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

# Copyright 2024 Cohere 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.

# This file is based on the LLama model definition file in transformers
"""PyTorch Cohere model."""

from collections.abc import Iterable
from itertools import islice

import torch
from torch import nn
from transformers import Cohere2Config, CohereConfig

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,
    tensor_model_parallel_all_reduce,
)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    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 VocabParallelEmbedding
from vllm.model_executor.model_loader.weight_utils import row_parallel_weight_loader
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors

from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    extract_layer_index,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)


@torch.compile(backend=current_platform.simple_compile_backend)
def layer_norm_func(hidden_states, weight, variance_epsilon):
    input_dtype = hidden_states.dtype
    hidden_states = hidden_states.to(torch.float32)
    mean = hidden_states.mean(-1, keepdim=True)
    variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
    hidden_states = (hidden_states - mean) * torch.rsqrt(variance + variance_epsilon)
    hidden_states = weight.to(torch.float32) * hidden_states
    return hidden_states.to(input_dtype)


class LayerNorm(nn.Module):
    def __init__(self, param_shape=None, eps=1e-5):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(param_shape))
        self.variance_epsilon = eps
        set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader})

    def forward(self, hidden_states, residuals=None):
        hidden_states = layer_norm_func(
            hidden_states, self.weight, self.variance_epsilon
        )
        return hidden_states, residuals


@torch.compile(backend=current_platform.simple_compile_backend)
def rms_norm_func(hidden_states, weight, variance_epsilon):
    input_dtype = hidden_states.dtype
    hidden_states = hidden_states.to(torch.float32)

    variance = hidden_states.pow(2).mean(-1, keepdim=True)
    hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)

    hidden_states = weight.to(torch.float32) * hidden_states
    return hidden_states.to(input_dtype)


class RMSNorm(nn.Module):
    def __init__(self, param_shape=None, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(param_shape))
        self.variance_epsilon = eps
        set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader})

    def forward(self, hidden_states, residuals=None):
        hidden_states = rms_norm_func(hidden_states, self.weight, self.variance_epsilon)
        return hidden_states, residuals


def select_norm_impl(config: CohereConfig) -> tuple[type[nn.Module], float]:
    """Returns the normalization layer class and epsilon value to use.
    If `config.rms_norm_eps` is present, use RMSNorm.
    Otherwise default to LayerNorm with `config.layer_norm_eps`.
    """
    rms_eps = getattr(config, "rms_norm_eps", None)
    if rms_eps is not None:
        return RMSNorm, rms_eps

    return LayerNorm, config.layer_norm_eps


# Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
class CohereMLP(nn.Module):
    def __init__(
        self,
        config: CohereConfig,
        intermediate_size: int | None = None,
        quant_config: QuantizationConfig | None = None,
        # so we can reduce the attention and MLP outputs together
        reduce_results: bool = False,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        if intermediate_size is None:
            self.intermediate_size = config.intermediate_size
        else:
            self.intermediate_size = intermediate_size
        self.gate_up_proj = MergedColumnParallelLinear(
            self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class CohereAttention(nn.Module):
    def __init__(
        self,
        config: CohereConfig | Cohere2Config,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        tp_size = get_tensor_model_parallel_world_size()
        self.config = config
        self.layer_idx = extract_layer_index(prefix)
        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.total_num_heads = config.num_attention_heads
        self.num_heads = self.total_num_heads // tp_size
        if hasattr(config, "head_dim"):
            self.head_dim = config.head_dim
        else:
            self.head_dim = self.hidden_size // self.total_num_heads
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.max_position_embeddings = getattr(
            config, "model_max_length", None
        ) or getattr(config, "max_position_embeddings", 8192)
        assert isinstance(self.max_position_embeddings, int)
        self.use_qk_norm = getattr(config, "use_qk_norm", False)
        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
            # NOTE: reduction will happen in the decoder layer forward
            # so we can combine the attention and MLP outputs together
            reduce_results=False,
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=self.max_position_embeddings,
            rope_parameters=config.rope_parameters,
            is_neox_style=False,
        )

