########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################

import torch, types, os, gc, math, json
import numpy as np
import torch.nn as nn
from torch.nn import Module
from torch.nn import functional as F

np.set_printoptions(precision=4, suppress=True, linewidth=200)
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cuda.matmul.allow_tf32 = True
# torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
# torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
torch._C._jit_set_autocast_mode(False)

"""
This will load RWKV-7 "Goose" x070 and inference in GPT-mode (slower than RNN-mode for autoregressive generation)
"""

args = types.SimpleNamespace()

# model download: https://huggingface.co/BlinkDL/rwkv-7-world

MODEL_PATH = "/home/colin/.cache/modelscope/hub/Blink_DL/rwkv-7-world/RWKV-x070-World-0.1B-v2.8-20241210-ctx4096.pth"

# for 0.1B
args.n_layer = 12
args.n_embd = 768
D_DECAY_LORA = 64
D_AAA_LORA = 64
D_MV_LORA = 32
D_GATE_LORA = 128

args.vocab_size = 65536

# DTYPE = torch.bfloat16
DTYPE = torch.half  # better

args.head_size_a = 64  # don't change
HS = args.head_size_a

########################################################################################################
# RWKV Tokenizer (slow version)
########################################################################################################


class RWKV_TOKENIZER:
    table: list[list[list[bytes]]]
    good: list[set[int]]
    wlen: list[int]

    def __init__(self, file_name):
        self.idx2token = {}
        sorted = []  # must be already sorted
        lines = open(file_name, "r", encoding="utf-8").readlines()
        for l in lines:
            idx = int(l[: l.index(" ")])
            x = eval(l[l.index(" ") : l.rindex(" ")])
            x = x.encode("utf-8") if isinstance(x, str) else x
            assert isinstance(x, bytes)
            assert len(x) == int(l[l.rindex(" ") :])
            sorted += [x]
            self.idx2token[idx] = x

        self.token2idx = {}
        for k, v in self.idx2token.items():
            self.token2idx[v] = int(k)

        # precompute some tables for fast matching
        self.table = [[[] for j in range(256)] for i in range(256)]
        self.good = [set() for i in range(256)]
        self.wlen = [0 for i in range(256)]

        for i in reversed(range(len(sorted))):  # reverse order - match longer tokens first
            s = sorted[i]
            if len(s) >= 2:
                s0 = int(s[0])
                s1 = int(s[1])
                self.table[s0][s1] += [s]
                self.wlen[s0] = max(self.wlen[s0], len(s))
                self.good[s0].add(s1)

    def encodeBytes(self, src: bytes) -> list[int]:
        src_len: int = len(src)
        tokens: list[int] = []
        i: int = 0
        while i < src_len:
            s: bytes = src[i : i + 1]

            if i < src_len - 1:
                s1: int = int(src[i + 1])
                s0: int = int(src[i])
                if s1 in self.good[s0]:
                    sss: bytes = src[i : i + self.wlen[s0]]
                    try:
                        s = next(filter(sss.startswith, self.table[s0][s1]))
                    except:
                        pass
            tokens.append(self.token2idx[s])
            i += len(s)

        return tokens

    def decodeBytes(self, tokens):
        return b"".join(map(lambda i: self.idx2token[i], tokens))

    def encode(self, src: str):
        return self.encodeBytes(src.encode("utf-8"))

    def decode(self, tokens):
        return self.decodeBytes(tokens).decode("utf-8")

    def printTokens(self, tokens):
        for i in tokens:
            s = self.idx2token[i]
            try:
                s = s.decode("utf-8")
            except:
                pass
            print(f"{repr(s)}{i}", end=" ")
            # print(repr(s), i)
        print()


tokenizer = RWKV_TOKENIZER("rwkv_vocab_v20230424.txt")


########################################################################################################
# RWKV TimeMix
########################################################################################################


class RWKV_Tmix_x070(Module):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id

        self.head_size = args.head_size_a
        self.n_head = args.dim_att // self.head_size
        assert args.dim_att % self.n_head == 0

