model.py

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

import os, math, gc, importlib
import torch
# torch._C._jit_set_profiling_executor(True)
# torch._C._jit_set_profiling_mode(True)
import torch.nn as nn
from torch.nn import functional as F
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from pytorch_lightning.strategies import DeepSpeedStrategy
if importlib.util.find_spec('deepspeed'):
    import deepspeed
    from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam

# from deepspeed.runtime.fp16.onebit.zoadam import ZeroOneAdam

try:
    print('RWKV_MY_TESTING', os.environ["RWKV_MY_TESTING"])
except:
    os.environ["RWKV_MY_TESTING"] = ''

def __nop(ob):
    return ob


MyModule = nn.Module
MyFunction = __nop
if os.environ["RWKV_JIT_ON"] == "1":
    MyModule = torch.jit.ScriptModule
    MyFunction = torch.jit.script_method


########################################################################################################
# CUDA Kernel
########################################################################################################

from torch.utils.cpp_extension import load

HEAD_SIZE = int(os.environ["RWKV_HEAD_SIZE_A"])

if 'x060' in os.environ["RWKV_MY_TESTING"]:
    if os.environ["RWKV_TRAIN_TYPE"] == 'states':
        wkv6state_cuda = load(name="wkv6state", sources=["cuda/wkv6state_op.cpp", f"cuda/wkv6state_cuda.cu"],
                        verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}", f"-D_T_={int(os.environ['RWKV_CTXLEN'])}"])
            
        class WKV_6STATE(torch.autograd.Function):
            @staticmethod
            def forward(ctx, B, T, C, H, r, k, v, w, u, s):
                with torch.no_grad():
                    assert r.dtype == torch.bfloat16
                    assert k.dtype == torch.bfloat16
                    assert v.dtype == torch.bfloat16
                    assert w.dtype == torch.bfloat16
                    assert u.dtype == torch.bfloat16
                    assert s.dtype == torch.bfloat16
                    assert HEAD_SIZE == C // H
                    ctx.B = B
                    ctx.T = T
                    ctx.C = C
                    ctx.H = H
                    assert r.is_contiguous()
                    assert k.is_contiguous()
                    assert v.is_contiguous()
                    assert w.is_contiguous()
                    assert u.is_contiguous()
                    assert s.is_contiguous()
                    ctx.save_for_backward(r, k, v, w, u, s)
                    y = torch.empty((B, T, C), device=r.device, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    wkv6state_cuda.forward(B, T, C, H, r, k, v, w, u, s, y)
                    return y

            @staticmethod
            def backward(ctx, gy):
                with torch.no_grad():
                    assert gy.dtype == torch.bfloat16
                    B = ctx.B
                    T = ctx.T
                    C = ctx.C
                    H = ctx.H
                    assert gy.is_contiguous()
                    r, k, v, w, u, s = ctx.saved_tensors
                    gr = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gk = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gv = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gw = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gu = torch.empty((B, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gs = torch.empty((B, H, C//H, C//H), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    wkv6state_cuda.backward(B, T, C, H, r, k, v, w, u, s, gy, gr, gk, gv, gw, gu, gs)
                    gu = torch.sum(gu, 0).view(H, C//H)
                    gs = torch.sum(gs, 0).view(H, C//H, C//H)
                    return (None, None, None, None, gr, gk, gv, gw, gu, gs)

        def RUN_CUDA_RWKV6_STATE(B, T, C, H, r, k, v, w, u, s):
            return WKV_6STATE.apply(B, T, C, H, r, k, v, w, u, s)
    else:
        wkv6_cuda = load(name="wkv6", sources=["cuda/wkv6_op.cpp", f"cuda/wkv6_cuda.cu"],
                        verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}", f"-D_T_={int(os.environ['RWKV_CTXLEN'])}"])
            
        class WKV_6(torch.autograd.Function):
            @staticmethod
            def forward(ctx, B, T, C, H, r, k, v, w, u):
                with torch.no_grad():
                    assert r.dtype == torch.bfloat16
                    assert k.dtype == torch.bfloat16
                    assert v.dtype == torch.bfloat16
                    assert w.dtype == torch.bfloat16
                    assert u.dtype == torch.bfloat16
                    assert HEAD_SIZE == C // H
                    ctx.B = B
                    ctx.T = T
                    ctx.C = C
                    ctx.H = H
                    assert r.is_contiguous()
                    assert k.is_contiguous()
                    assert v.is_contiguous()
                    assert w.is_contiguous()
                    assert u.is_contiguous()
                    ctx.save_for_backward(r, k, v, w, u)
                    y = torch.empty((B, T, C), device=r.device, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    wkv6_cuda.forward(B, T, C, H, r, k, v, w, u, y)
                    return y

