DeepGEMM/README_NVFP4.md

# DeepGEMM NVFP4 Mega MoE Kernel

## Overview

A native NVFP4 mega MoE kernel for DeepGEMM that uses `kind::mxf4nvf4.block_scale.scale_vec::4X`
to consume NVFP4 weights (E2M1 + UE4M3 block scales, group_size=16) directly on B200 (SM100a).

**HARD RULE: MoE experts stay in NVFP4. Never convert to MXFP4.**

## SM100a (B200) Hardware Support

**B200 (SM100a) DOES support `kind::mxf4nvf4` with `scale_vec::4X`** (block16, UE4M3 scales).
Documented in PTX ISA 8.7 (CUDA 12.8+), confirmed by NVIDIA/CUTLASS/Colfax.

The key requirement: target **`sm_100a`** (not `sm_100`). The `a` suffix enables the FP4
block-scaled instructions including `mxf4nvf4`. Targeting plain `sm_100` will produce
"Feature '.scale_vec::4X' not supported on .target 'sm_100f'" errors.

## Kernel Architecture

```
sm100_fp8_nvfp4_mega_moe_impl
├── kGranK = 16 (NVFP4 native block size)
├── kind::mxf4nvf4.block_scale.scale_vec::4X PTX instruction
├── float_ue4m3_t instruction descriptor
├── SF layout: scale_vec::4X, 4 TMEM sub-columns per UMMA atom
├── UTCCP copy: i*8 stride (4X layout, 8 TMEM cols per 128-element group)
├── kNumSFATmemCols = SF_BLOCK_M / 32 * 4
├── kNumSFBTmemCols = SF_BLOCK_N / 32 * 4
├── kNumSFUint32 = kHidden / 64 (4 UE4M3 per int32)
├── UE4M3 L1 epilogue (float → e4m3 cast, sign bit cleared)
└── recipe = (1, 1, 16)
```

## Critical: mxf4nvf4 Requires FP4×FP4 (Packed)

**The `mxf4nvf4` PTX instruction requires BOTH A and B to be FP4 (E2M1 packed, 2 per byte).**

Unlike `mxf8f6f4` which reads FP4 from SMEM as if it were FP8 (1 byte/element, low nibble=value,
high nibble=ignored via `float_e2m1_unpacksmem_t`), `mxf4nvf4` reads FP4 **packed** from SMEM:
- 2 E2M1 values per byte (low nibble=first, high nibble=second)
- Advances K by 32 bytes per UMMA_K=64 atom (64 packed nibbles = 32 bytes)
- Byte stride of `BLOCK_K / 2` per K-row (not `BLOCK_K * sizeof(dtype_t)`)

### SMEM Layout for mxf4nvf4

```cpp
using a_dtype_t = cutlass::float_e2m1_t;  // packed, 4 bits/element
using b_dtype_t = cutlass::float_e2m1_t;

static_assert(cutlass::sizeof_bits_v<a_dtype_t> == 4,
    "mxf4nvf4 requires packed FP4 (4 bits/element) in SMEM");

constexpr uint32_t kSwizzleAMode = BLOCK_K / 2;  // 64 bytes (packed)
constexpr uint32_t kSwizzleBMode = BLOCK_K / 2;  // 64 bytes (packed)
constexpr uint32_t SMEM_A_SIZE_PER_STAGE = LOAD_BLOCK_M * BLOCK_K / 2;  // packed
constexpr uint32_t SMEM_B_SIZE_PER_STAGE = LOAD_BLOCK_N * BLOCK_K / 2;  // packed
```

### UMMA Descriptors

The `make_umma_desc` and `advance_umma_desc_lo` helpers use `sizeof(dtype_t)` for byte math.
Since `sizeof(float_e2m1_t) == 1` but the real stride is 4 bits/element, pass the effective
byte-stride parameters:

```cpp
// Use BLOCK_K/2 as the template param and uint8_t as dtype for correct byte math
auto a_desc = make_umma_desc<K, LOAD_BLOCK_M, BLOCK_K/2, kSwizzleAMode, false, uint8_t>(...);
// Advance by UMMA_K/2 bytes per atom (64 packed elements = 32 bytes)
a_desc.lo = advance_umma_desc_lo<K, LOAD_BLOCK_M, kSwizzleAMode, uint8_t>(..., k * (UMMA_K / 2));
```

### L1 Epilogue (Packed FP4 Output)

The L1 epilogue writes packed E2M1 to SMEM using direct `st.shared.u16` (no STSM, no swizzle for v1):

```
Each lane quantizes 4 BF16 → 4 E2M1 nibbles → 2 bytes (uint16)
Lane mapping: row_in_atom = lane_idx / 4, col_pair = lane_idx % 4
Row stride: L1_OUT_BLOCK_N / 2 bytes (packed)
kWarpBytesPerRow = L1_OUT_BLOCK_N / 8
```

TMA store: `block_n / 4` inner dim, no swizzle (`CU_TENSOR_MAP_SWIZZLE_NONE` for v1).

