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Add bitnet-embeddings-270m model adaptation with F16 and I2_S GGUF conversion
- Add LLM_ARCH_GEMMA3 in llama.cpp for gemma3_text model type (embedding scaling, GELU, post-attn/post-FFN norms, GQA) - Add GGUF conversion support for Gemma3-based 270m models (SPM tokenizer, RMSNorm w+1 offset, arch-specific tensor mapping) - Add tokenizer hash for multilingual-e5-0.6b-260311 - Add conversion documentation
This commit is contained in:
isHuangXin
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# BitNet Embeddings GGUF Conversion Implementation
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## 1. Background
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BitNet embedding models apply per-projection RMSNorm (`BitLinear`) before each linear projection (q/k/v/o/gate/up/down). Each projection has a `.norm.weight` that applies RMSNorm to the input **before** the matmul:
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```
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x → RMSNorm(x, norm.weight) → activation_quant(8bit) → matmul(weight_quant(ternary))
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```
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This pattern does **not** exist in any standard llama.cpp architecture:
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- Standard Qwen3/Gemma3: no per-projection norms
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- Standard BitNet: has `attn_sub_norm`/`ffn_sub_norm` at different positions (after attention/gate*up, not before each projection)
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Currently two base architectures are supported:
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| | bitnet-embeddings-0.6b (Qwen3) | bitnet-embeddings-270m (Gemma3) |
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|---|---|---|
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| Architecture | `Qwen3Model` | `Gemma3TextModel` |
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| hidden_size | 1024 | 640 |
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| num_attention_heads | 16 | 4 |
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| num_key_value_heads | 8 | 1 |
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| head_dim | 128 (note: != hidden_size/num_heads = 64) | 256 (note: != hidden_size/num_heads = 160) |
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| intermediate_size | 3072 | 2048 |
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| num_hidden_layers | 28 | 18 |
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| hidden_activation | SiLU | gelu_pytorch_tanh |
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| vocab_size | 151936 | 262144 |
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| rope_theta | 1000000 | 10000.0 |
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| rms_norm_eps | 1e-06 | 1e-06 |
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| query_pre_attn_scalar | N/A | 256 |
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| tie_word_embeddings | true | true |
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### Gemma3 vs Qwen3 Key Differences
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| Feature | Qwen3 | Gemma3 |
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|---------|-------|--------|
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| Post-attn norm | No | Yes (`post_attention_norm`) |
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| Post-FFW norm | No | Yes (`post_ffw_norm`) |
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| Pre-FFW norm naming | `post_attention_layernorm` → `ffn_norm` | `pre_feedforward_layernorm` → `ffn_norm` |
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| QK head norms | Yes | Yes |
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| Activation | SiLU | GELU |
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| Embedding scaling | No | sqrt(n_embd) |
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| EOS token override | Yes (`<\|endoftext\|>` 151643) | No (auto from tokenizer) |
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### Per-Layer Tensors (7 extra norm tensors per layer)
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| Tensor | Qwen3 Shape | Gemma3 Shape |
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|--------|-------------|--------------|
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| `self_attn.q_proj.norm.weight` | [1024] | [640] |
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| `self_attn.k_proj.norm.weight` | [1024] | [640] |
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| `self_attn.v_proj.norm.weight` | [1024] | [640] |
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| `self_attn.o_proj.norm.weight` | [2048] | [1024] |
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| `mlp.gate_proj.norm.weight` | [1024] | [640] |
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| `mlp.up_proj.norm.weight` | [1024] | [640] |
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| `mlp.down_proj.norm.weight` | [3072] | [2048] |
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---
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## 2. GGUF Tensor Name Mapping
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### Common Tensors (both architectures)
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| HF Name | GGUF Name | Notes |
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|----------|-----------|-------|
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| `embed_tokens.weight` | `token_embd.weight` | |
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| `norm.weight` | `output_norm.weight` | |
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| `layers.{i}.input_layernorm.weight` | `blk.{i}.attn_norm.weight` | |
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| `layers.{i}.self_attn.q_proj.weight` | `blk.{i}.attn_q.weight` | |
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| `layers.{i}.self_attn.k_proj.weight` | `blk.{i}.attn_k.weight` | |
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| `layers.{i}.self_attn.v_proj.weight` | `blk.{i}.attn_v.weight` | |
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| `layers.{i}.self_attn.o_proj.weight` | `blk.{i}.attn_output.weight` | |
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| `layers.{i}.self_attn.q_norm.weight` | `blk.{i}.attn_q_norm.weight` | QK head norm |
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| `layers.{i}.self_attn.k_norm.weight` | `blk.{i}.attn_k_norm.weight` | QK head norm |
