attention.py 145 KB
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# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
# See LICENSE for license information.

"""Attention."""
import os
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import warnings
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import math
from importlib.metadata import version
from contextlib import nullcontext
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from typing import Any, Callable, List, Optional, Tuple, Union, Dict
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from pkg_resources import packaging
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import numpy as np
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import torch
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import torch.nn.functional as F
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import transformer_engine_extensions as tex
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from transformer_engine.pytorch.cpp_extensions.fused_attn import (
    fused_attn_fwd_qkvpacked,
    fused_attn_bwd_qkvpacked,
    fused_attn_fwd_kvpacked,
    fused_attn_bwd_kvpacked,
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    fused_attn_fwd,
    fused_attn_bwd,
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    QKVLayout,
    AttnBiasType,
    AttnMaskType,
    FusedAttnBackend,
)
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from transformer_engine.pytorch.module import LayerNormLinear, Linear
from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
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    get_default_init_method,
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)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
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    AttnBiasTypes,
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    QKVLayouts,
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    dist_group_type,
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    TE_DType,
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)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
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    get_distributed_rank,
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    checkpoint,
)
from transformer_engine.pytorch.export import is_in_onnx_export_mode
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from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
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_flash_attn_version = packaging.version.Version(version("flash-attn"))
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_flash_attn_version_required = packaging.version.Version("1.0.6")
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_flash_attn_2_available = _flash_attn_version >= packaging.version.Version("2")
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_flash_attn_2_1_plus = _flash_attn_version >= packaging.version.Version("2.1")
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_flash_attn_2_3_plus = _flash_attn_version >= packaging.version.Version("2.3")
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if _flash_attn_2_available:
    from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_forward_func # pylint: disable=no-name-in-module
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    from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd # pylint: disable=no-name-in-module
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    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward as _flash_attn_forward # pylint: disable=no-name-in-module,ungrouped-imports
    from flash_attn.flash_attn_interface import _flash_attn_varlen_backward as _flash_attn_backward # pylint: disable=no-name-in-module
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else:
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    from flash_attn.flash_attn_interface import flash_attn_unpadded_func as flash_attn_forward_func # pylint: disable=no-name-in-module,ungrouped-imports
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    from flash_attn.flash_attn_interface import _flash_attn_forward, _flash_attn_backward
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_cu_seqlens_q, _cu_seqlens_kv, _indices_q, _indices_kv = None, None, None, None
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_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
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__all__ = ["DotProductAttention", "InferenceParams", "MultiheadAttention"]


class InferenceParams: # pylint: disable=too-few-public-methods
    """
    Inference parameters that are passed to the main model in order
    to efficienly calculate and store the context during inference.

    Parameters
    ----------
    max_batch_size : int
                    maximum batch size during inference.
    max_sequence_length : int
                         maximum sequence length during inference.
    """

    def __init__(self, max_batch_size, max_sequence_length):
        self.max_sequence_length = max_sequence_length
        self.max_batch_size = max_batch_size
        self.sequence_len_offset = 0
        self.batch_size_offset = 0
        self.key_value_memory_dict = {}

    def swap_key_value_dict(self, batch_indices):
        """
        Reorders the KV cache using the specified batch indices.

        Parameters
        ----------
        batch_indices : List[int]
                       Sequence of indices to reorder along the batch dimensions of
                       the KV cache. Must have a length equal to the batch size.
        """
        if len(self.key_value_memory_dict) == 0:
            raise ValueError("should not swap when dict in empty")

        for layer_number, inference_memory in self.key_value_memory_dict.items():
            inference_key_memory, inference_value_memory = inference_memory
            assert (
                len(batch_indices) == inference_key_memory.shape[1]
            )  # make sure batch size is the same
            new_inference_key_memory = inference_key_memory[:, batch_indices]
            new_inference_value_memory = inference_value_memory[:, batch_indices]
            self.key_value_memory_dict[layer_number] = (
                new_inference_key_memory,
                new_inference_value_memory,
            )
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@torch.no_grad()
def get_alibi(
    num_heads: int,
    max_seqlen_q: int,
    max_seqlen_kv: int,
) -> torch.Tensor:
    """
    Generate ALiBi bias in the shape of [1, num_heads, max_seqlen_q, max_seqlen_kv].
    """
    n = 2 ** math.floor(math.log2(num_heads))
    m_0 = 2.0 ** (-8.0 / n)
    m = torch.pow(m_0, torch.arange(1, 1 + n))

    if n < num_heads:
        m_hat_0 = 2.0 ** (-4.0 / n)
        m_hat = torch.pow(m_hat_0, torch.arange(1, 1 + 2 * (num_heads - n), 2))
        m = torch.cat([m, m_hat])

    a = torch.ones(max_seqlen_q, max_seqlen_kv)
    b = torch.triu(a,diagonal=1)
    c = b.cumsum(dim=-1)
    bb = torch.tril(a,diagonal=-1)
    cc = bb.cumsum(dim=0)
    d = c - cc
    bias = d.repeat(1, num_heads, 1, 1)

    for i in range(num_heads):
        bias[0,i,:,:] = m[i] * bias[0,i,:,:]

    bias = bias.to(dtype=torch.float32, device="cuda")
    return bias

def get_cu_seqlens(mask: torch.Tensor) -> torch.Tensor:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch.
    """
    mask = mask.squeeze(1).squeeze(1)
    reduced_mask = mask.sum(dim=1)
    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    return cu_seqlens

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def get_cu_seqlens_and_indices(mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
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    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch, and another int32 tensor of shape [batch_size * max_seqlen, 1, 1]
    containing the indices for the valid tokens.
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    """
    mask = mask.squeeze(1).squeeze(1)
    bs, seqlen = mask.shape

    reduced_mask = mask.sum(dim=1)
    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    mask = mask.reshape(-1)
    indices = mask.nonzero()
    indices = indices.unsqueeze(-1)

    num_nonzeros = indices.shape[0]
    pad_amount = bs * seqlen - num_nonzeros
    indices = F.pad(input=indices, pad=(0, 0, 0, 0, 0, pad_amount),
                    mode="constant", value=float(bs * seqlen))

    return cu_seqlens, indices


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def get_indices(max_seqlen: int, cu_seqlens: torch.Tensor) -> torch.Tensor:
    """
    Given max_seqlen and cu_seqlens of shape [batch_size + 1], returns an int32
    tensor of shape [batch_size * max_seqlen, 1, 1] containing the indices for
    the valid tokens in a batch.
    """
    bs = len(cu_seqlens) - 1
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
    indices = [i*max_seqlen + ii for i,j in enumerate(seqlens) for ii in range(j)]
    indices = torch.Tensor(indices).unsqueeze(1).unsqueeze(1).to(
                    dtype=torch.int64, device="cuda")

    num_nonzeros = indices.shape[0]
    pad_amount = bs * max_seqlen - num_nonzeros
    indices = F.pad(input=indices, pad=(0, 0, 0, 0, 0, pad_amount),
                    mode="constant", value=float(bs * max_seqlen))

    return indices


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@jit_fuser
def pack_tensor(
    indices: torch.Tensor,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Packs the given tensor using the `indices`.
    """
    padding_indice = torch.zeros(
        1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device)
    tensor = torch.cat((tensor, padding_indice), dim=0)

    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    packed = torch.gather(tensor, 0, indices)
    return packed


@jit_fuser
def pack_2_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Packs the given 2 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    return t1_packed, t2_packed


@jit_fuser
def pack_3_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Packs the given 3 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    t3_packed = pack_tensor(indices, t3)
    return t1_packed, t2_packed, t3_packed


@jit_fuser
def unpack_tensor(
    indices: torch.Tensor,
    dim0: int,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Inverse of `pack_tensor`.
    """
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    unpacked = torch.zeros(
        dim0 + 1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device)
    unpacked.scatter_(0, indices, tensor)
    unpacked = unpacked[0:-1,:,:]
    return unpacked


@jit_fuser
def unpack_2_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_2_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    return t1_unpacked, t2_unpacked


@jit_fuser
def unpack_3_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_3_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    t3_unpacked = unpack_tensor(indices, dim0, t3)
    return t1_unpacked, t2_unpacked, t3_unpacked


class PackTensors(torch.autograd.Function):
    """
    Autograd function to pack tensors.
    """
    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        *tensors: Tuple[torch.Tensor, ...]
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
        assert 1 <= len(tensors) <= 3, f"Packing {len(tensors)} tensors not supported."
        ctx.indices = indices
        ctx.dim0 = tensors[0].shape[0]
        if len(tensors) == 1:
            return pack_tensor(indices, *tensors)
        if len(tensors) == 2:
            return pack_2_tensors(indices, *tensors)
        return pack_3_tensors(indices, *tensors)

    @staticmethod
    def backward(ctx, *grad_outputs: Tuple[torch.Tensor, ...]):
        if len(grad_outputs) == 1:
            return None, unpack_tensor(ctx.indices, ctx.dim0, *grad_outputs)
        if len(grad_outputs) == 2:
            return None, *unpack_2_tensors(ctx.indices, ctx.dim0, *grad_outputs)
        return None, *unpack_3_tensors(ctx.indices, ctx.dim0, *grad_outputs)


class UnpackTensor(torch.autograd.Function):
    """
    Autograd function to unpack a tensor.
    """
    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        dim0: int,
        tensor: torch.Tensor,
    ) -> torch.Tensor:
        ctx.indices = indices
        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
        return None, None, pack_tensor(ctx.indices, grad_output)


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def flash_attn_p2p_communicate(rank, send_tensor, send_dst,
                               recv_tensor, recv_src,
                               cp_group, batch_p2p_comm):
    """Point-to-point communications of KV and dKV in Flash Attention with context parallelism"""
    send_recv_ops = []

    if batch_p2p_comm:
        if rank % 2 == 0:
            send_op = torch.distributed.P2POp(torch.distributed.isend,
                                              send_tensor,
                                              send_dst,
                                              cp_group)
            recv_op = torch.distributed.P2POp(torch.distributed.irecv,
                                              recv_tensor,
                                              recv_src,
                                              cp_group)
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
            recv_op = torch.distributed.P2POp(torch.distributed.irecv,
                                              recv_tensor,
                                              recv_src,
                                              cp_group)
            send_op = torch.distributed.P2POp(torch.distributed.isend,
                                              send_tensor,
                                              send_dst,
                                              cp_group)
            send_recv_ops.append(recv_op)
            send_recv_ops.append(send_op)
        send_recv_reqs = torch.distributed.batch_isend_irecv(send_recv_ops)
    else:
        if rank % 2 == 0:
            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
            send_recv_ops.append(recv_op)
            send_recv_ops.append(send_op)
        send_recv_reqs = send_recv_ops

    return send_recv_reqs


@torch.jit.script
def flash_attn_fwd_out_correction(out, out_per_step, softmax_lse, softmax_lse_per_step):
    """Merge partial outputs of each step in Flash Attention with context parallelism"""
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).transpose(1, 2)
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
    out_corrected = out_per_step*softmax_lse_corrected_exp
    out.add_(out_corrected)


@torch.jit.script
def flash_attn_fwd_softmax_lse_correction(softmax_lse, softmax_lse_per_step):
    """Merge softmax stats of each step in Flash Attention with context parallelism"""
    softmax_lse.exp_()
    softmax_lse.add_(softmax_lse_per_step.to(torch.double).exp())
    softmax_lse.log_()


class FlashAttnUnpaddedFuncWithCP(torch.autograd.Function):
    """
    Flash Attention implementation with context parallelism.
    Split flash attention compute into multiple steps, and overlap current-step
    compute with next-step communication.
    """

    @staticmethod
    def forward(ctx, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p,
                cp_group, cp_global_ranks, cp_stream, softmax_scale, causal, deterministic):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)
        send_dst = cp_global_ranks[(rank + 1) % cp_size]
        recv_src = cp_global_ranks[(rank + cp_size - 1) % cp_size]
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

        # [b, s, np, hn] -> [b, 2, s//2, np, hn]
        q, k, v = [x.view(x.shape[0], 2, x.shape[1]//2, *x.shape[2:]) for x in [q, k, v]]
        if _flash_attn_2_available:
            assert(q.shape[-1] % 8 == 0), "hidden size per attention head should be multiple of 8"
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
        # Flash Attn outputs
        out_per_step = [None for _ in range(cp_size)]
        softmax_lse_per_step = [None for _ in range(cp_size)]
        rng_states = [None for _ in range(cp_size)]

