"""
Implementation for Phi architecture.
"""

import dataclasses
from typing import Any, Dict, Optional, Union  # noqa: UP035

from tvm import tirx
from tvm.relax.frontend import nn
from tvm.relax.frontend.nn import Tensor, op

from mlc_llm import op as op_ext
from mlc_llm.model.model_utils import index_last_token
from mlc_llm.nn import PagedKVCache, RopeMode
from mlc_llm.support import logging
from mlc_llm.support import tensor_parallel as tp
from mlc_llm.support.config import ConfigBase
from mlc_llm.support.style import bold

logger = logging.getLogger(__name__)


@dataclasses.dataclass
class Phi1Config(ConfigBase):
    """Configuration of the Phi-1/Phi-1.5 model."""

    vocab_size: int = 51200
    hidden_size: int = 2048
    intermediate_size: int = 8192
    num_hidden_layers: int = 24
    num_attention_heads: int = 32
    layer_norm_eps: float = 1e-5
    position_embedding_base: int = 0
    partial_rotary_factor: float = 0.5
    num_key_value_heads: int = 0
    context_window_size: int = 0
    prefill_chunk_size: int = 0
    head_dim: int = 0
    tensor_parallel_shards: int = 1
    max_batch_size: int = 1
    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)  # noqa: UP006

    def __post_init__(self):
        if self.position_embedding_base == 0:
            if "rope_theta" in self.kwargs:
                self.position_embedding_base = self.kwargs.pop("rope_theta")
            else:
                self.position_embedding_base = 10000
        if self.context_window_size == 0:
            for name in ["max_position_embeddings", "max_sequence_length"]:
                if name in self.kwargs:
                    self.context_window_size = self.kwargs.pop(name)
                    logger.info(
                        "%s not found in config.json. Falling back to %s (%d)",
                        bold("context_window_size"),
                        bold(name),
                        self.context_window_size,
                    )
                    break
            else:
                raise ValueError(
                    "Unable to determine the maximum sequence length, because none of "
                    "`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
                    "provided in `config.json`."
                )
        if self.prefill_chunk_size == 0:
            logger.info(
                "%s defaults to %d",
                bold("prefill_chunk_size"),
                min(self.context_window_size, 8192),
            )
            self.prefill_chunk_size = min(self.context_window_size, 8192)
        elif self.prefill_chunk_size > self.context_window_size:
            logger.info(
                "Overriding %s from %d to %d",
                bold("prefill_chunk_size"),
                self.prefill_chunk_size,
                min(self.context_window_size, 8192),
            )
            self.prefill_chunk_size = min(self.context_window_size, 8192)
        if self.num_key_value_heads == 0 or self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads
        if self.intermediate_size == 0 or self.intermediate_size is None:
            self.intermediate_size = 4 * self.hidden_size
        if self.head_dim == 0:
            self.head_dim = self.hidden_size // self.num_attention_heads
        assert self.head_dim * self.num_attention_heads == self.hidden_size
        assert self.num_attention_heads % self.num_key_value_heads == 0


@dataclasses.dataclass
class PhiConfig(ConfigBase):
    """Configuration of the Phi-2 model."""

    model_type: str  # "phi", "phi-msft", "mixformer-sequential"
    vocab_size: int = 51200
    n_positions: int = 2048
    n_embd: int = 2560
    n_layer: int = 32
    n_inner: int = 0
    n_head: int = 32
    rotary_dim: int = 32
    position_embedding_base: int = 0
    layer_norm_epsilon: float = 1e-5
    context_window_size: int = 0
    prefill_chunk_size: int = 0
    n_head_kv: int = 0
    head_dim: int = 0
    tensor_parallel_shards: int = 1
    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)  # noqa: UP006

