attention.py 239 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."""
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import collections
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from contextlib import nullcontext
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from importlib.metadata import version as get_pkg_version
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import math
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import os
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import warnings
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import logging
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import numpy as np
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from packaging.version import Version as PkgVersion
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import torch
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import torch.nn.functional as F
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import transformer_engine_torch as tex
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import transformer_engine as te
from transformer_engine.pytorch.utils import get_cudnn_version
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from transformer_engine.pytorch.cpp_extensions import (
    cast_to_fp8,
    cast_from_fp8,
)
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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.fp8 import get_fp8_te_dtype
from transformer_engine.pytorch.float8_tensor import Float8Tensor
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from transformer_engine.pytorch.module import LayerNormLinear, Linear
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from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
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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,
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    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
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)
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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from transformer_engine.pytorch.graph import is_graph_capturing

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_flash_attn_version = PkgVersion(get_pkg_version("flash-attn"))
_flash_attn_version_required = PkgVersion("2.0.6")
_flash_attn_max_version = PkgVersion("2.5.8")
_flash_attn_2_1_plus = _flash_attn_version >= PkgVersion("2.1")
_flash_attn_2_3_plus = _flash_attn_version >= PkgVersion("2.3")
_flash_attn_2_4_plus = _flash_attn_version >= PkgVersion("2.4")
_flash_attn_2_4_1_plus = _flash_attn_version >= PkgVersion("2.4.1")
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if _flash_attn_version >= _flash_attn_version_required:
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    from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_forward_func
    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward as _flash_attn_forward
    from flash_attn.flash_attn_interface import _flash_attn_varlen_backward as _flash_attn_backward
    from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd
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META_QKV  = tex.FP8FwdTensors.GEMM1_OUTPUT
META_DQKV = tex.FP8BwdTensors.GRAD_OUTPUT1
META_O    = tex.FP8FwdTensors.GEMM2_INPUT
META_DO   = tex.FP8BwdTensors.GRAD_INPUT2
META_S    = tex.FP8FwdTensors.GEMM3_OUTPUT
META_DP   = tex.FP8BwdTensors.GRAD_INPUT3
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# NVTE_DEBUG = 0/1 # disables/enables debug mode, default = 0
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_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
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# NVTE_DEBUG_LEVEL = 0/1/2 # enables more and more verbose debug mode, default = 0
_NVTE_DEBUG_LEVEL = int(os.getenv("NVTE_DEBUG_LEVEL", "0"))
log_level = _NVTE_DEBUG * _NVTE_DEBUG_LEVEL
log_levels = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG}
logging.basicConfig(
    format='[%(levelname)-8s | %(name)-19s]: %(message)s',
    level=log_levels[log_level if log_level in [0,1,2] else 2],
)

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_alibi_cache = {
    "_num_heads": None,
    "_alibi_slopes": None,
    "_max_seqlen_q": None,
    "_max_seqlen_kv": None,
    "_alibi_bias": None,
    "_alibi_slopes_require_update": False,
    "_alibi_bias_require_update": False,
    }
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__all__ = ["DotProductAttention", "InferenceParams", "MultiheadAttention"]

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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,
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    alibi_slopes: Optional[torch.Tensor] = None,
    bias_dtype: Optional[torch.dtype] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
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    """
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    Parameters
    ----------
    num_heads: int
        Number of heads.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
    alibi_slopes: Optional[torch.Tensor], default = `None`
        Custom ALiBi slopes, FP32, CUDA tensor, in shape [num_heads] or [batch_size, num_heads].
    bias_dtype: Optional[torch.dtype], default = `None`
        Dtype of the generated ALiBi bias. If None, use torch.float32.
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    Returns
    ----------
    alibi_slopes: torch.Tensor
        ALiBi slopes in FP32 and shape [num_heads] or [batch_size, num_heads].
    alibi_bias: torch.Tensor
        ALiBi bias in FP32 or `bias_dtype`. If `alibi_slopes` is in [num_heads] shape,
        then `alibi_bias` is in [1, num_heads, max_seqlen_q, max_seqlen_kv], and if
        `alibi_slopes` is in [batch_size, num_heads], then the bias is in
        [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
    """
    global _alibi_cache
    if _alibi_cache["_alibi_slopes_require_update"]:
        if alibi_slopes is not None:
            _alibi_cache["_alibi_slopes"] = alibi_slopes
        else:
            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])

            _alibi_cache["_alibi_slopes"] = m.to(dtype=torch.float32, device="cuda")
        _alibi_cache["_num_heads"] = num_heads
        _alibi_cache["_alibi_slopes_require_update"] = False

    if _alibi_cache["_alibi_bias_require_update"]:
        assert _alibi_cache["_alibi_slopes"] is not None, "ALiBi slopes can not be None!"
        if _alibi_cache["_alibi_slopes"].dim() == 1:
            slopes_shape = torch.Size([1, _alibi_cache["_alibi_slopes"].shape[0], 1, 1])
        if _alibi_cache["_alibi_slopes"].dim() == 2:
            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
        bias = torch.arange(
            1 - max_seqlen_kv, 1, dtype=torch.int32, device="cuda").view(1, 1, 1, max_seqlen_kv)
        bias = bias - torch.arange(
            1 - max_seqlen_q, 1, dtype=torch.int32, device="cuda").view(1, 1, max_seqlen_q, 1)
        bias = bias.abs().mul(-1)
        bias = bias * _alibi_cache["_alibi_slopes"].view(slopes_shape)
        _alibi_cache["_max_seqlen_q"], _alibi_cache["_max_seqlen_kv"] = max_seqlen_q, max_seqlen_kv
        bias_dtype = torch.float32 if bias_dtype is None else bias_dtype
        _alibi_cache["_alibi_bias"] = bias.contiguous().to(dtype=bias_dtype, device="cuda")
        _alibi_cache["_alibi_bias_require_update"] = False

    return _alibi_cache["_alibi_slopes"], _alibi_cache["_alibi_bias"]
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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)
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    reduced_mask = mask.logical_not().sum(dim=1)
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    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

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    reduced_mask = mask.logical_not().sum(dim=1)
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    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)
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    indices = mask.logical_not().nonzero()
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    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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_cu_seqlens_cache = {}
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def _get_full_cu_seqlens(
    batch_size: int,
    max_seqlen: int,
    device: torch.device,
) -> torch.Tensor:
    """Cumulative sequence lengths in full data batch

    All sequences in batch have the maximum sequence length.

    """
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    global _cu_seqlens_cache
    if (batch_size, max_seqlen) not in _cu_seqlens_cache:
        _cu_seqlens_cache[(batch_size, max_seqlen)] = torch.arange(
            0,
            (batch_size + 1) * max_seqlen,
            step=max_seqlen,
            dtype=torch.int32,
            device=device,
        )
    return _cu_seqlens_cache[(batch_size, max_seqlen)]
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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."
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        ctx.save_for_backward(indices)
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        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, ...]):
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        (indices,) = ctx.saved_tensors
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        if len(grad_outputs) == 1:
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            return None, unpack_tensor(indices, ctx.dim0, *grad_outputs)
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        if len(grad_outputs) == 2:
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            return None, *unpack_2_tensors(indices, ctx.dim0, *grad_outputs)
        return None, *unpack_3_tensors(indices, ctx.dim0, *grad_outputs)
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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:
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        ctx.save_for_backward(indices)
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        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
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        (indices,) = ctx.saved_tensors
        return None, None, pack_tensor(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):
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    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
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    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


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


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@jit_fuser
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def flash_attn_fwd_softmax_lse_correction(softmax_lse, softmax_lse_per_step):
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    """Merge softmax stats of each step in Attention with context parallelism"""
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    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
    new_scale = max_scale + torch.log(1 + torch.exp(min_scale - max_scale))
    softmax_lse.copy_(new_scale)
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class AttnFuncWithCP(torch.autograd.Function):
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    """
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    Attention implementation with context parallelism.
    Split attention compute into multiple steps, and overlap current-step
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    compute with next-step communication.
    """

    @staticmethod
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    def forward(ctx, is_training, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o, dropout_p,
                cp_group, cp_global_ranks, cp_stream, softmax_scale, qkv_format, attn_mask_type,
                attn_bias_type, attn_bias, deterministic, use_fused_attention):
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        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]
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        recv_src = cp_global_ranks[(rank - 1) % cp_size]
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        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

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        causal = ("causal" in attn_mask_type)
        padding = ("padding" in attn_mask_type)
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        qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