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
        # synchronize fwd results correction across steps
        fwd_results_correction_done = torch.cuda.Event()

        p2p_comm_buffers = [None for _ in range(cp_size)]
        p2p_comm_buffers[0] = torch.cat((k.unsqueeze(0), v.unsqueeze(0)), dim=0)
        send_recv_reqs = [[], []]

        for i in range(cp_size+1):
            if i < cp_size:
                with torch.cuda.stream(flash_attn_streams[i%2]):
                    # wait until KV is received
                    for req in send_recv_reqs[(i+1)%2]:
                        req.wait()

                    if i < (cp_size-1):
                        p2p_comm_buffers[i+1] = torch.empty_like(p2p_comm_buffers[i])
                        send_recv_reqs[i%2] = flash_attn_p2p_communicate(rank,
                                                                         p2p_comm_buffers[i],
                                                                         send_dst,
                                                                         p2p_comm_buffers[i+1],
                                                                         recv_src,
                                                                         cp_group,
                                                                         batch_p2p_comm)

                    kv_inputs[i%2] = p2p_comm_buffers[i]
                    if causal:
                        if i == 0:
                            # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                            q_inputs[i%2] = q.view(-1, *q.shape[-2:])
                            # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                            kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                            if _flash_attn_2_available:
                                _, _, _, _, out_per_step[i], \
                                softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                                    dropout_p, softmax_scale, causal=True, return_softmax=False,
                                )
                            else:
                                out_per_step[i] = torch.empty_like(q_inputs[i%2])
                                _, softmax_lse_per_step[i], rng_states[i], _ = _flash_attn_forward( # pylint: disable=unbalanced-tuple-unpacking
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    out_per_step[i], cu_seqlens_q, cu_seqlens_k,
                                    max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale,
                                    causal=True, return_softmax=False,
                                )
                        elif i <= rank:
                            # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                            q_inputs[i%2] = q.view(-1, *q.shape[-2:])
                            # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
                            kv_inputs[i%2] = kv_inputs[i%2][:, :, 0, ...].contiguous()
                            kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                            if _flash_attn_2_available:
                                _, _, _, _, out_per_step[i], \
                                softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    cu_seqlens_q, cu_seqlens_k//2, max_seqlen_q, max_seqlen_k//2,
                                    dropout_p, softmax_scale, causal=False, return_softmax=False,
                                )
                            else:
                                out_per_step[i] = torch.empty_like(q_inputs[i%2])
                                _, softmax_lse_per_step[i], rng_states[i], _ = _flash_attn_forward( # pylint: disable=unbalanced-tuple-unpacking
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    out_per_step[i], cu_seqlens_q, cu_seqlens_k//2,
                                    max_seqlen_q, max_seqlen_k//2, dropout_p, softmax_scale,
                                    causal=False, return_softmax=False,
                                )
                        else:
                            # [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            q_inputs[i%2] = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
                            # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                            kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                            if _flash_attn_2_available:
                                _, _, _, _, out_per_step[i], \
                                softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    cu_seqlens_q//2, cu_seqlens_k, max_seqlen_q//2, max_seqlen_k,
                                    dropout_p, softmax_scale, causal=False, return_softmax=False,
                                )
                            else:
                                out_per_step[i] = torch.empty_like(q_inputs[i%2])
                                _, softmax_lse_per_step[i], rng_states[i], _ = _flash_attn_forward( # pylint: disable=unbalanced-tuple-unpacking
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    out_per_step[i], cu_seqlens_q//2, cu_seqlens_k,
                                    max_seqlen_q//2, max_seqlen_k, dropout_p, softmax_scale,
                                    causal=False, return_softmax=False,
                                )
                    else:
                        assert False, "Not implemented yet!"

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
                    flash_attn_streams[(i-1)%2].wait_event(fwd_results_correction_done)

                with torch.cuda.stream(flash_attn_streams[(i-1)%2]):
                    if causal:
                        if i == 1:
                            out = torch.empty_like(q).zero_()
                            softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
                            # [b, np, sq] -> [b, np, 2, sq//2]
                            softmax_lse_ = softmax_lse.view(
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1]//2
                            )
                        elif (i-1) <= rank:
                            flash_attn_fwd_softmax_lse_correction(softmax_lse,
                                                                  softmax_lse_per_step[i-1])
                        else:
                            flash_attn_fwd_softmax_lse_correction(softmax_lse_[..., 1, :],
                                                                  softmax_lse_per_step[i-1])
                    else:
                        assert False, "Not implemented yet!"

                if i < cp_size:
                    flash_attn_streams[(i-1)%2].record_event(fwd_results_correction_done)

        torch.cuda.current_stream().wait_stream(flash_attn_streams[1])

        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
            # [b*sq, np, hn] -> [b, sq, np, hn] or [b*sq//2, np, hn] -> [b, sq//2, np, hn]
            out_ = out_per_step[i].view(out.shape[0], -1, *out.shape[-2:])
            if i <= rank:
                flash_attn_fwd_out_correction(out.view(*out_.shape),
                                              out_,
                                              softmax_lse,
                                              softmax_lse_per_step[i])
            else:
                flash_attn_fwd_out_correction(out[:, 1, ...],
                                              out_,
                                              softmax_lse_[..., 1, :],
                                              softmax_lse_per_step[i])

        kv = p2p_comm_buffers[-1]
        out = out.view(-1, *out.shape[-2:])
        ctx.save_for_backward(q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k)
        ctx.rng_states = rng_states
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_k = max_seqlen_k
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        ctx.deterministic = deterministic
        return out

    @staticmethod
    def backward(ctx, dout):
        q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k = ctx.saved_tensors

        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
        send_dst = ctx.cp_global_ranks[(rank + cp_size - 1) % cp_size]
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size]
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

        # [b, np, sq] -> [b, np, 2, sq//2]
        softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, softmax_lse.shape[-1]//2)
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        softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
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        # [b*sq, np, hn] -> [b, 2, sq//2, np, hn]
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        # Flash Attn outputs
        dq = torch.empty_like(q)

        p2p_comm_buffers = [torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device), \
                            torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device)]
        p2p_comm_buffers[0][0].copy_(kv)
        send_recv_reqs = []

        fa_optional_backward_kwargs = {}
        if not _flash_attn_2_available:
            fa_optional_backward_kwargs["num_splits"] = 1 if ctx.deterministic else 0

        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

            send_tensor = p2p_comm_buffers[i%2]
            recv_tensor = p2p_comm_buffers[(i+1)%2]
            if i == 0:
                send_tensor = send_tensor[0]
                recv_tensor = recv_tensor[0]
            if i == (cp_size-1):
                send_tensor = send_tensor[1]
                recv_tensor = recv_tensor[1]

            send_recv_reqs = flash_attn_p2p_communicate(rank,
                                                        send_tensor,
                                                        send_dst,
                                                        recv_tensor,
                                                        recv_src,
                                                        ctx.cp_group,
                                                        batch_p2p_comm)

            kv = p2p_comm_buffers[i%2][0]
            # In reversed order of fwd
            if ctx.causal:
                if i == (cp_size-1):
                    # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                    q_ = q.view(-1, *q.shape[-2:])
                    dq_ = torch.empty_like(q_)
                    # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
                    # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
                    _flash_attn_backward(
                        dout_, q_, kv_[0], kv_[1], out_, softmax_lse,
                        dq_, dkv_[0], dkv_[1], cu_seqlens_q, cu_seqlens_k,
                        ctx.max_seqlen_q, ctx.max_seqlen_k,
                        ctx.dropout_p, ctx.softmax_scale, True,
                        rng_state=ctx.rng_states[cp_size-i-1],
                        **fa_optional_backward_kwargs
                    )
                elif i >= (cp_size-rank-1):
                    # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                    q_ = q.view(-1, *q.shape[-2:])
                    dq_ = torch.empty_like(q_)
                    # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
                    kv_ = kv[:, :, 0, ...].contiguous().view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
                    # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
                    _flash_attn_backward(
                        dout_, q_, kv_[0], kv_[1], out_, softmax_lse,
                        dq_, dkv_[0], dkv_[1], cu_seqlens_q, cu_seqlens_k//2,
                        ctx.max_seqlen_q, ctx.max_seqlen_k//2,
                        ctx.dropout_p, ctx.softmax_scale, False,
                        rng_state=ctx.rng_states[cp_size-i-1],
                        **fa_optional_backward_kwargs
                    )
                else:
                    # [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                    q_ = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
                    dq_ = torch.empty_like(q_)
                    # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
                    # [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                    out_ = out[:, 1, ...].contiguous().view(-1, *out.shape[-2:])
                    dout_ = dout[:, 1, ...].contiguous().view(-1, *dout.shape[-2:])
                    _flash_attn_backward(
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                        dout_, q_, kv_[0], kv_[1], out_, softmax_lse_,
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                        dq_, dkv_[0], dkv_[1], cu_seqlens_q//2, cu_seqlens_k,
                        ctx.max_seqlen_q//2, ctx.max_seqlen_k,
                        ctx.dropout_p, ctx.softmax_scale, False,
                        rng_state=ctx.rng_states[cp_size-i-1],
                        **fa_optional_backward_kwargs
                    )

                if i >= (cp_size-rank-1):
                    # [b*sq, np, hn] -> [b, 2, sq//2, np, hn]
                    dq_ = dq_.view(*dq.shape)
                else:
                    # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                    dq_ = dq_.view(dq.shape[0], *dq.shape[2:])

                if i > (cp_size-rank-1):
                    dq.add_(dq_)
                elif i == (cp_size-rank-1):
                    if rank == (cp_size-1):
                        dq.copy_(dq_)
                    else:
                        dq[:, 0, ...].copy_(dq_[:, 0, ...])
                        dq[:, 1, ...].add_(dq_[:, 1, ...])
                elif i > 0:
                    dq[:, 1, ...].add_(dq_)
                else:
                    dq[:, 1, ...].copy_(dq_)

                # wait until dKV is received
                for req in send_recv_reqs:
                    req.wait()

                dkv = p2p_comm_buffers[(i+1)%2][1]
                if i >= (cp_size-rank-1) and i != (cp_size-1):
                    # [2, b*sk//2, np, hn] -> [2, b, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape[0:2], *dkv.shape[3:])
                else:
                    # [2, b*sk, np, hn] -> [2, b, 2, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape)

                if i == (cp_size-1):
                    if rank == 0:
                        dkv[:, :, 0, ...].add_(dkv_[:, :, 0, ...])
                        dkv[:, :, 1, ...].copy_(dkv_[:, :, 1, ...])
                    else:
                        dkv.add_(dkv_)
                elif i >= (cp_size-rank-1):
                    if i == 0 and rank == (cp_size-1):
                        dkv[:, :, 0, ...].copy_(dkv_)
                    else:
                        dkv[:, :, 0, ...].add_(dkv_)
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
                assert False, "Not implemented yet!"

        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
        dq = dq.view(q.shape[0], -1, *q.shape[-2:])
        # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
        dkv = dkv.view(*kv.shape[0:2], -1, *kv.shape[-2:])
        return dq, dkv[0], dkv[1], None, None, None, None, None, None, None, None, None, None, None


def flash_attn_forward_func_with_cp(q, k, v, cu_seqlens_q, cu_seqlens_k,
                                    max_seqlen_q, max_seqlen_k, dropout_p,
                                    cp_group, cp_global_ranks, cp_stream,
                                    softmax_scale=None, causal=False,
                                    deterministic=False):
    """Flash Attention implementation with context parallelism"""
    out = FlashAttnUnpaddedFuncWithCP.apply(
        q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p,
        cp_group, cp_global_ranks, cp_stream, softmax_scale, causal, deterministic
    )
    return out


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class RotaryPositionEmbedding(torch.nn.Module):
    """
    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
    """
    def __init__(
        self,
        dim: int,
        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
        seq_len_interpolation_factor: int
            if not None, discrete positions will be interpolated by this factor via the trick in
            https://arxiv.org/abs/2306.15595
        pretrained_max_position_embeddings: int
            pre-trained max_position_embeddings before position interpolation
        """
        super().__init__()
        self.seq_len_interpolation_factor = seq_len_interpolation_factor
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer('inv_freq', inv_freq)
        self.pretrained_max_position_embeddings = pretrained_max_position_embeddings

    def forward(self, max_seq_len: int, offset: int = 0):
        """
        Create rotary position embedding frequencies

        Parameters
        ----------
        max_seq_len: int
            sequence length of a sample
        offset: int, default = 0
            fixed offset for freqencies
        """
        seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
        seq = seq.type_as(self.inv_freq)

        if (self.pretrained_max_position_embeddings is not None
            and self.seq_len_interpolation_factor is not None):
            if (max_seq_len >
                self.pretrained_max_position_embeddings * self.seq_len_interpolation_factor):
                # dynamic linear scaling (length > position we have learned)
                seq *= 1 / (max_seq_len / self.pretrained_max_position_embeddings)
            else:
                # fixed linear scaling
                seq *= 1 / self.seq_len_interpolation_factor

        freqs = torch.einsum('i , j -> i j', seq, self.inv_freq)
        # first part even vector components, second part odd vector components,
        #  2 * dim in dimension size
        emb = torch.cat((freqs, freqs), dim=-1)
        # emb [seq_length, .., dim]
        return emb.reshape(emb.size(0), 1, 1, emb.size(1))

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def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    """
    change sign so the last dimension becomes [-odd, +even]
    """
    x = x.view(x.shape[:-1] + torch.Size((2, x.shape[-1] // 2)))
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
    """
    input tensor t is of shape [seq_length, ..., dim]
    rotary positional embeding tensor `freqs` is of shape [seq_length, ..., dim]
    """
    rot_dim = freqs.shape[-1]
    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]

    # first part is cosine component
    # second part is sine component, need to change signs with _rotate_half method
    t = (t * freqs.cos()) + (_rotate_half(t) * freqs.sin())
    return torch.cat((t, t_pass), dim=-1)


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class _SplitAlongDim(torch.autograd.Function):
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    """"""

    @staticmethod
    def forward(ctx,
                mixed_x_layer: torch.Tensor,
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                split_dim: int,
                split_size_or_sections: Union[int, List[int], Tuple[int]],
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    ) -> Tuple[torch.Tensor, ...]:
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        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
        return torch.split(mixed_x_layer, split_size_or_sections, dim = split_dim)
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    @staticmethod
    def backward(ctx,
                 *grad_outputs):
        assert len(grad_outputs) > 0, "No gradients received for backprop!"