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| `layers.{i}.self_attn.q_proj.norm.weight` | `blk.{i}.attn_q_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.self_attn.k_proj.norm.weight` | `blk.{i}.attn_k_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.self_attn.v_proj.norm.weight` | `blk.{i}.attn_v_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.self_attn.o_proj.norm.weight` | `blk.{i}.attn_output_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.mlp.gate_proj.weight` | `blk.{i}.ffn_gate.weight` | |
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| `layers.{i}.mlp.up_proj.weight` | `blk.{i}.ffn_up.weight` | |
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| `layers.{i}.mlp.down_proj.weight` | `blk.{i}.ffn_down.weight` | |
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| `layers.{i}.mlp.gate_proj.norm.weight` | `blk.{i}.ffn_gate_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.mlp.up_proj.norm.weight` | `blk.{i}.ffn_up_norm_in.weight` | BitNet per-projection |
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| `layers.{i}.mlp.down_proj.norm.weight` | `blk.{i}.ffn_down_norm_in.weight` | BitNet per-projection |
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### Architecture-Specific Tensors
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**Qwen3:**
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| HF Name | GGUF Name |
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|----------|-----------|
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| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.ffn_norm.weight` |
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**Gemma3 (additional):**
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| HF Name | GGUF Name |
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|----------|-----------|
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| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.post_attention_norm.weight` |
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| `layers.{i}.pre_feedforward_layernorm.weight` | `blk.{i}.ffn_norm.weight` |
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| `layers.{i}.post_feedforward_layernorm.weight` | `blk.{i}.post_ffw_norm.weight` |
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---
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## 3. Conversion Script
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### `utils/convert-bitnet-embedding-to-gguf.py`
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Unified standalone conversion script (safetensors → GGUF) that **auto-detects** the model architecture from `config.json`'s `model_type` field (`qwen3` or `gemma3_text`). Key features:
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- Hardcoded HF→GGUF tensor name mapping (no dependency on llama.cpp's Python converter)
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- Auto-detection of architecture and GGUF arch string (`qwen3` / `gemma3`)
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- Supports three output types:
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- `--outtype f32`: all weights in float32
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- `--outtype f16`: 2D weights and embeddings as float16, norms as float16
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- `--outtype i2_s`: ternary weights packed in I2_S layout, non-ternary weights as float16
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- Writes `key_length` and `value_length` metadata for correct head_dim (critical: head_dim != hidden_size/num_heads for both models, default calculation would give wrong values)
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- BPE tokenizer handling with per-architecture pre-tokenizer hash verification:
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- Qwen3: GPT-2 BPE tokenizer
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- Gemma3: GemmaTokenizerFast (BPE)
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- Pooling type auto-detection from `modules.json` / `1_Pooling/config.json` (sentence-transformers convention)
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- Architecture-specific tokenizer handling:
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- Qwen3: EOS token override (`<|endoftext|>` 151643) + `add_eos_token(True)` for last-token pooling
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- Gemma3: EOS token auto-set by SpecialVocab from tokenizer_config.json (eos_token_id=1)
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- Gemma3: writes `query_pre_attn_scalar = 256` for correct attention scaling
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### I2_S Ternary Packing
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The I2_S format packs ternary weights {-1, 0, +1} into 2-bit representation:
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- Quantization: `scale = 1/mean(|w|)`, `q = round(w * scale).clamp(-1, 1)`
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- Encoding: `-1 → 0`, `0 → 1`, `+1 → 2`
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- Every 128 values form a block, packed into 32 bytes
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- Each byte stores 4 values: `byte = (c0 << 6) | (c1 << 4) | (c2 << 2) | c3`
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- Scale (float32) is appended at the end of the packed data buffer
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### Tensor Type Assignment
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| Tensor Type | f16 mode | i2_s mode |
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|-------------|----------|-----------|
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| 2D linear weights | float16 | I2_S ternary packed |
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| Embedding weights | float16 | float16 |
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| Norm weights (1D) | float16 | float16 |
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Note: `output.weight` (lm_head) is skipped for embedding models — it is not needed (no token generation).
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---
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## 4. C++ Modifications (`3rdparty/llama.cpp/src/llama.cpp`)
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### 4.1 New Architecture: `LLM_ARCH_GEMMA3`
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Added after `LLM_ARCH_GEMMA2` in the `llm_arch` enum with name mapping `"gemma3"`. Qwen3 (`LLM_ARCH_QWEN3`) was added by the 0.6b adaptation.
