# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Based on:
Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
Punica: Multi-Tenant LoRA Serving.
https://arxiv.org/abs/2310.18547
"""

from typing import final

import torch

from vllm.lora.layers import LoRAMapping
from vllm.lora.utils import get_captured_lora_counts
from vllm.triton_utils import HAS_TRITON, triton
from vllm.utils.math_utils import round_up

if HAS_TRITON:
    from vllm.lora.ops.triton_ops import (
        LoRAKernelMeta,
        fused_moe_lora,
        lora_expand,
        lora_shrink,
    )

from vllm import _custom_ops as ops

from .punica_base import PunicaWrapperBase


@final
class PunicaWrapperGPU(PunicaWrapperBase):
    """
    PunicaWrapperGPU is designed to manage and provide metadata for the punica
    kernel. The main function is to maintain the state information for
    Multi-LoRA, and to provide the interface for the punica triton kernel.
    """

    def __init__(
        self,
        max_num_batched_tokens: int,
        max_batches: int,
        device: torch.device | str,
        **kwargs,
    ):
        PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, device)

        self.lora_config = kwargs["lora_config"]
        self.max_loras = self.lora_config.max_loras

        # Compute captured LoRA counts for cudagraph specialization.
        captured_lora_counts = get_captured_lora_counts(
            self.max_loras, self.lora_config.specialize_active_lora
        )

        self.token_mapping_meta = LoRAKernelMeta.make(
            self.max_loras,
            max_num_batched_tokens,
            device=device,
            captured_lora_counts=captured_lora_counts,
        )

        # When speculative decoding is enabled, max_num_samples is
        # max_batches * (num_speculative_decoding_tokens + 1).
        # This line can be optimized by replacing max_num_batched_tokens
        # to  max_batches * (num_speculative_decoding_tokens + 1).
        self.prompt_mapping_meta = LoRAKernelMeta.make(
            self.max_loras,
            max_num_batched_tokens,
            device=device,
            captured_lora_counts=captured_lora_counts,
        )

    def update_metadata(
        self,
        mapping: LoRAMapping,
        lora_index_to_id: list[int | None],
        max_loras: int,
        vocab_size: int,
        **kwargs,
    ):
        self.is_prefill = mapping.is_prefill
        self._update_base_metadata(mapping, lora_index_to_id, max_loras, vocab_size)

        # Prepare cuda kernel metadata tensors
        self.token_mapping_meta.prepare_tensors(self.token_lora_indices)
        self.prompt_mapping_meta.prepare_tensors(self.sampler_indices)

    def add_shrink(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_a_stacked: tuple[torch.Tensor, ...],
        scale: float,
        **kwargs,
    ):
        """
        Performs GEMM  for multiple slices of lora_a.

        Semantics:
        for i in range(len(lora_a_stacked)):
            y[i] += (x @ lora_a_stacked[i]) * scale

        Args:
            y (torch.Tensor): Output tensors
            x (torch.Tensor): Input tensor
            lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weights
            scale (float): Scaling factor for the operation
        """

        x = x.view(-1, x.shape[-1])
        lora_shrink(
            x,
            lora_a_stacked,
            y,
            *self.token_mapping_meta.meta_args(
                x.size(0), self.lora_config.specialize_active_lora
            ),
            scale,
        )

    def add_expand(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_b_stacked: tuple[torch.Tensor, ...],
        output_slices: tuple[int, ...],
        offset_start: int = 0,
        add_inputs=True,
        **kwargs,
    ) -> None:
        """
        Performs GEMM for multiple slices of lora_b.

        Semantics:
            for i in range(len(lora_b_stacked)):
                slice = output_slices[i]
                y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i]
                offset += slice

        Args:
            y (torch.Tensor): Output tensor.
            x (torch.Tensor): Input tensors
            lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight
            output_slices (tuple[int, ...]): Every slice's size
            add_inputs (bool): If True, add LoRA output to y; if False, write
                LoRA-only output to y (used for dual-stream when base and LoRA
                run on different CUDA streams). Defaults to True.
        """
        y_org = y
        y = y.view(-1, y.shape[-1])

        assert x.ndim == 3
        assert x.size(0) == len(output_slices)
        num_tokens = x.size(1)  # first dimension is the num slices

        lora_expand(
            x,
            lora_b_stacked,
            y,
            *self.token_mapping_meta.meta_args(
                num_tokens, self.lora_config.specialize_active_lora
            ),
            offset_start=offset_start,
            add_inputs=add_inputs,
        )

        y = y.view_as(y_org)

    def add_lora_embedding(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_b_stacked: torch.Tensor,
        add_inputs: bool = True,
        **kwargs,
    ) -> None:
        """
        Applies lora  specifically for VocabParallelEmbeddingWithLoRA.