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        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
            assert (len(grad_outputs) == len(split_sizes)
                ), "Unequal number of gradients vs split sections for backprop!"
        if isinstance(ctx.split_size_or_sections, int):
            split_sizes = [ctx.split_size_or_sections] * len(grad_outputs)
        dims = len(grad_outputs[0].shape)
        split_dim = (ctx.split_dim + dims) % dims

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        noop_ok = True
        strides = grad_outputs[0].stride()
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        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
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        shape = list(grad_outputs[0].shape)
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        for i, tensor in enumerate(grad_outputs):
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            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
            offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim+1:])
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            if (tensor.stride() != strides or
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                list(tensor.shape) != shape_i or
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                tensor.untyped_storage().data_ptr() != data_ptr or
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                tensor.storage_offset() != offset_size):
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                noop_ok = False
                break

        if noop_ok:
            ret = torch.Tensor().to(device=grad_outputs[0].device,
                                    dtype=grad_outputs[0].dtype)
            new_shape = list(shape)
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            new_shape[split_dim] = sum(split_sizes)
            ret.set_(grad_outputs[0].untyped_storage(),
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                     grad_outputs[0].storage_offset(),
                     new_shape,
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                     strides
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            )
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            return ret, None, None
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        return torch.cat(grad_outputs, dim = split_dim), None, None
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class UnfusedDotProductAttention(torch.nn.Module):
    """Parallel attention w/o QKV and Proj Gemms
    BMM1 -> softmax + dropout -> BMM2
    """

    def __init__(
        self,
        norm_factor: float,
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

        self.norm_factor = norm_factor
        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

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        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
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        # Dropout. Note that for a single iteration, this layer will generate
        # different outputs on different number of parallel partitions but
        # on average it should not be partition dependent.
        self.attention_dropout = torch.nn.Dropout(attention_dropout)

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        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None)

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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None, # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None, # pylint: disable=unused-argument
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        attn_mask_type: str = "causal",
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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    ) -> torch.Tensor:
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        """Unfused attention fprop"""
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        assert (qkv_layout in QKVLayouts
            ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
        qkv_format = ''.join([i for i in qkv_layout.split('_')[0] if i.isalpha()])
        assert (qkv_format != 'thd'
            ), """UnfusedDotProductAttention does not support variable sequence lengths!"""
        if qkv_format == 'bshd':
            # convert to sbhd and use sbhd implementation for now
            query_layer, key_layer, value_layer = [x.transpose(0, 1)
                for x in [query_layer, key_layer, value_layer]]
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        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
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        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
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        # [b, np, sq, sk]
        output_size = (
            query_layer.size(1),
            query_layer.size(2),
            query_layer.size(0),
            key_layer.size(0),
        )

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        if key_layer.shape[2] != query_layer.shape[2]:
            assert (query_layer.shape[2]%key_layer.shape[2]==0
                ),"The number of attention heads must be divisible by the number of GQA groups!"
            key_layer = key_layer.repeat_interleave(
                    int(query_layer.shape[2]/key_layer.shape[2]), dim = 2)
            value_layer = value_layer.repeat_interleave(
                    int(query_layer.shape[2]/value_layer.shape[2]), dim = 2)

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        # [sq, b, np, hn] -> [sq, b * np, hn]
        query_layer = query_layer.reshape(
            output_size[2], output_size[0] * output_size[1], -1
        )
        # [sk, b, np, hn] -> [sk, b * np, hn]
        key_layer = key_layer.reshape(output_size[3], output_size[0] * output_size[1], -1)

        # preallocting result tensor: [b * np, sq, sk]
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        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
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        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
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            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
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            device=torch.cuda.current_device(),
        )

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        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

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        scale = self.norm_factor
        if apply_qk_layer_scaling:
            scale *= self.layer_number

        # Raw attention scores. [b * np, sq, sk]
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        if core_attention_bias_type == "no_bias":
            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
                alpha=(1.0 / scale),
            )

        elif core_attention_bias_type == "pre_scale_bias":
            assert core_attention_bias is not None, "core_attention_bias should not be None!"
            assert (core_attention_bias.shape == torch.Size(1, *output_size[1:])
                    ), "core_attention_bias must be in [1, h, sq, skv] shape!"
            matmul_result = torch.bmm(
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
            )
            matmul_result = (matmul_result.view(
                output_size[0], output_size[1], output_size[2], output_size[3])
                + core_attention_bias).view(-1, output_size[2], output_size[3])
            matmul_result /= scale

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        elif core_attention_bias_type in ["post_scale_bias", "alibi"]:
            if core_attention_bias_type == "post_scale_bias":
                assert core_attention_bias is not None, "core_attention_bias should not be None!"
                assert (core_attention_bias.shape == torch.Size([1, *output_size[1:]])
                        ), "core_attention_bias must be in [1, h, sq, skv] shape!"
            if core_attention_bias_type == "alibi":
                core_attention_bias = get_alibi(output_size[1], output_size[2], output_size[3])
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            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
                alpha=(1.0 / scale),
            )
            matmul_result = (matmul_result.view(
                output_size[0], output_size[1], output_size[2], output_size[3])
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                + core_attention_bias).view(-1, output_size[2], output_size[3]).to(
                dtype=query_layer.dtype)
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        # change view to [b, np, sq, sk]
        attention_scores = matmul_result.view(*output_size)

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
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        attention_probs = self.scale_mask_softmax(
            attention_scores, attention_mask, attn_mask_type, softmax_scale)
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        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        with self.attention_dropout_ctx():
            attention_probs = self.attention_dropout(attention_probs)

        # value_layer -> context layer.
        # [sk, b, np, hn] --> [b, np, sq, hn]
        output_size = (
            value_layer.size(1),
            value_layer.size(2),
            query_layer.size(0),
            value_layer.size(3),
        )

        # change view [sk, b * np, hn]
        value_layer = value_layer.reshape(
            value_layer.size(0), output_size[0] * output_size[1], -1
        )

        # change view [b * np, sq, sk]
        attention_probs = attention_probs.view(
            output_size[0] * output_size[1], output_size[2], -1
        )

        # matmul: [b * np, sq, hn]
        context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

        # change view [b, np, sq, hn]
        context_layer = context_layer.view(*output_size)

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        if qkv_format == 'sbhd':
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
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            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

        if qkv_format == 'bshd':
            # [b, np, sq, hn] --> [b, sq, np, hn]
            context_layer = context_layer.permute(0, 2, 1, 3).contiguous()

            # [b, sq, np, hn] --> [b, sq, hp]
            context_layer = context_layer.view(batch_size, seqlen, -1)
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        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
       to separate contiguous q, k, v tensors in (b, s, ...) layout."""

    @staticmethod
    def forward(ctx,
                query_layer: torch.Tensor,
                key_layer: torch.Tensor,
                value_layer: torch.Tensor
    ) -> torch.Tensor:
        # All inputs received are non-contiguous tensors.
        # The `query_layer` tensor is used to access the
        # full memory region of the QKV tensor.
        qkv = tex.fa_prepare_fwd(query_layer)
        q, k, v = split_tensor_along_dim(qkv, 0, 3)
        query_layer = torch.squeeze(q, 0)
        key_layer = torch.squeeze(k, 0)
        value_layer = torch.squeeze(v, 0)
        return query_layer, key_layer, value_layer

    @staticmethod
    def backward(ctx,
                 dq: torch.Tensor,
                 dk: torch.Tensor,
                 dv: torch.Tensor
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

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def _get_qkv_layout(
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        qkv_format: str = 'sbhd',
    ) -> str:
    """Get qkv layout.
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    Parameters
    ----------
    q: torch.Tensor
        Query tensor.
    k: torch.Tensor
        Key tensor.
    v: torch.Tensor
        Value tensor.
    qkv_format: str, default = `sbhd`
        Dimension format for `q`, `k` and `v`, {`sbhd`, `bshd`, `thd`}. `s` stands for
        the sequence length dimension, `b` batch size, `h` the number of attention heads,
        `d` head size, and `t` the total number of sequences in a batch, i.e.
        `t = sum(s_i) for i = 0...b-1`.

    Returns
    ----------
    qkv_layout: str
       Memory layout of `q`, `k` and `v`. Each `qkv_format` can be mapped to one of five
       memory layouts. For example, `sb3hd` means `q`, `k`, `v` are created as one chunk
       of memory and that they are interleaved in the `2`nd dimension. `sbhd_sbh2d` means
       `q` and `kv` are created in two chunks and that `q` itself is contiguous and `k`, `v`
       are interleaved with each other in the `3`rd dimension, `k = kv[:,:,:,0,:]` and
       `v = kv[:,:,:,1,:]`.
       Mapping:
       `sbhd`: {`sb3hd`, `sbh3d`, `sbhd_sb2hd`, `sbhd_sbh2d`, `sbhd_sbhd_sbhd`}
       `bshd`: {`bs3hd`, `bsh3d`, `bshd_bs2hd`, `bshd_bsh2d`, `bshd_bshd_bshd`}
       `thd` : {`t3hd`, `th3d`, `thd_t2hd`, `thd_th2d`, `thd_thd_thd`}
    """
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    check_last_dim_contiguous = all(x.stride(-1) == 1 for x in [q, k, v])
    assert check_last_dim_contiguous, "q, k and v must have stride 1 in their last dimension!"
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    def run_iteratively(q, k, v):
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
        stride = k.stride()
        check_strides_kv = all(stride == x.stride() for x in [k, v])

        shape = q.shape
        check_shapes_qkv = all(shape == x.shape for x in [q, k, v])
        shape = k.shape
        check_shapes_kv = all(shape == x.shape for x in [k, v])

        last_dim_size = q.shape[-1]
        check_last_dim_offsets_qkv = all(i * last_dim_size == x.storage_offset()
                            for i, x in enumerate([q, k, v]))
        last_dim_size = k.shape[-1]
        check_last_dim_offsets_kv = all(i * last_dim_size == x.storage_offset()
                            for i, x in enumerate([k, v]))

        last_two_dims_size = q.shape[-1] * q.shape[-2]
        check_last_two_dims_offsets_qkv = all(i * last_two_dims_size == x.storage_offset()
                            for i, x in enumerate([q, k, v]))
        last_two_dims_size = k.shape[-1] * k.shape[-2]
        check_last_two_dims_offsets_kv = all(i * last_two_dims_size == x.storage_offset()
                            for i, x in enumerate([k, v]))

        if (check_ptrs_qkv and check_strides_qkv and check_shapes_qkv
            and check_last_two_dims_offsets_qkv
            and not check_last_dim_offsets_qkv):
            # sb3hd, bs3hd, t3hd
            qkv_layout = qkv_format[:-2] + '3' + qkv_format[-2:]
        elif (check_ptrs_qkv and check_strides_qkv and check_shapes_qkv
            and check_last_dim_offsets_qkv):
            # sbh3d, bsh3d, th3d
            qkv_layout = qkv_format[:-1] + '3' + qkv_format[-1:]
        elif (check_ptrs_kv and check_strides_kv and check_shapes_kv
            and check_last_two_dims_offsets_kv
            and not check_last_dim_offsets_kv):
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
            qkv_layout = qkv_format + '_' + qkv_format[:-2] + '2' + qkv_format[-2:]
        elif (check_ptrs_kv and check_strides_kv and check_shapes_kv
            and check_last_dim_offsets_kv):
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
            qkv_layout = qkv_format + '_' + qkv_format[:-1] + '2' + qkv_format[-1:]
        elif check_strides_kv and check_shapes_kv:
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
            qkv_layout = '_'.join(list([qkv_format])*3)
        else:
            qkv_layout = 'not_supported'

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
    if qkv_layout == 'not_supported':
        # force q,k,v to be contiguous and run get_layout again
        q, k, v = [x.contiguous() for x in [q, k, v]]
        qkv_layout = run_iteratively(q, k, v)
    if qkv_layout == 'not_supported':
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        raise Exception("The provided qkv memory layout is not supported!")