### Host-side TMA Descriptors

All activation TMA descriptors use packed FP4 dimensions:
- K-dim: `hidden / 2` bytes (not `hidden` elements)
- Inner block: `block_k / 2` bytes
- Swizzle mode: `swizzle_acts_mode / 2`
- `fp4_unpacked_smem = false` for all activation descriptors
- L1 output: `block_n / 4` inner, swizzle=0 (no swizzle)

### Pybind Buffer Sizing

The `NVFP4SymmBuffer` uses packed byte counts:
- `fp4_token_layout = layout::Data(hidden / 2)` (packed bytes per token)
- `fp4_intermediate_token_layout = layout::Data(intermediate_hidden / 2)`
- Tensor shapes: `{M, hidden / 2}` for uint8 packed activations

## Weight Transformation Pipeline

```
NVFP4 Checkpoint                         Kernel Format
┌─────────────────────┐                 ┌────────────────────────┐
│ weight: uint8       │────────────────→│ int8 (E2M1, same)     │
│ (E2M1, 2 per byte)  │  .view(int8)    │ packed, interleaved    │
├─────────────────────┤                 ├────────────────────────┤
│ weight_scale:       │ 1. fold global  │ int32 (TMA-aligned     │
│ float8_e4m3fn       │ 2. pack 4→i32   │  UTCCP layout,         │
│ (UE4M3, group=16)   │ 3. transpose    │  gran_k=16)            │
├─────────────────────┤ 4. TMA-align    └────────────────────────┘
│ weight_scale_2:     │
│ float32 (global)    │──folded into block scales before packing
└─────────────────────┘
```

**NO UE4M3→UE8M0 conversion. NO block16→block32 merge.** The kernel consumes
native UE4M3 scales with block16 grouping.

## Key Differences from MXFP4 mega_moe

| Parameter | MXFP4 | NVFP4 (this kernel) |
|-----------|-------|---------------------|
| `kGranK` | 32 | 16 |
| PTX instruction | `mxf8f6f4.block_scale` | `mxf4nvf4.block_scale.scale_vec::4X` |
| Scale factor type | `float_ue8m0_t` | `float_ue4m3_t` |
| SF vector size | block32 / 2X | block16 / 4X |
| TMEM SF cols (SFA) | `SF_BLOCK_M / 32` | `SF_BLOCK_M / 32 * 4` |
| UTCCP col stride | `i * 4` | `i * 8` |
| `kNumSFUint32` | `kHidden / 128` | `kHidden / 64` |
| SMEM dtype | `float_e2m1_unpacksmem_t` (1 byte/elem) | `float_e2m1_t` (packed, 4 bits/elem) |
| SMEM row stride | `BLOCK_K * sizeof(dtype_t)` = 128 | `BLOCK_K / 2` = 64 (packed) |
| UMMA_K | 32 | 64 |
| L1 epilogue | STSM + UE8M0 scales | st.shared.u16 + UE4M3 scales, no swizzle |
| recipe | `(1, 1, 32)` | `(1, 1, 16)` |

## Build History

| Build | Error | Fix |
|-------|-------|-----|
| 1–6 | Dockerfile/build issues | NVRTC symlink, CPATH, PYTHONPATH |
| 7 | `kPackedFP4` type mismatch | uint8→int8 view |
| 9 | SF stride assertion | MN-major layout + TMA alignment |
| 10 | `transform_sf` no gran_k=16 | C++ fix |
| 11 | SF dtype float8_e4m3fn rejected | Pack UE4M3→int32 first |
| 12–14 | SF stride layout | Transpose to MN-major |
| 15 | SymmBuffer too small | NVFP4-specific SymmBuffer (2× SF) |
| 16 | `ImportError` | Python wrapper |
| **17** | **NVCC: `scale_vec::4X` not on sm_100f** | **Wrong arch: need `sm_100a`** |
| 18 | `scale_vec::2X` also failed | Same — `sm_100a` required |
| 19 | kGranK still 16 in C++ binding | Should stay 16 — was wrongly changed to 32 |
| 20 | `uint32 >> 23` fails | Cast to int32 first |
| 22 | Garbled output | Fell back to mxf8f6f4 — should use mxf4nvf4 on sm_100a |
| 23–24 | transform_nvfp4 l1_weight_scale_2 | Added global scale folding to Python |
| 25 | Triton float8→uint8 cast | Manual FP32→E4M3 bit manipulation |
| 25 | Triton `__rpow__` | IEEE 754 bit-level 2^exp |
| 26–28 | Unpacked SMEM (wrong for mxf4nvf4) | Half-zeroed accumulators |
| 29–31 | Packed FP4 SMEM + STSM | Wrong: STSM writes 1 byte/elem, MMA reads 2/elem |
| **32** | **Full packed FP4 revert** | **float_e2m1_t, BLOCK_K/2 swizzle, UMMA desc fixes** |
| 33 | Syntax error in patch | Orphan `@triton.jit` decorator |
| 34 | Triton nested `tl.where` | Sum-of-comparisons for E2M1 quantization |
| 35 | Triton `constexpr[0]` indexing | `tl.split()` for E2M1 pair packing |

## Remaining Work

- [ ] End-to-end quality test on B200 (Build 35+)
- [ ] Add 32B/64B swizzle to L1 epilogue for perf (v2)
- [ ] Optimize L1→L2 bandwidth with packed format
- [ ] Performance benchmarking vs standard FusedMoE path