        Semantics:
            y += x @ lora_b_stacked

        Args:
            y (torch.Tensor): Output tensor.
            x (torch.Tensor): Input tensor.
            lora_b_stacked (torch.Tensor): lora_b's weights.
            add_inputs (bool): Default to True.
        """

        lora_expand(
            x.unsqueeze(dim=0),
            (lora_b_stacked,),
            y,
            *self.token_mapping_meta.meta_args(
                x.size(0), self.lora_config.specialize_active_lora
            ),
            offset_start=0,
            add_inputs=add_inputs,
        )

    def add_lora_linear(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_a_stacked: tuple[torch.Tensor, ...],
        lora_b_stacked: tuple[torch.Tensor, ...],
        scale: float,
        output_slices: tuple[int, ...],
        *,
        buffer: torch.Tensor | None = None,
        **kwargs,
    ) -> None:
        """
        Applicable to linear-related lora.

        Semantics:
            for i in range(len(lora_a_stacked)):
                y[i] += (
                    x[i].unsqueeze(0)
                    @ lora_a_stacked[indices[i], layer_idx, :, :]
                    @ lora_b_stacked[indices[i], layer_idx, :, :]
                    * scale
                    ).squeeze(0)
        Args:
            y (torch.Tensor): Output tensor. Will be changed in-place.
            x (torch.Tensor): Input tensor
            lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weight.
            lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight.
            scale (float): Scaling factor.
            output_slices (tuple[int, ...]): Every slice's size.
            buffer (Optional[torch.Tensor]): Defaults to None.
        """

        assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)

        assert buffer is None, (
            "To minimize overhead, the buffer should be created by "
            ".add_lora_linear() instead of being passed in."
        )
        r = lora_b_stacked[0].size(-1)
        # We set the buffer to be float32 by default, refer to:
        # https://github.com/triton-lang/triton/issues/1387
        # Note: buffer is zeroed inside the shrink op
        buffer = torch.empty(
            (len(output_slices), x.size(0), r), dtype=torch.float32, device=x.device
        )
        add_inputs = kwargs.pop("add_inputs", True)
        self.add_shrink(
            buffer,  # type: ignore
            x,
            lora_a_stacked,
            scale,
            **kwargs,
        )
        self.add_expand(
            y,
            buffer,  # type: ignore
            lora_b_stacked,
            output_slices,
            add_inputs=add_inputs,
            **kwargs,
        )

    def add_lora_logits(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_a_stacked: torch.Tensor,
        lora_b_stacked: torch.Tensor,
        scale,
        *,
        buffer: torch.Tensor | None = None,
        **kwargs,
    ) -> None:
        """
        Applies lora  specifically for LogitsProcessorWithLoRA.

        Semantics:
            buffer = (x @ lora_a_stacked) * scale
            y += buffer @ lora_b_stacked

        Args:
            y (torch.Tensor): Output tensor.
            x (torch.Tensor): Input tensor.
            lora_a_stacked (torch.Tensor): lora_a's weights.
            lora_b_stacked (torch.Tensor): lora_b's weights.
            scale (float): Scaling factor.
            buffer (Optional[torch.Tensor]): Default to None.
        """
        y_org = y
        y = y.view(-1, y.shape[-1])
        x = x.view(-1, x.shape[-1])
        r = lora_b_stacked.size(-1)

        assert buffer is None, (
            "To minimize overhead, the buffer should be created by "
            ".add_lora_linear() instead of being passed in."
        )
        # We set the buffer to be float32 by default, refer to:
        # https://github.com/triton-lang/triton/issues/1387
        # Note: buffer is zeroed inside the shrink op
        buffer = torch.empty((x.size(0), r), dtype=torch.float32, device=x.device)

        lora_shrink(
            x,
            [lora_a_stacked],
            buffer.unsqueeze(dim=0),
            *self.prompt_mapping_meta.meta_args(
                x.size(0), self.lora_config.specialize_active_lora
            ),
            scale,
        )

        lora_expand(
            buffer.unsqueeze(dim=0),
            [lora_b_stacked],
            y,
            *self.prompt_mapping_meta.meta_args(
                buffer.size(0), self.lora_config.specialize_active_lora
            ),
            add_inputs=True,
        )
        y = y.view_as(y_org)

    def moe_lora_align_block_size(
        self,
        topk_ids: torch.Tensor,
        num_tokens: int,
        block_size: int,
        num_experts: int,
        max_loras: int,
        adapter_enabled: torch.Tensor,
        expert_map: torch.Tensor | None = None,
        pad_sorted_ids: bool = False,
        naive_block_assignment: bool = False,
        token_lora_mapping: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Aligns tokens and experts into block-sized chunks for LoRA-based
        mixture-of-experts (MoE) execution.