        # Model v2 has interleaved sliding windows, v1 does not
        self.v1 = isinstance(config, CohereConfig)

        # cohere swa layer sees [pos - sliding_window, pos], i.e. sliding_window + 1
        # tokens.  vLLM's FlashAttention backend does (value - 1, 0), so we pass
        # sliding_window + 1 here to match the training convention.  The same +1
        # propagates into the KV-cache eviction formula
        # (single_type_kv_cache_manager.py), keeping both paths consistent.
        self.sliding_window = None
        if not self.v1 and config.layer_types[self.layer_idx] == "sliding_attention":
            self.sliding_window = config.sliding_window + 1

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=self.sliding_window,
            prefix=f"{prefix}.attn",
        )
        norm_cls, norm_eps = select_norm_impl(config)
        if self.use_qk_norm:
            self.q_norm = norm_cls(
                param_shape=(self.num_heads, self.head_dim), eps=norm_eps
            )
            self.k_norm = norm_cls(
                param_shape=(self.num_kv_heads, self.head_dim), eps=norm_eps
            )

    def _apply_qk_norm(self, q, k):
        q = q.view(*q.shape[:-1], -1, self.head_dim)
        k = k.view(*k.shape[:-1], -1, self.head_dim)
        q, _ = self.q_norm(q)
        k, _ = self.k_norm(k)
        q = q.view(*q.shape[:-2], -1)
        k = k.view(*k.shape[:-2], -1)
        return q, k

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        if self.use_qk_norm:
            q, k = self._apply_qk_norm(q, k)
        if self.v1 or self.sliding_window:
            q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class CohereDecoderLayer(nn.Module):
    def __init__(
        self,
        config: CohereConfig | Cohere2Config,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.self_attn = CohereAttention(
            config,
            cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.mlp = CohereMLP(config, quant_config=quant_config, prefix=f"{prefix}.mlp")
        norm_cls, norm_eps = select_norm_impl(config)
        self.input_layernorm = norm_cls(param_shape=(config.hidden_size), eps=norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        residual = hidden_states
        hidden_states, residual = self.input_layernorm(hidden_states, residual)
        hidden_states_attention = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )
        hidden_states_mlp = self.mlp(hidden_states)
        parallel_block_output = hidden_states_attention + hidden_states_mlp
        # Add everything together
        if self.tp_size > 1:
            # do the reduction in 1 shot instead of 2 separate all reduce
            parallel_block_output = tensor_model_parallel_all_reduce(
                parallel_block_output
            )
        hidden_states = residual + parallel_block_output

        return hidden_states, residual


@support_torch_compile
class CohereModel(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.quant_config = quant_config

        self.config = config

        self.vocab_size = config.vocab_size

        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: CohereDecoderLayer(
                config, cache_config, quant_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers",
        )

        norm_cls, norm_eps = select_norm_impl(config)
        self.norm = norm_cls(param_shape=(config.hidden_size), eps=norm_eps)

        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], 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)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]
        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
            )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_stacked={
            # weight_name: (param_name, shard_id)
            ".q_proj": (".qkv_proj", "q"),
            ".k_proj": (".qkv_proj", "k"),
            ".v_proj": (".qkv_proj", "v"),
            ".gate_proj": (".gate_up_proj", 0),
            ".up_proj": (".gate_up_proj", 1),
        },
        # ModelOpt NVFP4 checkpoints carry raw quantizer-module state
        # (e.g. "*.weight_quantizer._double_scale"); drop them before loading.
        # See #41925.
        orig_to_new_substr={"_quantizer.": None},
        orig_to_new_prefix={"lm_head": None},
    )
    packed_modules_mapping: dict[str, list[str]] = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"],
    }
    # LoRA specific attributes
    embedding_modules = {"embed_tokens": "input_embeddings"}

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config

        self.config = config

        self.quant_config = quant_config
        self.logits_processor = LogitsProcessor(
            config.vocab_size, scale=config.logit_scale
        )
        self.model = CohereModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        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)

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

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        is_not_lora = hasattr(self.model.embed_tokens, "weight")
        if is_not_lora:
            logits = self.logits_processor(self.model.embed_tokens, hidden_states)
        else:
            logits = self.logits_processor(
                self.model.embed_tokens.base_layer, hidden_states
            )

        return logits

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