chore: import upstream snapshot with attribution

swift/sequence_parallel/zigzag_ring_attn.py

@@ -0,0 +1,711 @@
+# Some code borrowed from the awesome work: https://github.com/zhuzilin/ring-flash-attention
+# Copyright (c) ModelScope Contributors. All rights reserved.
+import inspect
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from functools import cache
+
+from .ring_utils import RingComm
+from .zigzag_ring_attn_npu import is_npu_tensor, npu_backward, npu_forward
+
+
+def get_half_index(cu_seqlens, *, front: bool):
+    """Get half of the index
+
+    Args:
+        cu_seqlens: The cu_seqlens passed into flash_attn
+        front: The head part or the tail part
+
+    Returns:
+        The slice or the tensor mask.
+    """
+
+    if len(cu_seqlens) == 2:
+        if front:
+            return slice(None, cu_seqlens[-1] // 2)
+        else:
+            return slice(cu_seqlens[-1] // 2, None)
+
+    index = torch.zeros((cu_seqlens[-1].item(), ), dtype=torch.bool)
+    for i in range(len(cu_seqlens) - 1):
+        start, end = cu_seqlens[i], cu_seqlens[i + 1]
+        if front:
+            end = (start + end) // 2
+        else:
+            start = (start + end) // 2
+        index[start:end] = True
+    return index
+
+
+@torch.jit.script
+def get_half_lse(lse, cu_seqlens, *, front: bool):
+    """Get half of the lse
+
+    Args:
+        lse: The input lse, with shape [num_heads, seqlen]
+        cu_seqlens: The cu_seqlens passed into flash_attn
+        front: The head part or the tail part
+
+    Returns:
+        The filtered lse with the same shape as lse
+    """
+    new_lse = torch.empty(
+        (lse.shape[0], lse.shape[1] // 2),
+        dtype=lse.dtype,
+        device=lse.device,
+    )
+    for i in range(len(cu_seqlens) - 1):
+        start, end = cu_seqlens[i].item(), cu_seqlens[i + 1].item()
+        new_start, new_end = start // 2, end // 2
+        if front:
+            end -= (end - start) // 2
+        else:
+            start += (end - start) // 2
+        new_lse[:, new_start:new_end] = lse[:, start:end]
+    return new_lse
+
+
+def update_out_and_lse(out, lse, block_out, block_lse):
+    """Update output and lse:
+    new_lse = lse + torch.log(1 + torch.exp(block_lse - lse))
+    torch.exp(lse - new_lse) * out + torch.exp(block_lse - new_lse) * block_out
+    # For additional context and discussion, please refer to:
+    # https://github.com/zhuzilin/ring-flash-attention/pull/34#issuecomment-2076126795
+
+    Args:
+        out: The accumulated output of shape [seqlen, num_heads, hidden_size]
+        lse: The accumulated lse of shape [num_heads, seqlen]
+        block_out: The current block output of shape [seqlen, num_heads, hidden_size]
+        block_lse: The current block lse of shape [num_heads, seqlen]
+
+    Returns:
+        The updated output[seqlen, num_heads, hidden_size] and lse (shape: [seqlen, num_heads, 1]) and
+            the intermediate value of torch.sigmoid(block_lse - lse) (shape: [seqlen, num_heads, 1])
+    """
+    if out is None:
+        out = block_out.to(torch.float32)
+        lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1)
+        sig_diff = None
+    else:
+        block_out = block_out.to(torch.float32)
