ColossalAI/colossalai/quantization/fp8.py

import os
from typing import Any, Optional, Tuple

import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from packaging.version import Version
from torch.distributed import ReduceOp

from .fp8_config import dynamic_kernel

SUPPORT_TORCH_COMPILE = Version(torch.__version__) >= Version("2.4.0")
SCALE_BYTES = 4
try:
    cuda_arch = int("".join(str(i) for i in torch.cuda.get_device_capability()))
except:
    cuda_arch = 0


class Handle:
    def __init__(self, handles=[], remain_ops=None) -> None:
        self.handles = handles
        self.remain_ops = remain_ops

    def wait(self):
        for handle in self.handles:
            handle.wait()
        if self.remain_ops:
            self.remain_ops()


def process_group_is_intranode(pg):
    if pg is None:
        from torch.distributed.distributed_c10d import _get_default_group

        pg = _get_default_group()

    local_world_size = None
    for var in ["LOCAL_WORLD_SIZE", "OMPI_COMM_WORLD_LOCAL_SIZE", "SLURM_TASKS_PER_NODE"]:
        if var in os.environ:
            local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
    if local_world_size is None:
        local_world_size = torch.cuda.device_count()

    group_ranks = dist.get_process_group_ranks(pg)
    group_ranks_node_ids = [rank // local_world_size for rank in group_ranks]
    return min(group_ranks_node_ids) == max(group_ranks_node_ids)


def cast_to_fp8(
    inp: torch.Tensor, fp8_format="e4m3", per_channel_scale=False, out=None
) -> Tuple[torch.Tensor, torch.Tensor]:
    r"""
    casting torch Tensor into specified fp8 tensor with per-channel scaling or per-tensor scaling.
    Args:
        inp: input torch Tensor, should be in torch.FloatTensor, torch.HalfTensor, torch.BFloat16Tensor.
        scale: scaling factor for fp8 casting. If it is None, then it is computed automatically. Per-channel scaling
        is applied if input tensor is 2 dimension, otherwise, per-tensor scaling is applied.
        fp8_format: e4m3 or e5m2

    Returns:
        Tuples: A tuple (fp8_tensor, scale)
    """

    if inp.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
        raise TypeError("Only float16, bfloat16, and float32 are allowed.")

    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    fp8_max = torch.finfo(fp8_type).max

    if inp.numel() == 0:
        return inp.to(fp8_type), torch.tensor([1.0], device=inp.device)
    else:
        if per_channel_scale:
            per_channel_max = inp.abs().max(dim=-1).values.float()
            per_channel_max = torch.where(per_channel_max > 0, per_channel_max, 1.0)
            scale = fp8_max / per_channel_max[:, None]
            scale_inv = per_channel_max / fp8_max
        else:
            per_tensor_max = inp.abs().max().float()
            per_tensor_max = torch.where(per_tensor_max > 0, per_tensor_max, 1.0)
            scale = fp8_max / per_tensor_max
            scale_inv = 1.0 / scale

    if out is not None:
        ret = torch.mul(scale, inp.float(), out=out)
    else:
        ret = (scale * inp.float()).to(fp8_type)
    return ret, torch.unsqueeze(scale_inv, dim=0)


def cast_from_fp8(
    inp: torch.Tensor, scale_inv: torch.Tensor, ret_type: torch.dtype, per_channel_scale=False, out=None
) -> torch.Tensor:
    r"""
    Args:
        inp: should be a fp8 torch tensor in one of the types: [torch.float8_e4m3fn, torch.float8_e5m2].
        scale: scaling factor returned by cast_to_fp8 function.
        ret_type: the datatype of the returned tensor.
    Returns:
        torch.Tensor
    """
    if inp.dtype not in [torch.float8_e4m3fn, torch.float8_e5m2]:
        raise TypeError("Only float8_e4m3fn and float8_e5m2 are allowed.")

    if per_channel_scale:
        if out is not None:
            return torch.mul(scale_inv[:, None], inp.float(), out=out)
        else:
            ret = scale_inv[:, None] * inp.float()
    else:
        if out is not None:
            return torch.mul(scale_inv, inp.float(), out=out)
        else:
            ret = scale_inv * inp.float()
    return ret.to(ret_type)


def _all_reduce_fp8(
    tensor: torch.Tensor, fp8_format="e4m3", op=ReduceOp.SUM, group=None, async_op: bool = False
) -> Optional[Handle]:
    r"""
    This is an in-place operation for compressed all_reduce using fp8.
    It works like dist.all_reduce but during communication the data is cast to fp8 format.