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    return qkv_layout, q, k, v
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def check_set_window_size(
        attn_mask_type: str,
        window_size: Tuple[int, int] = None,
    ):
    """Check if sliding window size is compliant with mask type and if not,
    assert or set it to the appropriate size
    """
    if "causal" in attn_mask_type:
        if window_size is None:
            window_size = (-1, 0)
        else:
            assert (
                window_size[1] == 0
            ), "window_size[1] should be 0 when self_attn_mask_type includes 'causal'!"
    else:
        if window_size is None:
            window_size = (-1, -1)
    return window_size
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class FlashAttention(torch.nn.Module):
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    """Dot product attention, using HazyResearch flash-attn package:
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    https://github.com/Dao-AILab/flash-attention
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    """

    def __init__(
        self,
        norm_factor: float,
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
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        attention_type: str = "self",
        layer_number: Optional[int] = None,
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        deterministic: bool = False,
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    ) -> None:
        super().__init__()

        assert (
            _flash_attn_version >= _flash_attn_version_required
        ), f"FlashAttention minimum version {_flash_attn_version_required} is required."

        self.norm_factor = norm_factor
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
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        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
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        self.deterministic = deterministic
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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
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        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        cp_group: Optional[dist_group_type] = None,
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        cp_global_ranks: List[int] = None,
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        cp_stream: torch.cuda.Stream = None,
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    ) -> torch.Tensor:
        """flash-attn fprop"""

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        window_size = check_set_window_size(attn_mask_type, window_size)

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        assert (
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            query_layer.dtype in [torch.float16, torch.bfloat16]
            and key_layer.dtype in [torch.float16, torch.bfloat16]
            and value_layer.dtype in [torch.float16, torch.bfloat16]
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            ), "FlashAttention currently only supports FP16 and BF16."
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            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
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            ), "FlashAttention currently only supports CUDA tensors."
        assert (
            qkv_layout in QKVLayouts
            ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"

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        context_parallel = (cp_group is not None) and (get_distributed_world_size(cp_group) != 1)

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        qkv_format = ''.join([i for i in qkv_layout.split('_')[0] if i.isalpha()])

        if qkv_format == 'sbhd':
            # For now just 128, will make it more general in the future
            if (query_layer.shape[-1] == 128 and
                query_layer.shape[0] * query_layer.shape[1] >= 512 and
                qkv_layout == "sbh3d"):
                query_layer, key_layer, value_layer = _PrepareQKVForFA.apply(query_layer,
                                                                             key_layer,
                                                                             value_layer)
            else:
                query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
                    for x in (query_layer, key_layer, value_layer)]
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        elif qkv_format == 'bshd':
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            query_layer, key_layer, value_layer = [x.contiguous()
                for x in (query_layer, key_layer, value_layer)]

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        global _cu_seqlens_q, _cu_seqlens_kv, _indices_q, _indices_kv
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        batch_size = query_layer.shape[0]
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        if qkv_format in ['sbhd', 'bshd']:
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            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
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            if not context_parallel:
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
                    x.view(x.shape[0] * x.shape[1], *x.shape[2:])
                    for x in [query_layer, key_layer, value_layer]
                ]

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            if 'padding' in attn_mask_type:
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                assert not context_parallel, "Padding mask not supported with context parallelism."

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
                    if self.layer_number == 1:
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                        if cu_seqlens_q is None:
                            assert (attention_mask is not None
                                ), "Please provide attention_mask for padding!"
                            _cu_seqlens_q, _indices_q = get_cu_seqlens_and_indices(attention_mask)
                        else:
                            _cu_seqlens_q = cu_seqlens_q
                            _indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
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                    _cu_seqlens_kv = _cu_seqlens_q
                    query_layer_packed, key_layer_packed, value_layer_packed = PackTensors.apply(
                        _indices_q, query_layer, key_layer, value_layer
                    )
                else:
                    if self.layer_number == 1:
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                        if cu_seqlens_q is None or cu_seqlens_kv is None:
                            assert (attention_mask is not None
                                ), "Please provide attention_mask for padding!"
                            _cu_seqlens_q, _indices_q = get_cu_seqlens_and_indices(
                                attention_mask[0])
                            _cu_seqlens_kv, _indices_kv = get_cu_seqlens_and_indices(
                                attention_mask[1])
                        else:
                            _cu_seqlens_q = cu_seqlens_q
                            _cu_seqlens_kv = cu_seqlens_kv
                            _indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                            _indices_kv = get_indices(max_seqlen_kv, cu_seqlens_kv)
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                    query_layer_packed = PackTensors.apply(_indices_q, query_layer)
                    key_layer_packed, value_layer_packed = PackTensors.apply(
                        _indices_kv, key_layer, value_layer
                    )
                query_layer, key_layer, value_layer = (
                    query_layer_packed, key_layer_packed, value_layer_packed)
                cu_seqlens_q, cu_seqlens_kv = _cu_seqlens_q, _cu_seqlens_kv
            else:
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                if self.layer_number == 1:
                    if cu_seqlens_q is None:
                        cu_seqlens_q = torch.arange(
                                0,
                                (batch_size + 1) * max_seqlen_q,
                                step=max_seqlen_q,
                                dtype=torch.int32,
                                device=query_layer.device)
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = torch.arange(
                                0,
                                (batch_size + 1) * max_seqlen_kv,
                                step=max_seqlen_kv,
                                dtype=torch.int32,
                                device=key_layer.device)
                    _cu_seqlens_q, _cu_seqlens_kv = cu_seqlens_q, cu_seqlens_kv
                else:
                    cu_seqlens_q, cu_seqlens_kv = _cu_seqlens_q, _cu_seqlens_kv
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        elif qkv_format == 'thd':
            assert not context_parallel, "thd format is not supported for context parallelism!"
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            assert (_flash_attn_2_available
                ), "flash-attn v2 is required for variable sequence length support!"
            assert (cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
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            if max_seqlen_q is None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                max_seqlen_q = seqlens_q.max().item()
            if max_seqlen_kv is None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                max_seqlen_kv = seqlens_kv.max().item()
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        if context_parallel:
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            assert (
                window_size in ((-1, -1), (-1, 0))
                ), "Sliding window attention is not supported with context parallelism."
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            with self.attention_dropout_ctx():
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                output = flash_attn_forward_func_with_cp(
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                    query_layer, key_layer, value_layer,
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                    cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv,
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                    self.attention_dropout if self.training else 0.0,
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                    cp_group, cp_global_ranks, cp_stream,
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                    softmax_scale=1.0/self.norm_factor,
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                    causal="causal" in attn_mask_type,
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                    deterministic=self.deterministic
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                )
        else:
            with self.attention_dropout_ctx():
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                fa_optional_forward_kwargs = {}
                if not _flash_attn_2_available:
                    fa_optional_forward_kwargs["deterministic"] = self.deterministic
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                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
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                output = flash_attn_forward_func(
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                    query_layer, key_layer, value_layer,
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                    cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv,
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                    self.attention_dropout if self.training else 0.0,
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                    softmax_scale=1.0/self.norm_factor, causal="causal" in attn_mask_type,
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                    **fa_optional_forward_kwargs
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                )
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        if 'padding' in attn_mask_type:
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            output = UnpackTensor.apply(_indices_q, batch_size * max_seqlen_q, output)

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        if qkv_format == 'sbhd':
            # (bs)hd -> bs(hd) -> sb(hd)
            output = output.view(batch_size, max_seqlen_q, -1).transpose(0, 1).contiguous()
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        elif qkv_format == 'bshd':
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            # (bs)hd -> bs(hd)
            output = output.view(batch_size, max_seqlen_q, -1).contiguous()
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        elif qkv_format == 'thd':
            # thd -> t(hd)
            output = output.view(output.shape[0], -1).contiguous()
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        return output
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class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
    def forward(ctx, is_training, max_seqlen, cu_seqlens, qkv, qkv_dtype, attn_bias, attn_scale,
                dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
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                rng_gen, fused_attention_backend, use_FAv2_bwd):
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        out, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
            is_training, max_seqlen, cu_seqlens, qkv, qkv_dtype,
            fused_attention_backend, attn_bias,
            None, None, None, None, None,
            attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
            rng_gen)

        ctx.save_for_backward(qkv, out, cu_seqlens)
        ctx.aux_ctx_tensors = aux_ctx_tensors
        ctx.max_seqlen = max_seqlen
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
        ctx.fused_attention_backend = fused_attention_backend
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        ctx.use_FAv2_bwd = use_FAv2_bwd
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        return out

    @staticmethod
    def backward(ctx, d_out):
        qkv, out, cu_seqlens = ctx.saved_tensors
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        if not ctx.aux_ctx_tensors[0].is_contiguous():
            ctx.aux_ctx_tensors[0] = ctx.aux_ctx_tensors[0].contiguous()
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        if ctx.use_FAv2_bwd:
            softmax_lse, rng_state = ctx.aux_ctx_tensors
            dqkv = torch.empty_like(qkv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
            d_out, q, k, v, out = [maybe_contiguous(x)
                for x in (d_out, qkv[:,0], qkv[:,1], qkv[:,2], out)]
            flash_attn_cuda_bwd(
                d_out, q, k, v, out, softmax_lse, dqkv[:,0], dqkv[:,1], dqkv[:,2],
                cu_seqlens, cu_seqlens, ctx.max_seqlen, ctx.max_seqlen,
                ctx.dropout_p, ctx.attn_scale, False,
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                "causal" in ctx.attn_mask_type, None, rng_state
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            )
            dqkv = dqkv[..., :d_out.shape[-1]]
        else:
            dqkv, *rest = fused_attn_bwd_qkvpacked(
                ctx.max_seqlen, cu_seqlens, qkv, out, d_out,
                ctx.qkv_dtype, ctx.aux_ctx_tensors,
                ctx.fused_attention_backend,
                None, None, None, None, None, None, None, None, None,
                ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
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        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
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            return (None, None, None, dqkv, None, None, None,
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
        return (None, None, None, dqkv, None, rest[0], None,
                None, None, None, None, None, None,
                None, None, None, None, None, None)

class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
    def forward(ctx, is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q, kv, qkv_dtype, attn_bias, attn_scale, dropout_p, fast_zero_fill,
                qkv_layout, attn_bias_type, attn_mask_type,
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                rng_gen, fused_attention_backend, use_FAv2_bwd):
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        out, aux_ctx_tensors = fused_attn_fwd_kvpacked(
            is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
            q, kv, qkv_dtype, fused_attention_backend, attn_bias,
            None, None, None, None, None,
            attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
            rng_gen)

        ctx.save_for_backward(q, kv, out, cu_seqlens_q, cu_seqlens_kv)
        ctx.aux_ctx_tensors = aux_ctx_tensors
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
        ctx.fused_attention_backend = fused_attention_backend
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        ctx.use_FAv2_bwd = use_FAv2_bwd
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        return out

    @staticmethod
    def backward(ctx, d_out):
        q, kv, out, cu_seqlens_q, cu_seqlens_kv = ctx.saved_tensors
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        if not ctx.aux_ctx_tensors[0].is_contiguous():
            ctx.aux_ctx_tensors[0] = ctx.aux_ctx_tensors[0].contiguous()
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        if ctx.use_FAv2_bwd:
            softmax_lse, rng_state = ctx.aux_ctx_tensors
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
            d_out, q, k, v, out = [maybe_contiguous(x)
                for x in (d_out, q, kv[:,0], kv[:,1], out)]
            flash_attn_cuda_bwd(
                d_out, q, k, v, out, softmax_lse, dq, dkv[:,0], dkv[:,1],
                cu_seqlens_q, cu_seqlens_kv, ctx.max_seqlen_q, ctx.max_seqlen_kv,
                ctx.dropout_p, ctx.attn_scale, False,
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            )
            dq = dq[..., :d_out.shape[-1]]
            dkv = dkv[..., :d_out.shape[-1]]
        else:
            dq, dkv, *rest = fused_attn_bwd_kvpacked(
                ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q, kv, out, d_out,
                ctx.qkv_dtype, ctx.aux_ctx_tensors,
                ctx.fused_attention_backend,
                None, None, None, None, None, None, None, None, None,
                ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
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        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
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            return (None, None, None, None, None, dq, dkv, None, None, None,
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
        return (None, None, None, None, None, dq, dkv, None, rest[0], None,
                None, None, None, None, None, None,
                None, None, None, None, None, None)

