feat(inf-002): Transformer Forward Pass — native LLM inference, 18 tests

transformer_forward.zig (977 lines):
- LLaMA-style transformer: RMSNorm, RoPE, GQA attention, SwiGLU FFN
- SIMD-friendly matVec with 8-wide unrolling and scalar tail
- KV cache with per-layer/position/kv-head storage
- RoPE with precomputed cos/sin cache, correct rotation formula
- Numerically stable softmax (subtract max before exp)
- Full generation loop: forward -> sample -> next token
- Top-p nucleus sampling + greedy argmax
- InferenceStats with FLOP counting and memory estimation
- LCG PRNG with Xavier-like weight init for deterministic tests
- 18 tests: norms, matmul, activations, attention, forward, generation
- build.zig wired: test-transformer-forward step
- Tech tree 49/56 (88%)

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

Co-Authored-By: Claude <noreply@anthropic.com>

Author: gHashTag

.ralph/TECH_TREE.md

@@ -82,7 +82,7 @@
 |CORE-002|Multi-Language Codegen|core|+42 target languages|
 |CORE-003|Bytecode VM|core|+500% execution speed vs interpreter|
 |**INF-001**|**GGUF Parser**|**inference**|**gguf_parser.zig (850 lines): GGUF v3 binary parser, ByteReader, 13 value types, tensor info, Q4_0/Q8_0 dequant, f16-to-f32, model config extraction, GGUFBuilder for round-trip tests, 20 tests, build.zig wired**|
-|INF-002|Transformer Forward Pass|inference|Native LLM inference|
+|**INF-002**|**Transformer Forward Pass**|**inference**|**transformer_forward.zig (960 lines): LLaMA-style transformer, RMSNorm, RoPE cache, SIMD matVec, GQA attention, SwiGLU FFN, KV cache, generation loop, top-p sampling, inference stats, 18 tests, build.zig wired**|
 |DEP-001|Docker Container|deployment|Portable deployment|
 |DEP-002|Fly.io Integration|deployment|Global edge deployment|
 |OPT-T01|Ternary Weight Quantization|optimization|20x weight compression|
@@ -104,7 +104,7 @@
 | Branch | Done | Total | % |
 |--------|------|-------|---|
 |Core|3|4|75%|
-|**Inference**|**3**|**5**|**60%**|
+|**Inference**|**4**|**5**|**80%**|
 |Deployment|2|4|50%|
 |**Optimization**|**16**|**16**|**100%**|
 |Hardware|0|3|0%|
@@ -114,10 +114,10 @@
 |Visualization|1|1|100%|
 |**Nexus**|**10**|**10**|**100%**|
 |Multilingual|3|3|100%|
-|**Total**|**48**|**56**|**86%**|
+|**Total**|**49**|**56**|**88%**|
 
 ## 🎯 Recommended Next (highest ROI)
-1. **INF-002** Transformer Forward Pass — native LLM inference with ternary ops
+1. **DEP-001** Docker Container — portable deployment, enables CI testing
 2. **CORE-004** JIT Compilation — needs HW-001 but provides 500% execution speed
 3. **DEP-003** Auto-Scaling — elastic infrastructure, prerequisite for DEP-004
 

build.zig

@@ -1882,4 +1882,17 @@ pub fn build(b: *std.Build) void {
     const gen_gguf_parser_step = b.step("test-gguf-parser", "Test INF-001 GGUF Parser — load any GGUF model");
     gen_gguf_parser_step.dependOn(&run_gen_gguf_parser_tests.step);
     test_step.dependOn(&run_gen_gguf_parser_tests.step);
+
+    // Generated Transformer Forward Pass tests (INF-002: Native LLM inference)
+    const gen_tfm_fwd_tests = b.addTest(.{
+        .root_module = b.createModule(.{
+            .root_source_file = b.path("generated/transformer_forward.zig"),
+            .target = target,
+            .optimize = optimize,
+        }),
+    });
+    const run_gen_tfm_fwd_tests = b.addRunArtifact(gen_tfm_fwd_tests);
+    const gen_tfm_fwd_step = b.step("test-transformer-forward", "Test INF-002 Transformer Forward Pass — native LLM inference");
+    gen_tfm_fwd_step.dependOn(&run_gen_tfm_fwd_tests.step);
+    test_step.dependOn(&run_gen_tfm_fwd_tests.step);
 }