    Args:
        tensor: torch.Tensor in fp32, fp16, bf16 datatype.
        fp8_format: e4m3 or e5m2
        op: ReduceOp.SUM or ReduceOp.AVG

    Returns:
        None
    """

    world_size = dist.get_world_size(group=group)
    input_type = tensor.dtype
    input_shape = tensor.shape
    input_device = tensor.device
    input_size = tensor.numel()
    flat_padded_x = tensor.flatten()

    assert op in [ReduceOp.SUM, ReduceOp.AVG], "op can only be ReduceOp.SUM or ReduceOp.AVG"

    if flat_padded_x.size(0) % world_size != 0:
        pad_size = world_size - flat_padded_x.size(0) % world_size
        flat_padded_x = F.pad(flat_padded_x, (0, pad_size))

    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    ret, scale = cast_to_fp8(flat_padded_x, fp8_format=fp8_format)

    inp = ret.view(torch.uint8)
    input_chunks = list(torch.chunk(inp, world_size, dim=0))
    output_chunks = list(torch.chunk(torch.empty_like(inp), world_size, dim=0))
    dist.all_to_all(output_chunks, input_chunks, group=group)
    scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
    dist.all_gather(scale_list, scale, group=group)
    summed_out = torch.zeros_like(output_chunks[0]).to(input_type)

    for scale, out in zip(scale_list, output_chunks):
        out = out.view(fp8_type)
        summed_out += cast_from_fp8(out, scale, input_type)

    if op == ReduceOp.AVG:
        summed_out.div_(world_size)

    summed_out_fp8, scale = cast_to_fp8(summed_out, fp8_format=fp8_format)
    gather_scale_handle = dist.all_gather(scale_list, scale, group=group, async_op=async_op)

    tensor_list = [torch.empty_like(summed_out_fp8.view(torch.uint8)) for _ in range(world_size)]
    gather_tensor_handle = dist.all_gather(
        tensor_list, summed_out_fp8.view(torch.uint8), group=group, async_op=async_op
    )

    def cat_op():
        for i in range(world_size):
            tensor_list[i] = tensor_list[i].view(fp8_type).to(input_type) * scale_list[i]
        out = torch.cat(tensor_list, dim=0)
        tensor.copy_(out[:input_size].view(input_shape).to(input_type))

    if async_op:
        return Handle([gather_scale_handle, gather_tensor_handle], cat_op)
    else:
        cat_op()


def all_reduce_fp8(
    tensor: torch.Tensor, fp8_format="e4m3", op=ReduceOp.SUM, group=None, async_op: bool = False
) -> Optional[Handle]:
    # fall back to default op due to performance issue
    return dist.all_reduce(tensor, op=op, group=group, async_op=async_op)


@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=False, disable=cuda_arch < 89)
def _all_to_all_single_fp8(
    output, input, output_split_sizes=None, input_split_sizes=None, fp8_format="e5m2", group=None, async_op=False
) -> Optional[Handle]:
    r"""
    This is an in-place operation for compressed all_reduce using fp8.
    It works like dist.all_to_all_single but during communication the data is cast to fp8 format.
    Args:
        tensor: torch.Tensor in fp32, fp16, bf16 datatype.
        fp8_format: e4m3 or e5m2
    Returns:
        None
    """
    world_size = dist.get_world_size(group=group)
    input_type = input.dtype
    input_shape = input.shape
    input_device = input.device
    input = input.flatten()