+        block_lse = block_lse.transpose(-2, -1).unsqueeze(dim=-1)
+
+        diff = block_lse - lse
+        sig_diff = torch.sigmoid(diff)
+
+        out = out - sig_diff * (out - block_out)  # (..., D)
+        lse = lse - F.logsigmoid(lse - block_lse)  # (..., 1)
+    return out, lse, sig_diff
+
+
+@cache
+def _get_default_args(func):
+    spec = inspect.getfullargspec(func)
+    defaults = spec.defaults if spec.defaults is not None else ()
+    padded_defaults = (None, ) * (len(spec.args) - len(defaults)) + defaults
+    args = dict(zip(spec.args, padded_defaults))
+    if 'softcap' in args:
+        args['softcap'] = 0.0
+    return args
+
+
+def get_default_args(func):
+    if inspect.isfunction(func):
+        return _get_default_args(func)
+    else:
+        # Use the origin _init_fn in CustomOpDef
+        return _get_default_args(func._init_fn)
+
+
+def squeeze_batch(*t):
+    """Squeeze the batch dim of tensors"""
+    tensors = []
+    for sub in t:
+        if sub.shape[0] == 1:
+            tensors.append(sub.squeeze(0))
+        else:
+            tensors.append(sub)
+    return tuple(tensors)
+
+
+def padding(tensor, cu_seqlens, padding_value, front):
+    """Pad the tensor according to the cu_seqlens
+
+    Args:
+        tensor: The input tensor of shape [seqlen, *]
+        cu_seqlens: The cu_seqlens
+        padding_value: The padding value
+        front: tensor is the head or tail part
+    """
+    if len(cu_seqlens) == 2:
+        if front:
+            return torch.cat((tensor, torch.full_like(tensor, padding_value).to(tensor.dtype).to(tensor.device)), dim=0)
+        else:
+            return torch.cat((torch.full_like(tensor, padding_value).to(tensor.dtype).to(tensor.device), tensor), dim=0)
+
+    output = []
+    acc = 0
+    for i in range(len(cu_seqlens) - 1):
+        start, end = cu_seqlens[i], cu_seqlens[i + 1]
+        half_len = (end - start) // 2
+        acc += half_len
+        half_start = start // 2
+        local_tensor = tensor[half_start:half_start + half_len]
+        if front:
+            output.append(local_tensor)
+            output.append(torch.full_like(local_tensor, padding_value).to(local_tensor.dtype).to(local_tensor.device))
+        else:
+            output.append(torch.full_like(local_tensor, padding_value).to(local_tensor.dtype).to(local_tensor.device))
+            output.append(local_tensor)
+    assert acc == tensor.shape[0]
+    return torch.cat(output)
+
+
+def forward(q, k, v, causal, cu_seqlens, max_seqlen, block_seq_len, dropout_p, softmax_scale, alibi_slopes,
+            window_size):
+    seqlen_q = q.shape[0]
+    seqlen_kv = k.shape[0]
+    half_cu_seqlens = cu_seqlens // 2
+    half_max_seqlen = max_seqlen // 2
+    cu_seqlens_q = half_cu_seqlens if seqlen_q == block_seq_len else cu_seqlens
+    max_seqlen_q = half_max_seqlen if seqlen_q == block_seq_len else max_seqlen
+    cu_seqlens_kv = half_cu_seqlens if seqlen_kv == block_seq_len else cu_seqlens
+    max_seqlen_kv = half_max_seqlen if seqlen_kv == block_seq_len else max_seqlen
+    if is_npu_tensor(q):
+        # Keep the ring schedule in this file unchanged; only the per-block
+        # flash-attn call is swapped to Ascend's TND varlen attention kernel.