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        if causal:
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            if qkv_format == "bshd":
                # [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]]
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
                q, k, v = [x.view(2, x.shape[0]//2, *x.shape[1:]) for x in [q, k, v]]
        if attn_bias is not None:
            assert (len(attn_bias.shape) == 4), (
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_bias_ = attn_bias.view( \
                *attn_bias.shape[:-2], \
                2, attn_bias.shape[-2]//2, \
                2*cp_size, attn_bias.shape[-1]//(2*cp_size) \
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
            attn_bias = attn_bias.view( \
                *attn_bias.shape[:-1], \
                2*cp_size, attn_bias.shape[-1]//(2*cp_size) \
            )
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        assert(q.shape[-1] % 8 == 0), "hidden size per attention head should be multiple of 8"
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        fa_optional_forward_kwargs = {}
        if _flash_attn_2_3_plus:
            fa_optional_forward_kwargs["window_size"] = [-1, 0] if causal else [-1, -1]
        if _flash_attn_2_4_plus:
            fa_optional_forward_kwargs["alibi_slopes"] = None
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        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
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        attn_bias_inputs = [None, None]
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        # 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)]
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        attn_biases = [None for _ in range(cp_size)]
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        # 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:
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                            if use_fused_attention:
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                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                    q_inputs[i%2] = q.view(q.shape[0], -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, k.shape[0], -1, *k.shape[-2:])
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                    q_inputs[i%2] = q.view(-1, *q.shape[-3:])
                                    # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, -1, *k.shape[-3:])
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                                elif qkv_format == "thd":
                                    q_inputs[i%2] = q
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                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
                                    attn_bias_inputs[i%2] = torch.cat(
                                        (attn_bias[..., idx, :], \
                                         attn_bias[..., (2*cp_size-idx-1), :]),
                                        dim=-1
                                    ).contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
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                                fused_attn_fwd(
                                    is_training, max_seqlen_q, max_seqlen_k, cu_seqlens_q,
                                    cu_seqlens_k, q_inputs[i%2], kv_inputs[i%2][0],
                                    kv_inputs[i%2][1], TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                    attn_scale=softmax_scale, dropout=dropout_p,
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                                    qkv_layout=qkv_layout, attn_mask_type=attn_mask_type,
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                                    attn_bias_type=attn_bias_type, attn_bias=attn_bias_inputs[i%2],
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                                    seq_offsets_q=seq_offsets_q, seq_offsets_k=seq_offsets_k,
                                    seq_offsets_v=seq_offsets_v, seq_offsets_o=seq_offsets_o,
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                                )
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                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
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                            else:
                                # [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:])
                                _, _, _, _, 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,
                                    **fa_optional_forward_kwargs
                                )
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                        elif i <= rank:
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                            if use_fused_attention:
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                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                    q_inputs[i%2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2][:, :, 0, ...].contiguous()
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                    q_inputs[i%2] = q.view(-1, *q.shape[-3:])
                                    # [2, 2, sk//2, b, np, hn] -> [2, sk//2, b, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2][:, 0, ...].contiguous()
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                                elif qkv_format == "thd":
                                    q_inputs[i%2] = q
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
                                    kv_inputs[i%2] = tex.thd_read_half_tensor(
                                        kv_inputs[i%2], cu_seqlens_k, 0)
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                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
                                    attn_bias_inputs[i%2] = attn_bias[..., idx, :].contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
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                                fused_attn_fwd(
                                    is_training, max_seqlen_q, max_seqlen_k//2, cu_seqlens_q,
                                    cu_seqlens_k//2, q_inputs[i%2], kv_inputs[i%2][0],
                                    kv_inputs[i%2][1], TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
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                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i%2],
                                    seq_offsets_q=seq_offsets_q,
                                    seq_offsets_k=None if seq_offsets_k is None \
                                        else seq_offsets_k//2,
                                    seq_offsets_v=None if seq_offsets_v is None \
                                        else seq_offsets_v//2,
                                    seq_offsets_o=seq_offsets_o,
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                                )
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                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
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                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                                q_inputs[i%2] = q.view(-1, *q.shape[-2:])
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                                if qkv_format == "thd":
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
                                    kv_inputs[i%2] = tex.thd_read_half_tensor(
                                        kv_inputs[i%2], cu_seqlens_k, 0)
                                else:
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2][:, :, 0, ...].contiguous()
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                                # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
                                kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                                if _flash_attn_2_3_plus:
                                    fa_optional_forward_kwargs["window_size"] = [-1, -1]
                                _, _, _, _, 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,
                                    **fa_optional_forward_kwargs
                                )
                        else:
                            if use_fused_attention:
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                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                                    q_inputs[i%2] = q[:, 1, ...].contiguous()
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, k.shape[0], -1, *k.shape[-2:])
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                                    q_inputs[i%2] = q[1].contiguous()
                                    # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, -1, *k.shape[-3:])
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                                elif qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
                                    q_inputs[i%2] = tex.thd_read_half_tensor(q, cu_seqlens_q, 1)
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                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
                                    attn_bias_inputs[i%2] = torch.cat(
                                        (attn_bias_[..., 1, :, idx, :], \
                                         attn_bias_[..., 1, :, (2*cp_size-idx-1), :]),
                                        dim=-1
                                    ).contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
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                                fused_attn_fwd(
                                    is_training, max_seqlen_q//2, max_seqlen_k, cu_seqlens_q//2,
                                    cu_seqlens_k, q_inputs[i%2], kv_inputs[i%2][0],
                                    kv_inputs[i%2][1], TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
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                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i%2],
                                    seq_offsets_q=None if seq_offsets_q is None \
                                        else seq_offsets_q//2,
                                    seq_offsets_k=seq_offsets_k,
                                    seq_offsets_v=seq_offsets_v,
                                    seq_offsets_o=None if seq_offsets_o is None \
                                        else seq_offsets_o//2,
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                                )
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                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
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                            else:
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                                if qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
                                    q_inputs[i%2] = tex.thd_read_half_tensor(q, cu_seqlens_q, 1)
                                else:
                                    # [b, 2, sq//2, np, hn]->[b, sq//2, np, hn]->[b*sq//2, np, hn]
                                    q_inputs[i%2] = \
                                        q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
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                                # [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_3_plus:
                                    fa_optional_forward_kwargs["window_size"] = [-1, -1]
                                _, _, _, _, 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,
                                    **fa_optional_forward_kwargs
                                )
                    else:
                        if use_fused_attention:
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                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
                                attn_bias_inputs[i%2] = torch.cat(
                                    (attn_bias[..., idx, :], attn_bias[..., (2*cp_size-idx-1), :]),
                                    dim=-1
                                ).contiguous()
                            out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
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                            fused_attn_fwd(
                                is_training, max_seqlen_q, max_seqlen_k, cu_seqlens_q,
                                cu_seqlens_k, q, kv_inputs[i%2][0],
                                kv_inputs[i%2][1], TE_DType[q.dtype],
                                tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                attn_scale=softmax_scale, dropout=dropout_p,
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                                qkv_layout=qkv_layout, attn_mask_type=attn_mask_type,
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                                attn_bias_type=attn_bias_type, attn_bias=attn_bias_inputs[i%2],
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                                seq_offsets_q=seq_offsets_q, seq_offsets_k=seq_offsets_k,
                                seq_offsets_v=seq_offsets_v, seq_offsets_o=seq_offsets_o,
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                            )
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                            if len(rest) > 0:
                                attn_biases[i] = rest[0]
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                        else:
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                            # [b, sq, np, hn] -> [b*sq, np, hn]
                            q_inputs[i%2] = q.view(-1, *q.shape[-2:])
                            # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
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                            kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
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                            _, _, _, _, 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],
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                                cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                                dropout_p, softmax_scale, causal=False, return_softmax=False,
                                **fa_optional_forward_kwargs
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                            )
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            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)

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                if use_fused_attention:
                    # [b, np, sq, 1] -> [b, np, sq]
                    softmax_lse_per_step[i-1].squeeze_(-1)

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                with torch.cuda.stream(flash_attn_streams[(i-1)%2]):
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                    if i == 1:
                        out = torch.empty_like(q).zero_()
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
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                        if causal and qkv_format != "thd":
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                            # [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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                    elif (i-1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(softmax_lse,
                                                              softmax_lse_per_step[i-1])
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                    else:
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                        if qkv_format == "thd":
                            tex.thd_second_half_lse_correction(softmax_lse,
                                                               softmax_lse_per_step[i-1],
                                                               cu_seqlens_q,
                                                               q.size(0))
                        else:
                            flash_attn_fwd_softmax_lse_correction(softmax_lse_[..., 1, :],
                                                                  softmax_lse_per_step[i-1])
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                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)
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        if qkv_format in ["bshd", "sbhd"]:
            seq_dim = qkv_format.index("s")
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        for i in range(cp_size):
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875
876
877
            if qkv_format == "bshd":
                out_per_step[i] = out_per_step[i].view(out.shape[0], -1, *out.shape[-2:])
                out_ = out[:, 1, ...]
            elif qkv_format == "sbhd":
                out_per_step[i] = out_per_step[i].view(-1, *out.shape[-3:])
                out_ = out[1]
878

879
            if i <= rank or not causal:
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
                if qkv_format in ["bshd", "sbhd"]:
                    flash_attn_fwd_out_correction(out.view(*out_per_step[i].shape),
                                                  out_per_step[i],
                                                  seq_dim,
                                                  softmax_lse,
                                                  softmax_lse_per_step[i])
                elif qkv_format == "thd":
                    tex.thd_out_correction(out,
                                           out_per_step[i],
                                           softmax_lse,
                                           softmax_lse_per_step[i],
                                           cu_seqlens_q,
                                           False)
                else:
                    assert False, f"{qkv_format} is an unsupported qkv_format!"
895
            else:
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
                if qkv_format in ["bshd", "sbhd"]:
                    flash_attn_fwd_out_correction(out_,
                                                  out_per_step[i],
                                                  seq_dim,
                                                  softmax_lse_[..., 1, :],
                                                  softmax_lse_per_step[i])
                elif qkv_format == "thd":
                    tex.thd_out_correction(out,
                                           out_per_step[i],
                                           softmax_lse,
                                           softmax_lse_per_step[i],
                                           cu_seqlens_q,
                                           True)
                else:
                    assert False, f"{qkv_format} is an unsupported qkv_format!"
911
912

        kv = p2p_comm_buffers[-1]
913
        if use_fused_attention:
914
915
916
917
            if qkv_format == "bshd":
                out = out.view(out.shape[0], -1, *out.shape[-2:])
            elif qkv_format == "sbhd":
                out = out.view(-1, *out.shape[-3:])
918
919
        else:
            out = out.view(-1, *out.shape[-2:])
920

921
922
923
924
925
        ctx.save_for_backward(
            q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            *rng_states, *attn_biases
        )
926
927
928
929
930
931
        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
932
        ctx.qkv_format = qkv_format
933
        ctx.attn_mask_type = attn_mask_type
934
935
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
936
        ctx.deterministic = deterministic
937
        ctx.use_fused_attention = use_fused_attention
938
939
940
941
        return out

    @staticmethod
    def backward(ctx, dout):
942
        (q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k) = ctx.saved_tensors[:6]
943
        (seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o) = ctx.saved_tensors[6:10]
944
        cp_size = get_distributed_world_size(ctx.cp_group)
945
946
        rng_states = ctx.saved_tensors[10:10+cp_size]
        attn_biases = ctx.saved_tensors[10+cp_size:10+cp_size*2]
947

948
        rank = get_distributed_rank(ctx.cp_group)
949
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size]
950
951
952
        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)

953
954
        causal = ("causal" in ctx.attn_mask_type)
        padding = ("padding" in ctx.attn_mask_type)
955
956
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

957
        if attn_biases[0] is not None:
958
959
960
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
                *ctx.attn_bias_shape,
961
962
                dtype=attn_biases[0].dtype,
                device=attn_biases[0].device
963
964
965
966
967
968
969
970
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3]//2, *attn_dbias.shape[-2:]
            )
        else:
            attn_dbias = None

971
        if causal:
972
973
974
975
976
977
978
979
980
981
982
            if ctx.qkv_format == "thd":
                softmax_lse_ = tex.thd_read_second_half_lse(softmax_lse, cu_seqlens_q, q.size(0))
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
                softmax_lse_ = \
                    softmax_lse.view(*softmax_lse.shape[:-1], 2, softmax_lse.shape[-1]//2)
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)

983
984
985
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
986
987
988
989
990
991
992
993
994
995
        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 = []

996
997
998
999
1000
1001
        fa_optional_backward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_backward_kwargs["alibi_slopes"] = None
        if _flash_attn_2_4_1_plus:
            fa_optional_backward_kwargs["deterministic"] = ctx.deterministic