    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2

    ret, scale = cast_to_fp8(input, fp8_format=fp8_format)

    inp = ret.view(torch.uint8)
    if input_split_sizes is not None:
        input_split_sizes = [input_split_sizes[i] * np.prod(input_shape[1:]) for i in range(world_size)]
        input_chunks = list(torch.split(inp, input_split_sizes))
    else:
        input_chunks = list(torch.chunk(inp, world_size, dim=0))

    if output_split_sizes is not None:
        output_chunks = [
            torch.empty((output_split_sizes[i] * np.prod(input_shape[1:]),), device=input_device, dtype=inp.dtype)
            for i in range(world_size)
        ]
    else:
        if dist.get_rank() == world_size - 1:
            output_chunks = [torch.empty_like(input_chunks[-1]) for _ in range(world_size)]
        else:
            output_chunks = [torch.empty_like(input_chunks[0]) for _ in range(world_size)]

    chunk_handle = dist.all_to_all(output_chunks, input_chunks, group=group, async_op=async_op)
    scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
    scale_hanle = dist.all_gather(scale_list, scale, group=group, async_op=async_op)

    def cast_op():
        cast_output_chunk = [
            cast_from_fp8(out.view(fp8_type), scale, input_type) for scale, out in zip(scale_list, output_chunks)
        ]

        tensor_out = torch.cat(cast_output_chunk, dim=0)
        outputs_shape = list(input_shape)
        if output_split_sizes is not None:
            outputs_shape[0] = sum(output_split_sizes)
        else:
            outputs_shape = input_shape
        output.data = tensor_out.view(outputs_shape).to(input_type)

    if async_op:
        return Handle([chunk_handle, scale_hanle], cast_op)
    else:
        cast_op()


def all_to_all_single_fp8(
    output, input, output_split_sizes=None, input_split_sizes=None, fp8_format="e5m2", group=None, async_op=False
) -> Optional[Handle]:
    r"""
    This is wrapper for _all_to_all_single_fp8.
    """
    if process_group_is_intranode(group):
        return dist.all_to_all_single(
            output,
            input,
            output_split_sizes=output_split_sizes,
            input_split_sizes=input_split_sizes,
            group=group,
            async_op=async_op,
        )
    else:
        return _all_to_all_single_fp8(
            output,
            input,
            fp8_format=fp8_format,
            output_split_sizes=output_split_sizes,
            input_split_sizes=input_split_sizes,
            group=group,
            async_op=async_op,
        )


def cast_to_fp8_pipeline(inp: Any) -> None:
    """
    Cast the hidden_states tensor of inp object to fp8 format before p2p communication in pipeline.
    The activations tensor is indexed by 'hidden_states' in the inp dict.
    After FP8 casting, the resulting tensor is saved as float16 or bfloat16 format but the size becomes halved.
    Metadata such as fp8_scale is saved into inp dict for communication.
    """
    if inp is None:
        return
    # In pipeline parallelism, when inp is torch.Tensor, it only contains one element, thus can be omitted.
    if type(inp) == torch.Tensor:
        return

    assert "hidden_states" in inp, "required by pipeline parallelism."
    assert (
        inp["hidden_states"].size(-1) % 2 == 0
    ), "tensor size(-1) must be divisible by 2 to view Float8_e4m3fn as BFloat16 or Float16"
    inp_tensor = inp["hidden_states"]
    inp_dtype = inp_tensor.dtype

    min_val, max_val = inp_tensor.aminmax()
    amax = torch.maximum(min_val.abs(), max_val.abs())

    finfo = torch.finfo(torch.float8_e4m3fn)
    if amax > finfo.max:
        fp8_type = torch.float8_e5m2
        fp8_view_type = torch.float16
    else:
        fp8_type = torch.float8_e4m3fn
        fp8_view_type = torch.bfloat16

    finfo = torch.finfo(fp8_type)
    scale = torch.tensor(1.0).to(inp_tensor.device) if amax == 0.0 else finfo.max / amax.float()
    q_tensor = inp_tensor.data.float() * scale
    # Todo: Currently we use fp8_view_type <float16, bfloat16> to indicate which fp8 format is used. This is a temporary workaround due to 'Only support tensor for fast send'.
    #  inp_tensor needs to be a float datatype to avoid error during gradient placement.
    inp_tensor.data = q_tensor.to(fp8_type).view(fp8_view_type)

    inp["fp8_scale"] = scale.float().reciprocal()
    inp["dtype"] = torch.zeros_like(scale).to(inp_dtype)


def cast_from_fp8_pipeline(inp: Any, del_metadata=True) -> None:
    """
    Cast the FP8 encoded hidden_states tensor back to original dtype after p2p communication in pipeline.
    del_metadata = False is useful when this function is called before p2p communication.
    """
    if inp is None:
        return
    if type(inp) == torch.Tensor:
        return