        When `token_lora_mapping` is provided, it overrides the global mapping
        read from `self.token_mapping_meta`. This is how EP+LoRA injects the
        per-rank-local token→LoRA map after all-to-all dispatch.
        """
        (
            token_lora_mapping_meta,
            _,
            _,
            _,
            lora_ids,
            _,
            _,
        ) = self.token_mapping_meta.meta_args(
            num_tokens, self.lora_config.specialize_active_lora
        )
        if token_lora_mapping is None:
            token_lora_mapping = token_lora_mapping_meta
        # Under EP the caller passes local_num_experts but topk_ids carries
        # GLOBAL expert indices. The CUDA kernel uses num_experts to size
        # its bucketing table; with EP we must size by global_num_experts
        # so global topk_ids don't overflow. expert_map inside the kernel
        # then translates global→local so the output expert_ids are local
        # (mirrors the non-LoRA moe_align_block_size behavior).
        kernel_num_experts = (
            expert_map.numel() if expert_map is not None else num_experts
        )
        if naive_block_assignment:
            expert_ids = topk_ids.reshape(-1)
            sorted_ids = None
            num_tokens_post_pad = None
        else:
            max_num_tokens_padded = topk_ids.numel() + kernel_num_experts * (
                block_size - 1
            )
            if pad_sorted_ids:
                max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
            if topk_ids.numel() < kernel_num_experts:
                max_num_tokens_padded = topk_ids.numel() * block_size
            sorted_ids = torch.empty(
                (max_loras * max_num_tokens_padded,),
                dtype=torch.int32,
                device=topk_ids.device,
            )
            max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
            # Expert ids are initialized to -1 so unused (lora, expert)
            # slots don't drive the LoRA Triton kernel into the wrong bucket.
            # The kernel overwrites only active slots.
            expert_ids = torch.full(
                (max_loras * max_num_m_blocks,),
                -1,
                dtype=torch.int32,
                device=topk_ids.device,
            )
            num_tokens_post_pad = torch.empty(
                (max_loras), dtype=torch.int32, device=topk_ids.device
            )

            ops.moe_lora_align_block_size(
                topk_ids,
                token_lora_mapping,
                kernel_num_experts,
                block_size,
                max_loras,
                max_num_tokens_padded,
                max_num_m_blocks,
                sorted_ids,
                expert_ids,
                num_tokens_post_pad,
                adapter_enabled,
                lora_ids,
                expert_map,
            )

        return token_lora_mapping, sorted_ids, expert_ids, num_tokens_post_pad

    def add_lora_fused_moe(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_a_stacked: tuple[torch.Tensor, ...],
        lora_b_stacked: tuple[torch.Tensor, ...],
        topk_weights: torch.Tensor,
        sorted_token_ids: torch.Tensor | None,
        expert_ids: torch.Tensor,
        num_tokens_post_padded: torch.Tensor | None,
        max_lora_rank: int,
        top_k_num: int,
        shrink_config,
        expand_config,
        adapter_enabled: torch.Tensor,
        mul_routed_weight=False,
        fully_sharded: bool = False,
        offset: int = 0,
        token_lora_mapping: torch.Tensor | None = None,
        add_inputs: bool = True,
    ):
        """
        Performs a fused forward computation for LoRA of Mixture-of-Experts (MoE) layer.
        """
        (
            token_lora_mapping_meta,
            _,
            _,
            _,
            lora_ids,
            no_lora_flag,
            num_active_loras,
        ) = self.token_mapping_meta.meta_args(
            x.size(0), self.lora_config.specialize_active_lora
        )

        assert no_lora_flag.numel() == 1
        if no_lora_flag.item():
            # None of the inputs require LoRA.
            return