        H = self.n_head
        HS = self.head_size
        C = args.n_embd

        self.x_r = nn.Parameter(torch.empty(1, 1, C))
        self.x_w = nn.Parameter(torch.empty(1, 1, C))
        self.x_k = nn.Parameter(torch.empty(1, 1, C))
        self.x_v = nn.Parameter(torch.empty(1, 1, C))
        self.x_a = nn.Parameter(torch.empty(1, 1, C))
        self.x_g = nn.Parameter(torch.empty(1, 1, C))

        self.w0 = nn.Parameter(torch.empty(1, 1, C))
        self.w1 = nn.Parameter(torch.empty(C, D_DECAY_LORA))
        self.w2 = nn.Parameter(torch.empty(D_DECAY_LORA, C))

        self.a0 = nn.Parameter(torch.empty(1, 1, C))
        self.a1 = nn.Parameter(torch.empty(C, D_AAA_LORA))
        self.a2 = nn.Parameter(torch.empty(D_AAA_LORA, C))

        self.v0 = nn.Parameter(torch.empty(1, 1, C))
        self.v1 = nn.Parameter(torch.empty(C, D_MV_LORA))
        self.v2 = nn.Parameter(torch.empty(D_MV_LORA, C))

        self.g1 = nn.Parameter(torch.empty(C, D_GATE_LORA))
        self.g2 = nn.Parameter(torch.empty(D_GATE_LORA, C))

        self.k_k = nn.Parameter(torch.empty(1, 1, C))
        self.k_a = nn.Parameter(torch.empty(1, 1, C))
        self.r_k = nn.Parameter(torch.empty(H, HS))

        self.receptance = nn.Linear(C, C, bias=False)
        self.key = nn.Linear(C, C, bias=False)
        self.value = nn.Linear(C, C, bias=False)
        self.output = nn.Linear(C, C, bias=False)
        self.ln_x = nn.GroupNorm(H, C, eps=64e-5)  # !!! notice eps value !!!

    def forward(self, x, v_first):
        B, T, C = x.size()  # seq_len
        H = self.n_head  # 12

        xx = torch.zeros_like(x)  # time_shift  [1, seq_len, 768] -> [1, seq_len, 768]
        xx[:, 0, :] = -x[:, 0, :]
        xx[:, 1:, :] = x[:, :-1, :] - x[:, 1:, :]

        xr = x + xx * self.x_r  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]
        xw = x + xx * self.x_w  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]
        xk = x + xx * self.x_k  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]
        xv = x + xx * self.x_v  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]
        xa = x + xx * self.x_a  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]
        xg = x + xx * self.x_g  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]

        r = self.receptance(xr)  # Linear [1, seq_len, 768] -> [1, seq_len, 768]

        xw = torch.tanh(xw @ self.w1) # -> [1, seq_len, 64]
        xw = xw @ self.w2 + self.w0 # -> [1, seq_len, 768]
        xw = -F.softplus(-xw)  # 函数的输出范围为 [0,∞)
        w = xw - 0.5  # -> [1, seq_len, 768]

        k = self.key(xk)  # Linear [1, seq_len, 768] -> [1, seq_len, 768]

        v = self.value(xv)  # Linear [1, seq_len, 768] -> [1, seq_len, 768]

        if self.layer_id == 0:
            v_first = v  # store the v of the first layer
        else:
            xv = (xv @ self.v1) @ self.v2
            xv = xv + self.v0
            xv = torch.sigmoid(xv)
            v = v + (v_first - v) * xv  # add value residual #  -> [1, seq_len, 768]

        xa = (xa @ self.a1) @ self.a2
        xa = xa + self.a0
        a = torch.sigmoid(xa)  # -> [1, seq_len, 768]

        xg = xg @ self.g1
        xg = torch.sigmoid(xg)
        g = xg @ self.g2  # -> [1, seq_len, 768]

        kk = k * self.k_k  # [1, seq_len, 768] * [1, 1, 768] -> [1, seq_len, 768]
        kk = F.normalize(kk.view(B, T, H, -1), dim=-1, p=2.0).view(B, T, C)  # -> [1, seq_len, 768]

        k = k * (1 + (a - 1) * self.k_a)  # -> [1, seq_len, 768]