+        return npu_forward(
+            q,
+            k,
+            v,
+            causal,
+            cu_seqlens_q,
+            cu_seqlens_kv,
+            dropout_p,
+            softmax_scale,
+            deterministic=False,
+            window_size=window_size,
+        )
+    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward
+    params = get_default_args(_flash_attn_varlen_forward).copy()
+    params.update({
+        'q': q,
+        'k': k,
+        'v': v,
+        # the first half and the second half are the same
+        'cu_seqlens_q': cu_seqlens_q,
+        'cu_seqlens_k': cu_seqlens_kv,
+        'max_seqlen_q': max_seqlen_q,
+        'max_seqlen_k': max_seqlen_kv,
+        'dropout_p': dropout_p,
+        'softmax_scale': softmax_scale,
+        'causal': causal,
+        'alibi_slopes': alibi_slopes,
+        'return_softmax': True and dropout_p > 0,
+    })
+    if 'window_size' in params:
+        params.update({'window_size': window_size})
+    else:
+        params.update({
+            'window_size_left': window_size[0],
+            'window_size_right': window_size[1],
+        })
+    assert k.shape[-0] == cu_seqlens_kv[-1]
+    assert q.shape[-0] == cu_seqlens_q[-1]
+    assert max_seqlen_q == (cu_seqlens_q[1:] - cu_seqlens_q[:-1]).max().item()
+    assert max_seqlen_kv == (cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]).max().item()
+    outputs = _flash_attn_varlen_forward(**params)
+    if len(outputs) == 8:
+        block_out, _, _, _, _, block_lse, _, _ = outputs
+    else:
+        assert len(outputs) == 4
+        block_out, block_lse, _, _ = outputs
+    return block_out, block_lse
+
+
+def backward(dout, q, k, v, out, softmax_lse, causal, cu_seqlens, max_seqlen, block_seq_len, dq_buffer, dk_buffer,
+             dv_buffer, dropout_p, softmax_scale, alibi_slopes, deterministic, window_size):
+    seqlen_q = q.shape[0]
+    seqlen_kv = k.shape[0]
+
+    half_cu_seqlens = cu_seqlens // 2
+    half_max_seqlen = max_seqlen // 2
+    cu_seqlens_q = half_cu_seqlens if seqlen_q == block_seq_len else cu_seqlens
+    max_seqlen_q = half_max_seqlen if seqlen_q == block_seq_len else max_seqlen
+    cu_seqlens_kv = half_cu_seqlens if seqlen_kv == block_seq_len else cu_seqlens
+    max_seqlen_kv = half_max_seqlen if seqlen_kv == block_seq_len else max_seqlen
+    from flash_attn.flash_attn_interface import _flash_attn_varlen_backward
+    params = get_default_args(_flash_attn_varlen_backward).copy()
+    params.update({
+        'dout': dout,
+        'q': q,
+        'k': k,
+        'v': v,
+        'out': out,
+        'softmax_lse': softmax_lse,
+        'dq': dq_buffer[:seqlen_q],
+        'dk': dk_buffer[:seqlen_kv],
+        'dv': dv_buffer[:seqlen_kv],
+        # the first half and the second half are the same
+        'cu_seqlens_q': cu_seqlens_q,
+        'cu_seqlens_k': cu_seqlens_kv,
+        'max_seqlen_q': max_seqlen_q,
+        'max_seqlen_k': max_seqlen_kv,
+        'dropout_p': dropout_p,
+        'softmax_scale': softmax_scale,
+        'causal': causal,
+        'alibi_slopes': alibi_slopes,
+        'deterministic': deterministic,
+    })
+    assert dout.shape[0] == q.shape[0]
+    assert dout.shape[0] == out.shape[0]
+    assert softmax_lse.shape[1] == q.shape[0]
+    assert k.shape[0] == cu_seqlens_kv[-1]
+    assert q.shape[0] == cu_seqlens_q[-1]
+    assert max_seqlen_q == (cu_seqlens_q[1:] - cu_seqlens_q[:-1]).max().item()
+    assert max_seqlen_kv == (cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]).max().item()
+    if 'window_size' in params:
+        params.update({'window_size': window_size})
+    else:
+        params.update({
+            'window_size_left': window_size[0],
+            'window_size_right': window_size[1],
+        })
+    _flash_attn_varlen_backward(**params)
+
+
+def lse_grad(out, lse, block_out, block_lse, sig, grad_out, grad_lse):
+    """Calculate the grad of each block.
