# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# adapted from https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/persimmon/modeling_persimmon.py
# Copyright 2023 The vLLM team.
# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only persimmon model compatible with HuggingFace weights."""

from collections.abc import Iterable
from itertools import islice

import torch
from torch import nn
from transformers import PersimmonConfig

from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.sequence import IntermediateTensors

from .interfaces import SupportsPP
from .utils import (
    AutoWeightsLoader,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)


class PersimmonMLP(nn.Module):
    def __init__(
        self,
        config: PersimmonConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config,
            prefix=f"{prefix}.dense_h_to_4h",
        )
        self.dense_4h_to_h = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.dense_4h_to_h",
        )
        self.act = get_act_fn(config.hidden_act)

    def forward(self, hidden_states) -> torch.Tensor:
        hidden_states, _ = self.dense_h_to_4h(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.dense_4h_to_h(hidden_states)
        return hidden_states


class PersimmonAttention(nn.Module):
    def __init__(
        self,
        config: PersimmonConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        tensor_parallel_world_size = get_tensor_model_parallel_world_size()

        self.hidden_size = config.hidden_size
        self.total_num_heads = config.num_attention_heads
        self.num_heads = self.total_num_heads // tensor_parallel_world_size
        self.head_dim = self.hidden_size // self.total_num_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.is_causal = True

        assert (self.head_dim * self.total_num_heads) == self.hidden_size
        assert self.total_num_heads % tensor_parallel_world_size == 0

        self.query_key_value = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.query_key_value",
        )
        self.dense = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.dense",
        )
        self.is_qk_layernorm = config.qk_layernorm

        if self.is_qk_layernorm:
            self.q_layernorm = nn.LayerNorm(self.head_dim)
            self.k_layernorm = nn.LayerNorm(self.head_dim)

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=self.max_position_embeddings,
            rope_parameters=config.rope_parameters,
        )
        self.scaling = self.head_dim**-0.5
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            scale=self.scaling,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

    def _split_heads(self, x: torch.Tensor) -> torch.Tensor:
        # [seq_length, hidden_size] -> [seq_length, num_heads, head_dim]
        seq_length = x.shape[0]
        return x.view(seq_length, self.num_heads, self.head_dim)

    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
        # [seq_length, num_heads, head_dim] -> [seq_length, hidden_size]
        seq_length = x.shape[0]
        return x.view(seq_length, self.num_heads * self.head_dim)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        # [seq_length, 3 x hidden_size]
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)

        if self.is_qk_layernorm:
            # [seq_length, num_heads, head_dim]
            q = self._split_heads(q)
            k = self._split_heads(k)

            q = self.q_layernorm(q)
            k = self.k_layernorm(k)

            q = self._merge_heads(q)
            k = self._merge_heads(k)

        q, k = self.rotary_emb(position_ids, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.dense(attn_output)
        return output


class PersimmonDecoderLayer(nn.Module):
    def __init__(
        self,
        config: PersimmonConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = PersimmonAttention(
            config=config,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.mlp = PersimmonMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        self.input_layernorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps
        )
        self.post_attention_layernorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps
        )

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states = self.self_attn(
            position_ids=position_ids,
            hidden_states=hidden_states,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)

        hidden_states = hidden_states + residual

        outputs = hidden_states
        return outputs


@support_torch_compile
class PersimmonModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        self.vocab_size = config.vocab_size
        self.config = config
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size, config.hidden_size
        )
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: PersimmonDecoderLayer(
                config, cache_config, quant_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers",
        )
        self.final_layernorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps
        )
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states"], config.hidden_size
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_input_ids(input_ids)
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states = layer(positions, hidden_states)
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": hidden_states})
        hidden_states = self.final_layernorm(hidden_states)
        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if is_pp_missing_parameter(name, self):
                continue
            param = params_dict[name]

            if "query_key_value" in name:
                # copy from vllm/model_executor/models/bloom.py
                # NOTE: Persimmon's fused QKV's output_dim has the shape of
                # (num_heads * 3 * head_size), while the
                # required shape is (3 * num_heads * head_size).
                # Thus, we need weight conversion.
                output_dim = getattr(param, "output_dim", None)
                num_heads = self.config.num_attention_heads
                if output_dim is not None:
                    loaded_weight_shape = loaded_weight.shape
                    loaded_weight = loaded_weight.view(
                        loaded_weight_shape[:output_dim]
                        + (num_heads, 3, -1)
                        + loaded_weight_shape[output_dim + 1 :]
                    )
                    loaded_weight = loaded_weight.transpose(output_dim, output_dim + 1)
                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)

            weight_loader = getattr(param, "weight_loader", default_weight_loader)
            weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class PersimmonForCausalLM(nn.Module, SupportsPP):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        self.config = config
        self.vocab_size = config.vocab_size
        self.model = PersimmonModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            bias=False,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ):
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)