        # start op
        a_op = -kk
        b_op = kk * a

        B, T, C = r.size()  # 768
        H = C // HS  # 12

        r_op = r.view(B, T, H, HS, 1).float()  # -> [1, seq_len, 12, 64, 1]
        k_op = k.view(B, T, H, 1, HS).float()  # -> [1, seq_len, 12, 1, 64]
        v_op = v.view(B, T, H, HS, 1).float()  # -> [1, seq_len, 12, 64, 1]
        a_op = a_op.view(B, T, H, HS, 1).float()  # -> [1, seq_len, 12, 64, 1]
        b_op = b_op.view(B, T, H, 1, HS).float()  # -> [1, seq_len, 12, 1, 64]

        w_op = w.view(B, T, H, HS).float()
        w_op = torch.exp(-torch.exp(w_op))  # -> [1, seq_len, 12, 64]
        w_op = w_op.view(B, T, H, 1, HS)  # -> [1, seq_len, 12, 1, 64]

        out = torch.zeros((B, T, H, HS), device=r_op.device, dtype=torch.float)  # -> [1, seq_len, 12, 64]
        state = torch.zeros((B, H, HS, HS), device=r_op.device, dtype=torch.float)  # -> [1, seq_len, 12, 64]

        for t in range(T):
            rr_op = r_op[:, t, :]  # [1, seq_len, 12, 64, 1] -> [1, 12, 64, 1]
            kk_op = k_op[:, t, :]  # [1, seq_len, 12, 1, 64] -> [1, 12, 1, 64]
            vv_op = v_op[:, t, :]  # [1, seq_len, 12, 64, 1] -> [1, 12, 64, 1]
            aa_op = a_op[:, t, :]  # [1, seq_len, 12, 64, 1] -> [1, 12, 64, 1]
            bb_op = b_op[:, t, :]  # [1, seq_len, 12, 1, 64] -> [1, 12, 1, 64]
            ww_op = w_op[:, t, :]  # [1, seq_len, 12, 64] -> [1, 12, 1, 64]

            state = state * ww_op + state @ aa_op @ bb_op + vv_op @ kk_op  # -> [1, 12, 64, 64]
            out[:, t, :] = (state @ rr_op).view(B, H, HS)  # -> [1, seq_len, 12, 64]

        x = out.view(B, T, C).to(dtype=DTYPE)  # -> [1, seq_len, 768]
        # end op

        x = self.ln_x(x.view(B * T, C)).view(B, T, C)  # -> [1, seq_len, 768]

        xx = r.view(B, T, H, -1) * k.view(B, T, H, -1)  # -> [1, seq_len, 12, 64]
        xx = xx * self.r_k  # -> [1, seq_len, 12, 64]
        xx = xx.sum(dim=-1, keepdim=True)  # -> [1, seq_len, 12, 1]
        xx = xx * v.view(B, T, H, -1)  # [1, seq_len, 12, 1] x [1, seq_len, 12, 64] -> [1, seq_len, 12, 64]
        xx = xx.view(B, T, C)  # -> [1, seq_len, 768]
        x = x + xx  # -> [1, seq_len, 768]
        x = self.output(x * g)  # Linear -> [1, seq_len, 768]
        return x, v_first


########################################################################################################
# RWKV ChannelMix
########################################################################################################


class RWKV_CMix_x070(Module):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id

        with torch.no_grad():
            self.x_k = nn.Parameter(torch.empty(1, 1, args.n_embd))

        self.key = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
        self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)

    def forward(self, x):

        shift = torch.zeros_like(x)
        shift[:, 1:, :] = x[:, :-1, :]

        xx = shift - x  # time_shift -> [1, seq_len, 768]

        k = x + xx * self.x_k  # -> [1, seq_len, 768]
        k = torch.relu(self.key(k)) ** 2  # Linear -> [1, seq_len, 768]
        return self.value(k)  # Linear -> [1, seq_len, 768]