    def __post_init__(self):
        if self.position_embedding_base == 0:
            if "rope_theta" in self.kwargs:
                self.position_embedding_base = self.kwargs.pop("rope_theta")
            else:
                self.position_embedding_base = 10000
        if self.context_window_size == 0:
            for name in ["max_position_embeddings", "max_sequence_length"]:
                if name in self.kwargs:
                    self.context_window_size = self.kwargs.pop(name)
                    logger.info(
                        "%s not found in config.json. Falling back to %s (%d)",
                        bold("context_window_size"),
                        bold(name),
                        self.context_window_size,
                    )
                    break
            else:
                self.context_window_size = self.n_positions
                logger.info(
                    "%s not found in config.json. Falling back to %s (%d)",
                    bold("context_window_size"),
                    "n_positions",
                    self.context_window_size,
                )
        if self.prefill_chunk_size == 0:
            self.prefill_chunk_size = self.context_window_size
        self.prefill_chunk_size = min(self.prefill_chunk_size, self.context_window_size)
        if self.n_head_kv == 0 or self.n_head_kv is None:
            self.n_head_kv = self.n_head
        if self.n_inner == 0 or self.n_inner is None:
            self.n_inner = 4 * self.n_embd
        if self.head_dim == 0:
            self.head_dim = self.n_embd // self.n_head
        assert self.head_dim * self.n_head == self.n_embd
        assert self.n_head % self.n_head_kv == 0

    @staticmethod
    def from_phi1(config: Phi1Config) -> "PhiConfig":
        "Build PhiConig from a Phi1Config."
        return PhiConfig(
            model_type="phi",
            vocab_size=config.vocab_size,
            n_positions=config.context_window_size,
            n_embd=config.hidden_size,
            n_layer=config.num_hidden_layers,
            n_inner=config.intermediate_size,
            n_head=config.num_attention_heads,
            rotary_dim=int(config.partial_rotary_factor * config.head_dim),
            position_embedding_base=config.position_embedding_base,
            layer_norm_epsilon=config.layer_norm_eps,
            context_window_size=config.context_window_size,
            prefill_chunk_size=config.prefill_chunk_size,
            n_head_kv=config.num_key_value_heads,
            head_dim=config.head_dim,
            tensor_parallel_shards=config.tensor_parallel_shards,
            kwargs=config.kwargs,
        )


class PhiMLP(nn.Module):
    def __init__(self, config: PhiConfig):
        super().__init__()
        if config.n_inner % config.tensor_parallel_shards != 0:
            raise ValueError(
                f"Cannot split MLP intermediate size {config.n_inner} "
                f"evenly to {config.tensor_parallel_shards} GPUs."
            )
        self.intermediate_size = config.n_inner // config.tensor_parallel_shards
        self.fc1 = nn.Linear(config.n_embd, self.intermediate_size)
        self.fc2 = nn.Linear(self.intermediate_size, config.n_embd)

    def forward(self, hidden_states: Tensor):
        hidden_states = self.fc1(hidden_states)
        hidden_states = op.gelu(hidden_states, approximate="tanh")
        hidden_states = self.fc2(hidden_states)

        return hidden_states


class PhiMHA(nn.Module):
    def __init__(self, config: PhiConfig):
        self.num_q_heads = config.n_head // config.tensor_parallel_shards
        assert config.n_head % config.tensor_parallel_shards == 0, (
            f"n_head({config.n_head}) must be divisible by tensor_parallel_shards"
        )
        self.n_head_kv = config.n_head_kv // config.tensor_parallel_shards
        assert config.n_head_kv % config.tensor_parallel_shards == 0, (
            f"n_head({config.n_head_kv}) must be divisible by tensor_parallel_shards"
        )
        self.head_dim = config.head_dim
        op_size = self.head_dim * (self.num_q_heads + 2 * self.n_head_kv)
        hidden_size = config.n_embd

        self.Wqkv = nn.Linear(hidden_size, op_size, bias=True)
        self.out_proj = nn.Linear(self.num_q_heads * self.head_dim, hidden_size, bias=True)