            @staticmethod
            def backward(ctx, gy):
                with torch.no_grad():
                    assert gy.dtype == torch.bfloat16
                    B = ctx.B
                    T = ctx.T
                    C = ctx.C
                    H = ctx.H
                    assert gy.is_contiguous()
                    r, k, v, w, u = ctx.saved_tensors
                    gr = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gk = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gv = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gw = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    gu = torch.empty((B, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
                    wkv6_cuda.backward(B, T, C, H, r, k, v, w, u, gy, gr, gk, gv, gw, gu)
                    gu = torch.sum(gu, 0).view(H, C//H)
                    return (None, None, None, None, gr, gk, gv, gw, gu)

        def RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u):
            return WKV_6.apply(B, T, C, H, r, k, v, w, u)

elif 'x052' in os.environ["RWKV_MY_TESTING"]:
    wkv5_cuda = load(name="wkv5", sources=["cuda/wkv5_op.cpp", f"cuda/wkv5_cuda.cu"],
                    verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}"])
        
    class WKV_5(torch.autograd.Function):
        @staticmethod
        def forward(ctx, B, T, C, H, r, k, v, w, u):
            with torch.no_grad():
                assert r.dtype == torch.bfloat16
                assert k.dtype == torch.bfloat16
                assert v.dtype == torch.bfloat16
                assert w.dtype == torch.bfloat16
                assert u.dtype == torch.bfloat16
                assert HEAD_SIZE == C // H
                ctx.B = B
                ctx.T = T
                ctx.C = C
                ctx.H = H
                assert r.is_contiguous()
                assert k.is_contiguous()
                assert v.is_contiguous()
                assert w.is_contiguous()
                assert u.is_contiguous()
                ew = (-torch.exp(w.float())).contiguous()
                eew = (torch.exp(ew)).contiguous()
                ctx.save_for_backward(r, k, v, eew, ew, u)
                y = torch.empty((B, T, C), device=r.device, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
                wkv5_cuda.forward(B, T, C, H, r, k, v, eew, u, y)
                return y

        @staticmethod
        def backward(ctx, gy):
            with torch.no_grad():
                assert gy.dtype == torch.bfloat16
                B = ctx.B
                T = ctx.T
                C = ctx.C
                H = ctx.H
                assert gy.is_contiguous()
                r, k, v, eew, ew, u = ctx.saved_tensors
                gr = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
                gk = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
                gv = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
                gw = torch.empty((B, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
                gu = torch.empty((B, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
                wkv5_cuda.backward(B, T, C, H, r, k, v, eew, ew, u, gy, gr, gk, gv, gw, gu)
                gw = torch.sum(gw, 0).view(H, C//H)
                gu = torch.sum(gu, 0).view(H, C//H)
                return (None, None, None, None, gr, gk, gv, gw, gu)

    def RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w, u):
        return WKV_5.apply(B, T, C, H, r, k, v, w, u)

elif 'mamba' in os.environ["RWKV_MY_TESTING"]:
    from mamba_ssm import Mamba

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

class RWKV_Tmix_x052(MyModule):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id

        self.head_size = args.head_size_a
        assert HEAD_SIZE == self.head_size # change HEAD_SIZE to match args.head_size_a
        self.n_head = args.dim_att // self.head_size
        assert args.dim_att % self.n_head == 0
        self.head_size_divisor = args.head_size_divisor

        with torch.no_grad():
            ratio_0_to_1 = layer_id / (args.n_layer - 1)  # 0 to 1
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd

            # fancy time_mix
            self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
            self.time_mix_v = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1)
            self.time_mix_r = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
            self.time_mix_g = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))

            # fancy time_decay
            decay_speed = torch.ones(args.dim_att)
            for n in range(args.dim_att):
                decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
            self.time_decay = nn.Parameter(decay_speed.reshape(self.n_head, self.head_size))
            # print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy())

            tmp = torch.zeros(args.dim_att)
            for n in range(args.dim_att):
                zigzag = ((n + 1) % 3 - 1) * 0.1
                tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag

            self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.receptance = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.key = nn.Linear(args.n_embd, args.dim_att, bias=False)

        self.value = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
        self.gate = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.ln_x = nn.GroupNorm(self.n_head, args.dim_att)

    @MyFunction
    def jit_func(self, x):
        B, T, C = x.size()

        xx = self.time_shift(x) # Mix x with the previous timestep to produce xk, xv, xr
        xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
        xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
        xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
        xg = x * self.time_mix_g + xx * (1 - self.time_mix_g)

        r = self.receptance(xr)
        k = self.key(xk)
        v = self.value(xv)
        g = F.silu(self.gate(xg))

        return r, k, v, g

    @MyFunction
    def jit_func_2(self, x, g):
        B, T, C = x.size()
        x = x.view(B * T, C)
        
        x = self.ln_x(x / self.head_size_divisor).view(B, T, C)
        x = self.output(x * g)
        return x