1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
        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
1026
            if causal:
1027
                if i == (cp_size-1):
1028
                    if ctx.use_fused_attention:
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
                            # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                            kv_ = kv.view(*kv.shape[0:2], -1, *kv.shape[-2:])
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
                            # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
1045
1046
                        elif ctx.qkv_format == "thd":
                            q_, kv_, out_, dout_ = q, kv, out, dout
1047
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size-i-1]]
1048
                        if attn_dbias is not None:
1049
                            aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1050
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1051
1052
                            ctx.max_seqlen_q, ctx.max_seqlen_k,
                            cu_seqlens_q, cu_seqlens_k,
1053
1054
                            q_, kv_[0], kv_[1], out_, dout_,
                            TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1055
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
1056
                            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
1057
1058
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1059
                            qkv_layout=qkv_layout,
1060
                            attn_mask_type=ctx.attn_mask_type,
1061
                            attn_bias_type=ctx.attn_bias_type,
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
                        )
                    else:
                        # [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:])
                        if _flash_attn_2_3_plus:
                            fa_optional_backward_kwargs["window_size"] = [-1, 0]
                        _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,
1080
                            rng_state=rng_states[cp_size-i-1],
1081
1082
1083
1084
                            **fa_optional_backward_kwargs
                        )
                elif i >= (cp_size-rank-1):
                    if ctx.use_fused_attention:
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
                            # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous()
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
                            # [2, 2, sk//2, b, np, hn] -> [2, sk//2, b, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
1101
1102
1103
1104
                        elif ctx.qkv_format == "thd":
                            q_, out_, dout_ = q, out, dout
                            # [2, t, np, hn] -> [2, t/2, np, hn]
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_k, 0)
1105
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size-i-1]]
1106
                        if attn_dbias is not None:
1107
                            aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1108
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1109
1110
                            ctx.max_seqlen_q, ctx.max_seqlen_k//2,
                            cu_seqlens_q, cu_seqlens_k//2,
1111
1112
                            q_, kv_[0], kv_[1], out_, dout_,
                            TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1113
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
1114
1115
                            seq_offsets_q, None if seq_offsets_k is None else seq_offsets_k//2,
                            None if seq_offsets_v is None else seq_offsets_v//2, seq_offsets_o,
1116
1117
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1118
                            qkv_layout=qkv_layout,
1119
                            attn_mask_type="padding" if padding else "no_mask",
1120
                            attn_bias_type=ctx.attn_bias_type,
1121
1122
1123
1124
1125
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
                        dq_ = torch.empty_like(q_)
1126
1127
1128
1129
1130
1131
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_k, 0)
                        else:
                            # [2, b, 2, sk//2, np, hn]->[2, b, sk//2, np, hn]->[2, b*sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous().view(2, -1, *kv.shape[-2:])
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
                        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:])
                        if _flash_attn_2_3_plus:
                            fa_optional_backward_kwargs["window_size"] = [-1, -1]
                        _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,
1143
                            rng_state=rng_states[cp_size-i-1],
1144
1145
1146
1147
                            **fa_optional_backward_kwargs
                        )
                else:
                    if ctx.use_fused_attention:
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
                            # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                            kv_ = kv.view(*kv.shape[0:2], -1, *kv.shape[-2:])
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous()
                            dout_ = dout[:, 1, ...].contiguous()
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            q_ = q[1].contiguous()
                            # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
1164
1165
1166
1167
1168
1169
                        elif ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q, 1)
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q, 1)
                            kv_ = kv
1170
                        aux_ctx_tensors = [softmax_lse_, rng_states[cp_size-i-1]]
1171
                        if attn_dbias is not None:
1172
                            aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1173
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1174
1175
                            ctx.max_seqlen_q//2, ctx.max_seqlen_k,
                            cu_seqlens_q//2, cu_seqlens_k,
1176
1177
                            q_, kv_[0], kv_[1], out_, dout_,
                            TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1178
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
1179
1180
                            None if seq_offsets_q is None else seq_offsets_q//2, seq_offsets_k,
                            seq_offsets_v, None if seq_offsets_o is None else seq_offsets_o//2,
1181
1182
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1183
                            qkv_layout=qkv_layout,
1184
                            attn_mask_type="padding" if padding else "no_mask",
1185
                            attn_bias_type=ctx.attn_bias_type,
1186
1187
                        )
                    else:
1188
1189
1190
1191
1192
1193
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q, 1)
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
1194
1195
1196
1197
                        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_)
1198
1199
1200
1201
1202
1203
1204
                        if ctx.qkv_format == "thd":
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q, 1)
                        else:
                            # [b, 2, sq//2, np, hn] -> [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:])
1205
1206
1207
1208
1209
1210
1211
                        if _flash_attn_2_3_plus:
                            fa_optional_backward_kwargs["window_size"] = [-1, -1]
                        _flash_attn_backward(
                            dout_, q_, kv_[0], kv_[1], out_, softmax_lse_,
                            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,
1212
                            rng_state=rng_states[cp_size-i-1],
1213
1214
1215
1216
                            **fa_optional_backward_kwargs
                        )
            else:
                if ctx.use_fused_attention:
1217
                    aux_ctx_tensors = [softmax_lse, rng_states[cp_size-i-1]]
1218
                    if attn_dbias is not None:
1219
                        aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1220
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1221
1222
                        ctx.max_seqlen_q, ctx.max_seqlen_k,
                        cu_seqlens_q, cu_seqlens_k,
1223
1224
                        q, kv[0], kv[1], out, dout,
                        TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1225
                        tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
1226
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
1227
1228
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
1229
                        qkv_layout=qkv_layout,
1230
                        attn_mask_type=ctx.attn_mask_type,
1231
                        attn_bias_type=ctx.attn_bias_type,
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                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
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                    q_ = q.view(-1, *q.shape[-2:])
                    dq_ = torch.empty_like(q_)
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                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
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                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
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                    # [b, sq, np, hn] -> [b*sq, np, hn]
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                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
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                    if _flash_attn_2_3_plus:
                        fa_optional_backward_kwargs["window_size"] = [-1, -1]
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                    _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, False,
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                        **fa_optional_backward_kwargs
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                    )

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            if i >= (cp_size-rank-1) or not causal:
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                # [b*sq, np, hn] -> [b, 2, sq//2, np, hn] if causal
                # [b*sq, np, hn] -> [b, sq, np, hn] if not causal
                dq_ = dq_.view(*dq.shape)
            else:
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                if ctx.qkv_format == "bshd":
                    # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                    dq_ = dq_.view(dq.shape[0], *dq.shape[2:])
                elif ctx.qkv_format == "sbhd":
                    # [b*sq//2, np, hn] -> [sq//2, b, np, hn]
                    dq_ = dq_.view(-1, *dq.shape[-3:])
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            if causal:
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                if i > (cp_size-rank-1):
                    dq.add_(dq_)
                elif i == (cp_size-rank-1):
                    if rank == (cp_size-1):
                        dq.copy_(dq_)
                    else:
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                        if ctx.qkv_format == "bshd":
                            dq[:, 0, ...].copy_(dq_[:, 0, ...])
                            dq[:, 1, ...].add_(dq_[:, 1, ...])
                        elif ctx.qkv_format == "sbhd":
                            dq[0].copy_(dq_[0])
                            dq[1].add_(dq_[1])
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                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q, "copy", "add")
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                elif i > 0:
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                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
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                    elif ctx.qkv_format == "thd":
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q, "none", "add")
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                else:
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                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
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                    elif ctx.qkv_format == "thd":
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q, "none", "copy")
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            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
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            if attn_dbias is not None:
                idx = (rank+i+1)%cp_size
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                if i == (cp_size - 1) or not causal:
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                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1]//2)
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
                    attn_dbias[..., (2*cp_size-idx-1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size-rank-1):
                    # [b, np, sq, sk//(2*cp)]
                    attn_dbias[..., idx, :].copy_(dbias_)
                else:
                    # [b, np, sq//2, sk//cp] -> [b, np, sq//2, 2, sk//(2*cp)]
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1]//2)
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
                    attn_dbias_[..., 1, :, (2*cp_size-idx-1), :].copy_(dbias_[..., 1, :])

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            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
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            dkv = p2p_comm_buffers[(i+1)%2][1]
            if ctx.use_fused_attention:
                dkv_ = torch.cat((dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0)
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            if causal and i >= (cp_size-rank-1) and i != (cp_size-1):
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                if ctx.qkv_format == "bshd":
                    # [2, b*sk//2, np, hn] -> [2, b, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape[0:2], *dkv.shape[3:])
                elif ctx.qkv_format == "sbhd":
                    # [2, b*sk//2, np, hn] -> [2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(dkv.shape[0], -1, *dkv.shape[-3:])
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            else:
                # [2, b*sk, np, hn] -> [2, b, 2, sk//2, np, hn] if causal
                # [2, b*sk, np, hn] -> [2, b, sk, np, hn] if not causal
                dkv_ = dkv_.view(*dkv.shape)
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            if causal:
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                if i == (cp_size-1):
                    if rank == 0:
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                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_[:, :, 0, ...])
                            dkv[:, :, 1, ...].copy_(dkv_[:, :, 1, ...])
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_[:, 0, ...])
                            dkv[:, 1, ...].copy_(dkv_[:, 1, ...])
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                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_k, "add", "copy")
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                    else:
                        dkv.add_(dkv_)
                elif i >= (cp_size-rank-1):
                    if i == 0 and rank == (cp_size-1):
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                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
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                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_k, "copy", "none")
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                    else:
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                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
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                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_k, "add", "none")
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                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
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                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

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        if causal:
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            if ctx.qkv_format == "bshd":
                # [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:])
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                dq = dq.view(-1, *q.shape[-3:])
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                dkv = dkv.view(kv.shape[0], -1, *kv.shape[-3:])

        if attn_dbias is not None:
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, sq, sk]
            attn_dbias = attn_dbias.view(*attn_dbias.shape[:-2], -1)

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        return None, dq, dkv[0], dkv[1], None, None, None, None, None, None, None, None, \
                None, None, None, None, None, None, None, None, attn_dbias, None, None
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def attn_forward_func_with_cp(
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    is_training, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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    seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o, dropout_p,
    cp_group, cp_global_ranks, cp_stream, softmax_scale=None, qkv_format="bshd",
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    attn_mask_type="causal", attn_bias_type="no_bias", attn_bias=None, deterministic=False,
    use_fused_attention=False
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) -> torch.Tensor:
    """Attention implementation with context parallelism"""
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    assert(qkv_format in ["bshd", "sbhd", "thd"]
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        ), f"QKV format of {qkv_format} is not supported with context parallelism!"
    assert(qkv_format != "sbhd" or use_fused_attention
        ), "FlashAttention does not support sbhd format!"
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    assert (qkv_format != 'thd' or \
            not use_fused_attention or \
            attn_mask_type in ["padding", "padding_causal"]
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        ), f"Context parallelism is not supported for {attn_mask_type} mask type and " \
    f"{qkv_format} format with {'FusedAttention' if use_fused_attention else 'FlashAttention'}!"
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    assert (attn_bias is None or (use_fused_attention and "padding" not in attn_mask_type)
        ), """Attention bias is only supported with FusedAttention and "causal" """ \
           """or "no_mask" mask types!"""
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    out = AttnFuncWithCP.apply(
        is_training, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o, dropout_p,
        cp_group, cp_global_ranks, cp_stream, softmax_scale, qkv_format, attn_mask_type,
        attn_bias_type, attn_bias, deterministic, use_fused_attention
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    )
    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,
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        rotary_percent: float = 1.0,
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        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
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        rotary_percent: float
            Percent of rotary dimension to use for rotary position embeddings.
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        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__()
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        if rotary_percent < 1.0:
            dim = int(dim * rotary_percent)
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        self.seq_len_interpolation_factor = seq_len_interpolation_factor
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        inv_freq = 1.0 / (
            10000
            ** (
                torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device())
                / dim
            )
        )
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        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
        """
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        seq = (
            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            + offset
        )
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        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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class FusedRoPEFunc(torch.autograd.Function):
    """
    Function for FusedRoPE

    This implementation assumes the input tensor to be in `sbhd`, `bshd` or `thd` format and
    the RoPE tensor to be of shape (s, 1, 1, d). It accepts arbitrary memory layouts to avoid
    the expensive `.contiguous()` calls, thus it may not achieve the best memory access pattern.
    """

    @staticmethod
    def forward(
        ctx,
        t: torch.Tensor,
        freqs: torch.Tensor,
        tensor_format: str = "sbhd",
        cu_seqlens: Union[torch.Tensor, None] = None,
    ) -> torch.Tensor:
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        if freqs.dtype != torch.float32:
            freqs = freqs.float()
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        if tensor_format == "sbhd":
            output = tex.fused_rope_forward(t, freqs, False)
        elif tensor_format == "bshd":
            output = tex.fused_rope_forward(
                t.transpose(0, 1), freqs, True
            ).transpose(0, 1)
        elif tensor_format == "thd":
            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format

        return output

    @staticmethod
    def backward(
        ctx, grad_output: torch.Tensor
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        freqs, cu_seqlens = ctx.saved_tensors
        if ctx.tensor_format == "sbhd":
            grad_input = tex.fused_rope_backward(grad_output, freqs, False)
        elif ctx.tensor_format == "bshd":
            grad_input = tex.fused_rope_backward(
                grad_output.transpose(0, 1), freqs, True
            ).transpose(0, 1)
        elif ctx.tensor_format == "thd":
            grad_input = tex.fused_rope_thd_backward(grad_output, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

        return grad_input, None, None, None, None


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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)


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def apply_rotary_pos_emb(
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    t: torch.Tensor,
    freqs: torch.Tensor,
    tensor_format: str = "sbhd",
    fused: bool = False,
    cu_seqlens: Union[torch.Tensor, None] = None,
) -> torch.Tensor:
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    """
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    Apply rotary positional embedding tensor to the input tensor.
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    Parameters
    ----------
    t: torch.Tensor
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        rotary positional embedding will be applied.
    freqs: torch.Tensor
        Rotary positional embedding tensor of shape `[s2, 1, 1, d2]` and dtype 'float',
        with `s2 >= s` and `d2 <= d`.
    fused: bool, default = False
        Whether to use a fused applying RoPE implementation.
    tensor_format: {'sbhd', 'bshd', 'thd'}, default = 'sbhd'
        is `bshd` if `t` is of shape `[bs, seq, ...]`, or `sbhd` if `t` is
        of shape `[seq, bs, ...]`. 'thd' is only supported when `fused` is True.
    cu_seqlens: torch.Tensor, default = None.
        Cumulative sum of sequence lengths in a batch for `t`, with shape [b + 1] and
        dtype torch.int32. Only valid when `tensor_format` is 'thd'.
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    """
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    if fused:
        assert (
            tensor_format != "thd" or cu_seqlens is not None
        ), "cu_seqlens must not be None when tensor_format is 'thd'."
        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens)

    assert tensor_format in ("sbhd", "bshd"), (
        "Only formats `sbhd` or `bshd` are supported for input tensor `t` "
        f"when fused is False, got {tensor_format}."
    )