+
+    Args:
+        out: The accumulated output of shape [seqlen, num_heads, hidden_size]
+        lse: The accumulated lse of shape [num_heads, seqlen, 1]
+        block_out: The current block output of shape [seqlen, num_heads, hidden_size]
+        block_lse: The current block lse of shape [num_heads, seqlen, 1]
+        grad_out: The input grad of output of the current block shape [seqlen, num_heads, hidden_size]
+        grad_lse: The input grad of lse of the current block shape [num_heads, seqlen, 1]
+
+    Returns:
+        The accumulated grad of out and lse, and the grad of out and lse of the current block
+    """
+    grad_out_input = grad_out * (1 - sig)
+
+    grad_block_out = grad_out * sig
+
+    d_new_out_d_lse = (out - block_out) * (sig * (1 - sig))
+    grad_lse_input = (grad_out * d_new_out_d_lse).sum(dim=-1, keepdim=True)
+    grad_lse_input_final = grad_lse_input + grad_lse * torch.sigmoid(lse - block_lse)
+
+    grad_block_lse = -grad_lse_input_final + grad_lse
+
+    return grad_out_input, grad_lse_input_final, grad_block_out, grad_block_lse
+
+
+def zigzag_ring_flash_attn_varlen_forward(
+        process_group,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        cu_seqlens,
+        max_seqlen,
+        half_index0,
+        half_index1,
+        softmax_scale,
+        dropout_p=0,
+        causal=True,
+        window_size=(-1, -1),
+        alibi_slopes=None,
+        deterministic=False,
+):
+    assert causal, 'zigzag ring is meaningless for causal=False'
+    comm = RingComm(process_group)
+    q, k, v = squeeze_batch(q, k, v)
+    q1 = q[half_index1]
+    # Input cu_seqlens is the total length, divided by world_size to fit the split ones
+    cu_seqlens = cu_seqlens // comm.world_size
+    # Same with above
+    max_seqlen = max_seqlen // comm.world_size
+    block_seq_len = q.shape[0] // 2
+    out = None
+    lse = None
+    next_k, next_v = None, None
+
+    for step in range(comm.world_size):
+        # from step 0 to the last
+        if step + 1 != comm.world_size:
+            next_k, next_v = comm.send_recv_kv(k, v)
+        """
+        world_size = 4, total 8 parts
+        0/7 is group0
+        1/6 is group1
+        2/5 is group2
+        3/4 is group3
+        consider 1/6，take the query as the left axis, key as the top axis:
+        step 0:
+           1   6
+        1  ✅  ❎
+        6  ✅  ✅
+        all needed, causal=True
+        step 1(step <= comm.rank):
+           0   7
+        1  ✅  ❎
+        6  ✅  ❎
+        the first part of kv is needed, causal=False
+        step 2(step > comm.rank):
+           3   4
+        1  ❎  ❎
+        6  ✅  ✅
+        the second part of q is needed, causal=False
+        """
+        # Here block_lse shape: [num_heads, seqlen]
+        # lse shape: [seqlen, num_heads, 1]
+        if step == 0:
+            block_out, block_lse = forward(q, k, v, True, cu_seqlens, max_seqlen, block_seq_len, dropout_p,
+                                           softmax_scale, alibi_slopes, window_size)
+            out, lse, sig_diff = update_out_and_lse(out, lse, block_out, block_lse)
+        elif step <= comm.rank:
+            k0 = k[half_index0]
+            v0 = v[half_index0]
+            block_out, block_lse = forward(q, k0, v0, False, cu_seqlens, max_seqlen, block_seq_len, dropout_p,
+                                           softmax_scale, alibi_slopes, window_size)
+            out, lse, sig_diff = update_out_and_lse(out, lse, block_out, block_lse)
+        else:
+            block_out, block_lse = forward(q1, k, v, False, cu_seqlens, max_seqlen, block_seq_len, dropout_p,
+                                           softmax_scale, alibi_slopes, window_size)
+            out[half_index1], lse[half_index1], sig_diff = update_out_and_lse(out[half_index1], lse[half_index1],
+                                                                              block_out, block_lse)
+
+        if step + 1 != comm.world_size:
+            comm.wait()
+            k, v = next_k, next_v
+
+    out = out.to(q.dtype)
+    lse = lse.squeeze(dim=-1).transpose(0, 1)  # [num_heads, seqlen]
+    return out.unsqueeze(0), lse.unsqueeze(0)
+
+
+def zigzag_ring_flash_attn_varlen_backward(
+        process_group,
+        dout,
+        q,
+        k,
+        v,
+        out,
+        softmax_lse,
+        cu_seqlens,
+        max_seqlen,
+        half_index0,
+        half_index1,
+        softmax_scale,
+        dropout_p=0,
+        causal=True,
+        window_size=(-1, -1),
+        alibi_slopes=None,
+        deterministic=False,
+):
+    assert causal, 'zigzag ring is meaningless for causal=False'
+    if is_npu_tensor(q):
+        # NPU backward uses native flash-attn grad with the final ring out/lse
+        # patched into each block ctx. Missing kernel support should fail loudly.