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class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
    def forward(ctx, is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q, k, v, qkv_dtype, attn_bias, attn_scale, dropout_p, fast_zero_fill,
                qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen, fused_attention_backend, use_FAv2_bwd):
        out, aux_ctx_tensors = fused_attn_fwd(
            is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
            q, k, v, qkv_dtype, fused_attention_backend, attn_bias,
            None, None, None, None, None,
            attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
            rng_gen)

        ctx.save_for_backward(q, k, v, out, cu_seqlens_q, cu_seqlens_kv)
        ctx.aux_ctx_tensors = aux_ctx_tensors
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
        ctx.fused_attention_backend = fused_attention_backend
        ctx.use_FAv2_bwd = use_FAv2_bwd

        return out

    @staticmethod
    def backward(ctx, d_out):
        q, k, v, out, cu_seqlens_q, cu_seqlens_kv = ctx.saved_tensors
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        if not ctx.aux_ctx_tensors[0].is_contiguous():
            ctx.aux_ctx_tensors[0] = ctx.aux_ctx_tensors[0].contiguous()
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        if ctx.use_FAv2_bwd:
            softmax_lse, rng_state = ctx.aux_ctx_tensors
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
            d_out, q, k, v, out = [maybe_contiguous(x)
                for x in (d_out, q, k, v, out)]
            flash_attn_cuda_bwd(
                d_out, q, k, v, out, softmax_lse, dq, dk, dv,
                cu_seqlens_q, cu_seqlens_kv, ctx.max_seqlen_q, ctx.max_seqlen_kv,
                ctx.dropout_p, ctx.attn_scale, False,
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            )
            dq = dq[..., :d_out.shape[-1]]
            dk = dk[..., :d_out.shape[-1]]
            dv = dv[..., :d_out.shape[-1]]
        else:
            dq, dk, dv, *rest = fused_attn_bwd(
                ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q, k, v, out, d_out,
                ctx.qkv_dtype, ctx.aux_ctx_tensors,
                ctx.fused_attention_backend,
                None, None, None, None, None, None, None, None, None,
                ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)

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        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
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            return (None, None, None, None, None, dq, dk, dv, None, None, None,
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
        return (None, None, None, None, None, dq, dk, dv, None, rest[0], None,
                None, None, None, None, None, None,
                None, None, None, None, None, None)

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class FusedAttention(torch.nn.Module):
    """Dot product attention, with multiple backends:

    1. FusedAttnBackend["F16_max512_seqlen"]
       cuDNN based fused attention for FP16/BF16 and <=512 sequence length.
    2. FusedAttnBackend["F16_arbitrary_seqlen"]
       cuDNN based fused attention for FP16/BF16 and any sequence length.

    Support matrix:

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    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
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    | attn_type     | self/cross              | self/cross                     |
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    | qkv_layout    |                         |                                |
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    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
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    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
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    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
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    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
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    | dropout       | yes                     | yes                            |
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    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
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    | output dtype  | fp16/bf16               | fp16/bf16                      |
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    """

    def __init__(
        self,
        norm_factor: float,
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
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        layer_number: Optional[int] = None,
        deterministic: bool = False,
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    ) -> None:
        super().__init__()

        self.norm_factor = norm_factor
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
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        self.use_FAv2_bwd = (os.getenv("NVTE_FUSED_ATTN_USE_FAv2_BWD", "0") == "1"
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                        and _flash_attn_2_available
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                        and get_device_compute_capability() == (9, 0))
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        self.layer_number = 1 if layer_number is None else layer_number
        if deterministic:
            # workspace optimization path is deterministic
            os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "-1"

        # CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT
        # - unset:       enables workspace optimization when required workspace is <= 256MB
        #                or when bias gradient needs to be computed
        # - n:           enables workspace optimization when required workspace is <= n bytes
        # - -1:          enables workspace optimization always
        # - 0:           disables workspace optimization always
        if "NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT" in os.environ:
            if os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] == "0":
                os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "0"
            if os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] == "1":
                os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "-1"
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    @no_torch_dynamo()
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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
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        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        attn_mask_type: str = "causal",
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        fused_attention_backend:
            tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
    ) -> torch.Tensor:
        """fused attention fprop"""

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        assert (fused_attention_backend
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            != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
            ), 'No fused attention backend supports this input combination!'
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        assert (
            (query_layer.dtype in [torch.float16, torch.bfloat16])
            and (key_layer.dtype in [torch.float16, torch.bfloat16])
            and (value_layer.dtype in [torch.float16, torch.bfloat16])
            ), 'FusedAttention only supports FP16 and BF16 data types.'
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), 'FusedAttention only supports CUDA tensors.'
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        assert (
            qkv_layout in QKVLayouts
            ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"

        qkv_format = ''.join([i for i in qkv_layout.split('_')[0] if i.isalpha()])
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        assert (
            qkv_format != 'thd'
            ), 'FusedAttention does not support qkv_format = thd!'

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        if qkv_format in ['sbhd', 'bshd']:
            if qkv_format == 'sbhd':
                batch_size, max_seqlen_q, max_seqlen_kv = (
                    query_layer.shape[1], query_layer.shape[0], key_layer.shape[0])
            if qkv_format == 'bshd':
                batch_size, max_seqlen_q, max_seqlen_kv = (
                    query_layer.shape[0], query_layer.shape[1], key_layer.shape[1])
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            if 'padding' in attn_mask_type:
                global _cu_seqlens_q, _cu_seqlens_kv
                if (cu_seqlens_q is not None and cu_seqlens_kv is not None):
                    # use cu_seqlens when both cu_seqlens and attention_mask are present
                    if self.layer_number == 1:
                        _cu_seqlens_q, _cu_seqlens_kv = cu_seqlens_q, cu_seqlens_kv
                elif attention_mask is not None:
                    if self.attention_type == "self":
                        if self.layer_number == 1:
                            _cu_seqlens_q = get_cu_seqlens(attention_mask)
                            _cu_seqlens_kv = _cu_seqlens_q
                    else:
                        if self.layer_number == 1:
                            _cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                            _cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
                else:
                    raise Exception("Please provide attention_mask or cu_seqlens for padding!")
                cu_seqlens_q, cu_seqlens_kv = _cu_seqlens_q, _cu_seqlens_kv
            else:
                if self.layer_number == 1:
                    if cu_seqlens_q is None:
                        cu_seqlens_q = torch.arange(
                                0,
                                (batch_size + 1) * max_seqlen_q,
                                step=max_seqlen_q,
                                dtype=torch.int32,
                                device=query_layer.device)
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = torch.arange(
                                0,
                                (batch_size + 1) * max_seqlen_kv,
                                step=max_seqlen_kv,
                                dtype=torch.int32,
                                device=key_layer.device)
                    _cu_seqlens_q, _cu_seqlens_kv = cu_seqlens_q, cu_seqlens_kv
                else:
                    cu_seqlens_q, cu_seqlens_kv = _cu_seqlens_q, _cu_seqlens_kv
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        qkv_dtype = TE_DType[query_layer.dtype]

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        use_FAv2_bwd = (self.use_FAv2_bwd
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                and (core_attention_bias_type == "no_bias")
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                and (fused_attention_backend
                    == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen))
        with self.attention_dropout_ctx():
            output = FusedAttnFunc.apply(
                self.training,
                max_seqlen_q, max_seqlen_kv,
                cu_seqlens_q, cu_seqlens_kv,
                query_layer, key_layer, value_layer,
                qkv_dtype,
                core_attention_bias,
                1.0/self.norm_factor,
                self.attention_dropout if self.training else 0.0,
                fast_zero_fill,
                qkv_layout,
                core_attention_bias_type,
                attn_mask_type,
                None, # rng_gen
                fused_attention_backend,
                use_FAv2_bwd,
            )
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        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
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class DotProductAttention(torch.nn.Module):
    """Allows the model to jointly attend to information from different
    representation subspaces as described in the paper:
    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    .. note::

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        Argument :attr:`attention_mask` in the `forward` call is only used when
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        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
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    .. warning::

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        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
        deterministic behavior at the cost of performance, use FlashAttention version < `2.0.0`
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
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    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
    kv_channels : int
                number of key-value channels.
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    num_gqa_groups : Optional[int] = None
                    number of GQA groups in the transformer layer.
                    Grouped Query Attention is described in
                    `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                    This only affects the keys and values, not the queries.
                    GQA-1 is equivalent to Multi-Query Attention
                    (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                    is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
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    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
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    attn_mask_type: str, default = `causal`
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                   type of attention mask passed into softmax operation, options are "`no_mask`",
                   "`padding`", "`causal`", "`padding,causal`", "`causal,padding`", and
                   "`arbitrary`", where "`padding,causal`" and "`causal,padding`" are equivalent.
                   This arg can be overridden by :attr:`attn_mask_type` in the `forward` method.
                   It is useful for cases involving compilation/tracing, e.g. ONNX export, and the
                   forward arg is useful for dynamically changing mask types, e.g. a different mask
                   for training and inference. For "`no_mask`", no attention mask is applied. For
                   "`causal`" or the causal mask in "`padding,causal`", TransformerEngine calculates
                   and applies an upper triangular mask to the softmax input. No user input is
                   needed. For "`padding`" or the padding mask in "`padding,causal`", users need to
                   provide the locations of padded tokens either via :attr:`cu_seqlens_q` and
                   :attr:`cu_seqlens_kv` in the shape of [batch_size + 1] or :attr:`attention_mask`
                   in the shape [batch_size, 1, 1, max_seq_len]. For the "`arbitrary`" mask, users
                   need to provide a mask that is broadcastable to the shape of softmax input.
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    window_size: Optional[Tuple[int, int]], default = `None`
                sliding window size for local attention, where query at position i attends to keys
                in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
                + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
                window and causal mask specifically. Similar to :attr:`attn_mask_type`, it can
                be overridden by :attr:`window_size` in `forward` as well.
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    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
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    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
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    qkv_format: str, default = `sbhd`
               dimension format for `query_layer`, `key_layer` and `value_layer`,
               {`sbhd`, `bshd`, `thd`}. `s` stands for the sequence length, `b` batch size,
               `h` the number of heads, `d` head size, and `t` the total number of sequences
               in a batch, with `t = sum(s_i), for i = 0...b-1`. `sbhd` and `bshd` formats
               are used for when sequences in a batch are of equal length or padded to
               equal length, and the `thd` format is used for when sequences in a batch
               have different lengths. Please note that these formats do not reflect how
               tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
               For that, please use `_get_qkv_layout` to gain the layout information.
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    Parallelism parameters
    ----------------------
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_size : int, default = 1
             tensor parallel world size.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
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    cp_group : ProcessGroup, default = `None`
              context parallel process group.
    cp_global_ranks : list of global rank IDs, default = `None`
                     global rank IDs of GPUs that are in cp_group.
    cp_stream : CUDA stream, default = `None`
               context parallelism splits flash attention into multiple steps for
               compute and communication overlapping. To address the wave quantization
               issue of each split step, we add an additional CUDA stream so that we
               can overlap two flash attention kernels.
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    """

    def __init__(
        self,
        num_attention_heads: int,
        kv_channels: int,
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        num_gqa_groups: Optional[int] = None,
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        attention_dropout: float = 0.0,
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        qkv_format: str = "sbhd",
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        attn_mask_type: str = "causal",
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        window_size: Optional[Tuple[int, int]] = None,
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        sequence_parallel: bool = False,
        tp_size: int = 1,
        get_rng_state_tracker: Optional[Callable] = None,
        tp_group: Optional[dist_group_type] = None,
        layer_number: Optional[int] = None,
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        attention_type: str = "self",
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        cp_group: Optional[dist_group_type] = None,
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        cp_global_ranks: List[int] = None,
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        cp_stream: torch.cuda.Stream = None,
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    ) -> None:
        super().__init__()