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    max_seq_len = freqs.shape[0]
    cur_seq_len = t.shape[1] if tensor_format == "bshd" else t.shape[0]

    # Only apply the rotary embeddings up to the sequence length of the running
    # input.
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    assert cur_seq_len <= max_seq_len, (
        f"Rotary Embeddings only supported up to {max_seq_len} sequence length!"
    )
    freqs = freqs[:cur_seq_len]
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    if tensor_format == "bshd":
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        freqs = freqs.transpose(0, 1)  # [seq, 1, 1, dim] -> [1, seq, 1, dim]
    # cos/sin first then dtype conversion for better precision
    cos_ = torch.cos(freqs).to(t.dtype)
    sin_ = torch.sin(freqs).to(t.dtype)
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    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
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    t = (t * cos_) + (_rotate_half(t) * sin_)
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    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
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        if isinstance(mixed_x_layer, Float8Tensor):
            return tuple(Float8Tensor.make_like(
                mixed_x_layer,
                data=x,
                ) for x in torch.split(
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
                    dim=split_dim))
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        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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        if isinstance(grad_outputs[0], Float8Tensor):
            noop_ok = True
            strides = grad_outputs[0].stride()
            data_ptr = grad_outputs[0]._data.untyped_storage().data_ptr()
            shape = list(grad_outputs[0].shape)
            for i, tensor in enumerate(grad_outputs):
                shape_i = shape
                shape_i[split_dim] = split_sizes[i]
                offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim+1:])
                if (tensor.stride() != strides or
                    list(tensor.shape) != shape_i or
                    tensor._data.untyped_storage().data_ptr() != data_ptr or
                    tensor.storage_offset() != offset_size):
                    noop_ok = False
                    break
            if noop_ok:
                ret = torch.Tensor().to(device=grad_outputs[0].device,
                                        dtype=grad_outputs[0]._data.dtype)
                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
                ret.set_(grad_outputs[0]._data.untyped_storage(),
                         grad_outputs[0]._data.storage_offset(),
                         new_shape,
                         strides
                )
                return Float8Tensor.make_like(grad_outputs[0], data=ret), None, None

            grad_outputs_data = [x._data for x in grad_outputs]
            return Float8Tensor.make_like(
                grad_outputs[0],
                data=torch.cat(grad_outputs_data, dim = split_dim)), None, None
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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,
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        softmax_scale: float,
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        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

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        self.softmax_scale = softmax_scale
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        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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        alibi_slopes: 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()])
        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.softmax_scale
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        if apply_qk_layer_scaling:
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            scale /= self.layer_number
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        # 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,
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                alpha=scale,
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            )

        elif core_attention_bias_type == "pre_scale_bias":
            assert core_attention_bias is not None, "core_attention_bias should not be None!"
            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])
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            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!"
            if core_attention_bias_type == "alibi":
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                _, core_attention_bias = get_alibi(
                    output_size[1], output_size[2], output_size[3], alibi_slopes=alibi_slopes)
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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,
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                alpha=scale,
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            )
            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
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    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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        # 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
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    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
        dv: torch.Tensor
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    ) -> 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,
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        softmax_scale: float,
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        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."
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        assert (
            _flash_attn_version <= _flash_attn_max_version
        ), f"FlashAttention maximum version {_flash_attn_max_version} is supported."
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        self.softmax_scale = softmax_scale
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        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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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
2101
2102
2103
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
2104
2105
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
2106
        attn_mask_type: str = "causal",
2107
        window_size: Optional[Tuple[int, int]] = None,
2108
        alibi_slopes: Optional[torch.Tensor] = None,
2109
        cp_group: Optional[dist_group_type] = None,
2110
        cp_global_ranks: List[int] = None,
2111
        cp_stream: torch.cuda.Stream = None,
2112
2113
2114
    ) -> torch.Tensor:
        """flash-attn fprop"""

2115
2116
        window_size = check_set_window_size(attn_mask_type, window_size)

2117
        assert (
2118
2119
2120
            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]
2121
            ), "FlashAttention currently only supports FP16 and BF16."
2122
2123
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
2124
2125
2126
2127
2128
            ), "FlashAttention currently only supports CUDA tensors."
        assert (
            qkv_layout in QKVLayouts
            ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"

2129
2130
        context_parallel = (cp_group is not None) and (get_distributed_world_size(cp_group) != 1)

2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
        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)]
2144
        elif qkv_format in ['bshd', 'thd']:
2145
2146
2147
            query_layer, key_layer, value_layer = [x.contiguous()
                for x in (query_layer, key_layer, value_layer)]

2148
        batch_size = query_layer.shape[0]
2149

2150
        if qkv_format in ['sbhd', 'bshd']:
2151
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
2152
2153
2154
2155
2156
2157
2158
            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]
                ]

2159
            if 'padding' in attn_mask_type:
2160
                assert not context_parallel, "Padding mask not supported with context parallelism!"
2161
2162
2163
2164
2165

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
2166
2167
                    if cu_seqlens_q is None:
                        assert (attention_mask is not None
2168
                                ), "Please provide attention_mask for padding!"
2169
2170
2171
2172
2173
2174
                        cu_seqlens_q, indices_q = get_cu_seqlens_and_indices(attention_mask)
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                    cu_seqlens_kv = cu_seqlens_q
                    query_layer, key_layer, value_layer = PackTensors.apply(
                        indices_q, query_layer, key_layer, value_layer
2175
2176
                    )
                else:
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
                    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:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                        indices_kv = get_indices(max_seqlen_kv, cu_seqlens_kv)
                    query_layer = PackTensors.apply(indices_q, query_layer)
                    key_layer, value_layer = PackTensors.apply(
                        indices_kv, key_layer, value_layer
2190
2191
                    )
            else:
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
                # Cumulative sequence lengths for unpadded data
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
2205
        elif qkv_format == 'thd':
2206
2207
            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!"
2208
2209
2210
2211
2212
2213
            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()
2214

2215
        if context_parallel:
2216
2217
2218
            assert (
                window_size in ((-1, -1), (-1, 0))
                ), "Sliding window attention is not supported with context parallelism."
2219
2220
2221
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
2222
            with self.attention_dropout_ctx():
2223
2224
                output = attn_forward_func_with_cp(
                    self.training, query_layer, key_layer, value_layer,
2225
                    cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv,
2226
                    None, None, None, None,
2227
                    self.attention_dropout if self.training else 0.0,
2228
                    cp_group, cp_global_ranks, cp_stream,
2229
                    softmax_scale=self.softmax_scale,
2230
                    qkv_format="bshd" if qkv_format=="sbhd" else qkv_format,
2231
                    attn_mask_type=attn_mask_type,
2232
                    deterministic=self.deterministic
2233
2234
                )
        else:
2235
2236
2237
2238
2239
2240
2241
2242

            from .cpu_offload import CPUOffloadEnabled
            if CPUOffloadEnabled:
                tensor_list = [query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv]
                for tensor in tensor_list:
                    if tensor is not None:
                        tensor.activation_offloading = True

2243
            with self.attention_dropout_ctx():
2244
                fa_optional_forward_kwargs = {}
2245
2246
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
2247
2248
2249
2250
                if _flash_attn_2_4_plus:
                    fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                if _flash_attn_2_4_1_plus:
                    fa_optional_forward_kwargs["deterministic"] = self.deterministic
2251
                output = flash_attn_forward_func(
2252
                    query_layer, key_layer, value_layer,
2253
                    cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv,
2254
                    self.attention_dropout if self.training else 0.0,
2255
                    softmax_scale=self.softmax_scale, causal="causal" in attn_mask_type,
2256
                    **fa_optional_forward_kwargs,
2257
                )
2258

2259
        if qkv_format in ['sbhd', 'bshd'] and 'padding' in attn_mask_type:
2260
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
2261

2262
2263
2264
        if qkv_format == 'sbhd':
            # (bs)hd -> bs(hd) -> sb(hd)
            output = output.view(batch_size, max_seqlen_q, -1).transpose(0, 1).contiguous()
2265
        elif qkv_format == 'bshd':
2266
2267
            # (bs)hd -> bs(hd)
            output = output.view(batch_size, max_seqlen_q, -1).contiguous()
2268
2269
2270
        elif qkv_format == 'thd':
            # thd -> t(hd)
            output = output.view(output.shape[0], -1).contiguous()
2271
2272

        return output
2273

2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
def _combine_tensors(
        tensors: List[torch.Tensor],
        dim: int,
    ) -> torch.Tensor:
    """Combine tensors along a particular dimension"""

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    new_stride = list(tensors[0].stride())
    new_stride.insert(dim, int(new_stride[dim-1]/num_tensors))
    if isinstance(tensors[0], Float8Tensor):
        combined_tensor = torch.Tensor().to(
            device=tensors[0].device, dtype=tensors[0]._data.dtype)
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
            new_shape, new_stride)
        combined_tensor = Float8Tensor.make_like(
            tensors[0], data=combined_tensor)
    else:
        combined_tensor = torch.Tensor().to(
            device=tensors[0].device, dtype=tensors[0].dtype)
        combined_tensor.set_(
            tensors[0].untyped_storage(),
            tensors[0].storage_offset(),
            new_shape, new_stride)

    return combined_tensor
2303

2304
2305
2306
2307
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
2308
2309
2310
    def forward(ctx, is_training, max_seqlen, cu_seqlens,
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
                qkv, qkv_dtype, attn_bias, attn_scale,
2311
                dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
2312
                rng_gen, fused_attention_backend, use_FAv2_bwd,
2313
                fp8, fp8_meta):
2314
        logger = logging.getLogger("FusedAttnFunc_qkvpacked")
2315
        if fp8:
2316
            logger.debug("Running forward in FP8")
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
            if fp8_meta["recipe"].fp8_mha:
                assert (isinstance(qkv, Float8Tensor)), "qkv must be Float8Tensors for FP8 MHA."
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = qkv._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            # 1: qkv packed, 2: kv packed, 3: qkv separate
            qkv_group = len(qkv_layout.split('_'))
            assert (qkv_group == 1
                ), f"qkv layout should conform to 3hd or h3d, e.g. sb3hd, \
                but found {qkv_layout}."
            if fp8_meta["recipe"].fp8_mha:
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                qkv_fp8 = cast_to_fp8(qkv_c,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward).view(qkv.shape)
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
                is_training, max_seqlen, cu_seqlens,
                qkv_fp8, fp8_dtype_forward, fused_attention_backend, attn_bias,
2337
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
                attn_scale, dropout_p, fast_zero_fill, qkv_layout,
                attn_bias_type, attn_mask_type, rng_gen)
            if fp8_meta["recipe"].fp8_mha:
                out_ret = Float8Tensor(data=out_fp8,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=qkv.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                qkv = cast_from_fp8(qkv_c._data,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward, TE_DType[qkv.dtype]).view(qkv.shape)
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            fp8_tensors = (qkv_fp8, out_fp8,
                fp8_meta["scaling_fwd"].scale.clone(),
                fp8_meta["scaling_fwd"].scale_inv.clone())
        else:
2373
            logger.debug("Running forward in %s",qkv.dtype)
2374
2375
2376
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
                is_training, max_seqlen, cu_seqlens, qkv, qkv_dtype,
                fused_attention_backend, attn_bias,
2377
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2378
2379
2380
2381
2382
2383
2384
2385
                None, None, None, None, None, None,
                attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen)
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
2386
2387
2388
        ctx.save_for_backward(*qkvo_tensors, cu_seqlens,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            *fp8_tensors, *aux_ctx_tensors)
2389
        ctx.fp8_meta = fp8_meta
2390
2391
2392
2393
2394
2395
2396
2397
        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
2398
2399
        ctx.fused_attention_backend = \
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
2400
        ctx.use_FAv2_bwd = use_FAv2_bwd
2401

2402
        return out_ret
2403
2404
2405

    @staticmethod
    def backward(ctx, d_out):
2406
        logger = logging.getLogger("FusedAttnFunc_qkvpacked")
2407
2408
2409
2410
2411
2412
        if ctx.fp8_meta["recipe"].fp8_mha:
            assert (isinstance(d_out, Float8Tensor)
                ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
            d_out_f8tensor = d_out
            d_out = d_out._data