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### 4.2 New Tensor Enums (shared across architectures)
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Added 7 new entries after `LLM_TENSOR_FFN_SUB_NORM`:
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```cpp
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LLM_TENSOR_ATTN_Q_NORM_IN,
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LLM_TENSOR_ATTN_K_NORM_IN,
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LLM_TENSOR_ATTN_V_NORM_IN,
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LLM_TENSOR_ATTN_OUT_NORM_IN,
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LLM_TENSOR_FFN_GATE_NORM_IN,
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LLM_TENSOR_FFN_UP_NORM_IN,
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LLM_TENSOR_FFN_DOWN_NORM_IN,
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```
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### 4.3 Layer Struct Fields
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Added to `struct llama_layer`:
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```cpp
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struct ggml_tensor * attn_q_norm_in;
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struct ggml_tensor * attn_k_norm_in;
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struct ggml_tensor * attn_v_norm_in;
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struct ggml_tensor * attn_out_norm_in;
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struct ggml_tensor * ffn_gate_norm_in;
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struct ggml_tensor * ffn_up_norm_in;
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struct ggml_tensor * ffn_down_norm_in;
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```
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### 4.4 Tensor Name Mappings
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Both `LLM_ARCH_QWEN3` and `LLM_ARCH_GEMMA3` include the 7 per-projection norm tensor mappings plus standard tensors (see Section 2 for full mapping). Key differences:
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- Qwen3 includes `LLM_TENSOR_OUTPUT` (`"output"`); Gemma3 does not (uses tied embeddings directly)
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- Gemma3 additionally includes `LLM_TENSOR_ATTN_POST_NORM` (`"blk.%d.post_attention_norm"`) and `LLM_TENSOR_FFN_POST_NORM` (`"blk.%d.post_ffw_norm"`)
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### 4.5 load_tensors
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Both architectures load the 7 per-projection norm tensors as optional (`TENSOR_NOT_REQUIRED`):
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```cpp
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layer.attn_q_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.attn_k_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.attn_v_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.attn_out_norm_in = create_tensor(tn(...), {n_embd_head_k * n_head}, TENSOR_NOT_REQUIRED);
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layer.ffn_gate_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.ffn_up_norm_in = create_tensor(tn(...), {n_embd}, TENSOR_NOT_REQUIRED);
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layer.ffn_down_norm_in = create_tensor(tn(...), {n_ff}, TENSOR_NOT_REQUIRED);
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```
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Note: `o_proj.norm` input dimension is `n_embd_head_k * n_head` (Qwen3: 2048, Gemma3: 1024), `down_proj.norm` input dimension is `n_ff` (Qwen3: 3072, Gemma3: 2048).
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Both graph functions use the same per-projection norm pattern. The logic is fully backward compatible — when no `*_norm_in` tensors exist, behavior is identical to the original.
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**Attention per-projection norms:**
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```
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// Before Q/K/V matmul:
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if (layer.attn_q_norm_in) {
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cur_q = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps);
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cur_q = ggml_mul(ctx, cur_q, layer.attn_q_norm_in);
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} else {
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cur_q = cur;
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}
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Qcur = ggml_mul_mat(ctx, layer.wq, cur_q);
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// QK head norms applied after projection
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Qcur = ggml_rms_norm(ctx, Qcur, hparams.f_norm_rms_eps);
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Qcur = ggml_mul(ctx, Qcur, layer.attn_q_norm);
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```
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**O_proj norm** requires special handling because `llm_build_kv()` normally applies `wo` internally. Solution: pass `wo=NULL` to `llm_build_kv()`, then apply norm + wo manually:
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```
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cur = llm_build_kv(..., wo=NULL, ...); // returns attention output without o_proj
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if (layer.attn_out_norm_in) {
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cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps);
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cur = ggml_mul(ctx, cur, layer.attn_out_norm_in);
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}
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cur = ggml_mul_mat(ctx, layer.wo, cur);
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```
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**FFN per-projection norms:**
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```
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// Instead of llm_build_ffn(), manually:
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if (layer.ffn_gate_norm_in) {
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tmp_gate = rms_norm(cur) * gate_norm_in;
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} else {
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tmp_gate = cur;
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}
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tmp_gate = matmul(gate_proj, tmp_gate);
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tmp_gate = activation(tmp_gate); // SiLU for Qwen3, GELU for Gemma3
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// Similarly for up_proj
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tmp = tmp_gate * tmp_up;
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if (layer.ffn_down_norm_in) {
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tmp = rms_norm(tmp) * down_norm_in;
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}
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cur = matmul(down_proj, tmp);
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```
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**Gemma3-specific differences:**
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- Embedding scaling by `sqrt(n_embd)` (Gemma convention)
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- GELU activation instead of SiLU
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- Post-attention and post-FFN layer norms
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- `query_pre_attn_scalar` for attention scaling
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---
