Add bitnet-embeddings-0.6b model adaptation and GGUF conversion tools

docs/bitnet-embeddings-qwen3-gguf-conversion.md

@@ -0,0 +1,302 @@
+# 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
+```

src/ggml-bitnet-lut.cpp

@@ -5,9 +5,16 @@
 #include <stdio.h>
 #include <stdlib.h>
 
+#ifdef __x86_64__
+#include <immintrin.h>
+#endif
+
 #include "ggml-bitnet.h"
 #include "ggml-quants.h"
+
+#if defined(GGML_BITNET_ARM_TL1) || defined(GGML_BITNET_X86_TL2)
 #include "bitnet-lut-kernels.h"
+#endif
 
 #if defined(GGML_BITNET_ARM_TL1)