[blackwell][draft] sm100: INT4 grouped GEMM Triton kernel (#230)

batchgen_kernels/triton/__init__.py

@@ -33,3 +33,7 @@
 from batchgen_kernels.triton.fused_dequant_gemm import fused_fp8_bf16_gemm
 from batchgen_kernels.triton.fused_q_absorb import fused_q_absorb_query_states
 from batchgen_kernels.triton.fused_out_absorb import fused_out_absorb_reshape
+from batchgen_kernels.triton.int4_grouped_gemm import (
+    int4_grouped_gemm,
+    int4_moe_grouped_gemm,
+)

batchgen_kernels/triton/int4_grouped_gemm.py

@@ -0,0 +1,163 @@
+"""INT4 (W4A16) GEMM Triton kernels for SM100 (Blackwell).
+
+Pure-Triton port of the Hopper WGMMA INT4 grouped MoE kernels (K2.5 decode)
+whose `.cu` uses `wgmma`/TMA intrinsics not built for sm_100a.
+
+Weight / scale layout (matches the SM90a `.cu`, e.g.
+`single_expert_int4_wgmma.cu::load_decode_rhs_int4_swizzled`):
+  * `w_packed` : [N, K // 2] uint8 — output-channel major, two INT4 values per
+    byte along K. The **low** nibble `(byte & 0x0F)` is the even K index, the
+    **high** nibble `((byte >> 4) & 0x0F)` is the odd K index.
+  * `scale`    : [N, K // group_size] bf16 — one scale per group of
+    `group_size` (=32) contiguous K values, shared across the group.
+  * dequant    : `w[n, k] = (nibble - 8) * scale[n, k // group_size]`
+  * GEMM       : `out[m, n] = sum_k x[m, k] * w[n, k]`  (i.e. `x @ dequant(W).T`).
+"""
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_M': 16, 'BLOCK_N': 64, 'BLOCK_KP': 64}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 32, 'BLOCK_N': 128, 'BLOCK_KP': 64}, num_stages=3, num_warps=4),
+        triton.Config({'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_KP': 64}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_KP': 32}, num_stages=3, num_warps=8),
+    ],
+    key=['M', 'K', 'N', 'GROUP_SIZE'],
+)
+@triton.jit
+def _int4_gemm_kernel(
+    x_ptr, w_ptr, scale_ptr, out_ptr,
+    M, K, N,
+    stride_xm, stride_xk,
+    stride_wn, stride_wk,      # w_packed: [N, K//2] uint8
+    stride_sn, stride_sk,      # scale:    [N, K//group_size] bf16
+    stride_om, stride_on,
+    GROUP_SIZE: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_KP: tl.constexpr,    # packed-K (bytes) per iteration; real-K = 2*BLOCK_KP
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    offs_kp = tl.arange(0, BLOCK_KP)
+
+    Kp = K // 2
+    # packed-K bytes per scale group (group_size K-values -> group_size//2 bytes)
+    GROUP_BYTES = GROUP_SIZE // 2
+
+    m_mask = offs_m[:, None] < M
+    n_mask_out = offs_n[None, :] < N      # [1, BLOCK_N] for output/x-side
+    n_mask_w = offs_n[:, None] < N        # [BLOCK_N, 1] for weight/scale rows
+
+    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
+    for kp_start in range(0, Kp, BLOCK_KP):
+        kp = kp_start + offs_kp                      # [BLOCK_KP] packed indices
+        kp_mask = kp < Kp
+
+        # x even / odd columns: x[:, 2*kp] and x[:, 2*kp+1]
+        x_even = tl.load(
+            x_ptr + offs_m[:, None] * stride_xm + (2 * kp[None, :]) * stride_xk,
+            mask=m_mask & kp_mask[None, :], other=0.0,
+        )
+        x_odd = tl.load(
+            x_ptr + offs_m[:, None] * stride_xm + (2 * kp[None, :] + 1) * stride_xk,
+            mask=m_mask & kp_mask[None, :], other=0.0,
+        )
+
+        # packed weight byte [BLOCK_N, BLOCK_KP]
+        wb = tl.load(
+            w_ptr + offs_n[:, None] * stride_wn + kp[None, :] * stride_wk,
+            mask=n_mask_w & kp_mask[None, :], other=0,
+        )
+        lo = ((wb & 0x0F).to(tl.int32) - 8).to(tl.bfloat16)