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## 5. GGUF Conversion Process
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Each model variant requires two GGUF files from **two different source models**:
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### 5.1 Qwen3 (0.6b)
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| GGUF Output | Source Model | Description |
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|-------------|-------------|-------------|
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| `embeddings-0.6b-f16.gguf` | `multilingual-e5-0.6b` (standard Qwen3) | F16 baseline |
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| `bitnet-embeddings-0.6b-f16-i2_s.gguf` | `bitnet-embeddings-0.6b` (BitNet ternary) | I2_S ternary packed |
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**F16 (from standard Qwen3 model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/multilingual-e5-0.6b \
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--outtype f16 \
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--outfile embeddings-0.6b-f16.gguf
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```
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What happens:
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1. Load `model.safetensors` (standard Qwen3 weights, bfloat16)
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2. Convert all 2D weights (projections, embeddings) to float16
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3. Convert norm weights to float16
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4. Write GGUF with `qwen3` architecture metadata and tokenizer
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**Output:** ~1.11 GiB (595.78M params)
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**I2_S (from BitNet model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/bitnet-embeddings-0.6b \
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--outfile bitnet-embeddings-0.6b-f16-i2_s.gguf --outtype i2_s
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```
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What happens:
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1. Load `model.safetensors` (BitNet ternary weights, bfloat16)
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2. Map HF tensor names to GGUF names, including 7 extra `*_norm_in` tensors per layer
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3. For each 2D linear weight: quantize to I2_S ternary packed format
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4. Keep embeddings (`token_embd.weight`) in float16
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5. Keep all norm weights in float16
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6. Skip `output.weight` (lm_head, not needed for embedding models)
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7. Write GGUF with `I2_S` type tag for quantized tensors
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**Output:** ~699 MiB (~50% of F16 size)
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### 5.2 Gemma3 (270m)
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| GGUF Output | Source Model | Description |
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|-------------|-------------|-------------|
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| `multilingual-e5-270m-f16.gguf` | `multilingual-e5-270m-260311` (standard Gemma3) | F16 baseline |
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| `bitnet-embeddings-270m-i2_s.gguf` | `bitnet-embeddings-270m` (BitNet ternary) | I2_S ternary packed |
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**F16 (from standard Gemma3 model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/multilingual-e5-270m-260311 \
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--outtype f16
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```
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What happens:
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1. Load `model.safetensors` (standard Gemma3 weights, bfloat16)
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2. Convert all 2D weights (projections, embeddings) to float16
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3. Convert norm weights to float16
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4. Write GGUF with `gemma3` architecture metadata and tokenizer
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**I2_S (from BitNet model):**
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```bash
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python3 utils/convert-bitnet-embedding-to-gguf.py \
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/path/to/bitnet-embeddings-270m \
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--outtype i2_s
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```
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What happens:
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1. Load `model.safetensors` (BitNet ternary weights, bfloat16)
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2. Map HF tensor names to GGUF names, including 7 extra `*_norm_in` tensors per layer
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3. For each 2D linear weight: quantize to I2_S ternary packed format
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4. Keep embeddings (`token_embd.weight`) in float16
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5. Keep all norm weights in float16
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6. Skip `output.weight` (lm_head, not needed for embedding models)
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7. Write GGUF with `I2_S` type tag for quantized tensors
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### 5.3 Why Two Different Source Models?
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- `multilingual-e5-*` is the **teacher/baseline model** with standard float weights, used as the F16 performance reference
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- `bitnet-embeddings-*` is the **1-bit quantized student model** with ternary weights and per-projection BitLinear norms, converted to I2_S for efficient CPU inference
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- Benchmarking compares both to measure the throughput gain and quality trade-off of ternary quantization
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### 5.4 Tensor Type Summary
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| Tensor | F16 (baseline) | I2_S (BitNet) |
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|--------|----------------|---------------|
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| Linear projections (q/k/v/o/gate/up/down) | float16 | I2_S (2-bit packed + float32 scale) |
|
||||
| Embedding (`token_embd.weight`) | float16 | float16 |
|
||||
| Per-projection norms (`*_norm_in`) | N/A (not present) | float16 |
|
||||
| Layer norms (attn_norm, ffn_norm, etc.) | float16 | float16 |
|
||||
| QK head norms (`attn_q_norm`, `attn_k_norm`) | float16 | float16 |
|
||||
| `output.weight` (lm_head) | skipped | skipped |
|
||||
|
||||
---
|
||||
|
||||
## 6. Additional Changes
|
||||
|
||||
### 6.1 ggml.c: F16 Norm Weight Support
|
||||
|
||||
Added `ggml_compute_forward_mul_f32_f16()` function to support element-wise multiplication where norm weights are stored in float16. Modified `ggml_compute_forward_mul()` to dispatch based on `src1->type`.
|
||||
|
||||
### 6.2 gguf-py: I2_S Type
|
||||
|
||||
Added `I2_S = 36` to `GGMLQuantizationType` enum and `(4, 1)` quant size in `constants.py`.
|
||||
|
||||
### 6.3 CMakeLists.txt: BitNet LUT Kernels Guard
|
||||
|
||||
Guarded `bitnet-lut-kernels.h` include with `if (GGML_BITNET_ARM_TL1 OR GGML_BITNET_X86_TL2)` to prevent build errors when LUT kernels are not available.