2413
        d_out = d_out.contiguous()
2414
2415
2416
        (qkv, out, cu_seqlens,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            qkv_fp8, out_fp8,
2417
2418
2419
            fwd_scales, fwd_scale_invs, *aux_ctx_tensors) = ctx.saved_tensors
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
2420
        if ctx.use_FAv2_bwd:
2421
            softmax_lse, rng_state = aux_ctx_tensors
2422
2423
2424
2425
2426
2427
2428
2429
            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,
2430
                "causal" in ctx.attn_mask_type, None, rng_state
2431
2432
2433
            )
            dqkv = dqkv[..., :d_out.shape[-1]]
        else:
2434
2435
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
2436
                    logger.debug("Running backward in FP8")
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
                    fp8_dtype_forward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=False)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO] = d_out_f8tensor._scale_inv
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                            ).view(d_out.shape)
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
                        ctx.max_seqlen, cu_seqlens,
                        qkv_fp8, out_fp8, d_out_fp8,
2452
                        fp8_dtype_forward, fp8_dtype_backward, aux_ctx_tensors,
2453
                        ctx.fused_attention_backend,
2454
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
                        fwd_scale_invs[META_QKV], # d_scale_qkv,
                        fwd_scale_invs[META_S], # d_scale_s,
                        fwd_scale_invs[META_O], # d_scale_o,
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO], # d_scale_do
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DP], # d_scale_dp
                        fwd_scales[META_S], # q_scale_s
                        ctx.fp8_meta['scaling_bwd'].scale[META_DP], # q_scale_dp
                        ctx.fp8_meta['scaling_bwd'].scale[META_DQKV], # q_scale_dqkv
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DP], # amax_dp
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DQKV], # amax_dqkv
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        dqkv = Float8Tensor(data=dqkv_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                    else:
                        dqkv_c_fp8 = dqkv_fp8.view(-1,
                            dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1])
                        dqkv = cast_from_fp8(dqkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"], META_DQKV,
                            fp8_dtype_backward, ctx.qkv_dtype).view(dqkv_fp8.shape)
                else:
2482
                    logger.debug("Running backward in %s",qkv.dtype)
2483
2484
2485
2486
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
                        ctx.max_seqlen, cu_seqlens, qkv, out, d_out,
2487
                        ctx.qkv_dtype, ctx.qkv_dtype, aux_ctx_tensors,
2488
                        ctx.fused_attention_backend,
2489
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2490
2491
2492
                        None, 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)
2493

2494
2495
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
2496
            return (None, None, None, None, None, None, None, dqkv, None, None, None,
2497
2498
2499
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
2500
        return (None, None, None, None, None, None, None, dqkv, None, rest[0], None,
2501
2502
2503
                None, None, None, None, None, None,
                None, None, None, None, None, None)

2504

2505
2506
2507
2508
2509
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,
2510
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2511
                q, kv, qkv_dtype, attn_bias, attn_scale, dropout_p, fast_zero_fill,
2512
                qkv_layout, attn_bias_type, attn_mask_type, rng_gen, fused_attention_backend,
2513
                use_FAv2_bwd, fp8, fp8_meta):
2514
        logger = logging.getLogger("FusedAttnFunc_kvpacked")
2515
        if fp8:
2516
            logger.debug("Running forward in FP8")
2517
2518
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2540
            if fp8_meta["recipe"].fp8_mha:
                assert (isinstance(q, Float8Tensor)
                    and isinstance(kv, Float8Tensor)), "q/kv must be Float8Tensors for FP8 MHA."
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
                qkv_group = len(qkv_layout.split('_'))
                assert (qkv_group == 2
                    ), f"qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd, \
                    but found {qkv_layout}."
                q_fp8 = cast_to_fp8(q,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward).view(q.shape)
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                kv_fp8 = cast_to_fp8(kv_c,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward).view(kv.shape)
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
                is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q_fp8, kv_fp8, fp8_dtype_forward, fused_attention_backend, attn_bias,
2541
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2542
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2579
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
                attn_scale, dropout_p, fast_zero_fill, qkv_layout,
                attn_bias_type, attn_mask_type, rng_gen)
            if fp8_meta["recipe"].fp8_mha:
                out_ret = Float8Tensor(data=out_fp8,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                q = cast_from_fp8(q._data,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward, TE_DType[q.dtype]).view(q.shape)
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                kv = cast_from_fp8(kv_c._data,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward, TE_DType[kv.dtype]).view(kv.shape)
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            fp8_tensors = (q_fp8, kv_fp8, out_fp8,
                fp8_meta["scaling_fwd"].scale.clone(),
                fp8_meta["scaling_fwd"].scale_inv.clone())
        else:
2580
            logger.debug("Running forward in %s",q.dtype)
2581
2582
2583
            out_ret, 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,
2584
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2585
2586
2587
2588
2589
2590
2591
2592
                None, None, None, None, None, None,
                attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen)
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
2593
        ctx.save_for_backward(*qkvo_tensors, cu_seqlens_q, cu_seqlens_kv,
2594
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2595
            *fp8_tensors, *aux_ctx_tensors)
2596
        ctx.fp8_meta = fp8_meta
2597
2598
2599
2600
2601
2602
2603
2604
2605
        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
2606
2607
        ctx.fused_attention_backend = \
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
2608
        ctx.use_FAv2_bwd = use_FAv2_bwd
2609

2610
        return out_ret
2611
2612
2613

    @staticmethod
    def backward(ctx, d_out):
2614
        logger = logging.getLogger("FusedAttnFunc_kvpacked")
2615
2616
2617
2618
2619
2620
        if ctx.fp8_meta["recipe"].fp8_mha:
            assert (isinstance(d_out, Float8Tensor)
                ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
            d_out_f8tensor = d_out
            d_out = d_out._data

2621
        d_out = d_out.contiguous()
2622
2623
2624
        (q, kv, out, cu_seqlens_q, cu_seqlens_kv,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            q_fp8, kv_fp8, out_fp8,
2625
2626
2627
            fwd_scales, fwd_scale_invs, *aux_ctx_tensors) = ctx.saved_tensors
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
2628
        if ctx.use_FAv2_bwd:
2629
            softmax_lse, rng_state = aux_ctx_tensors
2630
2631
2632
2633
2634
2635
2636
2637
2638
            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,
2639
                "causal" in ctx.attn_mask_type, None, rng_state
2640
2641
2642
2643
            )
            dq = dq[..., :d_out.shape[-1]]
            dkv = dkv[..., :d_out.shape[-1]]
        else:
2644
2645
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
2646
                    logger.debug("Running backward in FP8")
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
                    fp8_dtype_forward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=False)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO] = d_out_f8tensor._scale_inv
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                            ).view(d_out.shape)
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
                        ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                        q_fp8, kv_fp8, out_fp8, d_out_fp8,
2662
                        fp8_dtype_forward, fp8_dtype_backward, aux_ctx_tensors,
2663
                        ctx.fused_attention_backend,
2664
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2665
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2695
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2699
2700
2701
2702
                        fwd_scale_invs[META_QKV], # d_scale_qkv,
                        fwd_scale_invs[META_S], # d_scale_s,
                        fwd_scale_invs[META_O], # d_scale_o,
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO], # d_scale_do
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DP], # d_scale_dp
                        fwd_scales[META_S], # q_scale_s
                        ctx.fp8_meta['scaling_bwd'].scale[META_DP], # q_scale_dp
                        ctx.fp8_meta['scaling_bwd'].scale[META_DQKV], # q_scale_dqkv
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DP], # amax_dp
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DQKV], # amax_dqkv
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        dq = Float8Tensor(data=dq_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                        dkv = Float8Tensor(data=dkv_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DQKV,
                            fp8_dtype_backward, ctx.qkv_dtype).view(dq_fp8.shape)
                        dkv_c_fp8 = dkv_fp8.view(-1,
                            dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1])
                        dkv = cast_from_fp8(dkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"], META_DQKV,
                            fp8_dtype_backward, ctx.qkv_dtype).view(dkv_fp8.shape)
                else:
2703
                    logger.debug("Running backward in %s",q.dtype)
2704
2705
2706
2707
2708
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    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,
2709
                        ctx.qkv_dtype, ctx.qkv_dtype, aux_ctx_tensors,
2710
                        ctx.fused_attention_backend,
2711
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2712
2713
2714
                        None, 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)
2715

2716
2717
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
2718
            return (None, None, None, None, None, None, None, None, None, dq, dkv, None, None, None,
2719
2720
2721
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
2722
        return (None, None, None, None, None, None, None, None, None, dq, dkv, None, rest[0], None,
2723
2724
2725
                None, None, None, None, None, None,
                None, None, None, None, None, None)

2726
2727
2728
2729
2730
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,
2731
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2732
                q, k, v, qkv_dtype, attn_bias, attn_scale, dropout_p, fast_zero_fill,
2733
                qkv_layout, attn_bias_type, attn_mask_type, rng_gen, fused_attention_backend,
2734
                use_FAv2_bwd, fp8, fp8_meta):
2735
        logger = logging.getLogger("FusedAttnFunc")
2736
        if fp8:
2737
            logger.debug("Running forward in FP8")
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
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2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
                assert (isinstance(q, Float8Tensor)
                    and isinstance(k, Float8Tensor)
                    and isinstance(v, Float8Tensor)), "q/k/v must be Float8Tensors for FP8 MHA."
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                q_fp8, k_fp8, v_fp8 = q._data, k._data, v._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
                qkv_group = len(qkv_layout.split('_'))
                if qkv_group == 1:
                    dim = qkv_layout.find('3')
                    qkv = _combine_tensors([q,k,v], dim)
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                    qkv_fp8 = cast_to_fp8(qkv_c,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(qkv.shape)
                    q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1,1,1])
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
                    q_fp8 = cast_to_fp8(q,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(q.shape)
                    dim = qkv_layout.split('_')[1].find('2')
                    kv = _combine_tensors([k,v], dim)
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                    kv_fp8 = cast_to_fp8(kv_c,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(kv.shape)
                    k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1,1])
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
                    q_fp8 = cast_to_fp8(q,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(q.shape)
                    k_fp8 = cast_to_fp8(k,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(k.shape)
                    v_fp8 = cast_to_fp8(v,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(v.shape)
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
                is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q_fp8, k_fp8, v_fp8, fp8_dtype_forward, fused_attention_backend, attn_bias,
2783
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2784
2785
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2849
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
                attn_scale, dropout_p, fast_zero_fill, qkv_layout,
                attn_bias_type, attn_mask_type, rng_gen)
            if fp8_meta["recipe"].fp8_mha:
                out_ret = Float8Tensor(data=out_fp8,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            out_save = out_ret

            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                # 1: qkv packed, 2: kv packed, 3: qkv separate
                qkv_group = len(qkv_layout.split('_'))
                if qkv_group == 1:
                    dim = qkv_layout.find('3')
                    qkv = _combine_tensors([q,k,v], dim)
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                    qkv_no_fp8 = cast_from_fp8(qkv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[qkv.dtype]).view(qkv.shape)
                    q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1,1,1])
                    q, k, v = [x.squeeze(dim) for x in [q, k, v]]
                if qkv_group == 2:
                    q = cast_from_fp8(q._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[q.dtype]).view(q.shape)
                    dim = qkv_layout.split('_')[1].find('2')
                    kv = _combine_tensors([k,v], dim)
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                    kv_no_fp8 = cast_from_fp8(kv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[kv.dtype]).view(kv.shape)
                    k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1,1])
                    k, v = [x.squeeze(dim) for x in [k, v]]
                if qkv_group == 3:
                    q = cast_from_fp8(q._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[q.dtype]).view(q.shape)
                    k = cast_from_fp8(k._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[k.dtype]).view(k.shape)
                    v = cast_from_fp8(v._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[v.dtype]).view(v.shape)
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8,
                fp8_meta["scaling_fwd"].scale.clone(),
                fp8_meta["scaling_fwd"].scale_inv.clone())
        else:
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            logger.debug("Running forward in %s",q.dtype)
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            out_ret, 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,
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                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
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                None, None, None, None, None, None,
                attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen)
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
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        from .cpu_offload import CPUOffloadEnabled
        if CPUOffloadEnabled:
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            tensor_list = [q, k, v, out_save, cu_seqlens_q, cu_seqlens_kv]
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            qkv_layout = 'sbhd_sbhd_sbhd'
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