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        self.qkv_format = qkv_format
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        attn_mask_type = attn_mask_type.replace(",","_")
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
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        self.attn_mask_type = attn_mask_type
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        self.window_size = window_size
        self.window_size = check_set_window_size(attn_mask_type, self.window_size)
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        self.tp_size = tp_size if tp_group is None else get_distributed_world_size(tp_group)
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        self.tp_group = tp_group
        self.get_rng_state_tracker = get_rng_state_tracker
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        self.num_attention_heads = num_attention_heads
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        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
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        self.hidden_size_per_attention_head = kv_channels
        self.num_gqa_groups = (
            num_attention_heads if num_gqa_groups is None else num_gqa_groups
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        )
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        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)

        assert (num_attention_heads % self.num_gqa_groups == 0
                ), "The number of attention heads must be divisible by the number of GQA groups!"
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        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
            attention_dropout_ctx = get_rng_state_tracker().fork

        norm_factor = math.sqrt(self.hidden_size_per_attention_head)

        self.device_compute_capability = get_device_compute_capability()
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        self.deterministic = not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1"))) \
                             or torch.are_deterministic_algorithms_enabled()
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        self.use_flash_attention = (
            int(os.getenv("NVTE_FLASH_ATTN", "1"))
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            and self.device_compute_capability >= (8, 0)
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        )
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        if _flash_attn_2_available and self.deterministic:
            self.use_flash_attention = False
            warnings.warn(
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                "Disabling usage of FlashAttention since version 2 does not support deterministic "
                "execution. In order to use FA with deterministic behavior, please install "
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                "FlashAttention version 1."
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            )

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        self.use_fused_attention = (
            int(os.getenv("NVTE_FUSED_ATTN", "1"))
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            and self.device_compute_capability >= (8, 0)
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        )
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        assert (
            attention_type in AttnTypes
        ), f"attention_type {attention_type} not supported"

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

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        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

        if self.use_flash_attention:
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            self.flash_attention = FlashAttention(norm_factor,
                                                  attention_type=attention_type,
                                                  layer_number=layer_number,
                                                  deterministic=self.deterministic,
                                                  **attn_kwargs)

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        # Instantiating three types since use of flash-attn and FusedAttention
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        # might be ruled out due to forward inputs.
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        if self.use_fused_attention:
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            self.fused_attention = FusedAttention(norm_factor,
                                                  attention_type=attention_type,
                                                  layer_number=layer_number,
                                                  deterministic=self.deterministic,
                                                  **attn_kwargs)
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        self.unfused_attention = UnfusedDotProductAttention(
            norm_factor, **attn_kwargs, layer_number=layer_number)

    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
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        **forward_kwargs: Dict[str, Any],
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    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

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        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
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        hidden_states = checkpoint(
            custom_forward,
            False,
            self.get_rng_state_tracker,
            self.tp_group,
            *forward_args,
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            **forward_kwargs,
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        )

        return hidden_states

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    def set_context_parallel_group(
        self,
        cp_group: Union[dist_group_type, None],
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
    ) -> None:
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        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
        """
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        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream

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    @no_torch_dynamo(recursive=False)
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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
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        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        attn_mask_type: Optional[str] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        checkpoint_core_attention: bool = False,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
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    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

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            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
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        .. note::

            Input tensors :attr:`query_layer`, :attr:`key_layer`, and :attr:`value_layer`
            must each be of shape (:attr:`sequence_length`, :attr:`batch_size`,
            :attr:`num_attention_heads`, :attr:`kv_channels`). Output of shape
            (:attr:`sequence_length`, :attr:`batch_size`, :attr:`num_attention_heads`
            * :attr:`kv_channels`) is returned.

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        .. note::

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            DotProductAttention supports three backends: 1) FlashAttention which calls
            HazyResearch/Dao-AILab's `flash-attn <https://arxiv.org/pdf/2305.13245.pdf>`_
            PyTorch API, 2) FusedAttention which has multiple fused attention implementations
            based on `cuDNN Graph API
            <https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html#op-fusion>`_
            (see :attr:`FusedAttention` for more details on FusedAttention backends), and 3)
            UnfusedDotProductAttention which is the native PyTorch implementation
            with fused scaled masked softmax.

        .. note::

            Users can use environment variables :attr:`NVTE_FLASH_ATTN`, :attr:`NVTE_FUSED_ATTN`,
            and :attr:`NVTE_FUSED_ATTN_BACKEND` to control which DotProductAttention backend,
            and FusedAttention backend if applicable, to use. TransformerEngine prioritizes
            FlashAttention over FusedAttention and over UnfusedDotProductAttention.
            If FusedAttention is being used, users can also choose to switch to flash-attn's
            implementation for backward by setting :attr:`NVTE_FUSED_ATTN_USE_FAv2_BWD=1`
            (default: 0), because of the performance differences between various versions of
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            flash-attn and FusedAttention. Further, :attr:`NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT`
            can be used to enable (:attr:`1`) or disable (:attr:`0`) the workspace related
            optimizations in FusedAttention. When unset, TransformerEngine determines the code path
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
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        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
             It should be 'None' for 'causal' and 'no_mask' types. For 'padding' masks, it should be
             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
             for cross-attention. For the 'arbitrary' mask type, it should be in a shape that is
             broadcastable to [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
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        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths in a batch for `key_layer` and `value_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
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        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
                      Calculated from `cu_seqlens_q` if not provided.
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
                       Calculated from `cu_seqlens_kv` if not provided.
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        attn_mask_type: {`no_mask`, `padding`, `causal`, `padding,causal`, `causal,padding`,
                       `arbitrary`}, default = `None`. Type of attention mask passed into
                       softmax operation. 'padding,causal' and 'causal,padding' are equivalent.
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        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
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        checkpoint_core_attention : bool, default = `False`
                                   If true, forward activations for attention are recomputed
                                   during the backward pass in order to save memory that would
                                   otherwise be occupied to store the forward activations until
                                   backprop.
2204
        core_attention_bias_type: str, default = `no_bias`
2205
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
2206
        core_attention_bias: Optional[torch.Tensor], default = `None`
2207
2208
                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
2209
        fast_zero_fill: bool, default = `True`
2210
                    Whether to use the fast path to set output tensors to 0 or not.
2211
2212
        """

2213
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        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), 'DotProductAttention only supports CUDA tensors.'

2217
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        assert (key_layer.shape == value_layer.shape
            ), "Keys and values must have the same shape!"

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        if attn_mask_type is not None:
            window_size = check_set_window_size(attn_mask_type, window_size)
2222
        if attn_mask_type is None:
2223
            attn_mask_type = self.attn_mask_type
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        else:
            attn_mask_type = attn_mask_type.replace(",","_")
            if attn_mask_type == "causal_padding":
                attn_mask_type = "padding_causal"

        assert (attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"

2232
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        if window_size is None:
            window_size = self.window_size

2235
2236
        if qkv_format is None:
            qkv_format = self.qkv_format
2237

2238
        assert (key_layer.shape[-2] == self.num_gqa_groups_per_partition
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            and value_layer.shape[-2] == self.num_gqa_groups_per_partition
            ), f"Keys and values must have num_gqa_group = {self.num_gqa_groups} heads!"
        assert (qkv_format in ['sbhd', 'bshd', 'thd']
            ), "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

        if qkv_format == 'thd':
            assert (all(len(x.shape) == 3 for x in (query_layer, key_layer, value_layer))
                ), "Queries, keys and values must be 3D tensors when qkv_format = thd!"
            assert (cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
            assert (cu_seqlens_q.shape == cu_seqlens_kv.shape
                and len(cu_seqlens_q.shape) == 1
                and len(cu_seqlens_kv.shape) == 1
                ), "cu_seqlens_q and cu_seqlens_q must both have shape [batch_size + 1]!"
            assert (cu_seqlens_q.dtype == torch.int32
                and cu_seqlens_kv.dtype == torch.int32
                ), "cu_seqlens_q and cu_seqlens_q must both be in dtype torch.int32!"
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            if max_seqlen_q is None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                max_seqlen_q = seqlens_q.max().item()
            if max_seqlen_kv is None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                max_seqlen_kv = seqlens_kv.max().item()
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        if qkv_format in ['sbhd', 'bshd']:
            assert (all(len(x.shape) == 4 for x in (query_layer, key_layer, value_layer))
                ), f"Queries, keys and values must be 4D tensors when qkv_format = {qkv_format}!"
            if qkv_format == 'sbhd':
                max_seqlen_q, max_seqlen_kv = (query_layer.shape[0], key_layer.shape[0])
            if qkv_format == 'bshd':
                max_seqlen_q, max_seqlen_kv = (query_layer.shape[1], key_layer.shape[1])
            if cu_seqlens_q is not None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                assert (all(seqlens_q <= max_seqlen_q)
                    ), """Sequence lengths indicated by cu_seqlens_q must be no greater than
                    the sequence dimention in 'query_layer'!"""
            if cu_seqlens_kv is not None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                assert (all(seqlens_kv <= max_seqlen_kv)
                    ), """Sequence lengths indicated by cu_seqlens_kv must be no greater than
                    the sequence dimention in 'key_layer' and 'value_layer'!"""

2281
2282
        qkv_layout, query_layer, key_layer, value_layer = _get_qkv_layout(
            query_layer, key_layer, value_layer, qkv_format = qkv_format)
2283

2284
2285
        # The priority for attention backends (subject to availability and clearing the filters)
        # is: FlashAttention > FusedAttention (cuDNN) > UnfusedDotProductAttention.
2286
        use_flash_attention = self.use_flash_attention
2287
        use_fused_attention = self.use_fused_attention
2288
        use_unfused_attention = True
2289

2290
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2293
        # The following section filters out some backends based on
        # certain asserts before executing the forward pass.

        # Filter: Input type.
2294
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2298
        if (query_layer.dtype not in [torch.bfloat16, torch.float16]
            or key_layer.dtype not in [torch.bfloat16, torch.float16]
            or value_layer.dtype not in [torch.bfloat16, torch.float16]
        ):
            use_flash_attention = False
2299
            use_fused_attention = False
2300

2301
        # Filter: Device and dimensions.
2302
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2304
2305
2306
2307
2308
2309
        # FAv1 supports head_dim <= 128, and for >64 requires sm80/sm90
        # FAv2 supports head_dim <= 256, and for >192 requires sm80/sm90
        # Both FAv1 and FAv2 require head_dim % 8 == 0
        if not _flash_attn_2_available:
            if (key_layer.shape[-1] > 128
                or key_layer.shape[-1] % 8 != 0
                or (key_layer.shape[-1] > 64
                    and self.device_compute_capability not in ((8, 0), (9, 0)))):
2310
                use_flash_attention = False
2311
2312
2313
2314
2315
        if _flash_attn_2_available:
            if (key_layer.shape[-1] > 256
                or key_layer.shape[-1] % 8 != 0
                or (key_layer.shape[-1] > 192
                    and self.device_compute_capability not in ((8, 0), (9, 0)))):
2316
2317
                use_flash_attention = False

2318
        # Filter: MQA/GQA.
2319
2320
2321
        if not _flash_attn_2_available and self.num_gqa_groups != self.num_attention_heads:
            use_flash_attention = False

2322
        # Filter: cross attention + causal mask.
2323
        if (_flash_attn_2_1_plus
2324
            and "causal" in attn_mask_type
2325
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2327
2328
2329
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2332
            and max_seqlen_q != max_seqlen_kv):
            warnings.warn(
                "Disabling the use of FlashAttention since version 2.1+ has changed its behavior "
                "for causal mask in cross attention. See "
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False

2333
        # Filter: bias.
2334
2335
2336
        if core_attention_bias_type != "no_bias" or core_attention_bias is not None:
            use_flash_attention = False

2337
2338
2339
2340
2341
2342
2343
2344
2345
        # Filter: sliding window attention.
        # UnfusedDotProductAttention can support SWA via arbitrary attention mask.
        if window_size not in ((-1, -1), (-1, 0)):
            use_fused_attention = False
            context_parallel = (self.cp_group is not None
                and get_distributed_world_size(self.cp_group) != 1)
            if (not _flash_attn_2_3_plus) or context_parallel:
                use_flash_attention = False

2346
        # Filter: ONNX export.
2347
2348
        if is_in_onnx_export_mode():
            use_flash_attention = False
2349
2350
            use_fused_attention = False

2351
        # Filter: Attention mask type.
2352
        #   attn_mask_type(s)    |     supported backends
2353
        # ------------------------------------------------
2354
2355
        #   no_mask              |     All
        #   padding              |     UnfusedDotProductAttention, FlashAttention, FusedAttention
2356
        #   causal               |     All
2357
        #   padding + causal     |     FlashAttention, FusedAttention
2358
2359
2360
2361
2362
        #   arbitrary            |     UnfusedDotProductAttention
        #
        if attn_mask_type == "arbitrary":
            use_flash_attention = False
            use_fused_attention = False
2363
2364
        if "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
            use_unfused_attention = False
2365

