bf16_kernel for reshape
- Update nn.cu dispatch to use new FP16/BF16 kernels
- Update Python ops/basic.py to route FP16/BF16 to native kernels

Previously these operations fell back to CPU for FP16, causing slow
inference. Now they run natively on GPU.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
refactor(llm): Add ModelSpec abstraction for model-specific differences
dcd041b 
Introduces ModelSpec data structure to unify model differences:
- ModelSpec: frozen dataclass with weight patterns and arch flags
- GPT2_SPEC: LayerNorm, GELU, combined QKV, position embeddings
- LLAMA_SPEC: RMSNorm, SiLU, RoPE, GQA
- QWEN3_SPEC: RMSNorm, SiLU, RoPE, GQA, QK Norm

New generic loader:
- load_model_from_safetensors(): auto-detects model type
- detect_model_spec(): detects from tensor names
- MODEL_SPECS registry for model type lookup

Existing loaders preserved unchanged for backward compatibility:
- load_gpt2_from_safetensors()
- load_llama_from_safetensors()
- load_qwen3_from_safetensors()

This is a structural refactor only - no behavior changes.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
refactor(llm): convert model loaders to thin wrappers
3ddd416 
Convert load_gpt2_from_safetensors, load_llama_from_safetensors, and
load_qwen3_from_safetensors to thin wrappers that delegate to the
generic load_model_from_safetensors function with appropriate ModelSpec.

This completes the ModelSpec abstraction refactor:
- All three loaders now use load_model_from_safetensors internally
- Backward compatibility preserved via legacy model class wrappers
- Config parameters are now ignored (auto-detected from tensor shapes)
- ~150 lines of duplicated code removed

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
refactor(llm): unify all models into CausalTransformerModel
fc1beee 
Final ModelSpec cleanup for v0.2.9:

- CausalTransformerModel is now the ONLY runtime model
- Add `spec` attribute to store ModelSpec used for loading
- GPT2Model, LlamaModel are now simple type aliases
- RMSNorm, LayerNorm, etc. are simple aliases to Norm
- Remove all legacy wrapper classes with constructors
- Simplify loaders to direct load_model_from_safetensors calls
- Remove redundant config parameters from loaders

Code reduction: 188 deletions, 48 insertions (-140 net lines)

All model-specific behavior is now controlled via:
- model.spec.use_rope
- model.spec.use_qk_norm
- model.spec.activation
- model.spec.norm_type

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
test(llm): add unified interface tests for v0.2.9
750dd02 
Add comprehensive tests for the ModelSpec refactor:
- Type aliases (GPT2Model = LlamaModel = CausalTransformerModel)
- Component aliases (RMSNorm = Norm, CausalSelfAttention = Attention)
- ModelSpec instances and registry
- Automatic model detection from tensor names
- Simplified loader signatures (no config parameter)
- CausalTransformerModel spec attribute
- All expected exports from pygpukit.llm

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
demo(llm): update E2E demo for unified ModelSpec interface
03e4e3a 
- Use load_model_from_safetensors with detect_model_spec
- Add --dtype argument for FP16/FP32 selection
- Show ModelSpec info in detection output
- Display model.spec attribute after loading

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
docs: clarify tokenizer is experimental, update README for v0.2.9
376ccbc 
- Add tokenizer policy section: PyGPUkit delegates tokenization to HuggingFace
- Mark pygpukit.llm.Tokenizer as EXPERIMENTAL with detailed docstring
- Update README LLM section with unified interface examples
- Add v0.2.9 to roadmap (unified LLM interface, ModelSpec abstraction)
- Update API stability table with CausalTransformerModel

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
docs(readme): add What's New in v0.2.9 section
0676eb4 
- Unified LLM Interface (CausalTransformerModel + ModelSpec)
- Multi-architecture support: GPT-2, LLaMA 2/3, Qwen3
- Hybrid attention execution (CPU decode / GPU prefill)
- New LLM operations: sdpa_causal, rope_inplace, silu, rmsnorm
- Sharded model support for large models
- Updated documentation table

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan @claude
fix(llm): resolve mypy type errors in model.py
13bd676 
- Add assertions for num_kv_heads (set in __post_init__)
- Add type annotations for _cos/_sin as Optional[ndarray]
- Use hidden_np for numpy array, hidden for GPUArray
- Fix return type annotations for __call__ method
- Add assertions for _cos/_sin before indexing

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
@m96-chan m96-chan merged commit 10fc369 into main Dec 16, 2025
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This was referenced Dec 16, 2025
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@m96-chan m96-chan deleted the feature/v0.2.9 branch December 26, 2025 09:38
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