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        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
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        ctx.save_for_backward(*qkvo_tensors, cu_seqlens_q, cu_seqlens_kv,
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            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
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            *fp8_tensors, *aux_ctx_tensors)
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        ctx.fp8_meta = fp8_meta
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        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
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        ctx.fused_attention_backend = \
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
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        ctx.use_FAv2_bwd = use_FAv2_bwd

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        return out_ret
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    @staticmethod
    def backward(ctx, d_out):
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        logger = logging.getLogger("FusedAttnFunc")
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        if ctx.fp8_meta["recipe"].fp8_mha:
            assert (isinstance(d_out, Float8Tensor)
                ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
            d_out_f8tensor = d_out
            d_out = d_out._data

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        d_out = d_out.contiguous()
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        (q, k, v, out, cu_seqlens_q, cu_seqlens_kv,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            q_fp8, k_fp8, v_fp8, out_fp8,
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            fwd_scales, fwd_scale_invs, *aux_ctx_tensors) = ctx.saved_tensors
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
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        if ctx.use_FAv2_bwd:
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            softmax_lse, rng_state = aux_ctx_tensors
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            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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                "causal" in ctx.attn_mask_type, None, rng_state
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            )
            dq = dq[..., :d_out.shape[-1]]
            dk = dk[..., :d_out.shape[-1]]
            dv = dv[..., :d_out.shape[-1]]
        else:
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            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
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                    logger.debug("Running backward in FP8")
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                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=False)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO] = d_out_f8tensor._scale_inv
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                            ).view(d_out.shape)
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
                        ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                        q_fp8, k_fp8, v_fp8, out_fp8, d_out_fp8,
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                        fp8_dtype_forward, fp8_dtype_backward, aux_ctx_tensors,
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                        ctx.fused_attention_backend,
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                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
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                        fwd_scale_invs[META_QKV], # d_scale_qkv,
                        fwd_scale_invs[META_S], # d_scale_s,
                        fwd_scale_invs[META_O], # d_scale_o,
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO], # d_scale_do
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DP], # d_scale_dp
                        fwd_scales[META_S], # q_scale_s
                        ctx.fp8_meta['scaling_bwd'].scale[META_DP], # q_scale_dp
                        ctx.fp8_meta['scaling_bwd'].scale[META_DQKV], # q_scale_dqkv
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DP], # amax_dp
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DQKV], # amax_dqkv
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        dq = Float8Tensor(data=dq_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                        dk = Float8Tensor(data=dk_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                        dv = Float8Tensor(data=dv_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                    else:
                        qkv_group = len(ctx.qkv_layout.split('_'))
                        if qkv_group == 1:
                            dim = ctx.qkv_layout.find('3')
                            dqkv_fp8 = _combine_tensors([dq_fp8,dk_fp8,dv_fp8], dim)
                            dqkv_c_fp8 = dqkv_fp8.view(-1,
                                dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1])
                            dqkv = cast_from_fp8(dqkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dqkv_fp8.shape)
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1,1,1])
                            dq, dk, dv = [x.squeeze(dim) for x in [dq, dk, dv]]
                        if qkv_group == 2:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dq_fp8.shape)
                            dim = ctx.qkv_layout.split('_')[1].find('2')
                            dkv_fp8 = _combine_tensors([dk_fp8,dv_fp8], dim)
                            dkv_c_fp8 = dkv_fp8.view(-1,
                                dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1])
                            dkv = cast_from_fp8(dkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dkv_fp8.shape)
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1,1])
                            dk, dv = [x.squeeze(dim) for x in [dk, dv]]
                        if qkv_group == 3:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dq_fp8.shape)
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dk_fp8.shape)
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dv_fp8.shape)
                else:
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                    logger.debug("Running backward in %s",q.dtype)
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                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    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,
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                        ctx.qkv_dtype, ctx.qkv_dtype, aux_ctx_tensors,
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                        ctx.fused_attention_backend,
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                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
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                        None, 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, None,
                    None, None, None, dq, dk, dv, None, None, None,
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                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
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        return (None, None, None, None, None, None,
                None, None, None, dq, dk, dv, None, rest[0], None,
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                None, None, None, None, None, None,
                None, None, None, None, None, None)

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class FusedAttention(TransformerEngineBaseModule):
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    """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,
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        softmax_scale: float,
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        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__()

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        self.logger = logging.getLogger("FusedAttention")
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        self.softmax_scale = softmax_scale
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        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 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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        def remove_extra_states_check(self, incompatible_keys): # pylint: disable=unused-argument
            """
            Temporarily remove fused_attention._extra_state as a missing key
            when loading older TransformerEngine checkpoints. Will phase out
            this hook in TransformerEngine 2.0.
            """
            for key in incompatible_keys.missing_keys:
                if 'fused_attention._extra_state' in key:
                    incompatible_keys.missing_keys.remove(key)
        self.register_load_state_dict_post_hook(remove_extra_states_check)

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    def get_fp8_weights_scratchpad(
        self,
        is_first_microbatch: Union[bool, None],
    ) -> List[Float8Tensor]:
        """Needs override."""

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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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        seq_offsets_q: Optional[torch.Tensor] = None,
        seq_offsets_k: Optional[torch.Tensor] = None,
        seq_offsets_v: Optional[torch.Tensor] = None,
        seq_offsets_o: 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,
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        cp_group: Optional[dist_group_type] = None,
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
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        is_first_microbatch: Optional[bool] = None,
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    ) -> 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 (
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            (query_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
            and (key_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
            and (value_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
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            ), '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}!"

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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()])
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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:
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                assert not context_parallel, "Padding mask not supported with context parallelism!"

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                if cu_seqlens_q is None or cu_seqlens_kv is None:
                    if attention_mask is None:
                        raise RuntimeError(
                            "Please provide attention_mask or cu_seqlens for padding!"
                        )
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                    if self.attention_type == "self":
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                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
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                    else:
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                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
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            else:
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                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
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        if qkv_format == 'thd':
            assert (max_seqlen_q is not None
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
                ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
            if (seq_offsets_q is None
                or seq_offsets_k is None
                or seq_offsets_v is None
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                or seq_offsets_o is None
                or context_parallel):
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                qkv_group = ''.join([x for x in qkv_layout if x not in 'bst'])
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                qkv_group = 'hd_hd_hd' if context_parallel else qkv_group
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                num_heads = query_layer.shape[-2]
                num_gqa_groups = key_layer.shape[-2]
                head_dim = query_layer.shape[-1]
                seq_offsets_o = num_heads * head_dim * cu_seqlens_q
                if qkv_group == 'hd_hd_hd':
                    seq_offsets_q = num_heads * head_dim * cu_seqlens_q
                    seq_offsets_k = num_gqa_groups * head_dim * cu_seqlens_kv
                    seq_offsets_v = num_gqa_groups * head_dim * cu_seqlens_kv
                if qkv_group in ['3hd', 'h3d']:
                    seq_offsets_q = num_heads * head_dim * 3 * cu_seqlens_q
                    seq_offsets_k = num_heads * head_dim * 3 * cu_seqlens_q
                    seq_offsets_v = num_heads * head_dim * 3 * cu_seqlens_q
                if qkv_group in ['hd_2hd', 'hd_h2d']:
                    seq_offsets_q = num_heads * head_dim * cu_seqlens_q
                    seq_offsets_k = num_gqa_groups * head_dim * 2 * cu_seqlens_kv
                    seq_offsets_v = num_gqa_groups * head_dim * 2 * 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))
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        if context_parallel:
            assert (fused_attention_backend
                == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
                ), f"{fused_attention_backend} does not work with context parallelism!"
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            assert (
                core_attention_bias_type not in ["alibi"]
            ), f"{core_attention_bias_type} is not supported with context parallelism!"
            query_layer, key_layer, value_layer = [x.contiguous()
                for x in (query_layer, key_layer, value_layer)]
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            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
                    query_layer, key_layer, value_layer,
                    cu_seqlens_q, cu_seqlens_kv,
                    max_seqlen_q, max_seqlen_kv,
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                    seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
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                    self.attention_dropout if self.training else 0.0,
                    cp_group, cp_global_ranks, cp_stream,
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                    softmax_scale=self.softmax_scale,
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                    qkv_format=qkv_format,
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                    attn_mask_type=attn_mask_type,
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                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
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                    use_fused_attention=True,
                )
        else:
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            with self.prepare_forward(query_layer,
                is_first_microbatch,
                num_gemms=3,
                allow_non_contiguous=True) as query_layer:
                with self.attention_dropout_ctx():
                    forced_fp8_dpa = ""
                    if self.fp8_meta["recipe"].fp8_mha:
                        if not self.fp8_meta["recipe"].fp8_dpa:
                            self.fp8_meta["recipe"].fp8_dpa = True
                            forced_fp8_dpa = " (forced)"
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                    if fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_FP8:
                        self.logger.debug(
                            "Running with fp8_recipe.fp8_mha=%s, "
                            "fp8_recipe.fp8_dpa=%s%s, and NVTE_FP8_DPA_BWD=%s",
                            self.fp8_meta["recipe"].fp8_mha,
                            self.fp8_meta["recipe"].fp8_dpa,
                            forced_fp8_dpa,
                            int(os.getenv("NVTE_FP8_DPA_BWD", "1")))
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                    output = FusedAttnFunc.apply(
                        self.training,
                        max_seqlen_q, max_seqlen_kv,
                        cu_seqlens_q, cu_seqlens_kv,
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                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
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                        query_layer, key_layer, value_layer,
                        qkv_dtype,
                        core_attention_bias,
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                        self.softmax_scale,
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                        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,
                        self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        self.fp8_meta,
                    )
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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
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        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
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        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
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                number of key-query-value channels per attention head.
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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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    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
                `1.0 / math.sqrt(kv_channels)`.
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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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        softmax_scale: Optional[float] = None,
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    ) -> None:
        super().__init__()

3425
        self.logger = logging.getLogger("DotProductAttention")
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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.layer_number = 1 if layer_number is None else layer_number
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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
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        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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        self.rng_states_tracker = None
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        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
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            self.rng_states_tracker = get_rng_state_tracker()
            set_all_rng_states(self.rng_states_tracker.get_states())
            attention_dropout_ctx = self.rng_states_tracker.fork
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        if softmax_scale is None:
            softmax_scale = 1.0 / math.sqrt(kv_channels)
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        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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Tim Moon committed
3469
            and self.device_compute_capability >= (8, 0)
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        )
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        if int(os.getenv("NVTE_FLASH_ATTN", "1")) == 0:
            self.logger.debug("Disabling FlashAttention due to NVTE_FLASH_ATTN=0")
        if self.device_compute_capability < (8, 0):
            self.logger.debug("Disabling FlashAttention for compute capability < sm80")

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        if not _flash_attn_2_4_1_plus and self.deterministic:
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            self.use_flash_attention = False
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            self.logger.warning(
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                "Disabling usage of FlashAttention since version <2.4.1 does not support "
                "deterministic execution. In order to use FA with deterministic behavior,"
                " please install FlashAttention version >=2.4.1."
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            )

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        self.use_fused_attention = (
            int(os.getenv("NVTE_FUSED_ATTN", "1"))
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3486
            and self.device_compute_capability >= (8, 0)
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        )
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        if int(os.getenv("NVTE_FUSED_ATTN", "1")) == 0:
            self.logger.debug("Disabling FusedAttention due to NVTE_FUSED_ATTN=0")
        if self.device_compute_capability < (8, 0):
            self.logger.debug("Disabling FusedAttention for compute capability < sm80")
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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(softmax_scale,
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                                                  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(softmax_scale,
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                                                  attention_type=attention_type,
                                                  layer_number=layer_number,
                                                  deterministic=self.deterministic,
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                                                  **attn_kwargs)
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        self.unfused_attention = UnfusedDotProductAttention(
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            softmax_scale, **attn_kwargs, layer_number=layer_number)
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    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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3536

        hidden_states = checkpoint(
            custom_forward,
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            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
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            *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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        seq_offsets_q: Optional[torch.Tensor] = None,
        seq_offsets_k: Optional[torch.Tensor] = None,
        seq_offsets_v: Optional[torch.Tensor] = None,
        seq_offsets_o: 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,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        fast_zero_fill: bool = True,
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        inference_params: Optional[InferenceParams] = None,
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        is_first_microbatch: Optional[bool] = None,
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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::

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            Input tensor :attr:`query_layer` must be of shape
            (:attr:`sequence_length`, :attr:`batch_size`, :attr:`num_attention_heads`,
            :attr:`kv_channels`) and the tensors :attr:`key_layer` and :attr:`value_layer`
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            must each be of shape (:attr:`sequence_length`, :attr:`batch_size`,
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            :attr:`num_gqa_groups`, :attr:`kv_channels`). Output of shape
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3612
            (:attr:`sequence_length`, :attr:`batch_size`, :attr:`num_attention_heads`
            * :attr:`kv_channels`) is returned.