+        return npu_backward(
+            process_group,
+            dout,
+            q,
+            k,
+            v,
+            out,
+            softmax_lse,
+            cu_seqlens,
+            max_seqlen,
+            half_index0,
+            half_index1,
+            softmax_scale,
+            dropout_p=dropout_p,
+            window_size=window_size,
+            deterministic=deterministic,
+        )
+    kv_comm = RingComm(process_group)
+    d_kv_comm = RingComm(process_group)
+    dk_comm_buffer = dv_comm_buffer = None
+    dq, dk, dv = None, None, None
+    next_dk, next_dv = None, None
+    next_k, next_v = None, None
+
+    # squeeze the axis of batch
+    dout, q, k, v, out, softmax_lse = squeeze_batch(dout, q, k, v, out, softmax_lse)
+    q1 = q[half_index1]
+    # Input cu_seqlens is the total length, divided by world_size to fit the split ones
+    cu_seqlens = cu_seqlens // kv_comm.world_size
+    # Same as above
+    max_seqlen = max_seqlen // kv_comm.world_size
+    # half of the part
+    block_seq_len = q.shape[0] // 2
+
+    # repeatly allocating buffer may be slow...
+    dq_buffer = torch.empty(q.shape, dtype=q.dtype, device=q.device)
+    dk_buffer = torch.empty(k.shape, dtype=k.dtype, device=k.device)
+    dv_buffer = torch.empty(v.shape, dtype=v.dtype, device=v.device)
+    origin_q, origin_k, origin_v = q, k, v
+
+    out_lse = []
+    fout = None
+    flse = None
+    # Recalculate forward with the same qkv to generate out_lse, used to calculate the grad
+    for step in range(kv_comm.world_size):
+        if step + 1 != kv_comm.world_size:
+            next_k, next_v = kv_comm.send_recv_kv(k, v)
+
+        if step == 0:
+            block_out, block_lse = forward(q, k, v, True, cu_seqlens, max_seqlen, block_seq_len, dropout_p,
+                                           softmax_scale, alibi_slopes, window_size)
+            fout, flse, sig_diff = update_out_and_lse(fout, flse, block_out, block_lse)
+        elif step <= kv_comm.rank:
+            k0 = k[half_index0]
+            v0 = v[half_index0]
+            block_out, block_lse = forward(q, k0, v0, False, cu_seqlens, max_seqlen, block_seq_len, dropout_p,
+                                           softmax_scale, alibi_slopes, window_size)
+            fout, flse, sig_diff = update_out_and_lse(fout, flse, block_out, block_lse)
+        else:
+            block_out, block_lse = forward(q1, k, v, False, cu_seqlens, max_seqlen, block_seq_len, dropout_p,
+                                           softmax_scale, alibi_slopes, window_size)
+            fout[half_index1], flse[half_index1], sig_diff = update_out_and_lse(fout[half_index1], flse[half_index1],
+                                                                                block_out, block_lse)
+
+        block_lse = block_lse.transpose(0, 1).unsqueeze(-1)
+        if step > kv_comm.rank:
+            # cat zeros because there are may be a half of the out/lse
+            block_out = padding(block_out, cu_seqlens, 0, front=False)
+            block_lse = padding(block_lse, cu_seqlens, -1e5, front=False)
+            sig_diff = padding(sig_diff, cu_seqlens, 0, front=False)
+
+        # save to out_lse
+        out_lse.append((fout, flse, block_out, block_lse, sig_diff))
+
+        if step + 1 != kv_comm.world_size:
+            kv_comm.wait()
+            k, v = next_k, next_v
+
+    current_dout = dout
+    current_dlse = torch.zeros_like(softmax_lse.transpose(0, 1).unsqueeze(-1))
+    block_gradients = {}
+
+    for i in reversed(range(len(out_lse))):
+        if i == 0:
+            # the first step does not need
+            continue
+        stored_out, stored_lse, stored_block_out, stored_block_lse, stored_sig = out_lse[i]
+        grad_out_input, grad_lse_input, grad_block_out, grad_block_lse = lse_grad(stored_out, stored_lse,
+                                                                                  stored_block_out, stored_block_lse,