|
||||
|
||||
### 6.4 ggml-bitnet-mad.cpp: AVX512 SIMD
|
||||
|
||||
Added AVX512BW SIMD paths for I2_S dot product functions:
|
||||
- `ggml_vec_dot_i2_i8_s_1x1`
|
||||
- `ggml_vec_dot_i2_i8_s_1xN`
|
||||
- `ggml_vec_dot_i2_i8_s_Nx1`
|
||||
|
||||
---
|
||||
|
||||
## 7. Build and Run
|
||||
|
||||
```bash
|
||||
# Build with BitNet repo (includes I2_S support)
|
||||
cmake -S /path/to/BitNet -B build -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build --target llama-embedding llama-bench -j$(nproc)
|
||||
|
||||
# Run embedding inference (Qwen3 example)
|
||||
build/bin/llama-embedding -m bitnet-embeddings-0.6b-f16-i2_s.gguf \
|
||||
-p "hello world" --embd-normalize 2 --embd-output-format array
|
||||
|
||||
# Run embedding inference (Gemma3 example)
|
||||
build/bin/llama-embedding -m bitnet-embeddings-270m-i2_s.gguf \
|
||||
-p "hello world" --embd-normalize 2 --embd-output-format array
|
||||
|
||||
# Benchmark: F16 vs I2_S (Qwen3)
|
||||
build/bin/llama-bench -m embeddings-0.6b-f16.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
build/bin/llama-bench -m bitnet-embeddings-0.6b-f16-i2_s.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
# Benchmark: F16 vs I2_S (Gemma3)
|
||||
build/bin/llama-bench -m multilingual-e5-270m-f16.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
build/bin/llama-bench -m bitnet-embeddings-270m-i2_s.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
```
|
||||
@@ -1,302 +0,0 @@
|
||||
# BitNet Embeddings (Qwen3) GGUF Conversion Implementation
|
||||
|
||||
## 1. Background
|
||||
|
||||
`bitnet-embeddings-0.6b` is a Qwen3-based embedding model with BitNet per-projection RMSNorm (`BitLinear`). Each linear projection (q/k/v/o/gate/up/down) has a `.norm.weight` that applies RMSNorm to the input **before** the matmul:
|
||||
|
||||
```
|
||||
x → RMSNorm(x, norm.weight) → activation_quant(8bit) → matmul(weight_quant(ternary))
|
||||
```
|
||||
|
||||
This pattern does **not** exist in any standard llama.cpp architecture:
|
||||
- Standard Qwen3: no per-projection norms
|
||||
- Standard BitNet: has `attn_sub_norm`/`ffn_sub_norm` at different positions (after attention/gate*up, not before each projection)
|
||||
|
||||
### Model Config
|
||||
|
||||
- Architecture: `Qwen3Model`
|
||||
- hidden_size: 1024, num_attention_heads: 16, num_key_value_heads: 8
|
||||
- head_dim: 128 (note: != hidden_size/num_heads = 64)
|
||||
- intermediate_size: 3072, num_hidden_layers: 28
|
||||
- tie_word_embeddings: true
|
||||
- rope_theta: 1000000, rms_norm_eps: 1e-06
|
||||
|
||||
### Per-Layer Tensors (7 extra norm tensors per layer)
|
||||
|
||||
| Tensor | Shape |
|
||||
|--------|-------|
|
||||
| `self_attn.q_proj.norm.weight` | [1024] |
|
||||
| `self_attn.k_proj.norm.weight` | [1024] |
|
||||
| `self_attn.v_proj.norm.weight` | [1024] |
|
||||
| `self_attn.o_proj.norm.weight` | [2048] |
|
||||
| `mlp.gate_proj.norm.weight` | [1024] |
|
||||
| `mlp.up_proj.norm.weight` | [1024] |
|
||||
| `mlp.down_proj.norm.weight` | [3072] |
|
||||
|
||||
---
|
||||
|
||||
## 2. GGUF Tensor Name Mapping
|
||||
|
||||
| HF Name | GGUF Name | Notes |
|
||||
|----------|-----------|-------|
|
||||
| `embed_tokens.weight` | `token_embd.weight` | |
|
||||
| `norm.weight` | `output_norm.weight` | |
|
||||
| `layers.{i}.input_layernorm.weight` | `blk.{i}.attn_norm.weight` | |
|
||||
| `layers.{i}.post_attention_layernorm.weight` | `blk.{i}.ffn_norm.weight` | |
|
||||
| `layers.{i}.self_attn.q_proj.weight` | `blk.{i}.attn_q.weight` | |
|
||||
| `layers.{i}.self_attn.k_proj.weight` | `blk.{i}.attn_k.weight` | |
|
||||
| `layers.{i}.self_attn.v_proj.weight` | `blk.{i}.attn_v.weight` | |
|
||||
| `layers.{i}.self_attn.o_proj.weight` | `blk.{i}.attn_output.weight` | |
|
||||
| `layers.{i}.self_attn.q_norm.weight` | `blk.{i}.attn_q_norm.weight` | QK head norm |
|
||||
| `layers.{i}.self_attn.k_norm.weight` | `blk.{i}.attn_k_norm.weight` | QK head norm |
|
||||