2366
2367
2368
2369
2370
2371
        if use_fused_attention:
            fused_attention_backend = tex.get_fused_attn_backend(
                TE_DType[query_layer.dtype],
                TE_DType[key_layer.dtype],
                QKVLayout[qkv_layout],
                AttnBiasType[core_attention_bias_type],
2372
                AttnMaskType[attn_mask_type],
2373
                self.attention_dropout,
2374
2375
2376
2377
2378
2379
                query_layer.shape[-2], # num_attn_heads
                key_layer.shape[-2], # num_gqa_groups
                max_seqlen_q,
                max_seqlen_kv,
                query_layer.shape[-1], # head_dim
            )
2380
2381
2382
2383
            # DPA does not support FP8; for FP8, use cpp_extensions modules directly
            is_backend_avail = (fused_attention_backend in
                [FusedAttnBackend["F16_max512_seqlen"], FusedAttnBackend["F16_arbitrary_seqlen"]])
            use_fused_attention = (use_fused_attention
2384
2385
                                  and is_backend_avail)

2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
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2398
2399
2400
2401
2402
2403
        # Filter: determinism.
        # backend                                  | deterministic
        # ---------------------------------------------------------
        # flash-attn v1                            | yes
        # flash-attn v2                            | no
        # FusedAttnBackend["F16_max512_seqlen"]    | yes
        # FusedAttnBackend["F16_arbitrary_seqlen"] | workspace optimization path: yes; otherwise: no
        # UnfusedDotProductAttention               | yes
        #
        # Note that FusedAttnBackend["F16_arbitrary_seqlen"] only has workspace optimization path
        # on sm90 architectures.
        #
        if (use_fused_attention
            and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
            and self.deterministic
            and self.device_compute_capability != (9, 0)):
            use_fused_attention = False

2404
2405
2406
2407
2408
2409
        # Select FusedAttention on sm90 and FlashAttention on others for performance
        if (use_flash_attention
            and use_fused_attention
            and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]):
            if self.device_compute_capability == (9, 0):
                use_flash_attention = False
2410
2411

        if use_flash_attention:
2412
2413
            if _NVTE_DEBUG:
                print("[DotProductAttention]: using flash-attn",_flash_attn_version)
2414
2415
2416
2417
2418
2419
2420
2421
            return self.flash_attention(query_layer,
                                        key_layer,
                                        value_layer,
                                        attention_mask=attention_mask,
                                        qkv_layout=qkv_layout,
                                        cu_seqlens_q=cu_seqlens_q,
                                        cu_seqlens_kv=cu_seqlens_kv,
                                        attn_mask_type=attn_mask_type,
2422
                                        window_size=window_size,
2423
2424
                                        cp_group=self.cp_group,
                                        cp_global_ranks=self.cp_global_ranks,
2425
2426
2427
                                        cp_stream=self.cp_stream,
                                        max_seqlen_q=max_seqlen_q,
                                        max_seqlen_kv=max_seqlen_kv)
2428
2429
2430
2431

        assert (
            self.cp_group is None or get_distributed_world_size(self.cp_group) == 1
        ), "Context parallelism is only implemented with Flash Attention!"
2432

2433
        if use_fused_attention:
2434
2435
2436
            if _NVTE_DEBUG:
                print("[DotProductAttention]: using cuDNN fused attention (backend "
                    + str(int(fused_attention_backend)) + ")")
2437
2438
            if checkpoint_core_attention:
                return self._checkpointed_attention_forward(self.fused_attention,
2439
2440
2441
                              query_layer,
                              key_layer,
                              value_layer,
2442
2443
2444
2445
                              qkv_layout = qkv_layout,
                              cu_seqlens_q = cu_seqlens_q,
                              cu_seqlens_kv = cu_seqlens_kv,
                              attn_mask_type = attn_mask_type,
2446
                              attention_mask = attention_mask,
2447
2448
2449
                              fused_attention_backend = fused_attention_backend,
                              core_attention_bias_type = core_attention_bias_type,
                              core_attention_bias = core_attention_bias,
2450
2451
2452
                              fast_zero_fill = fast_zero_fill,
                              max_seqlen_q=max_seqlen_q,
                              max_seqlen_kv=max_seqlen_kv)
2453
            return self.fused_attention(query_layer, key_layer, value_layer,
2454
2455
2456
2457
                              qkv_layout = qkv_layout,
                              cu_seqlens_q = cu_seqlens_q,
                              cu_seqlens_kv = cu_seqlens_kv,
                              attn_mask_type = attn_mask_type,
2458
                              attention_mask = attention_mask,
2459
2460
2461
                              fused_attention_backend = fused_attention_backend,
                              core_attention_bias_type = core_attention_bias_type,
                              core_attention_bias = core_attention_bias,
2462
2463
2464
                              fast_zero_fill = fast_zero_fill,
                              max_seqlen_q=max_seqlen_q,
                              max_seqlen_kv=max_seqlen_kv)
2465

2466
2467
        if _NVTE_DEBUG:
            print("[DotProductAttention]: using unfused DPA")
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
        if use_unfused_attention:
            if checkpoint_core_attention:
                return self._checkpointed_attention_forward(
                    self.unfused_attention,
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout = qkv_layout,
                    cu_seqlens_q = cu_seqlens_q,
                    cu_seqlens_kv = cu_seqlens_kv,
                    attn_mask_type = attn_mask_type,
                    attention_mask = attention_mask,
                    core_attention_bias_type = core_attention_bias_type,
                    core_attention_bias = core_attention_bias)
            return self.unfused_attention(query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout = qkv_layout,
                    cu_seqlens_q = cu_seqlens_q,
                    cu_seqlens_kv = cu_seqlens_kv,
                    attn_mask_type = attn_mask_type,
                    attention_mask = attention_mask,
                    core_attention_bias_type = core_attention_bias_type,
                    core_attention_bias = core_attention_bias)

        raise Exception("No dot product attention support for the provided inputs!")
2494
2495


2496
2497
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2499
2500
2501
2502
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

2503
2504
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
2505

2506
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2523
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2525
2526
2527
2528
2529
2530
    Parameters
    ----------
    hidden_size : int
                 size of each input sample.
    num_attention_heads : int
                         number of attention heads in the transformer layer.
    kv_channels: int, default = `None`
                number of key-value channels. defaults to
                :attr:`hidden_size` / :attr:`num_attention_heads` if `None`.
    attention_dropout: float, default = 0.1
                      dropout probability for the dropout op during multi-head attention.
    layernorm_epsilon : float, default = 1e-5
                       a value added to the denominator of layer normalization
                       for numerical stability.
    init_method : Callable, default = `None`
                 used for initializing weights of QKV and FC1 weights in the following way:
                 `init_method(weight)`. When set to `None`, defaults to
                 `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    output_layer_init_method : Callable, default = `None`
                              used for initializing weights of PROJ and FC2 in the following way:
                              `output_layer_init_method(weight)`. When set to `None`, defaults to
                              `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    layer_number: int, default = `None`
                 layer number of the current `TransformerLayer` when multiple such modules are
                 concatenated to form a transformer block.
2531
2532
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal' 'arbitrary'},
                   default = `causal`
2533
2534
2535
2536
2537
                   type of attention mask passed into softmax operation. Overridden by
                   :attr:`attn_mask_type` in the `forward` method. The forward
                   arg is useful for dynamically changing mask types, e.g. a different
                   mask for training and inference. The init arg is useful for cases
                   involving compilation/tracing, e.g. ONNX export.
2538
2539
2540
2541
2542
2543
    window_size: Optional[Tuple[int, int]], default = `None`
                sliding window size for local attention, where query at position i attends to keys
                in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
                + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
                window and causal mask specifically. Similar to :attr:`attn_mask_type`, it can
                be overridden by :attr:`window_size` in `forward` as well.
2544
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2607
2608
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2610
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2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
    num_gqa_groups : int, default = `None`
                         number of GQA groups in the transformer layer.
                         Grouped Query Attention is described in
                         `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                         This only affects the keys and values, not the querys.
                         GQA-1 is equivalent to Multi-Query Attention
                         (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                         is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
    return_layernorm_output : bool, default = `False`
                             if set to `True`, output of layernorm is returned from the forward
                             together with the output of the linear transformation.
                             Example use case: residual connection for transformer module is
                             taken post layernorm.
    input_layernorm: bool, default = `True`
                     if set to `False`, layer normalization to the input is not applied.
    attention_type: { 'self', 'cross' }, default = 'self'
                   type of attention applied.
    zero_centered_gamma : bool, default = 'False'
                         if set to 'True', gamma parameter in LayerNorm is initialized to 0 and
                         the LayerNorm formula changes to

                         .. math::
                            y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                            (1 + \gamma) + \beta
    normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm'
                   type of normalization applied.
    qkv_weight_interleaved : bool, default = `True`
                            if set to `False`, the QKV weight is interpreted as a concatenation of
                            query, key, and value weights along the `0th` dimension. The default
                            interpretation is that the individual `q`, `k`, and `v` weights for each
                            attention head are interleaved. This parameter is set to `False` when
                            using :attr:`fuse_qkv_params=False`.
    bias : bool, default = `True`
          if set to `False`, the transformer layer will not learn any additive biases.
    device : Union[torch.device, str], default = "cuda"
          The device on which the parameters of the model will allocated. It is the user's
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.

    Parallelism parameters
    ----------------------
    set_parallel_mode : bool, default = `False`
                      if set to `True`, QKV and FC1 layers are used as Column Parallel
                      whereas PROJ and FC2 is used as Row Parallel as described
                      `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
    tp_size : int, default = 1
             used as TP (tensor parallel) world size when TP groups are not formed during
             initialization. In this case, users must call the
             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
             forward pass to supply the tensor parallel group needed for tensor and sequence
             parallel collectives.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient. When enabled, it is assumed that the weights
                             have an additional `main_grad` attribute (used instead of the
                             regular `grad`) which is a pre-allocated buffer of the correct
                             size to accumulate gradients in.
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
                  it controls the type used to allocate the initial parameters. Useful when
                  the model is trained with lower precision and the original FP32 parameters
                  would not fit in GPU memory.
    return_bias : bool, default = `False`
                 when set to `True`, this module will not apply the additive bias itself, but
                 instead return the bias value during the forward pass together with the
                 output of the linear transformation :math:`y = xA^T`. This is useful when
                 the bias addition can be fused to subsequent operations.
    fuse_qkv_params: bool, default = 'False'
                    if set to `True`, `TransformerLayer` module exposes a single fused
                    parameter for query-key-value. This enables optimizations such as QKV
                    fusion without concatentations/splits and also enables the argument
                    `fuse_wgrad_accumulation`.
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    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
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        kv_channels: Optional[int] = None,
        attention_dropout: float = 0.1,
        layernorm_epsilon: float = 1e-5,
        init_method: Optional[Callable] = None,
        output_layer_init_method: Optional[Callable] = None,
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        layer_number: Optional[int] = None,
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        attn_mask_type: str = "causal",
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        window_size: Optional[Tuple[int, int]] = None,
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        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
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        num_gqa_groups: Optional[int] = None,
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        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
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        params_dtype: Optional[torch.dtype] = None,
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        return_bias: bool = False,
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        return_layernorm_output: bool = False,
        input_layernorm: bool = False,
        attention_type: str = "self",
        set_parallel_mode: bool = False,
        fuse_qkv_params: bool = False,
        zero_centered_gamma: bool = False,
        qkv_weight_interleaved: bool = True,
        ub_bulk_wgrad: bool = False,
        ub_bulk_dgrad: bool = False,
        ub_split_rs: bool = False,
        ub_split_ag: bool = False,
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        ub_atomic_gemm_rs: bool = False,
        ub_atomic_gemm_ag: bool = False,
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        bias: bool = True,
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        normalization: str = "LayerNorm",
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        device: Union[torch.device, str] = "cuda",
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    ) -> None:
        super().__init__()
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        self.attn_mask_type = attn_mask_type
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        self.window_size = window_size
        self.window_size = check_set_window_size(attn_mask_type, self.window_size)
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        self.layer_number = layer_number
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        self.input_layernorm = input_layernorm
        self.attention_type = attention_type
        self.get_rng_state_tracker = get_rng_state_tracker
        self.tp_group = tp_group
        self.return_layernorm_output = return_layernorm_output
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        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
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        self.num_attention_heads = num_attention_heads
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        self.return_bias = return_bias

        kv_channels = kv_channels if kv_channels else (hidden_size // num_attention_heads)

        if init_method is None:
            init_method = get_default_init_method()
        if output_layer_init_method is None:
            output_layer_init_method = get_default_init_method()
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        if not fuse_qkv_params:
            qkv_weight_interleaved = False
        self.qkv_weight_interleaved = qkv_weight_interleaved