+                                                                                  stored_sig, current_dout,
+                                                                                  current_dlse)
+        current_dout = grad_out_input
+        current_dlse = grad_lse_input
+        block_gradients[i] = {'grad_block_out': grad_block_out, 'grad_block_lse': grad_block_lse}
+
+    q, k, v = origin_q, origin_k, origin_v
+
+    for step in range(kv_comm.world_size):
+        _, _, block_out, block_lse, _ = out_lse[step]
+        if block_out.isnan().any() or block_lse.isnan().any():
+            raise
+        block_lse = block_lse.transpose(0, 1).squeeze(2)
+
+        if step + 1 != kv_comm.world_size:
+            next_k, next_v = kv_comm.send_recv_kv(k, v)
+
+        if step == 0:
+            # if step == 0, use the final current_dout
+            block_dout = current_dout
+        else:
+            # else use the grad in the block_gradients
+            block_dout = block_gradients[step]['grad_block_out']
+
+        if block_dout.isnan().any():
+            raise
+
+        if step == 0:
+            backward(
+                block_dout.to(dout.dtype), q, k, v, block_out, block_lse, True, cu_seqlens, max_seqlen, block_seq_len,
+                dq_buffer, dk_buffer, dv_buffer, dropout_p, softmax_scale, alibi_slopes, deterministic, window_size)
+            dq = dq_buffer.to(torch.float32)
+            dk = dk_buffer.to(torch.float32)
+            dv = dv_buffer.to(torch.float32)
+            if dq.isnan().any() or dk.isnan().any() or dv.isnan().any():
+                raise
+        else:
+            if step <= kv_comm.rank:
+                k0 = k[half_index0]
+                v0 = v[half_index0]
+                backward(
+                    block_dout.to(dout.dtype), q, k0, v0, block_out, block_lse, False, cu_seqlens, max_seqlen,
+                    block_seq_len, dq_buffer, dk_buffer, dv_buffer, dropout_p, softmax_scale, alibi_slopes,
+                    deterministic, window_size)
+                dq += dq_buffer
+            else:
+                backward(block_dout[half_index1].to(dout.dtype), q1, k, v, block_out[half_index1],
+                         get_half_lse(block_lse, cu_seqlens,
+                                      front=False), False, cu_seqlens, max_seqlen, block_seq_len, dq_buffer, dk_buffer,
+                         dv_buffer, dropout_p, softmax_scale, alibi_slopes, deterministic, window_size)
+                # only need to add to the tail half, because the head half does not match the causal condition
+                dq[half_index1] += dq_buffer[:block_seq_len]
+
+            d_kv_comm.wait()
+            # dk_comm_buffer, dv_comm_buffer = dk, dv
+            # avoid d_kv_comm.send_recv_kv causing dk_comm_buffer reuse the same memory with next_dk and dk
+            dk_comm_buffer = torch.empty_like(dk)
+            dv_comm_buffer = torch.empty_like(dv)
+            dk_comm_buffer.copy_(dk)
+            dv_comm_buffer.copy_(dv)
+            # next_dk, next_dv comes from a previous gpu, add kv grad to them, and pass them to the next gpu
+            dk, dv = next_dk, next_dv
+
+            if step <= kv_comm.rank:
+                # only need to add to the head part, because the tail part does not match the causal condition
+                dk[half_index0] += dk_buffer[:block_seq_len]
+                dv[half_index0] += dv_buffer[:block_seq_len]
+            else:
+                dk += dk_buffer
+                dv += dv_buffer
+            if dq.isnan().any() or dk.isnan().any() or dv.isnan().any():
+                raise
+        if step + 1 != kv_comm.world_size:
+            kv_comm.wait()
+            k, v = next_k, next_v
+
+        next_dk, next_dv = d_kv_comm.send_recv_kv(dk, dv, dk_comm_buffer, dv_comm_buffer)
+
+    d_kv_comm.wait()
+