########################################################################################################
# RWKV Block
########################################################################################################


class Block(Module):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id

        self.ln0 = nn.LayerNorm(args.n_embd)  # only used in block 0, should be fused with emb
        self.ln1 = nn.LayerNorm(args.n_embd)
        self.ln2 = nn.LayerNorm(args.n_embd)

        self.att = RWKV_Tmix_x070(args, layer_id)
        self.ffn = RWKV_CMix_x070(args, layer_id)

    def forward(self, x, v_first):

        if self.layer_id == 0:
            x = self.ln0(x)  # LayerNorm -> [1, seq_len, 768]   normal at dim 768  * γ + β
        ln = self.ln1(x)  # LayerNorm -> [1, seq_len, 768]   normal at dim 768  * γ + β

        xx, v_first = self.att(ln, v_first)  # [1, seq_len, 768] -> [1, seq_len, 768]
        x = x + xx  # [1, seq_len, 768] -> [1, seq_len, 768]
        x = x + self.ffn(self.ln2(x))  # [1, seq_len, 768] -> [1, seq_len, 768]

        return x, v_first


########################################################################################################
# RWKV Model
########################################################################################################


class RWKV(nn.Module):
    def __init__(self, args):
        super().__init__()
        args.dim_att = args.n_embd
        args.dim_ffn = args.n_embd * 4
        self.emb = nn.Embedding(args.vocab_size, args.n_embd)

        self.blocks = nn.ModuleList([Block(args, i) for i in range(args.n_layer)])

        self.ln_out = nn.LayerNorm(args.n_embd)
        self.head = nn.Linear(args.n_embd, args.vocab_size, bias=False)

    def forward(self, idx):

        x = self.emb(idx)  # [1, seq_len] -> [1, seq_len, 768]

        v_first = torch.empty_like(x)  # -> [1, seq_len, 768]
        for block in self.blocks:
            x, v_first = block(x, v_first)

        x = self.ln_out(x)  # [1, seq_len, 768] -> [1, seq_len, 768]
        x = self.head(x)  # [1, seq_len, 768] -> [1, seq_len, 65536]

        return x


########################################################################################################
# RWKV Inference
########################################################################################################

model_params = torch.load(MODEL_PATH, map_location="cpu")

with torch.no_grad():

    model = RWKV(args).to(dtype=DTYPE).cuda()
    model.load_state_dict(model_params, strict=False)  # we will ignore blocks.0.att.v0/v1/v2

    ########################################################################################################

    prompt = "中国的首都是在"
    input = tokenizer.encode(prompt)
    print(f"\nInput:\n{input}")

    # 中国的首都是在
    # 北 [probability 4.99%]
    # 中 [probability 4.22%]
    # 这 [probability 3.38%]
    # 上 [probability 2.74%]
    # 东 [probability 2.28%]
    # 台 [probability 2.23%]
    # 南 [probability 1.86%]
    # 广 [probability 1.83%]
    # 华 [probability 1.63%]
    # 河 [probability 1.47%]

    out = model.forward(torch.tensor(input).reshape(1, -1).cuda())
    print(f"\nOutput:\n{out}")

    # logits of the last token => prediction for the next token
    out = out[0, -1]

    probs = F.softmax(out.float(), dim=-1)  # compute softmax in float (more accurate)

    print(f"\n{prompt}")

    _, indices = torch.topk(probs, 10)  # print top-10 possibilities
    for i in range(len(indices)):
        token_id = indices[i].item()
        token = tokenizer.decode([token_id])
        token_prob = probs[token_id].item()
        print(token, f"[probability {token_prob:.2%}]")

    ########################################################################################################

    with open(f"misc/lambada_test.jsonl", "r", encoding="utf-8") as f:
        todo = [json.loads(line) for line in f]
        todo = [[doc["text"].rsplit(" ", 1)[0], " " + doc["text"].rsplit(" ", 1)[1]] for doc in todo]

    print("\nCheck LAMBADA...")
    xsum = 0
    xcnt = 0
    xacc = 0
    for d in todo:
        src = [0] + tokenizer.encode(d[0])
        dst = tokenizer.encode(d[1])

        logits = 0
        correct = True
        out = model.forward(torch.tensor(src + dst).reshape(1, -1).cuda())
        for i in range(len(dst)):
            ooo = out[0, len(src) - 1 + i].float()
            probs = F.softmax(ooo, dim=-1)
            logits += math.log(probs[dst[i]])
            if torch.argmax(probs).item() != dst[i]:
                correct = False

        xcnt += 1
        xsum += logits
        xacc += 1 if correct else 0
        if xcnt % 100 == 0 or xcnt == len(todo):
            print(xcnt, "ppl", round(math.exp(-xsum / xcnt), 2), "acc", round(xacc / xcnt * 100, 2))