    assert "hidden_states" in inp, "required by pipeline parallelism."
    inp_tensor = inp["hidden_states"]
    scale = inp["fp8_scale"]

    fp8_view_type = inp_tensor.dtype
    if fp8_view_type == torch.float16:
        fp8_type = torch.float8_e5m2
    elif fp8_view_type == torch.bfloat16:
        fp8_type = torch.float8_e4m3fn
    else:
        raise TypeError("Only float16, bfloat16 are implemented.")

    inp_tensor.data = inp_tensor.data.view(fp8_type).to(inp["dtype"]) * scale

    if del_metadata:
        del inp["fp8_scale"]
        del inp["dtype"]


def _reduce_scatter_fp8(
    output: torch.Tensor, input_list, group, fp8_format="e5m2", async_op: bool = False
) -> Optional[Handle]:
    r"""
    This is an in-place operation for compressed reduce_scatter using fp8.
    It works like dist.reduce_scatter but during communication the data is cast to fp8 format.

    Args:
        tensor: torch.Tensor in fp32, fp16, bf16 datatype.
        fp8_format: e4m3 or e5m2

    Returns:
        None
    """

    input_type = output.dtype

    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    scale_list = []
    cast_input_list = []
    output_chunks = []
    output_scale_list = []
    for input in input_list:
        ret, scale = cast_to_fp8(input, fp8_format=fp8_format)
        scale_list.append(scale)
        ret = ret.view(torch.uint8)
        cast_input_list.append(ret)
        output_chunks.append(torch.empty_like(ret))
        output_scale_list.append(torch.empty_like(scale))
    chunk_handle = dist.all_to_all(output_chunks, cast_input_list, group=group, async_op=async_op)
    scale_handle = dist.all_to_all(output_scale_list, scale_list, group=group, async_op=async_op)

    def cast_op():
        summed_out = torch.zeros_like(output_chunks[0]).to(input_type)
        for scale, out in zip(output_scale_list, output_chunks):
            out = out.view(fp8_type)
            summed_out += cast_from_fp8(out, scale, input_type)
        output.data = summed_out

    if async_op:
        return Handle([chunk_handle, scale_handle], cast_op)
    else:
        cast_op()


def reduce_scatter_fp8(
    output: torch.Tensor, input_list, group, fp8_format="e5m2", async_op: bool = False
) -> Optional[Handle]:
    # fall back to default op due to performance issue
    return dist.reduce_scatter(output, input_list, group=group, async_op=async_op)


def fp8_compress_ddp_grad_comm_hook_async(
    process_group: dist.ProcessGroup,
    bucket: dist.GradBucket,
    fp8_format: str = "e5m2",
) -> torch.futures.Future[torch.Tensor]:
    """
    Compress by casting ``GradBucket`` to FP8 floating-point format divided by process group size.

    This DDP communication hook implements a simple gradient compression approach that casts ``GradBucket`` tensor
    to FP8 floating-point format (``torch.float8_e5m2`` or ``torch.bfloat16_e4m3``), and then divides it
    by the process group size.
    Once compressed gradient tensors are allreduced, the chained callback ``decompress`` casts it back
    to the input data type (such as ``float32``).

    Example::
        >>> ddp_model.register_comm_hook(process_group, fp8_compress_ddp_grad_comm_hook_async)
    """
    group_to_use = process_group if process_group is not None else dist.group.WORLD

    input_tensor = bucket.buffer()
    world_size = dist.get_world_size()
    input_type = input_tensor.dtype
    input_device = input_tensor.device
    flat_padded_x = input_tensor.flatten()

    if flat_padded_x.size(0) % world_size != 0:
        pad_size = world_size - flat_padded_x.size(0) % world_size
        flat_padded_x = F.pad(flat_padded_x, (0, pad_size))