| `layers.{i}.self_attn.q_proj.norm.weight` | `blk.{i}.attn_q_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.self_attn.k_proj.norm.weight` | `blk.{i}.attn_k_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.self_attn.v_proj.norm.weight` | `blk.{i}.attn_v_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.self_attn.o_proj.norm.weight` | `blk.{i}.attn_output_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.mlp.gate_proj.weight` | `blk.{i}.ffn_gate.weight` | |
|
||||
| `layers.{i}.mlp.up_proj.weight` | `blk.{i}.ffn_up.weight` | |
|
||||
| `layers.{i}.mlp.down_proj.weight` | `blk.{i}.ffn_down.weight` | |
|
||||
| `layers.{i}.mlp.gate_proj.norm.weight` | `blk.{i}.ffn_gate_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.mlp.up_proj.norm.weight` | `blk.{i}.ffn_up_norm_in.weight` | BitNet per-projection |
|
||||
| `layers.{i}.mlp.down_proj.norm.weight` | `blk.{i}.ffn_down_norm_in.weight` | BitNet per-projection |
|
||||
|
||||
---
|
||||
|
||||
## 3. Conversion Script
|
||||
|
||||
### `utils/convert-bitnet-embedding-to-gguf.py`
|
||||
|
||||
Standalone conversion script (safetensors → GGUF). Key features:
|
||||
|
||||
- Hardcoded HF→GGUF tensor name mapping (no dependency on llama.cpp's Python converter)
|
||||
- Supports three output types:
|
||||
- `--outtype f32`: all weights in float32
|
||||
- `--outtype f16`: 2D weights and embeddings as float16, norms as float16
|
||||
- `--outtype i2_s`: ternary weights packed in I2_S layout, non-ternary weights as float16
|
||||
- Writes `key_length` and `value_length` metadata for head_dim=128 (critical: default calculation would give wrong value 64)
|
||||
- GPT-2 BPE tokenizer handling with pre-tokenizer hash verification
|
||||
- Pooling type auto-detection from `modules.json` / `1_Pooling/config.json` (sentence-transformers convention)
|
||||
- EOS token override: uses `<|endoftext|>` (151643) for correct last-token pooling
|
||||
- Architecture string: `"qwen3"`
|
||||
|
||||
### I2_S Ternary Packing
|
||||
|
||||
The I2_S format packs ternary weights {-1, 0, +1} into 2-bit representation:
|
||||
|
||||
- Quantization: `scale = 1/mean(|w|)`, `q = round(w * scale).clamp(-1, 1)`
|
||||
- Encoding: `-1 → 0`, `0 → 1`, `+1 → 2`
|
||||
- Every 128 values form a block, packed into 32 bytes
|
||||
- Each byte stores 4 values: `byte = (c0 << 6) | (c1 << 4) | (c2 << 2) | c3`
|
||||
- Scale (float32) is appended at the end of the packed data buffer
|
||||
|
||||
### Tensor Type Assignment
|
||||
|
||||
| Tensor Type | f16 mode | i2_s mode |
|
||||
|-------------|----------|-----------|
|
||||
| 2D linear weights | float16 | I2_S ternary packed |
|
||||
| Embedding weights | float16 | float16 |
|
||||
| Norm weights (1D) | float16 | float16 |
|
||||
|
||||
Note: `output.weight` (lm_head) is skipped for embedding models — it is not needed (no token generation).
|
||||
|
||||
---
|
||||
|
||||
## 4. C++ Modifications (`3rdparty/llama.cpp/src/llama.cpp`)
|
||||
|
||||
### 4.1 New Tensor Enums
|
||||
|
||||
Added 7 new entries after `LLM_TENSOR_FFN_SUB_NORM`:
|
||||
|
||||
```cpp
|
||||
LLM_TENSOR_ATTN_Q_NORM_IN,
|
||||
LLM_TENSOR_ATTN_K_NORM_IN,
|
||||
LLM_TENSOR_ATTN_V_NORM_IN,
|
||||
LLM_TENSOR_ATTN_OUT_NORM_IN,
|
||||
LLM_TENSOR_FFN_GATE_NORM_IN,
|
||||
LLM_TENSOR_FFN_UP_NORM_IN,
|
||||
LLM_TENSOR_FFN_DOWN_NORM_IN,
|
||||
```
|
||||
|
||||
### 4.2 Tensor Name Mappings
|
||||
|
||||
Added to `LLM_ARCH_QWEN3` tensor name map:
|
||||
|
||||
```cpp
|
||||
{ LLM_TENSOR_ATTN_Q_NORM_IN, "blk.%d.attn_q_norm_in" },
|
||||
{ LLM_TENSOR_ATTN_K_NORM_IN, "blk.%d.attn_k_norm_in" },
|
||||
{ LLM_TENSOR_ATTN_V_NORM_IN, "blk.%d.attn_v_norm_in" },
|
||||
{ LLM_TENSOR_ATTN_OUT_NORM_IN, "blk.%d.attn_output_norm_in" },
|
||||
{ LLM_TENSOR_FFN_GATE_NORM_IN, "blk.%d.ffn_gate_norm_in" },
|
||||
{ LLM_TENSOR_FFN_UP_NORM_IN, "blk.%d.ffn_up_norm_in" },
|
||||
{ LLM_TENSOR_FFN_DOWN_NORM_IN, "blk.%d.ffn_down_norm_in" },
|
||||
```
|
||||
|
||||
### 4.3 Layer Struct Fields
|
||||
|