        if token_lora_mapping is None:
            token_lora_mapping = token_lora_mapping_meta
        fused_moe_lora(
            y,
            x,
            lora_a_stacked,
            lora_b_stacked,
            topk_weights,
            sorted_token_ids,
            expert_ids,
            num_tokens_post_padded,
            token_lora_mapping,
            max_lora_rank,
            top_k_num,
            lora_ids,
            num_active_loras,
            adapter_enabled,
            shrink_config.get("BLOCK_SIZE_M", 64),
            shrink_config.get("BLOCK_SIZE_N", 64),
            shrink_config.get("BLOCK_SIZE_K", 32),
            shrink_config.get("GROUP_SIZE_M", 8),
            shrink_config.get("NUM_WARPS", 4),
            shrink_config.get("NUM_STAGES", 3),
            shrink_config.get("SPLIT_K", 1),
            expand_config.get("BLOCK_SIZE_M", 64),
            expand_config.get("BLOCK_SIZE_N", 64),
            expand_config.get("BLOCK_SIZE_K", 32),
            expand_config.get("GROUP_SIZE_M", 8),
            expand_config.get("NUM_WARPS", 4),
            expand_config.get("NUM_STAGES", 3),
            expand_config.get("SPLIT_K", 1),
            mul_routed_weight,
            fully_sharded,
            offset,
            add_inputs,
        )

    def add_lora_w13(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_a_stacked: tuple[torch.Tensor, ...],
        lora_b_stacked: tuple[torch.Tensor, ...],
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        expert_map: torch.Tensor | None,
        w1: torch.Tensor,
        w2: torch.Tensor,
        num_tokens: int,
        top_k_num: int,
        max_loras: int,
        adapter_enabled: torch.Tensor,
        local_num_experts: int,
        top_k: int,
        num_slices: int,
        fully_sharded: bool,
        use_tuned_config: bool,
        add_inputs: bool = True,
        token_lora_mapping: torch.Tensor | None = None,
    ) -> tuple[
        torch.Tensor | None,
        torch.Tensor | None,
        torch.Tensor | None,
        torch.Tensor | None,
    ]:
        import functools

        from vllm.lora.layers.utils import try_get_optimal_moe_lora_config
        from vllm.lora.ops.triton_ops.utils import (
            _normalize_lora_config_keys,
            get_lora_op_configs,
        )
        from vllm.model_executor.layers.fused_moe.config import _get_config_dtype_str

        config_dtype = _get_config_dtype_str(
            dtype=x.dtype,
            use_fp8_w8a8=False,
            use_int8_w8a16=False,
            use_int4_w4a16=False,
        )
        max_lora_rank = lora_a_stacked[0].shape[-2]

        if use_tuned_config:
            shrink_config = get_lora_op_configs(
                op_type="fused_moe_lora_w13_shrink",
                max_loras=max_loras,
                batch=num_tokens,
                hidden_size=x.shape[-1],
                rank=max_lora_rank,
                num_slices=num_slices,
                moe_intermediate_size=lora_b_stacked[0].shape[-2],
            )
            expand_config = get_lora_op_configs(
                op_type="fused_moe_lora_w13_expand",
                max_loras=max_loras,
                batch=num_tokens,
                hidden_size=x.shape[-1],
                rank=max_lora_rank,
                num_slices=num_slices,
                moe_intermediate_size=lora_b_stacked[0].shape[-2],
            )
        else:
            get_config = functools.partial(
                try_get_optimal_moe_lora_config,
                w1_shape=w1.shape,
                w2_shape=w2.shape,
                rank=max_lora_rank,
                top_k=top_k,
                dtype=config_dtype,
                M=num_tokens,
            )
            shrink_config = get_config(op_type="fused_moe_lora_w13_shrink")
            expand_config = get_config(op_type="fused_moe_lora_w13_expand")

        shrink_config = _normalize_lora_config_keys(shrink_config)
        expand_config = _normalize_lora_config_keys(expand_config)

        SPARSITY_FACTOR = 8
        naive_block_assignment = (
            not fully_sharded
            and expert_map is None
            and num_tokens * top_k * SPARSITY_FACTOR <= local_num_experts * max_loras
        )

        (
            token_lora_mapping,
            sorted_token_ids_lora,
            expert_ids_lora,
            num_tokens_post_padded_lora,
        ) = self.moe_lora_align_block_size(
            topk_ids,
            num_tokens,
            int(shrink_config.get("BLOCK_SIZE_M") or 64),
            local_num_experts,
            max_loras,
            adapter_enabled,
            expert_map,
            naive_block_assignment=naive_block_assignment,
            token_lora_mapping=token_lora_mapping,
        )