    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
        d, h_q, h_kv = self.head_dim, self.num_q_heads, self.n_head_kv
        b, s, _ = hidden_states.shape
        # QKV Projection
        qkv = self.Wqkv(hidden_states)
        qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
        # Attention
        output = op.reshape(
            paged_kv_cache.attention_with_fused_qkv(
                layer_id, qkv, self.num_q_heads, sm_scale=self.head_dim**-0.5
            ),
            (b, s, h_q * d),
        )
        return self.out_proj(output)


class PhiParallelBlock(nn.Module):
    def __init__(self, config: PhiConfig):
        super().__init__()

        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.mixer = PhiMHA(config)
        self.mlp = PhiMLP(config)

        def _set_tp():
            def _set(param, hint):
                param.attrs["shard_strategy"] = hint

            hd = config.head_dim
            q = self.mixer.num_q_heads * hd
            k = self.mixer.n_head_kv * hd
            v = self.mixer.n_head_kv * hd
            _set(
                self.mixer.Wqkv.weight,
                tp.ShardSingleDim("_shard_qkv_weight", segs=[q, k, v], dim=0),
            )
            _set(
                self.mixer.Wqkv.bias,
                tp.ShardSingleDim("_shard_qkv_bias", segs=[q, k, v], dim=0),
            )
            _set(self.mixer.out_proj.weight, tp.ShardSingleDim("_shard_o_weight", dim=1))
            _set(self.mlp.fc1.weight, tp.ShardSingleDim("_shard_mlp_fc1_weight", dim=0))
            _set(self.mlp.fc1.bias, tp.ShardSingleDim("_shard_mlp_fc1_bias", dim=0))
            _set(self.mlp.fc2.weight, tp.ShardSingleDim("_shard_mlp_fc2_weight", dim=1))

        self.tensor_parallel_shards = config.tensor_parallel_shards
        _set_tp()

    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
        residual = hidden_states
        hidden_states = self.ln(hidden_states)

        with (
            tp.shard_bias(self.mixer.out_proj, self.tensor_parallel_shards),
            tp.shard_bias(self.mlp.fc2, self.tensor_parallel_shards),
        ):
            attn_outputs = self.mixer(hidden_states, paged_kv_cache, layer_id)
            feed_forward_hidden_states = self.mlp(hidden_states)

        hidden_states = self._apply_parallel_residual(
            attn_outputs, feed_forward_hidden_states, residual
        )

        return hidden_states

    def _apply_parallel_residual(self, attn_out, mlp_out, residual):
        if self.tensor_parallel_shards > 1:
            return op.ccl_allreduce(
                attn_out + mlp_out + residual / self.tensor_parallel_shards, "sum"
            )
        return attn_out + mlp_out + residual


class PhiCausalLMHead(nn.Module):
    def __init__(self, config: PhiConfig) -> None:
        super().__init__()

        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.linear = nn.Linear(config.n_embd, "vocab_size")

    def forward(self, hidden_states: Tensor):
        hidden_states = self.ln(hidden_states)
        logits = self.linear(hidden_states)

        if logits.dtype != "float32":
            logits = logits.astype("float32")
        return logits


class PhiModel(nn.Module):
    def __init__(self, config: PhiConfig) -> None:
        super().__init__()
        self.embd = nn.Embedding(config.vocab_size, config.n_embd)
        self.h = nn.ModuleList([PhiParallelBlock(config) for _ in range(config.n_layer)])

    def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
        hidden_states = input_embed
        for layer_id, layer in enumerate(self.h):
            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)

        return hidden_states


class PhiForCausalLM(nn.Module):
    def __init__(self, config: Union[PhiConfig, Phi1Config]) -> None:
        super().__init__()

        if isinstance(config, Phi1Config):
            config = PhiConfig.from_phi1(config)

        self.transformer = PhiModel(config)
        self.lm_head = PhiCausalLMHead(config)
        self.num_hidden_layers = config.n_layer
        self.num_attention_heads = config.n_head
        self.num_key_value_heads = config.n_head_kv
        self.head_dim = config.head_dim
        self.hidden_size = config.n_embd
        self.vocab_size = config.vocab_size
        self.rope_theta = config.position_embedding_base
        self.tensor_parallel_shards = config.tensor_parallel_shards
        self.rotary_dim = config.rotary_dim
        self.dtype = "float32"