    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    ret, scale = cast_to_fp8(flat_padded_x, fp8_format=fp8_format)

    inp = ret.view(torch.uint8)
    output_chunks_single = torch.empty_like(inp)
    split_sizes = [inp.numel() // world_size for _ in range(world_size)]
    fut0 = dist.all_to_all_single(
        output_chunks_single,
        inp,
        output_split_sizes=split_sizes,
        input_split_sizes=split_sizes,
        group=group_to_use,
        async_op=True,
    ).get_future()

    scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
    fut1 = dist.all_gather_into_tensor(
        torch.cat(scale_list, dim=0), scale, group=group_to_use, async_op=True
    ).get_future()
    all_to_all_fut = torch.futures.collect_all([fut0, fut1])

    def sum_and_allgather(fut):
        output_chunks_single = fut.value()[0].wait()[0]
        scale_list_single = fut.value()[1].wait()[0]

        output_chunks = list(torch.chunk(output_chunks_single, world_size, dim=0))
        scale_list = scale_list_single.chunk(world_size, dim=0)

        summed_out = torch.zeros_like(output_chunks[0]).to(input_type)
        for scale, out in zip(scale_list, output_chunks):
            out = out.view(fp8_type)
            summed_out += cast_from_fp8(out, scale, input_type)
        summed_out.div_(world_size)

        summed_out_fp8, scale = cast_to_fp8(summed_out, fp8_format=fp8_format)

        tensor_list_single = torch.empty(summed_out_fp8.size(0) * world_size, device=input_device, dtype=torch.uint8)
        fut2 = dist.all_gather_into_tensor(
            tensor_list_single, summed_out_fp8.view(torch.uint8), group=group_to_use, async_op=True
        ).get_future()

        scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
        fut3 = dist.all_gather_into_tensor(
            torch.cat(scale_list, dim=0), scale, group=group_to_use, async_op=True
        ).get_future()
        fut_combined2 = torch.futures.collect_all([fut2, fut3])
        return fut_combined2

    def decompress(fut):
        tensor_list_single = fut.value().wait()[0].value()[0]
        scale_list_single = fut.value().wait()[1].value()[0]

        tensor_list = list(torch.chunk(tensor_list_single, world_size, dim=0))
        scale_list = scale_list_single.chunk(world_size, dim=0)

        for i in range(world_size):
            tensor_list[i] = tensor_list[i].view(fp8_type).to(input_type) * scale_list[i]
        out = torch.cat(tensor_list, dim=0)

        input_tensor_size = input_tensor.numel()
        input_shape = input_tensor.shape
        out = out[:input_tensor_size]

        input_tensor.copy_(out.view(input_shape).to(input_type))
        return input_tensor

    return all_to_all_fut.then(sum_and_allgather).then(decompress)


def fp8_compress_ddp_grad_comm_hook_sync(
    process_group: dist.ProcessGroup,
    bucket: dist.GradBucket,
    fp8_format="e5m2",
) -> torch.futures.Future[torch.Tensor]:
    """
    Return a future that wraps the input, after the input is allreduced. However, the allreduce commnunication is synchronized.
    This breaks the overlapping between allreduce communication and backward compuation.

    This hook should **only** be used for debugging purposes, instead of the normal gradient synchronization.
    For asynchronized implementation, use fp8_compress_ddp_grad_comm_hook_async instead.

    Example::
        >>> # xdoctest: +SKIP
        >>> ddp_model.register_comm_hook(None, fp8_compress_ddp_grad_comm_hook_sync)
    """

    buffer = bucket.buffer()
    all_reduce_fp8(buffer, fp8_format=fp8_format)

    fut: torch.futures.Future[torch.Tensor] = torch.futures.Future()
    fut.set_result(bucket.buffer())

    return fut


def fp8_compress_fsdp_grad_comm_hook(
    state: object,
    unsharded_gradient_flattened: torch.Tensor,
    sharded_gradient: torch.Tensor,
    group=None,
    fp8_format="e5m2",
) -> None:
    """
    This communication hook implements a simple gradient compression approach that casts unsharded_gradient_flattened tensor
    to FP8 floating-point format (``torch.float8_e5m2`` or ``torch.bfloat16_e4m3``), and then perform scatter_allreduce logic
    by using all_to_all and all_gather among the process group.