3613
3614
        .. note::

3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
            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
3633
3634
3635
3636
3637
            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.
3638

3639
3640
3641
3642
3643
3644
3645
3646
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
3647
3648
3649
3650
3651
3652
        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
3653
3654
3655
             broadcastable to [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value
             means the corresponding position is masked out and a `False` means that position is
             allowed to participate in attention.
3656
3657
3658
3659
3660
3661
3662
3663
        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.
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
        seq_offsets_q: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
        seq_offsets_k: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for `key_layer`,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
        seq_offsets_v: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for `value_layer`,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
        seq_offsets_o: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for forward output,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
3676
3677
3678
3679
3680
3681
        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.
3682
3683
3684
        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.
3685
        window_size: Optional[Tuple[int, int]], default = `None`
3686
                    Sliding window size for local attention.
3687
3688
3689
3690
3691
        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.
3692
        core_attention_bias_type: str, default = `no_bias`
3693
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
3694
        core_attention_bias: Optional[torch.Tensor], default = `None`
3695
3696
                    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.
3697
3698
3699
3700
        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
3701
        fast_zero_fill: bool, default = `True`
3702
                    Whether to use the fast path to set output tensors to 0 or not.
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
        inference_params: Optional[InferenceParams], default = `None`
            Optimizes execution performance during inference by caching Keys and Values of the
            current decoding iteration. These cached values are appended to the K and V values
            computed in previous iterations, eliminating the need to recalculate them for the
            entire sequence.
            Initialization of `inference_params` is required prior to use to ensure sufficient
            memory allocation.
            Adjustments of the sequence_len_offset should be done after a complete forward pass.
            If rotary positional embeddings (RoPE) are utilized, they must be prepared beforehand.
            Supports "sbhd" and "bshd" layouts, with the "sbhd" layout being more efficient.
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
        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)
3726
3727
        """

3728
3729
3730
3731
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), 'DotProductAttention only supports CUDA tensors.'

3732
3733
3734
        assert (key_layer.shape == value_layer.shape
            ), "Keys and values must have the same shape!"

3735
3736
        if attn_mask_type is not None:
            window_size = check_set_window_size(attn_mask_type, window_size)
3737
        if attn_mask_type is None:
3738
            attn_mask_type = self.attn_mask_type
3739
3740
3741
3742
3743
3744
3745
        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!"
3746
3747
3748
        if qkv_format == 'thd':
            assert ('padding' in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
3749

3750
3751
3752
3753
3754
3755
3756
3757
        if self.rng_states_tracker is not None and is_graph_capturing():
            assert (
                isinstance(self.rng_states_tracker, CudaRNGStatesTracker)
            ), "Unsupported RNG states tracker."
            assert (
                graph_safe_rng_available()
            ), "Upgrade PyTorch version to get RNG manipulation support for cuda graph capture."

3758
3759
3760
        if window_size is None:
            window_size = self.window_size

3761
3762
        if qkv_format is None:
            qkv_format = self.qkv_format
3763

3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
        if inference_params is not None:
            assert self.layer_number is not None, "Layer number must be set!"

            if qkv_format == "bshd":
                key_layer = key_layer.transpose(0, 1)
                value_layer = value_layer.transpose(0, 1)

            (inference_key_memory, inference_value_memory,
            ) = inference_params.key_value_memory_dict[self.layer_number]

            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 keys and values into KV-cache
            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, ...]

            if qkv_format == "bshd":
                key_layer = key_layer.transpose(0, 1)
                value_layer = value_layer.transpose(0, 1)

            key_layer = key_layer.contiguous()
            value_layer = value_layer.contiguous()

3797
        assert (key_layer.shape[-2] == self.num_gqa_groups_per_partition
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
            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!"
3815
3816
            if max_seqlen_q is None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
3817
                max_seqlen_q = pow(2, math.ceil(math.log2(seqlens_q.max().item())))
3818
3819
            if max_seqlen_kv is None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
3820
                max_seqlen_kv = pow(2, math.ceil(math.log2(seqlens_kv.max().item())))
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839

        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'!"""

3840
3841
3842
3843
3844
3845
3846
3847
        if (isinstance(query_layer, Float8Tensor)
            and isinstance(key_layer, Float8Tensor)
            and isinstance(value_layer, Float8Tensor)):
            qkv_layout, query_layer._data, key_layer._data, value_layer._data = _get_qkv_layout(
                query_layer._data, key_layer._data, value_layer._data, qkv_format = qkv_format)
        else:
            qkv_layout, query_layer, key_layer, value_layer = _get_qkv_layout(
                query_layer, key_layer, value_layer, qkv_format = qkv_format)
3848

3849
3850
        # The priority for attention backends (subject to availability and clearing the filters)
        # is: FlashAttention > FusedAttention (cuDNN) > UnfusedDotProductAttention.
3851
        use_flash_attention = self.use_flash_attention
3852
        use_fused_attention = self.use_fused_attention
3853
        use_unfused_attention = True
3854

3855
3856
3857
        # The following section filters out some backends based on
        # certain asserts before executing the forward pass.

3858
        # Filter: QKV layout.
3859
3860
        if use_unfused_attention and qkv_format == 'thd':
            self.logger.debug("Disabling UnusedDotProductAttention for qkv_format = thd")
3861
3862
            use_unfused_attention = False

3863
3864
        # Filter: ONNX export.
        if is_in_onnx_export_mode():
3865
3866
            if use_flash_attention:
                self.logger.debug("Disabling FlashAttention for ONNX mode")
3867
            use_flash_attention = False
3868
3869
            if use_fused_attention:
                self.logger.debug("Disabling FusedAttention for ONNX mode")
3870
3871
            use_fused_attention = False

3872
        # Filter: Input type.
3873
3874
3875
3876
3877
        if (use_flash_attention
            and (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]
                or any(isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]))
3878
        ):
3879
3880
3881
3882
3883
            self.logger.debug(
                "Disabling FlashAttention due to unsupported QKV data types. "
                "Supported: [torch.bfloat16, torch.float16]. "
                "Found: query_layer.dtype=%s, key_layer.dtype=%s, value_layer.dtype=%s.",
                query_layer.dtype, key_layer.dtype, value_layer.dtype)
3884
            use_flash_attention = False
3885
3886
3887
3888
        if (use_fused_attention
            and (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])
3889
        ):
3890
3891
3892
3893
3894
            self.logger.debug(
                "Disabling FusedAttention due to unsupported QKV data types. "
                "Supported: [torch.bfloat16, torch.float16, Float8Tensor]. "
                "Found: query_layer.dtype=%s, key_layer.dtype=%s, value_layer.dtype=%s.",
                query_layer.dtype, key_layer.dtype, value_layer.dtype)
3895
            use_fused_attention = False
3896

3897
        # Filter: Device and dimensions.
3898
        # FAv2 supports head_dim <= 256, and for >192 requires sm80/sm90
3899
        # FAv2 requires head_dim % 8 == 0
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
        if (use_flash_attention
            and (query_layer.shape[-1] > 256
                or query_layer.shape[-1] % 8 != 0
                or (query_layer.shape[-1] > 192
                    and self.device_compute_capability not in ((8, 0), (9, 0))))):
            self.logger.debug(
                "Disabling FlashAttention due to unsupported head_dim. "
                "Supported: %%8 == 0, and <= 256; sm80/90 for >192. "
                "Found: query_layer.shape[-1]=%s, key_layer.shape[-1]=%s, sm=%s",
                query_layer.shape[-1], key_layer.shape[-1],
                '.'.join([str(i) for i in self.device_compute_capability]))
3911
3912
            use_flash_attention = False

3913
        # Filter: cross attention + causal mask.
3914
        # (in training mode)
3915
3916
        if (use_flash_attention
            and inference_params is None
3917
            and _flash_attn_2_1_plus
3918
            and "causal" in attn_mask_type
3919
3920
            and max_seqlen_q != max_seqlen_kv
        ):
3921
            self.logger.warning(
3922
3923
                "In training mode, disable the use of FlashAttention since version 2.1+ has "
                "changed its behavior for causal mask in cross attention. See "
3924
3925
3926
3927
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False

3928
3929
3930
        context_parallel = (self.cp_group is not None and \
            get_distributed_world_size(self.cp_group) != 1)

3931
3932
3933
        # Filter: sliding window attention.
        # UnfusedDotProductAttention can support SWA via arbitrary attention mask.
        if window_size not in ((-1, -1), (-1, 0)):
3934
3935
            if use_fused_attention:
                self.logger.debug("Disabling FusedAttention for SWA")
3936
3937
            use_fused_attention = False
            if (not _flash_attn_2_3_plus) or context_parallel:
3938
3939
3940
3941
                if use_flash_attention:
                    self.logger.debug(
                        "Disabling FusedAttention as it requires flash-attn 2.3+ "
                        "and no context parallelism")
3942
3943
                use_flash_attention = False

3944
        # Filter: Attention mask type.
3945
        #   attn_mask_type(s)    |     supported backends
3946
        # ------------------------------------------------
3947
3948
        #   no_mask              |     All
        #   padding              |     UnfusedDotProductAttention, FlashAttention, FusedAttention
3949
        #   causal               |     All
3950
        #   padding + causal     |     FlashAttention, FusedAttention
3951
3952
3953
        #   arbitrary            |     UnfusedDotProductAttention
        #
        if attn_mask_type == "arbitrary":
3954
3955
            if use_flash_attention:
                self.logger.debug("Disabling FlashAttention for arbitrary mask")
3956
            use_flash_attention = False
3957
3958
            if use_fused_attention:
                self.logger.debug("Disabling FusedAttention for arbitrary mask")
3959
            use_fused_attention = False
3960

3961
3962
        if (use_unfused_attention
            and inference_params is None
3963
3964
3965
            and "causal" in attn_mask_type
            and max_seqlen_q != max_seqlen_kv
        ):
3966
            self.logger.debug("Disabling UnusedDotProductAttention for qkv_format = thd")
3967
            use_unfused_attention = False
3968

3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
        # Filter: bias.
        global _alibi_cache
        if alibi_slopes is not None:
            assert (core_attention_bias_type == "alibi"
                ), "core_attention_bias_type must be alibi in order to use alibi_slopes!"
            if self.layer_number == 1:
                _alibi_cache["_alibi_slopes_require_update"] = True
                _alibi_cache["_alibi_bias_require_update"] = True
        if core_attention_bias_type == "alibi":
            assert (core_attention_bias is None
                ), "core_attention_bias must be None when core_attention_bias_type is alibi!"
            if (_alibi_cache["_num_heads"] != query_layer.shape[-2]
                or _alibi_cache["_max_seqlen_q"] != max_seqlen_q
                or _alibi_cache["_max_seqlen_kv"] != max_seqlen_kv
                or _alibi_cache["_alibi_slopes"] is None):
                _alibi_cache["_alibi_slopes_require_update"] = True
                _alibi_cache["_alibi_bias_require_update"] = True

