# Copy and modify https://github.com/huggingface/text-generation-inference/blob/main/server/text_generation_server/utils/layers.py
from typing import List

import torch
import torch.distributed
from accelerate import init_empty_weights
from torch import nn
from torch.nn import functional as F


# Monkey patching
@classmethod
def load_layer_norm(cls, prefix, weights, eps):
    weight = weights.get_tensor(f"{prefix}.weight")
    bias = weights.get_tensor(f"{prefix}.bias")
    with init_empty_weights():
        ln = cls(weight.shape, eps=eps)

    ln.weight = nn.Parameter(weight)
    ln.bias = nn.Parameter(bias)
    return ln


@classmethod
def load_layer_norm_no_bias(cls, prefix, weights, eps):
    weight = weights.get_tensor(f"{prefix}.weight")
    with init_empty_weights():
        ln = cls(weight.shape, eps=eps)

    ln.weight = nn.Parameter(weight)
    ln.bias = None
    return ln


torch.nn.LayerNorm.load = load_layer_norm
torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias


class FastLinear(nn.Module):
    def __init__(
        self,
        weight,
        bias,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(weight)
        if bias is not None:
            self.bias = nn.Parameter(bias)
        else:
            self.bias = None

    @classmethod
    def load(cls, config, prefix: str, weights, bias: bool):
        weight = weights.get_tensor(f"{prefix}.weight")
        if bias:
            bias = weights.get_tensor(f"{prefix}.bias")
        else:
            bias = None
        return cls(weight, bias)

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return F.linear(input, self.weight, self.bias)


def get_linear(weight, bias):
    linear = FastLinear(weight, bias)
    return linear


class SuperLayer(nn.Module):
    def __init__(self, linear):
        super().__init__()
        self.linear = linear

    def forward(self, x):
        return self.linear.forward(x)


class TensorParallelHead(SuperLayer):
    def __init__(self, linear, process_group, should_gather: bool):
        super().__init__(linear)
        self.process_group = process_group
        self.should_gather = should_gather

    @staticmethod
    def load(config, prefix: str, weights):
        if weights.process_group.size() > 1:
            try:
                weight = weights.get_sharded(f"{prefix}.weight", dim=0)
                should_gather = True
            except AssertionError:
                # If the vocab size is not divisible by number of shards
                # just load the entire thing.
                weight = weights.get_tensor(f"{prefix}.weight")
                should_gather = False
        else:
            weight = weights.get_tensor(f"{prefix}.weight")
            should_gather = False

        return TensorParallelHead(
            get_linear(weight, bias=None),
            process_group=weights.process_group,
            should_gather=should_gather,
        )

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        if not self.should_gather:
            return super().forward(input)

        world_size = self.process_group.size()
        if len(input.shape) == 2 and isinstance(self.linear, FastLinear):
            out_dim = self.linear.weight.shape[0]

            if input.shape[0] == 1:
                world_out = input.new_empty(1, out_dim * world_size)
                local_out = input.new_empty(1, out_dim)
                gather_input = local_out
            else:
                world_out = input.new_empty(out_dim * world_size, input.shape[0])
                gather_input = input.new_empty(out_dim, input.shape[0])
                local_out = gather_input.T

            torch.mm(input, self.linear.weight.T, out=local_out)

            torch.distributed.all_gather_into_tensor(world_out, gather_input, group=self.process_group)

            if input.shape[0] == 1:
                return world_out
            return world_out.T

        output = super().forward(input)
        world_output = [torch.empty_like(output) for _ in range(self.process_group.size())]
        torch.distributed.all_gather(world_output, output, group=self.process_group)
        world_output = torch.cat(world_output, dim=-1)
        return world_output


class TensorParallelColumnLinear(SuperLayer):
    @classmethod
    def load(cls, config, prefix: str, weights, bias: bool):
        return cls.load_multi(config, [prefix], weights, bias, dim=0)

    @classmethod
    def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
        weight = weights.get_multi_weights_col(prefixes, dim=dim, quantize=config.quantize)

        if bias:
            b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
            bias = torch.cat(b, dim=dim)
        else:
            bias = None
        linear = get_linear(weight, bias)
        return cls(linear)


class TensorParallelRowLinear(SuperLayer):
    def __init__(self, linear, process_group):
        super().__init__(linear)
        self.process_group = process_group

    @classmethod
    def load(cls, config, prefix: str, weights, bias: bool):
        weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)

        if bias and weights.process_group.rank() == 0:
            # Rank is only on the first rank process
            bias = weights.get_tensor(f"{prefix}.bias")
        else:
            bias = None
        return cls(
            get_linear(weight, bias),
            process_group=weights.process_group,
        )

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        out = super().forward(input)
        if self.process_group.size() > 1:
            torch.distributed.all_reduce(out, group=self.process_group)
        return out


class TensorParallelEmbedding(nn.Module):
    def __init__(self, prefix: str, weights, reduce=True):
        super().__init__()
        weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0)
        num_embeddings = weights.get_shape(f"{prefix}.weight")[0]

        process_group = weights.process_group

        world_size = process_group.size()
        rank = process_group.rank()

        block_size = num_embeddings // world_size
        self.min_id = rank * block_size
        self.max_id = min(num_embeddings, (rank + 1) * block_size)
        self.null_idx = block_size
        self.process_group = weights.process_group
        self.reduce = reduce

        """Additional 0 entry used for masking"""
        self.weight = nn.Parameter(F.pad(weight, (0, 0, 0, 1)))

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        # default all out of bounds values to `self.null_idx` that will then be mapped to 0
        # translate for [0, self.max_id - self.min_id[
        input = torch.where(
            (self.min_id > input) | (input >= self.max_id),
            self.null_idx,
            input - self.min_id,
        )
        out = torch.nn.functional.embedding(input, self.weight)
        if self.reduce and self.process_group.size() > 1:
            torch.distributed.all_reduce(out, group=self.process_group)
        return out


try:
    import dropout_layer_norm

    class FastLayerNorm(nn.LayerNorm):
        def forward(self, hidden_states, residual=None):
            if hidden_states.shape[-1] > 8192:
                if residual is not None:
                    hidden_states += residual
                residual = hidden_states

                return super(FastLayerNorm, self).forward(hidden_states), residual
            else:
                (
                    normed_hidden_states,
                    residual,
                    *rest,
                ) = dropout_layer_norm.dropout_add_ln_fwd(
                    hidden_states,
                    residual,
                    self.weight,
                    self.bias,
                    None,
                    None,
                    None,
                    None,
                    0.0,
                    self.eps,
                    1.0,
                    0,
                    None,
                    False,
                    False,
                )
                if residual is None:
                    residual = hidden_states

                return normed_hidden_states, residual

except ImportError:
    pass