    Example::
        >>> fsdp_model.register_comm_hook(None, fp8_compress_fsdp_grad_comm_hook)
    """
    grad = unsharded_gradient_flattened
    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    input_type = grad.dtype
    input_device = grad.device
    world_size = dist.get_world_size(group=group)

    grad_fp8, scale = cast_to_fp8(grad, fp8_format=fp8_format)
    uint8_buffer = torch.empty_like(grad_fp8).view(torch.uint8)
    dist.all_to_all_single(uint8_buffer, grad_fp8.view(torch.uint8), group=group)

    scale_list = [torch.ones(1, dtype=scale.dtype, device=input_device) for _ in range(world_size)]
    dist.all_gather(scale_list, scale, group=group)

    buffer_list = list(torch.chunk(uint8_buffer.view(fp8_type), world_size, dim=0))
    sharded_gradient.zero_()
    for tensor, scale in zip(buffer_list, scale_list):
        sharded_gradient += cast_from_fp8(tensor, scale, input_type)


def fp8_compress_fsdp_params_comm_hook(
    state: object,
    padded_unsharded_flat_param: torch.Tensor,
    sharded_flat_param: torch.Tensor,
    group=None,
    fp8_format="e5m2",
) -> None:
    """
        This hook is pending the official support for parameters communication hook in FSDP, e.g. register_params_comm_hook.

    Example::
        >>> fsdp_model.register_params_comm_hook(None, fp8_compress_fsdp_params_comm_hook)
    """

    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    fp8_max = torch.finfo(fp8_type).max
    inp = sharded_flat_param
    out = padded_unsharded_flat_param

    per_tensor_max = inp.abs().max().float()
    per_tensor_max = torch.where(per_tensor_max > 0, per_tensor_max, 1.0)
    dist.all_reduce(per_tensor_max, op=torch.distributed.ReduceOp.MAX, group=group)

    scale = fp8_max / per_tensor_max
    fp8_sharded_flat_param = (scale * inp.float()).to(fp8_type).view(torch.uint8)

    fp8_out = torch.empty(out.shape, dtype=torch.uint8, device=out.device)
    dist.all_gather_into_tensor(
        fp8_out,
        fp8_sharded_flat_param,
        group=group,
    )
    padded_unsharded_flat_param.copy_((fp8_out.view(fp8_type).float() / scale).to(out.dtype))


def split_chunk_by_channel(
    chunk: torch.Tensor, channel_size: int, num_channels: int, rank: int = 0, world_size: int = 1
):
    offset = chunk.numel() * rank
    end = offset + chunk.numel()
    break_points = [x for x in range(0, channel_size * num_channels + 1, channel_size) if offset <= x <= end]
    if len(break_points) == 0 or break_points[0] > offset:
        break_points.insert(0, offset)
    if break_points[-1] < end:
        break_points.append(end)
    sizes = [b - a for a, b in zip(break_points[:-1], break_points[1:])]
    return chunk.split(sizes)


@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=False, disable=cuda_arch < 89)
def _all_to_all_fp8(output_list, input_list, group=None, fp8_format="e5m2", async_op=False):
    world_size = dist.get_world_size(group)
    input_type = input_list[0].dtype
    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
    scale_list = []
    tensor_list = []

    for i in range(world_size):
        input_tensor = input_list[i]
        ret, scale = cast_to_fp8(input_tensor, fp8_format=fp8_format)
        scale_list.append(scale)
        ret = ret.view(torch.uint8)
        tensor_list.append(ret)

    output_scale_list = [torch.empty_like(x) for x in scale_list]
    output_tensor_list = [torch.empty_like(x) for x in tensor_list]
    tensor_hanle = dist.all_to_all(output_tensor_list, tensor_list, group=group, async_op=async_op)
    scale_handle = dist.all_to_all(output_scale_list, scale_list, group=group, async_op=async_op)

    def cast_op():
        for i in range(world_size):
            scale = output_scale_list[i]
            tensor = output_tensor_list[i]
            tensor = tensor.view(fp8_type)
            output_list[i].copy_(cast_from_fp8(tensor, scale, input_type))

    if async_op:
        return Handle([tensor_hanle, scale_handle], cast_op)
    else:
        cast_op()


def all_to_all_fp8(output_list, input_list, group=None, fp8_format="e5m2", async_op=False):
    if process_group_is_intranode(group):
        return dist.all_to_all(output_list, input_list, group=group, async_op=async_op)
    else:
        return _all_to_all_fp8(output_list, input_list, group=group, fp8_format=fp8_format, async_op=async_op)