+    return dq.to(q.dtype).unsqueeze(0), next_dk.to(q.dtype).unsqueeze(0), next_dv.to(q.dtype).unsqueeze(0)
+
+
+class ZigZagRingFlashAttnVarlenFunc(torch.autograd.Function):
+
+    @staticmethod
+    def forward(
+        ctx,
+        q,
+        k,
+        v,
+        cu_seqlens,
+        max_seqlen,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_softmax,
+        group,
+    ):
+        if softmax_scale is None:
+            softmax_scale = q.shape[-1]**(-0.5)
+
+        assert alibi_slopes is None
+        k = k.contiguous()
+        v = v.contiguous()
+        rp_world_size = dist.get_world_size(group)
+        half_index0 = get_half_index(cu_seqlens // rp_world_size, front=True)
+        half_index1 = get_half_index(cu_seqlens // rp_world_size, front=False)
+        out, softmax_lse = zigzag_ring_flash_attn_varlen_forward(
+            group,
+            q,
+            k,
+            v,
+            cu_seqlens,
+            max_seqlen,
+            half_index0,
+            half_index1,
+            softmax_scale=softmax_scale,
+            dropout_p=dropout_p,
+            causal=causal,
+            window_size=window_size,
+            alibi_slopes=alibi_slopes,
+            deterministic=False,
+        )
+        # this should be out_padded
+        is_half_index_tensor = isinstance(half_index0, torch.Tensor)
+        ctx.is_half_index_tensor = is_half_index_tensor
+        if is_half_index_tensor:
+            """
+            Shapes:
+            qkv: [1, seqlen, num_heads, hidden_size]
+            out: [1, seqlen, num_heads, hidden_size]
+            softmax_lse: [1, num_heads, seqlen]
+            """
+            ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens, half_index0, half_index1)
+        else:
+            ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens)
+            ctx.half_index0 = half_index0
+            ctx.half_index1 = half_index1
+        ctx.max_seqlen = max_seqlen
+        ctx.dropout_p = dropout_p
+        ctx.softmax_scale = softmax_scale
+        ctx.causal = causal
+        ctx.window_size = window_size
+        ctx.alibi_slopes = alibi_slopes
+        ctx.deterministic = deterministic
+        ctx.group = group
+        return out if not return_softmax else (out, softmax_lse, None)
+
+    @staticmethod
+    def backward(ctx, dout, *args):
+        if ctx.is_half_index_tensor:
+            (q, k, v, out, softmax_lse, cu_seqlens, half_index0, half_index1) = (ctx.saved_tensors)
+        else:
+            q, k, v, out, softmax_lse, cu_seqlens = ctx.saved_tensors
+            half_index0 = ctx.half_index0
+            half_index1 = ctx.half_index1
+        dq, dk, dv = zigzag_ring_flash_attn_varlen_backward(
+            ctx.group,
+            dout,
+            q,
+            k,
+            v,
+            out,
+            softmax_lse,
+            cu_seqlens,
+            ctx.max_seqlen,
+            half_index0,
+            half_index1,
+            softmax_scale=ctx.softmax_scale,
+            dropout_p=ctx.dropout_p,
+            causal=ctx.causal,
+            window_size=ctx.window_size,
+            alibi_slopes=ctx.alibi_slopes,
+            deterministic=ctx.deterministic,
+        )
+        return dq, dk, dv, None, None, None, None, None, None, None, None, None, None
+
+
+def zigzag_ring_flash_attn_varlen_func(
+    q,
+    k,
+    v,
+    cu_seqlens,
+    max_seqlen,
+    dropout_p=0.0,
+    softmax_scale=None,
+    causal=False,
+    window_size=(-1, -1),  # -1 means infinite context window
+    alibi_slopes=None,
+    deterministic=False,
+    return_attn_probs=False,
+    group=None,
+):
+    return ZigZagRingFlashAttnVarlenFunc.apply(
+        q,
+        k,
+        v,
+        cu_seqlens,
+        max_seqlen,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_size,
+        alibi_slopes,
+        deterministic,
+        return_attn_probs,
+        group,
+    )