Kernel for matmul while unpacking int2 weights

kernel.py

import torch

import triton
import triton.language as tl

def unpack_weights(packed: torch.Tensor, bits: int = 2) -> torch.Tensor:
    values_per_item = 8 // bits
    packed_shape = packed.shape

    if len(packed_shape) == 1:
        original_row_dim = packed_shape[0] * values_per_item
        unpacked_shape = (original_row_dim,)
    else:
        original_row_dim = packed_shape[0] * values_per_item
        unpacked_shape = (original_row_dim, *packed_shape[1:])

    unpacked = torch.zeros(unpacked_shape, device=packed.device, dtype=torch.uint8)

    for i in range(values_per_item):
        start = i * packed_shape[0]
        end = start + packed_shape[0]
        mask = (3 << (2 * i))
        unpacked[start:end] = (packed & mask) >> (2 * i)

    unpacked = unpacked.to(torch.float16) - 1
    return unpacked

def pack_weights(intweights: torch.Tensor, bits: int) -> torch.Tensor:
    intweights += 1
    original_shape = intweights.shape
    values_per_item = 8 // bits
    row_dim = (original_shape[0] + values_per_item - 1) // values_per_item

    if len(original_shape) == 1:
        packed_tensor_shape = (row_dim,)
    else:
        packed_tensor_shape = (row_dim, *original_shape[1:])

    packed = torch.zeros(packed_tensor_shape, device=intweights.device, dtype=torch.uint8)
    unpacked = intweights.to(torch.uint8)

    def lshift(t: torch.Tensor, bits: int):
        return t << bits

    it = min(values_per_item, (original_shape[0] // row_dim) + 1)
    for i in range(it):
        start = i * row_dim
        end = min(start + row_dim, original_shape[0])
        packed[: (end - start)] |= lshift(unpacked[start:end], bits * i)

    return packed


def get_cuda_autotune_config():
    return [
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3,
                      num_warps=8),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3,
                      num_warps=8),
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3,
                      num_warps=8),
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4}, num_stages=4,
                      num_warps=4)
    ]

@triton.autotune(
    configs=get_cuda_autotune_config(),
    key=['M', 'N', 'K'],
)
@triton.jit
def matmul_kernel(
        a_ptr, b_ptr, c_ptr,
        M, N, K,
        stride_am, stride_ak,  #
        stride_bk, stride_bn,  #
        stride_cm, stride_cn,
        BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,  
        GROUP_SIZE_M: tl.constexpr,  #
):

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)

    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.int32)
    for i in range(4) : 
        b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
        for j in range(0, tl.cdiv(K // 4, BLOCK_SIZE_K) ):
            k = i * tl.cdiv(K // 4, BLOCK_SIZE_K) + j 
            # BLOCK_SIZE_K must be a divisor of K / 4 
            a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0)
            b_uint8 = tl.load(b_ptrs, mask=offs_k[:, None] < K // 4 - j * BLOCK_SIZE_K, other=0)
            mask = 3<<(2*i)
            b = ((b_uint8 & mask) >> (2*i))
            # We accumulate along the K dimension.
            tensor_full = tl.full((1,), 1, dtype=tl.int8)

            accumulator += tl.dot(a, (b.to(tl.int8) - tensor_full), out_dtype=tl.int32)
            # Advance the ptrs to the next K block.
            a_ptrs += BLOCK_SIZE_K * stride_ak
            b_ptrs += BLOCK_SIZE_K * stride_bk
    
    c = accumulator

    # -----------------------------------------------------------
    # Write back the block of the output matrix C with masks.
    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, c, mask=c_mask)


def matmul(a, b):
    assert a.shape[1] == b.shape[0] * 4, "Incompatible dimensions, the weight matrix need to be packed"
    assert a.is_contiguous(), "Matrix A must be contiguous"
    M, K = a.shape
    _, N = b.shape
    c = torch.empty((M, N), device=a.device, dtype=torch.float16)
    grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
    matmul_kernel[grid](
        a, b, c,  #
        M, N, K,  #
        a.stride(0), a.stride(1),  #
        b.stride(0), b.stride(1),  #
        c.stride(0), c.stride(1),  #
    )
    return c



size = 2048
ht = torch.randint(-127, 127, (13, 1, size*4), device='cuda', dtype=torch.int8)
u = torch.randint(0,255,(size*4, size), device='cuda', dtype=torch.uint8)

B, M, N = ht.size()
triton_output = matmul(ht.view(B*M, N), u.T.contiguous()).view(B, M, -1)

assert (pack_weights(unpack_weights(u.T), 2) == u.T).all()
unpacked = unpack_weights(u.T, bits=2).T
torch_output =  torch.matmul(ht.half(), unpacked.T.contiguous())

print("triton = ",triton_output)
print("torch = ",torch_output)

if torch.allclose(triton_output, torch_output, atol=1e-2, rtol=1e-2):
    print("Results match")
else:
    print("Results differ")