        _sorted = sorted_token_ids_lora
        _eids = expert_ids_lora
        if _sorted is not None:
            _eids = _eids.view(max_loras, -1)
            _sorted = _sorted.view(max_loras, -1)

        self.add_lora_fused_moe(
            y.view(-1, top_k_num, y.shape[-1]),
            x,
            lora_a_stacked,
            lora_b_stacked,
            topk_weights,
            _sorted,
            _eids,
            num_tokens_post_padded_lora,
            max_lora_rank,
            top_k,
            shrink_config,
            expand_config,
            adapter_enabled,
            fully_sharded=fully_sharded,
            token_lora_mapping=token_lora_mapping,
            add_inputs=add_inputs,
        )

        return (
            sorted_token_ids_lora,
            expert_ids_lora,
            num_tokens_post_padded_lora,
            token_lora_mapping,
        )

    def add_lora_w2(
        self,
        y: torch.Tensor,
        x: torch.Tensor,
        lora_a_stacked: tuple[torch.Tensor, ...],
        lora_b_stacked: tuple[torch.Tensor, ...],
        topk_weights: torch.Tensor,
        sorted_token_ids_lora: torch.Tensor | None,
        expert_ids_lora: torch.Tensor | None,
        num_tokens_post_padded_lora: torch.Tensor | None,
        token_lora_mapping: torch.Tensor | None,
        num_tokens: int,
        w1: torch.Tensor,
        w2: torch.Tensor,
        top_k_num: int,
        max_loras: int,
        adapter_enabled: torch.Tensor,
        top_k: int,
        fully_sharded: bool,
        tp_rank: int,
        use_tuned_config: bool,
        add_inputs: bool = True,
    ) -> None:
        import functools

        from vllm.lora.layers.utils import try_get_optimal_moe_lora_config
        from vllm.lora.ops.triton_ops.utils import (
            _normalize_lora_config_keys,
            get_lora_op_configs,
        )
        from vllm.model_executor.layers.fused_moe.config import _get_config_dtype_str

        config_dtype = _get_config_dtype_str(
            dtype=x.dtype,
            use_fp8_w8a8=False,
            use_int8_w8a16=False,
            use_int4_w4a16=False,
        )
        max_lora_rank = lora_a_stacked[0].shape[-2]

        if use_tuned_config:
            shrink_config = get_lora_op_configs(
                op_type="fused_moe_lora_w2_shrink",
                max_loras=max_loras,
                batch=num_tokens,
                hidden_size=y.shape[-1],
                rank=max_lora_rank,
                num_slices=1,
                moe_intermediate_size=lora_a_stacked[0].shape[-1],
            )
            expand_config = get_lora_op_configs(
                op_type="fused_moe_lora_w2_expand",
                max_loras=max_loras,
                batch=num_tokens,
                hidden_size=y.shape[-1],
                rank=max_lora_rank,
                num_slices=1,
                moe_intermediate_size=lora_a_stacked[0].shape[-1],
            )
        else:
            get_config = functools.partial(
                try_get_optimal_moe_lora_config,
                w1_shape=w1.shape,
                w2_shape=w2.shape,
                rank=max_lora_rank,
                top_k=top_k,
                dtype=config_dtype,
                M=num_tokens,
            )
            shrink_config = get_config(op_type="fused_moe_lora_w2_shrink")
            expand_config = get_config(op_type="fused_moe_lora_w2_expand")

        shrink_config = _normalize_lora_config_keys(shrink_config)
        expand_config = _normalize_lora_config_keys(expand_config)

        _sorted = sorted_token_ids_lora
        _eids = expert_ids_lora
        if _sorted is not None:
            assert _eids is not None
            _eids = _eids.view(max_loras, -1)
            _sorted = _sorted.view(max_loras, -1)

        # w2_lora_b shape[-2] is hidden_size // tp_size when fully_sharded
        shard_size = lora_b_stacked[0].shape[-2]
        offset = shard_size * tp_rank if fully_sharded else 0

        self.add_lora_fused_moe(
            y,
            x,
            lora_a_stacked,
            lora_b_stacked,
            topk_weights,
            _sorted,
            _eids,
            num_tokens_post_padded_lora,
            max_lora_rank,
            top_k,
            shrink_config,
            expand_config,
            adapter_enabled,
            True,  # mul_routed_weight
            fully_sharded=fully_sharded,
            offset=offset,
            token_lora_mapping=token_lora_mapping,
            add_inputs=add_inputs,
        )