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        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
        if layer_number is not None:
            assert layer_number > 0, "layer_number must be a positive integer"
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        tp_size = tp_size if tp_group is None else get_distributed_world_size(tp_group)
        self.tp_size = tp_size
        self.sequence_parallel = (tp_size > 1) and sequence_parallel

        self.hidden_size_per_attention_head = kv_channels
        self.num_attention_heads_per_partition = divide(num_attention_heads, tp_size)
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        self.num_gqa_groups = (
            num_attention_heads if num_gqa_groups is None else num_gqa_groups
        )
        assert (num_attention_heads % self.num_gqa_groups == 0
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                ), "The number of attention heads must be divisible by the number of GQA groups!"
        assert (self.num_gqa_groups % tp_size == 0
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                ), "The number of GQA groups must be divisible by tensor parallel size!"
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
        self.hidden_size_kv = int(hidden_size * self.num_gqa_groups // num_attention_heads)
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        common_gemm_kwargs = {
            "fuse_wgrad_accumulation": fuse_wgrad_accumulation,
            "tp_group": tp_group,
            "tp_size": tp_size,
            "get_rng_state_tracker": get_rng_state_tracker,
            "sequence_parallel": sequence_parallel,
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            "params_dtype": self.params_dtype,
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            "device": device,
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        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

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        if self.attention_type == "self":
            parameters_split = {"query_": hidden_size,
                                "key_": self.hidden_size_kv,
                                "value_": self.hidden_size_kv} if not fuse_qkv_params else None
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            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
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                    hidden_size + 2 * self.hidden_size_kv,
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                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    return_layernorm_output=return_layernorm_output,
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                    parameters_split=parameters_split,
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                    zero_centered_gamma=zero_centered_gamma,
                    ub_bulk_wgrad=ub_bulk_wgrad,
                    ub_bulk_dgrad=ub_bulk_dgrad,
                    ub_split_ag=ub_split_ag,
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                    normalization=normalization,
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                    ub_atomic_gemm_ag=ub_atomic_gemm_ag,
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                    ub_name="qkv",
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                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
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                    hidden_size + 2 * self.hidden_size_kv,
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                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
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                    parameters_split=parameters_split,
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                    **common_gemm_kwargs,
                )
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        elif self.attention_type == "cross":
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            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
                    hidden_size,
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
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                    parameters_split=("query_",) if not fuse_qkv_params else None,
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                    return_layernorm_output=return_layernorm_output,
                    zero_centered_gamma=zero_centered_gamma,
                    ub_bulk_wgrad=ub_bulk_wgrad,
                    ub_bulk_dgrad=ub_bulk_dgrad,
                    ub_split_ag=ub_split_ag,
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                    normalization=normalization,
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                    ub_atomic_gemm_ag=ub_atomic_gemm_ag,
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                    ub_name="qkv",
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                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
                    hidden_size,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
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                2 * self.hidden_size_kv,
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                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
                parameters_split=("key_", "value_") if not fuse_qkv_params else None,
                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
            kv_channels,
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            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
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            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
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            layer_number=self.layer_number,
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        )

        # Linear
        self.proj = Linear(
            hidden_size,
            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
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            return_bias=return_bias,
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            parallel_mode="row" if set_parallel_mode else None,
            ub_split_rs=ub_split_rs,
            ub_split_ag=ub_split_ag,
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            ub_atomic_gemm_rs=ub_atomic_gemm_rs,
            ub_atomic_gemm_ag=ub_atomic_gemm_ag,
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            ub_name="proj",
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            **common_gemm_kwargs,
        )


    def _allocate_memory(
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        self, inference_max_sequence_len: int, batch_size: int, dtype: torch.dtype
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    ) -> torch.Tensor:
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
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            self.num_gqa_groups_per_partition,
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            self.hidden_size_per_attention_head,
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            dtype=dtype,
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            device=torch.cuda.current_device(),
        )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
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        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
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        self.tp_group = tp_group

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    def set_context_parallel_group(
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        self,
        cp_group: Union[dist_group_type, None],
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        cp_global_ranks: List[int],
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        cp_stream: torch.cuda.Stream,
    ) -> None:
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        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
        """
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        # Deep iterate but skip self to avoid infinite recursion.
        for index, child in enumerate(self.modules()):
            if index == 0:
                continue
            if hasattr(child, "set_context_parallel_group"):
                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        encoder_output: Optional[torch.Tensor] = None,
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        attn_mask_type: Optional[str] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
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        inference_params: Optional[InferenceParams] = None,
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        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
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    ) -> Tuple[Union[torch.Tensor, None], ...]:
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        """
        Forward propagation for MultiheadAttention layer.

        .. note::

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            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
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        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
             It should be 'None' for 'causal' and 'no_mask' types. For 'padding' masks, it should be
             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
             for cross-attention. For the 'arbitrary' mask type, it should be in a shape that is
             broadcastable to [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'arbitrary'},
                       default = `None`
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                       type of attention mask passed into softmax operation.
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        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
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        encoder_output : Optional[torch.Tensor], default = `None`
             Output of the encoder block to be fed into the decoder block if using
             `layer_type="decoder"`.
        is_first_microbatch : {True, False, None}, default = None
                             During training using either gradient accumulation or
                             pipeline parallelism a minibatch of data is further split
                             into microbatches. Between the microbatches of the same minibatch
                             the model weights are not updated. Setting this parameter indicates
                             whether the current microbatch is the first in a minibatch or not.
                             When set, this parameter enables additional optimizations:

                             * during FP8 training, it allows caching of the FP8 versions of
                               the weights
                             * it also allows skipping gradient accumulation during the
                               first microbatch (since it is the first gradient being
                               produced)
        checkpoint_core_attention: bool, default = `False`
                                  If true, forward activations for core attention are recomputed
                                  during the backward pass in order to save memory that would
                                  otherwise be occupied to store the forward activations until
                                  backprop.
        rotary_pos_emb: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], default = `None`
                       Embeddings for query and key tensors for applying rotary position
                       embedding. By default no input embedding is applied.
        core_attention_bias_type: str, default = `no_bias`
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                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
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        core_attention_bias: Optional[torch.Tensor], default = `None`
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                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
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        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
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        # hidden_states: [sq, b, h]

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        if attn_mask_type is not None:
            window_size = check_set_window_size(attn_mask_type, window_size)
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        if attn_mask_type is None:
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            attn_mask_type = self.attn_mask_type
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        if window_size is None:
            window_size = self.window_size
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        if "padding" in attn_mask_type and attention_mask is not None:
            for i,_ in enumerate(attention_mask):
                assert (
                    attention_mask[i].dtype == torch.bool
                ), "Attention mask must be in boolean type!"
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        assert (core_attention_bias_type in AttnBiasTypes
                ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
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        # =================================================
        # Pre-allocate memory for key-values for inference.
        # =================================================

        if inference_params and self.layer_number is not None:
            if self.layer_number not in inference_params.key_value_memory_dict:
                inf_max_seq_len = inference_params.max_sequence_len
                inf_max_batch_size = inference_params.max_batch_size
                inference_key_memory = self._allocate_memory(
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                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
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                )
                inference_value_memory = self._allocate_memory(
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                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
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                )
                inference_params.key_value_memory_dict[self.layer_number] = (
                    inference_key_memory,
                    inference_value_memory,
                )
            else:
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]

        # =====================
        # Query, Key, and Value
        # =====================

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        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
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            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )
                if self.return_layernorm_output:
                    mixed_x_layer, layernorm_output = layernorm_qkv_outputs
                else:
                    mixed_x_layer = layernorm_qkv_outputs
            else:
                mixed_x_layer = self.qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )

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            num_queries_per_key_value = (self.num_attention_heads_per_partition //
                                         self.num_gqa_groups_per_partition)
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            if self.qkv_weight_interleaved:
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                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
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                new_tensor_shape = mixed_x_layer.size()[:-1] + (
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                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
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                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
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            else:
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, (np/ng + 2), ng, hn]
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
                    (num_queries_per_key_value + 2),
                    self.num_gqa_groups_per_partition,
                    self.hidden_size_per_attention_head
                )
                # split along third last dimension
                split_dim = -3
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            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

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            # qkv_weight_interleaved:
            #  [sq, b, ng, (np/ng + 2), hn]
            #  --> [sq, b, ng, np/ng, hn], [sq, b, ng, 1, hn], [sq, b, ng, 1, hn]
            # not qkv_weight_interleaved:
            #  [sq, b, (np/ng + 2), ng, hn]
            #  --> [sq, b, np/ng, np, hn], [sq, b, 1, ng, hn], [sq, b, 1, ng, hn]
            if not is_in_onnx_export_mode():
                query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
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                )
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            else:
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                query_layer, key_layer, value_layer = torch.split(
                    mixed_x_layer, (num_queries_per_key_value, 1, 1), dim = split_dim,
                 )

            # query: -> [sq, b, np, hn]
            # key, value: -> [sq, b, ng, hn]
            query_layer, key_layer, value_layer = (x.reshape(x.size(0), x.size(1), -1,
                                                             self.hidden_size_per_attention_head)
                                                   for x in (query_layer, key_layer, value_layer))

        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
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            mixed_kv_layer = self.key_value(
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                encoder_output,
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                is_first_microbatch=is_first_microbatch,
            )

            if self.qkv_weight_interleaved:
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                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
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                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
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                    self.num_gqa_groups_per_partition,
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                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
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                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
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                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
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                    2 * self.num_gqa_groups_per_partition,
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                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2

            mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)

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            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_kv_layer, split_dim, mixed_kv_layer.shape[split_dim] // 2,
                )
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            else:
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                key_layer, value_layer = torch.split(
                    mixed_kv_layer, mixed_kv_layer.shape[split_dim] // 2, dim = split_dim,
                )
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            # Attention head [sq, b, h] --> [sq, b, hp]
            if self.input_layernorm:
                layernorm_query_outputs = self.layernorm_query(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )
                if self.return_layernorm_output:
                    query_layer, layernorm_output = layernorm_query_outputs
                else:
                    query_layer = layernorm_query_outputs
            else:
                query_layer = self.query_layer(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )

            # [sq, b, hp] --> [sq, b, np, hn]
            new_tensor_shape = query_layer.size()[:-1] + (
                self.num_attention_heads_per_partition,
                self.hidden_size_per_attention_head,
            )
            query_layer = query_layer.view(*new_tensor_shape)

        # ==================================
        # Adjust key and value for inference
        # ==================================

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        # duplicate the pos_emb for self attention
        if rotary_pos_emb is not None:
            if not isinstance(rotary_pos_emb, tuple):
                rotary_pos_emb = ((rotary_pos_emb,) * 2)

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        if inference_params and self.layer_number is not None:
            batch_start = inference_params.batch_size_offset
            batch_end = batch_start + key_layer.size(1)
            assert batch_end <= inference_key_memory.size(1)
            sequence_start = inference_params.sequence_len_offset
            sequence_end = sequence_start + key_layer.size(0)
            assert sequence_end <= inference_key_memory.size(0)
            # Copy key and values.
            inference_key_memory[
                sequence_start:sequence_end, batch_start:batch_end, ...
            ] = key_layer
            inference_value_memory[
                sequence_start:sequence_end, batch_start:batch_end, ...
            ] = value_layer
            key_layer = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
            value_layer = inference_value_memory[
                :sequence_end, batch_start:batch_end, ...
            ]

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            # adjust the key rotary positional embedding
            if rotary_pos_emb is not None:
                q_pos_emb, k_pos_emb = rotary_pos_emb
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                q_pos_emb = q_pos_emb[sequence_start:sequence_end, :, :, :]
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                k_pos_emb = k_pos_emb[:sequence_end, :, :, :]
                rotary_pos_emb = (q_pos_emb, k_pos_emb)

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        # ==================================
        # core attention computation
        # ==================================

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        # apply relative positional encoding (rotary embedding)
        if rotary_pos_emb is not None:
            q_pos_emb, k_pos_emb = rotary_pos_emb
            query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb)
            key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb)

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        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
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            qkv_format='sbhd',
            cu_seqlens_q=None,
            cu_seqlens_kv=None,
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            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
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            window_size=window_size,
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            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
            fast_zero_fill=fast_zero_fill,
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        )

        # =================
        # Output. [sq, b, h]
        # =================

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        projection_output = self.proj(
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            context_layer, is_first_microbatch=is_first_microbatch
        )

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        if self.return_bias:
            attention_output, attention_bias = projection_output
        else:
            attention_output, attention_bias = projection_output, None

        outputs = (attention_output,)
        if self.return_bias:
            outputs += (attention_bias,)
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        if self.input_layernorm and self.return_layernorm_output:
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            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]