@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=False, disable=cuda_arch < 89)
def _all_gather_fp8(output_list, input_, group=None, fp8_format="e5m2", async_op: bool = False) -> Optional[Handle]:
    world_size = dist.get_world_size(group)

    input_type = input_.dtype
    ret, scale = cast_to_fp8(input_, fp8_format=fp8_format)
    fp8_type = ret.dtype
    input_ = ret.view(torch.uint8)
    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
    scale_list = [torch.ones(1, dtype=scale.dtype, device=input_.device) for _ in range(world_size)]
    chunk_handle = dist.all_gather(tensor_list, input_, group=group, async_op=async_op)
    scale_hanle = dist.all_gather(scale_list, scale, group=group, async_op=async_op)

    def cast_op():
        for i in range(world_size):
            output = tensor_list[i].view(fp8_type)
            scale = scale_list[i]
            output_list[i].copy_(cast_from_fp8(output, scale, input_type))

    if async_op:
        return Handle([chunk_handle, scale_hanle], cast_op)
    else:
        cast_op()


def all_gather_fp8(output_list, input_, group=None, fp8_format="e5m2", async_op: bool = False) -> Optional[Handle]:
    if process_group_is_intranode(group):
        return dist.all_gather(output_list, input_, group=group, async_op=async_op)
    else:
        return _all_gather_fp8(output_list, input_, group=group, fp8_format=fp8_format, async_op=async_op)


@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=False, disable=cuda_arch < 89)
def all_gather_fp8_lagacy(
    output_list, input_, group=None, fp8_format="e5m2", async_op: bool = False
) -> Optional[Handle]:
    world_size = dist.get_world_size(group)
    shape = input_.shape
    input_type = input_.dtype
    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2

    combined_buffer = torch.empty(world_size * (SCALE_BYTES + input_.numel()), dtype=torch.uint8, device=input_.device)
    combined_buffers = list(combined_buffer.chunk(world_size, dim=0))
    cur_buffer = combined_buffers[dist.get_rank(group)]
    ret = cur_buffer[SCALE_BYTES:].view(fp8_type)
    ret, scale = cast_to_fp8(input_.view(-1), fp8_format=fp8_format, out=ret)
    cur_buffer[:SCALE_BYTES].view(torch.float)[0] = scale
    # cur_buffer[:SCALE_BYTES] = scale.unsqueeze(0).view(torch.uint8)
    dist.all_gather(combined_buffers, cur_buffer, group=group, async_op=async_op)
    for out, buf in zip(output_list, combined_buffers):
        scale = buf[:SCALE_BYTES].clone().view(scale.dtype)
        output = buf[SCALE_BYTES:].view(fp8_type)
        cast_from_fp8(output.view(shape), scale, input_type, out=out)
    # output = combined_buffer.view(world_size, -1)[:, SCALE_BYTES:].view(fp8_type)
    # scales = combined_buffer.view(world_size, -1)[:, :SCALE_BYTES].view(torch.float)
    # output = output.float() * scales
    # for i, out in enumerate(output_list):
    #     out.copy_(output[i].view(shape))


@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=False, disable=cuda_arch < 89)
def all_gather_fp8_ring(output_list, input_, group=None, fp8_format="e5m2", async_op: bool = False) -> Optional[Handle]:
    world_size = dist.get_world_size(group)
    rank = dist.get_rank(group)

    send_rank = (rank + 1) % world_size
    recv_rank = (rank - 1) % world_size

    shape = input_.shape
    input_type = input_.dtype
    fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2

    combined_buffer = torch.empty(world_size * (SCALE_BYTES + input_.numel()), dtype=torch.uint8, device=input_.device)
    combined_buffers = list(combined_buffer.chunk(world_size, dim=0))
    cur_buffer = combined_buffers[dist.get_rank(group)]
    ret = cur_buffer[SCALE_BYTES:].view(fp8_type)
    ret, scale = cast_to_fp8(input_.view(-1), fp8_format=fp8_format, out=ret)
    # cur_buffer[:SCALE_BYTES] = scale.unsqueeze(0).view(torch.uint8)
    cur_buffer[:SCALE_BYTES].view(torch.float)[0] = scale

