import triton
import triton.language as tl
import torch
import custom_autotune


# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
    configs=[
        triton.Config({'BLOCK_SIZE_M': 256, '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': 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),
        # These provided a benefit on a 3090
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, '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': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, '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': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, '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': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
    ],
    key=['M', 'N'],
    nearest_power_of_two=True,
)


@triton.jit
def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
                      scales_ptr, zeros_ptr, g_ptr,
                      M, N, K, bits, maxq,
                      stride_am, stride_ak,
                      stride_bk, stride_bn,
                      stride_cm, stride_cn,
                      stride_scales, stride_zeros,
                      BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
                      GROUP_SIZE_M: tl.constexpr):
    """
    Compute the matrix multiplication C = A x B.
    A is of shape (M, K) float16
    B is of shape (K//8, N) int32
    C is of shape (M, N) float16
    scales is of shape (G, N) float16
    zeros is of shape (G, N) float16
    g_ptr is of shape (K) int32 
    """
    infearure_per_bits = 32 // bits

    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_k = tl.cdiv(K, BLOCK_SIZE_K)
    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 % 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)
    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
    a_mask = (offs_am[:, None] < M)
    # b_ptrs is set up such that it repeats elements along the K axis 8 times
    b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
    g_ptrs = g_ptr + offs_k
    # shifter is used to extract the N bits of each element in the 32-bit word from B
    scales_ptrs = scales_ptr + offs_bn[None, :]
    zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits) 
    
    shifter = (offs_k % infearure_per_bits) * bits
    zeros_shifter = (offs_bn % infearure_per_bits) * bits
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
            
    for k in range(0, num_pid_k):
        g_idx = tl.load(g_ptrs)

        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
        scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        
        zeros = (zeros >> zeros_shifter[None, :]) & maxq
        zeros = (zeros + 1)
        
        a = tl.load(a_ptrs, mask=a_mask, other=0.0)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

        # Now we need to unpack b (which is N-bit values) into 32-bit values
        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
        b = (b - zeros) * scales  # Scale and shift
        # ! Convert to fp16
        b = b.to(tl.float16)
        a = a.to(tl.float16)

        accumulator += tl.dot(a, b)
        a_ptrs += BLOCK_SIZE_K
        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
        g_ptrs += BLOCK_SIZE_K

    c = accumulator.to(tl.float16)
    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
    tl.store(c_ptrs, c, mask=c_mask)
    

# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
    configs=[
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 256, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        # These provided a benefit on a 3090
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
    ],
    key=['M', 'K'],
    nearest_power_of_two=True,
)


@triton.jit
def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
                            scales_ptr, zeros_ptr, g_ptr,
                            M, N, K, bits, maxq,
                            stride_am, stride_ak,
                            stride_bk, stride_bn,
                            stride_cm, stride_cn,
                            stride_scales, stride_zeros,
                            BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
                            GROUP_SIZE_M: tl.constexpr):
    """
    Compute the matrix multiplication C = A x B.
    A is of shape (M, N) float16
    B is of shape (K//8, N) int32
    C is of shape (M, K) float16
    scales is of shape (G, N) float16
    zeros is of shape (G, N) float16
    g_ptr is of shape (K) int32 
    """
    infearure_per_bits = 32 // bits

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_k
    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 % group_size_m)
    pid_k = (pid % num_pid_in_group) // group_size_m

    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
    offs_n = tl.arange(0, BLOCK_SIZE_N)
    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
    a_mask = (offs_am[:, None] < M)
    # b_ptrs is set up such that it repeats elements along the K axis 8 times
    b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
    g_ptrs = g_ptr + offs_bk
    g_idx = tl.load(g_ptrs)
    
    # shifter is used to extract the N bits of each element in the 32-bit word from B
    scales_ptrs = scales_ptr + offs_n[None, :]  + g_idx[:, None] * stride_scales
    zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
    
    shifter = (offs_bk % infearure_per_bits) * bits
    zeros_shifter = (offs_n % infearure_per_bits) * bits
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
    
    for k in range(0, num_pid_n):
        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
        scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        
        zeros = (zeros >> zeros_shifter[None, :]) & maxq
        zeros = (zeros + 1)
        
        a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

        # Now we need to unpack b (which is N-bit values) into 32-bit values
        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
        b = (b - zeros) * scales  # Scale and shift
        b = tl.trans(b)
        # ! Convert to fp16
        b = b.to(tl.float16)
        a = a.to(tl.float16)

        accumulator += tl.dot(a, b)
        a_ptrs += BLOCK_SIZE_N
        b_ptrs += BLOCK_SIZE_N
        scales_ptrs += BLOCK_SIZE_N
        zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
        
    c = accumulator.to(tl.float16)
    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
    c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
    tl.store(c_ptrs, c, mask=c_mask)
    
    
def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
    assert input.shape[-1] == qweight.shape[0] * 32 // bits
    outshape = input.shape[:-1] + (qweight.shape[1],)
    input = input.reshape(-1, input.shape[-1])
    output = torch.empty((input.shape[0], qweight.shape[1]), device=scales.device, dtype=torch.float16)
    grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
    matmul_248_kernel[grid](input, qweight, output,
                            scales, qzeros, g_idx,
                            input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
                            input.stride(0), input.stride(1),
                            qweight.stride(0), qweight.stride(1),
                            output.stride(0), output.stride(1),
                            scales.stride(0), qzeros.stride(0))
    output = output.reshape(outshape)
    return output


def triton_matmul_transpose(input, qweight, scales, qzeros, g_idx, bits, maxq):
    assert input.shape[-1] == qweight.shape[1]
    out_dim = qweight.shape[0] * 32 // bits
    outshape = input.shape[:-1] + (out_dim,)
    input = input.reshape(-1, input.shape[-1])
    output_shape_mid = (input.shape[0], out_dim)
    output = torch.empty((output_shape_mid[0], output_shape_mid[1]), device=scales.device, dtype=torch.float16)
    grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_shape_mid[1], META['BLOCK_SIZE_K']),)
    trans_matmul_248_kernel[grid](input, qweight, output,
                            scales, qzeros, g_idx,
                            input.shape[0], qweight.shape[1], output_shape_mid[1], bits, maxq,
                            input.stride(0), input.stride(1),
                            qweight.stride(0), qweight.stride(1),
                            output.stride(0), output.stride(1),
                            scales.stride(0), qzeros.stride(0))
    output = output.reshape(outshape)
    return output