3987
3988
3989
3990
        if (use_flash_attention
            and (core_attention_bias_type not in ["no_bias", "alibi"]
                or core_attention_bias is not None)):
            self.logger.debug("Disabling FlashAttention for pre/post_scale_bias")
3991
3992
3993
3994
3995
3996
3997
3998
3999
            use_flash_attention = False

        fu_core_attention_bias_type = core_attention_bias_type
        fu_core_attention_bias = core_attention_bias
        if core_attention_bias_type == "alibi" and use_fused_attention and alibi_slopes is not None:
            fu_core_attention_bias_type = "post_scale_bias"
            _, fu_core_attention_bias = get_alibi(
                query_layer.shape[-2], max_seqlen_q, max_seqlen_kv, alibi_slopes=alibi_slopes,
                bias_dtype=query_layer.dtype)
4000
4001
4002
4003
4004
4005
4006
        if (use_fused_attention
            and fu_core_attention_bias_type == "post_scale_bias"
            and (fu_core_attention_bias.shape[0] != 1
            or fu_core_attention_bias.shape[1] != query_layer.shape[-2])):
            if fu_core_attention_bias.requires_grad:
                # remove this line when cuDNN adds bwd support for
                # [1, 1, s, s], [b, 1, s, s] and [b, h, s, s]
4007
                self.logger.debug("Disabling FusedAttention for dBias in [1, H, S, S] shape")
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                use_fused_attention = False
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            else:
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                # max512 backend will only support [1, h, s, s]
                os.environ["NVTE_FUSED_ATTN_BACKEND"] = "1"

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        if use_fused_attention:
            fused_attention_backend = tex.get_fused_attn_backend(
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                TE_DType[query_layer.dtype]
                if not isinstance(query_layer, Float8Tensor) else query_layer._fp8_dtype,
                TE_DType[key_layer.dtype]
                if not isinstance(key_layer, Float8Tensor) else key_layer._fp8_dtype,
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                QKVLayout[qkv_layout],
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                AttnBiasType[fu_core_attention_bias_type],
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                AttnMaskType[attn_mask_type],
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                self.attention_dropout,
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                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
            )
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            # DPA does not support FP8; for FP8, use cpp_extensions modules directly
            is_backend_avail = (fused_attention_backend in
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                [FusedAttnBackend["F16_max512_seqlen"],
                FusedAttnBackend["F16_arbitrary_seqlen"],
                FusedAttnBackend["FP8"]])
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            use_fused_attention = ( \
                use_fused_attention and is_backend_avail and \
                (not context_parallel or \
                 fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]))
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            if (fused_attention_backend == FusedAttnBackend["F16_max512_seqlen"]
                and fu_core_attention_bias_type == "post_scale_bias"
                and (fu_core_attention_bias.shape[0] != 1
                or fu_core_attention_bias.shape[1] != query_layer.shape[-2])):
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                self.logger.debug(
                    "Disabling FusedAttention as no backend supports the provided input")
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                use_fused_attention = False
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        # 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)):
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            self.logger.debug("Disabling FusedAttention for determinism reasons")
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            use_fused_attention = False

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        # 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):
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                self.logger.debug(
                    "Disabling FlashAttention to give FusedAttention preference on Hopper+ "
                    "for performance reasons")
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                use_flash_attention = False
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        run_config = {
            "compute_capability":"sm"+str((lambda x,y: x*10+y)(
                self.device_compute_capability[0],self.device_compute_capability[1])),
            "q_dtype":query_layer.dtype,
            "k_dtype":key_layer.dtype,
            "v_dtype":value_layer.dtype,
            "q_shape":list(query_layer.shape),
            "k_shape":list(key_layer.shape),
            "v_shape":list(value_layer.shape),
            "qkv_format":qkv_format,
            "qkv_layout":qkv_layout,
            "mask_type":attn_mask_type,
            "bias_type":core_attention_bias_type,
            "bias_shape":core_attention_bias.shape if core_attention_bias is not None else None,
            "dropout":self.attention_dropout,
            "context_parallel":context_parallel,
            "is_training":self.training,
            "transformer_engine_version":te.__version__,
            "flash_attn_version":_flash_attn_version,
            "cudnn_version":'.'.join([str(i) for i in get_cudnn_version()])}

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        if use_flash_attention:
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            self.logger.info("Running with FlashAttention backend ")
            self.logger.debug("Running with config=%s",run_config)
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            if core_attention_bias_type == "alibi":
                alibi_slopes, _ = get_alibi(
                    query_layer.shape[-2], max_seqlen_q, max_seqlen_kv, alibi_slopes=alibi_slopes)
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            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,
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                                        window_size=window_size,
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                                        alibi_slopes=alibi_slopes,
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                                        cp_group=self.cp_group,
                                        cp_global_ranks=self.cp_global_ranks,
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                                        cp_stream=self.cp_stream,
                                        max_seqlen_q=max_seqlen_q,
                                        max_seqlen_kv=max_seqlen_kv)
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        if use_fused_attention:
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            self.logger.info(
                "Running with FusedAttention backend (sub-backend %s)",
                int(fused_attention_backend))
            self.logger.debug("Running with config=%s",run_config)
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            if checkpoint_core_attention:
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                return self._checkpointed_attention_forward(
                    self.fused_attention,
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
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                    seq_offsets_q=seq_offsets_q,
                    seq_offsets_k=seq_offsets_k,
                    seq_offsets_v=seq_offsets_v,
                    seq_offsets_o=seq_offsets_o,
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                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
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                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
                    fused_attention_backend=fused_attention_backend,
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                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
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                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
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                    is_first_microbatch=is_first_microbatch)
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            return self.fused_attention(
                query_layer,
                key_layer,
                value_layer,
                qkv_layout=qkv_layout,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_kv,
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                seq_offsets_q=seq_offsets_q,
                seq_offsets_k=seq_offsets_k,
                seq_offsets_v=seq_offsets_v,
                seq_offsets_o=seq_offsets_o,
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                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
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                attn_mask_type=attn_mask_type,
                attention_mask=attention_mask,
                fused_attention_backend=fused_attention_backend,
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                core_attention_bias_type=fu_core_attention_bias_type,
                core_attention_bias=fu_core_attention_bias,
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                fast_zero_fill=fast_zero_fill,
                cp_group=self.cp_group,
                cp_global_ranks=self.cp_global_ranks,
                cp_stream=self.cp_stream,
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                is_first_microbatch=is_first_microbatch)
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        assert (not context_parallel), \
            "Context parallelism is only implemented with Flash Attention and Fused Attention!"
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        from .cpu_offload import CPUOffloadEnabled
        if CPUOffloadEnabled:
            warnings.warn(
                           "Attention activation Offloading is only implemented"
                           "with Flash Attention and Fused Attention!"
                         )

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        if use_unfused_attention:
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            self.logger.info("Running with UnfusedDotProductAttention backend")
            self.logger.debug("Running with config=%s",run_config)
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            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,
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                    core_attention_bias = core_attention_bias,
                    alibi_slopes = alibi_slopes)
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            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,
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                    core_attention_bias = core_attention_bias,
                    alibi_slopes = alibi_slopes)
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        raise Exception("No dot product attention support for the provided inputs!")
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class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

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        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
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    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.
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    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal' 'arbitrary'},
                   default = `causal`
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                   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.
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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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    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.
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    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
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    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.
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    qkv_format: str, default = `sbhd`
            dimension format for `query_layer`, `key_layer` and `value_layer`,
            {`sbhd`, `bshd`}. `s` stands for the sequence length, `b` batch size,
            `h` the number of heads and `d` head size. `sbhd` and `bshd` formats
            are used for when sequences in a batch are of equal length or padded to
            equal length. 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
    ----------------------
    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,
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        ub_overlap_rs_dgrad: bool = False,
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        ub_overlap_rs: bool = False,
        ub_overlap_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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        qkv_format: str = "sbhd"
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    ) -> None:
        super().__init__()
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        self.qkv_format = qkv_format
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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.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)
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        self.hidden_size_per_attention_head = kv_channels
        self.hidden_size_q = self.hidden_size_per_attention_head * num_attention_heads
        self.hidden_size_kv = self.hidden_size_per_attention_head * self.num_gqa_groups
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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":
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            parameters_split = None
            if not fuse_qkv_params:
                parameters_split = collections.OrderedDict([
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                    ("query", self.hidden_size_q),
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                    ("key", self.hidden_size_kv),
                    ("value", self.hidden_size_kv),
                ])
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            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
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                    self.hidden_size_q + 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,
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                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
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                    ub_overlap_ag=ub_overlap_ag,
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                    normalization=normalization,
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                    ub_name="qkv",
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                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
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                    self.hidden_size_q + 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,
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                    self.hidden_size_q,
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                    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,
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                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
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                    ub_overlap_ag=ub_overlap_ag,
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                    normalization=normalization,
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                    ub_name="qkv",
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                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
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                    self.hidden_size_q,
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                    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,
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                parameters_split=("key", "value") if not fuse_qkv_params else None,
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                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
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            self.hidden_size_per_attention_head,
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            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
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            qkv_format=self.qkv_format,
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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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            attention_type=self.attention_type,
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        )

        # Linear
        self.proj = Linear(
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            self.hidden_size_q,
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            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,
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            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_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,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        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
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             broadcastable to [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value
             means the corresponding position is masked out and a `False` means that position is
             allowed to participate in attention.
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        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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        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
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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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        # =================================================
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        # Pre-allocate memory for key-values for inference
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        # =================================================

        if inference_params and self.layer_number is not None:
            if self.layer_number not in inference_params.key_value_memory_dict:
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                inf_max_seq_len = inference_params.max_sequence_length
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                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]

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        # ======================
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        # Query, Key, and Value
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        # ======================
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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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                    is_first_module_in_mha=True, # specific to FP8 MHA
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                )

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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,
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                is_first_module_in_mha=True, # specific to FP8 MHA
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            )

            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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            key_layer, value_layer = (x.reshape(
                x.size(0), x.size(1), -1, self.hidden_size_per_attention_head,
                ) for x in (key_layer, value_layer))
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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,
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                    is_first_module_in_mha=True, # specific to FP8 MHA
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                )

            # [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)

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        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
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        if rotary_pos_emb is not None:
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            assert (not isinstance(query_layer, Float8Tensor)
                and not isinstance(key_layer, Float8Tensor)
                ), "RoPE is not supported for Float8Tensors!"
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            # duplicate the pos_emb for self attention
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            if not isinstance(rotary_pos_emb, tuple):
                rotary_pos_emb = ((rotary_pos_emb,) * 2)

            q_pos_emb, k_pos_emb = rotary_pos_emb
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            # adjust key and value for inference
            if inference_params is not None:
                if self.qkv_format == "sbhd":
                    sequence_length = key_layer.size(0)
                elif self.qkv_format == "bshd":
                    sequence_length = key_layer.size(1)

                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + sequence_length

                q_pos_emb = q_pos_emb[sequence_start:sequence_end, ...]
                k_pos_emb = k_pos_emb[sequence_start:sequence_end, ...]

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            query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb, self.qkv_format, fused=True)
            key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb, self.qkv_format, fused=True)
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        # ===========================
        # Core attention computation
        # ===========================

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        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
4897
            qkv_format=self.qkv_format,
4898
4899
            cu_seqlens_q=None,
            cu_seqlens_kv=None,
4900
4901
            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
4902
            window_size=window_size,
4903
4904
4905
            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
4906
            alibi_slopes=alibi_slopes,
4907
            fast_zero_fill=fast_zero_fill,
4908
            inference_params=inference_params,
4909
4910
        )

4911
        # ===================
4912
        # Output. [sq, b, h]
4913
        # ===================
4914

4915
        projection_output = self.proj(
4916
4917
            context_layer,
            is_first_microbatch=is_first_microbatch,
4918
4919
        )

4920
4921
4922
4923
4924
4925
4926
4927
        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,)
4928
        if self.input_layernorm and self.return_layernorm_output:
4929
4930
            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]