    def to(self, dtype: Optional[str] = None):
        super().to(dtype=dtype)
        if dtype is not None:
            self.dtype = dtype

    def batch_forward(
        self,
        input_embeds: Tensor,
        paged_kv_cache: PagedKVCache,
        logit_positions: Optional[Tensor] = None,
    ):
        op_ext.configure()

        hidden_states = self.transformer(input_embeds, paged_kv_cache)
        if logit_positions is not None:
            hidden_states = op.take(hidden_states, logit_positions, axis=1)
        lm_logits = self.lm_head(hidden_states)
        if lm_logits.dtype != "float32":
            lm_logits = lm_logits.astype("float32")
        return lm_logits

    def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
        op_ext.configure()

        hidden_states = self.transformer(input_embed, paged_kv_cache)
        hidden_states = index_last_token(hidden_states)
        logits = self.lm_head(hidden_states)

        if logits.dtype != "float32":
            logits = logits.astype("float32")

        return logits, paged_kv_cache

    def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
        op_ext.configure()

        hidden_states = self.transformer(input_embed, paged_kv_cache)
        logits = self.lm_head(hidden_states)
        if logits.dtype != "float32":
            logits = logits.astype("float32")
        return logits, paged_kv_cache

    def batch_prefill(
        self,
        input_embeds: Tensor,
        logit_positions: Tensor,
        paged_kv_cache: PagedKVCache,
    ):
        if self.tensor_parallel_shards > 1:
            logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
        logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
        return logits, paged_kv_cache

    def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
        logits = self.batch_forward(input_embeds, paged_kv_cache)
        return logits, paged_kv_cache

    def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
        logits = self.batch_forward(input_embeds, paged_kv_cache)
        return logits, paged_kv_cache

    def embed(self, input_ids: Tensor):
        if self.tensor_parallel_shards > 1:
            input_ids = op.ccl_broadcast_from_worker0(input_ids)
        embeds = self.transformer.embd(input_ids)
        return embeds

    def create_paged_kv_cache(
        self,
        max_batch_size: tirx.Var,
        max_total_seq_len: tirx.Var,
        prefill_chunk_size: tirx.Var,
        page_size: tirx.Var,
        support_sliding_window: tirx.Var,
    ) -> PagedKVCache:
        return PagedKVCache.create_generic(
            attn_kind="mha",
            max_batch_size=max_batch_size,
            max_total_seq_len=max_total_seq_len,
            prefill_chunk_size=prefill_chunk_size,
            page_size=page_size,
            support_sliding_window=support_sliding_window,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
            num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
            qk_head_dim=self.head_dim,
            v_head_dim=self.head_dim,
            rope_mode=RopeMode.NORMAL,
            rope_scale=1,
            rope_theta=self.rope_theta,
            rotary_dim=self.rotary_dim,
            dtype=self.dtype,
        )

    def get_default_spec(self):
        mod_spec = {
            "embed": {
                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
                "$": {
                    "param_mode": "packed",
                    "effect_mode": "none",
                },
            },
            "prefill": {
                "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
                "$": {
                    "param_mode": "packed",
                    "effect_mode": "none",
                },
            },
            "decode": {
                "input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
                "$": {
                    "param_mode": "packed",
                    "effect_mode": "none",
                },
            },
            "batch_prefill": {
                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
                "$": {
                    "param_mode": "packed",
                    "effect_mode": "none",
                },
            },
            "batch_decode": {
                "input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
                "$": {
                    "param_mode": "packed",
                    "effect_mode": "none",
                },
            },
            "batch_verify": {
                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
                "$": {
                    "param_mode": "packed",
                    "effect_mode": "none",
                },
            },
            "create_paged_kv_cache": {
                "max_batch_size": int,
                "max_total_seq_len": int,
                "prefill_chunk_size": int,
                "page_size": int,
                "support_sliding_window": int,
                "$": {
                    "param_mode": "none",
                    "effect_mode": "none",
                },
            },
        }
        return nn.spec.ModuleSpec.from_raw(mod_spec, self)