+        hi = (((wb >> 4) & 0x0F).to(tl.int32) - 8).to(tl.bfloat16)
+
+        # scale [BLOCK_N, BLOCK_KP]: group index = (2*kp) // group_size = kp // GROUP_BYTES
+        sg = kp // GROUP_BYTES
+        sc = tl.load(
+            scale_ptr + offs_n[:, None] * stride_sn + sg[None, :] * stride_sk,
+            mask=n_mask_w & kp_mask[None, :], other=0.0,
+        )
+        w_lo = (lo * sc)          # [BLOCK_N, BLOCK_KP] bf16
+        w_hi = (hi * sc)
+
+        acc += tl.dot(x_even, tl.trans(w_lo), out_dtype=tl.float32)
+        acc += tl.dot(x_odd, tl.trans(w_hi), out_dtype=tl.float32)
+
+    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
+    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=m_mask & n_mask_out)
+
+
+def int4_grouped_gemm(
+    x: torch.Tensor,          # [M, K] BF16
+    w_packed: torch.Tensor,   # [N, K // 2] uint8 (INT4 packed, output-major)
+    scales: torch.Tensor,     # [N, K // group_size] BF16
+    group_size: int = 32,
+) -> torch.Tensor:
+    """Single-expert INT4×BF16 GEMM with per-group dequant. Returns [M, N] BF16.
+
+    Computes `out = x @ dequant(w_packed, scales).T` where
+    `dequant[n, k] = ((nibble(n,k) - 8) * scales[n, k // group_size])`.
+    """
+    assert x.dim() == 2, f"x must be 2D, got {x.shape}"
+    M, K = x.shape
+    if w_packed.dtype == torch.int32:
+        w_packed = w_packed.view(torch.uint8)
+    assert w_packed.dtype == torch.uint8, f"w_packed must be uint8, got {w_packed.dtype}"
+    N, Kp = w_packed.shape
+    assert Kp == K // 2, f"w_packed K mismatch: x K={K} -> expected {K // 2}, got {Kp}"
+    assert K % group_size == 0, f"K={K} not divisible by group_size={group_size}"
+
+    x = x.contiguous()
+    w_packed = w_packed.contiguous()
+    scales = scales.contiguous()
+
+    out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
+    grid = lambda meta: (
+        triton.cdiv(M, meta['BLOCK_M']),
+        triton.cdiv(N, meta['BLOCK_N']),
+    )
+    _int4_gemm_kernel[grid](
+        x, w_packed, scales, out,
+        M, K, N,
+        x.stride(0), x.stride(1),
+        w_packed.stride(0), w_packed.stride(1),
+        scales.stride(0), scales.stride(1),
+        out.stride(0), out.stride(1),
+        GROUP_SIZE=group_size,
+    )
+    return out
+
+
+def int4_moe_grouped_gemm(
+    x: torch.Tensor,                  # [total_M, K] BF16 (sorted by expert)
+    w_packed_list,                    # list[Tensor [N, K//2] uint8] per expert
+    scales_list,                      # list[Tensor [N, K//group_size] bf16] per expert
+    m_offsets,                        # [num_experts + 1] int (row offsets into x)
+    group_size: int = 32,
+) -> torch.Tensor:
+    """Batched INT4 MoE GEMM across experts (decode: small M per expert).
+
+    Tokens are assumed pre-sorted by expert; `m_offsets[e]:m_offsets[e+1]` is the
+    contiguous row range for expert `e`. Returns [total_M, N] BF16.
+    """
+    total_M, K = x.shape
+    N = w_packed_list[0].shape[0]
+    out = torch.empty((total_M, N), dtype=torch.bfloat16, device=x.device)
+    num_experts = len(w_packed_list)
+    offs = m_offsets.tolist() if torch.is_tensor(m_offsets) else list(m_offsets)
+    for e in range(num_experts):
+        lo, hi = offs[e], offs[e + 1]
+        if hi <= lo:
+            continue
+        out[lo:hi] = int4_grouped_gemm(
+            x[lo:hi], w_packed_list[e], scales_list[e], group_size=group_size,
+        )
+    return out

docs/BLACKWELL_KERNELS_WIP.md

@@ -0,0 +1 @@
+# WIP: [wip] blackwell: sm100 INT4 grouped GEMM Triton stub