||||
Added to `struct llama_layer`:
|
||||
|
||||
```cpp
|
||||
struct ggml_tensor * attn_q_norm_in;
|
||||
struct ggml_tensor * attn_k_norm_in;
|
||||
struct ggml_tensor * attn_v_norm_in;
|
||||
struct ggml_tensor * attn_out_norm_in;
|
||||
struct ggml_tensor * ffn_gate_norm_in;
|
||||
struct ggml_tensor * ffn_up_norm_in;
|
||||
struct ggml_tensor * ffn_down_norm_in;
|
||||
```
|
||||
|
||||
### 4.4 load_tensors (LLM_ARCH_QWEN3)
|
||||
|
||||
Added optional loading with `TENSOR_NOT_REQUIRED`:
|
||||
|
||||
```cpp
|
||||
layer.attn_q_norm_in = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM_IN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.attn_k_norm_in = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM_IN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.attn_v_norm_in = create_tensor(tn(LLM_TENSOR_ATTN_V_NORM_IN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.attn_out_norm_in = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM_IN, "weight", i), {n_embd_head_k * n_head}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_gate_norm_in = create_tensor(tn(LLM_TENSOR_FFN_GATE_NORM_IN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up_norm_in = create_tensor(tn(LLM_TENSOR_FFN_UP_NORM_IN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_norm_in = create_tensor(tn(LLM_TENSOR_FFN_DOWN_NORM_IN, "weight", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
||||
```
|
||||
|
||||
Note: `o_proj.norm` input dimension is `n_embd_head_k * n_head` (=2048), `down_proj.norm` input dimension is `n_ff` (=3072).
|
||||
|
||||
### 4.5 build_qwen3() Graph Modifications
|
||||
|
||||
The `build_qwen3()` function was modified to conditionally apply per-projection RMSNorm. The logic is fully backward compatible — when no `*_norm_in` tensors exist, behavior is identical to original.
|
||||
|
||||
**Attention per-projection norms:**
|
||||
```
|
||||
// Before Q/K/V matmul:
|
||||
if (layer.attn_q_norm_in) {
|
||||
cur_q = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps);
|
||||
cur_q = ggml_mul(ctx, cur_q, layer.attn_q_norm_in);
|
||||
} else {
|
||||
cur_q = cur;
|
||||
}
|
||||
Qcur = ggml_mul_mat(ctx, layer.wq, cur_q);
|
||||
// Similarly for K, V
|
||||
```
|
||||
|
||||
**O_proj norm** requires special handling because `llm_build_kv()` normally applies `wo` internally. Solution: pass `wo=NULL` to `llm_build_kv()`, then apply norm + wo manually:
|
||||
|
||||
```
|
||||
cur = llm_build_kv(..., wo=NULL, ...); // returns attention output without o_proj
|
||||
if (layer.attn_out_norm_in) {
|
||||
cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps);
|
||||
cur = ggml_mul(ctx, cur, layer.attn_out_norm_in);
|
||||
}
|
||||
cur = ggml_mul_mat(ctx, layer.wo, cur);
|
||||
```
|
||||
|
||||
**FFN per-projection norms:**
|
||||
```
|
||||
// Instead of llm_build_ffn(), manually:
|
||||
if (layer.ffn_gate_norm_in) {
|
||||
tmp_gate = rms_norm(cur) * gate_norm_in;
|
||||
} else {
|
||||
tmp_gate = cur;
|
||||
}
|
||||
tmp_gate = matmul(gate_proj, tmp_gate);
|
||||
// Similarly for up_proj
|
||||
tmp = silu(tmp_gate) * tmp_up;
|
||||
|
||||
if (layer.ffn_down_norm_in) {
|
||||
tmp = rms_norm(tmp) * down_norm_in;
|
||||
}
|
||||
cur = matmul(down_proj, tmp);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. GGUF Conversion Process
|
||||
|
||||
There are two GGUF files to produce, from **two different source models**:
|
||||
|
||||
| GGUF Output | Source Model | Description |
|
||||
|-------------|-------------|-------------|
|
||||
| `embeddings-0.6b-f16.gguf` | `multilingual-e5-0.6b` (standard Qwen3) | F16 baseline, standard float16 weights |
|
||||
| `bitnet-embeddings-0.6b-f16-i2_s.gguf` | `bitnet-embeddings-0.6b` (BitNet ternary) | I2_S ternary packed weights |
|
||||
|
||||
### 5.1 F16 GGUF: from multilingual-e5-0.6b
|
||||
|
||||
The F16 GGUF is converted from the **standard (non-BitNet) model** `multilingual-e5-0.6b`, which has normal float weights and no per-projection RMSNorm. This uses llama.cpp's standard converter since it is a vanilla Qwen3 model:
|
||||
|
||||
```bash
|
||||
python3 /path/to/llama.cpp/convert_hf_to_gguf.py \
|
||||
/path/to/multilingual-e5-0.6b \
|
||||
--outtype f16 \
|
||||
--outfile embeddings-0.6b-f16.gguf
|
||||
```
|
||||
|
||||
**What happens:**
|
||||
1. Load `model.safetensors` (standard Qwen3 weights, bfloat16)
|
||||
2. Convert all 2D weights (projections, embeddings) to float16
|
||||
3. Convert norm weights to float32
|
||||
4. Write GGUF with `qwen3` architecture metadata and tokenizer
|
||||
|
||||
**Output:** ~1.11 GiB (595.78M params)
|
||||
|
||||
### 5.2 I2_S GGUF: from bitnet-embeddings-0.6b
|
||||
|
||||
The I2_S GGUF is converted from the **BitNet ternary model** `bitnet-embeddings-0.6b`, which has ternary weights {-1, 0, +1} and 7 extra per-projection RMSNorm tensors per layer. This uses the custom converter because the standard llama.cpp converter does not handle per-projection norms or I2_S quantization:
|
||||
|
||||
```bash
|
||||
python3 utils/convert-bitnet-embedding-to-gguf.py \
|
||||
/path/to/bitnet-embeddings-0.6b \
|
||||
--outfile bitnet-embeddings-0.6b-f16-i2_s.gguf --outtype i2_s
|
||||
```
|
||||
|
||||
**What happens:**
|
||||
1. Load `model.safetensors` (BitNet ternary weights, bfloat16)
|
||||
2. Map HF tensor names to GGUF names, including 7 extra `*_norm_in` tensors per layer (see Section 2)
|
||||
3. For each 2D linear weight (q/k/v/o/gate/up/down projections):
|
||||
- Compute scale: `scale = 1 / mean(|w|)`
|
||||
- Quantize: `q = round(w * scale).clamp(-1, 1)`
|
||||
- Encode: `-1 -> 0`, `0 -> 1`, `+1 -> 2`
|
||||
- Pack every 128 values into 32 bytes (4 values per byte, 2 bits each)
|
||||
- Append per-row float32 scale
|
||||
4. Keep embeddings (`token_embd.weight`) in float16 (not ternary)
|
||||
5. Keep all norm weights in float16
|
||||
6. Skip `output.weight` (lm_head, not needed for embedding models)
|
||||
7. Write GGUF with `I2_S` type tag for quantized tensors
|
||||
|
||||
**Output:** ~699 MiB (~50% of F16 size)
|
||||
|
||||
### 5.3 Why Two Different Source Models?
|
||||
|
||||
- `multilingual-e5-0.6b` is the **teacher/baseline model** with standard float weights, used as the F16 performance reference
|
||||
- `bitnet-embeddings-0.6b` is the **1-bit quantized student model** with ternary weights and per-projection BitLinear norms, converted to I2_S for efficient CPU inference
|
||||
- Benchmarking compares both to measure the throughput gain and quality trade-off of ternary quantization
|
||||
|
||||
### 5.4 Tensor Type Summary
|
||||
|
||||
| Tensor | F16 (from e5-0.6b) | I2_S (from bitnet-0.6b) |
|
||||
|--------|---------------------|-------------------------|
|
||||
| Linear projections (q/k/v/o/gate/up/down) | float16 | I2_S (2-bit packed + float32 scale) |
|
||||
| Embedding (`token_embd.weight`) | float16 | float16 |
|
||||
| Per-projection norms (`*_norm_in`) | N/A (not present) | float16 |
|
||||
| Layer norms (`attn_norm`, `ffn_norm`) | float32 | float16 |
|
||||
| QK head norms (`attn_q_norm`, `attn_k_norm`) | float32 | float32 |
|
||||
| `output.weight` (lm_head) | present | skipped |
|
||||
|
||||
---
|
||||
|
||||
## 6. Build and Run
|
||||
|
||||
```bash
|
||||
# Build with BitNet repo (includes I2_S support)
|
||||
cmake -S /path/to/BitNet -B build -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build --target llama-embedding llama-bench -j$(nproc)
|
||||
|
||||
# Run embedding inference
|
||||
build/bin/llama-embedding -m bitnet-embeddings-0.6b-f16-i2_s.gguf \
|
||||
-p "hello world" --embd-normalize 2 --embd-output-format array
|
||||
|
||||
# Benchmark: F16 vs I2_S
|
||||
build/bin/llama-bench -m embeddings-0.6b-f16.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
|
||||
build/bin/llama-bench -m bitnet-embeddings-0.6b-f16-i2_s.gguf \
|
||||
-t 8 -p 128,256,512,1024,2048 -n 32,64 -r 3 -ngl 0
|
||||
```
|
||||
Reference in New Issue
Block a user