    def send_recv(idx):
        send_idx = (rank - idx) % world_size
        recv_idx = (rank - idx - 1) % world_size
        ops = dist.batch_isend_irecv(
            [
                dist.P2POp(dist.isend, combined_buffers[send_idx], send_rank, group=group),
                dist.P2POp(dist.irecv, combined_buffers[recv_idx], recv_rank, group=group),
            ]
        )
        return ops

    def cast(idx):
        cast_idx = (rank - idx - 1) % world_size
        scale = combined_buffers[cast_idx][:SCALE_BYTES].clone().view(torch.float)
        output = combined_buffers[cast_idx][SCALE_BYTES:].view(fp8_type)
        cast_from_fp8(output.view(shape), scale, input_type, out=output_list[cast_idx])

    # warmup
    ops = send_recv(0)
    output_list[rank].copy_(input_)
    for op in ops:
        op.wait()
    ops = []

    # 1p-1c
    for i in range(1, world_size - 1):
        new_ops = send_recv(i)
        for op in ops:
            op.wait()
        cast(i - 1)
        ops = new_ops

    # cooldown
    for op in ops:
        op.wait()
    cast(world_size - 2)


class _LinearFp8(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx: Any,
        x: torch.Tensor,
        w: torch.Tensor,
        bias: Optional[torch.Tensor],
    ) -> Any:
        assert (
            x.dtype in (torch.bfloat16, torch.float16) and x.dtype == w.dtype
        ), "Only float16 and bfloat16 are allowed."
        if bias is not None:
            assert bias.dtype == x.dtype, "Bias should have the same dtype as input."
        # ensure x and w are row-major
        x = x.contiguous()
        w = w.contiguous()
        ctx.x_shape = x.shape
        ctx.has_bias = bias is not None
        ctx.out_dtype = x.dtype
        x = x.reshape(-1, x.shape[-1])

        x_fp8, inv_scale_x = cast_to_fp8(x, fp8_format="e4m3")
        w_fp8, inv_scale_w = cast_to_fp8(w, fp8_format="e4m3")
        ctx.x_fp8 = x_fp8
        ctx.w_fp8_t = w_fp8.t()
        ctx.inv_scale_x = inv_scale_x
        ctx.inv_scale_w = inv_scale_w
        out = torch._scaled_mm(
            x_fp8,
            ctx.w_fp8_t,
            bias=bias,
            out_dtype=ctx.out_dtype,
            scale_a=inv_scale_x,
            scale_b=inv_scale_w,
            use_fast_accum=True,
        )[0]
        return out.reshape(*ctx.x_shape[:-1], w.shape[0])

    @staticmethod
    def backward(ctx: Any, out_grad) -> Any:
        out_grad = out_grad.reshape(-1, out_grad.shape[-1])
        out_grad_fp8, out_grad_scale = cast_to_fp8(out_grad, fp8_format="e5m2")
        x_grad = torch._scaled_mm(
            out_grad_fp8,
            ctx.w_fp8_t.contiguous().t(),
            out_dtype=ctx.out_dtype,
            scale_a=out_grad_scale,
            scale_b=ctx.inv_scale_w,
            use_fast_accum=True,
        )[0]
        w_grad = torch._scaled_mm(
            out_grad_fp8.t().contiguous(),
            ctx.x_fp8.t().contiguous().t(),
            out_dtype=ctx.out_dtype,
            scale_a=out_grad_scale,
            scale_b=ctx.inv_scale_x,
            use_fast_accum=True,
        )[0]
        bias_grad = None
        if ctx.has_bias:
            bias_grad = out_grad.sum(0)
        return x_grad.reshape(ctx.x_shape), w_grad, bias_grad


@torch.compile(mode="max-autotune-no-cudagraphs", disable=not SUPPORT_TORCH_COMPILE, dynamic=dynamic_kernel)
def _linear_fp8(input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    return _LinearFp8.apply(input, weight, bias)


def linear_fp8(input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    if input.shape[-1] % 16 != 0 or np.prod(input.shape[:-1]) % 16 != 0:
        return F.linear(input, weight, bias)
    out = _linear_fp8(input, weight, bias)
    return out