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

        r, k, v, g = self.jit_func(x)

        x = RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w=self.time_decay, u=self.time_faaaa)

        return self.jit_func_2(x, g)

class RWKV_Tmix_x060(MyModule):
    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

        with torch.no_grad():
            ratio_0_to_1 = layer_id / (args.n_layer - 1)  # 0 to 1
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd

            # fancy time_mix
            self.time_maa_x = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_w = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_v = nn.Parameter(1.0 - (torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1))
            self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))
            self.time_maa_g = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))

            D_MIX_LORA = 32 # generate TIME_MIX for w,k,v,r,g
            self.time_maa_w1 = nn.Parameter(torch.zeros(args.n_embd, D_MIX_LORA*5))
            self.time_maa_w2 = nn.Parameter(torch.zeros(5, D_MIX_LORA, args.n_embd).uniform_(-0.01, 0.01))

            # fancy time_decay
            decay_speed = torch.ones(args.dim_att)
            for n in range(args.dim_att):
                decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
            self.time_decay = nn.Parameter(decay_speed.reshape(1,1,args.dim_att))

            D_DECAY_LORA = 64
            self.time_decay_w1 = nn.Parameter(torch.zeros(args.n_embd, D_DECAY_LORA))
            self.time_decay_w2 = nn.Parameter(torch.zeros(D_DECAY_LORA, args.dim_att).uniform_(-0.01, 0.01))

            tmp = torch.zeros(args.dim_att)
            for n in range(args.dim_att):
                zigzag = ((n + 1) % 3 - 1) * 0.1
                tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag

            self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.receptance = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.key = nn.Linear(args.n_embd, args.dim_att, bias=False)

        self.value = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
        self.gate = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.ln_x = nn.GroupNorm(self.n_head, args.dim_att, eps=(1e-5)*(args.head_size_divisor**2))

    @MyFunction
    def jit_func(self, x):
        B, T, C = x.size()

        xx = self.time_shift(x) - x

        xxx = x + xx * self.time_maa_x
        xxx = torch.tanh(xxx @ self.time_maa_w1).view(B*T, 5, -1).transpose(0, 1)
        xxx = torch.bmm(xxx, self.time_maa_w2).view(5, B, T, -1)
        mw, mk, mv, mr, mg = xxx.unbind(dim=0)

        xw = x + xx * (self.time_maa_w + mw)
        xk = x + xx * (self.time_maa_k + mk)
        xv = x + xx * (self.time_maa_v + mv)
        xr = x + xx * (self.time_maa_r + mr)
        xg = x + xx * (self.time_maa_g + mg)

        r = self.receptance(xr)
        k = self.key(xk)
        v = self.value(xv)
        g = F.silu(self.gate(xg))

        ww = torch.tanh(xw @ self.time_decay_w1) @ self.time_decay_w2
        w = self.time_decay + ww

        return r, k, v, g, w

    @MyFunction
    def jit_func_2(self, x, g):
        B, T, C = x.size()
        x = x.view(B * T, C)
        
        x = self.ln_x(x).view(B, T, C)
        x = self.output(x * g)
        return x

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

        r, k, v, g, w = self.jit_func(x)
        x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)

        return self.jit_func_2(x, g)

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

class RWKV_Tmix_x060_state(MyModule):
    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

        with torch.no_grad():
            ratio_0_to_1 = layer_id / (args.n_layer - 1)  # 0 to 1
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd

            # fancy time_mix
            self.time_maa_x = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_w = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_v = nn.Parameter(1.0 - (torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1))
            self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))
            self.time_maa_g = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))

            D_MIX_LORA = 32 # generate TIME_MIX for w,k,v,r,g
            self.time_maa_w1 = nn.Parameter(torch.zeros(args.n_embd, D_MIX_LORA*5))
            self.time_maa_w2 = nn.Parameter(torch.zeros(5, D_MIX_LORA, args.n_embd).uniform_(-0.01, 0.01))

            # fancy time_decay
            decay_speed = torch.ones(args.dim_att)
            for n in range(args.dim_att):
                decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
            self.time_decay = nn.Parameter(decay_speed.reshape(1,1,args.dim_att))

            D_DECAY_LORA = 64
            self.time_decay_w1 = nn.Parameter(torch.zeros(args.n_embd, D_DECAY_LORA))
            self.time_decay_w2 = nn.Parameter(torch.zeros(D_DECAY_LORA, args.dim_att).uniform_(-0.01, 0.01))

            tmp = torch.zeros(args.dim_att)
            for n in range(args.dim_att):
                zigzag = ((n + 1) % 3 - 1) * 0.1
                tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag

            self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))
            self.time_state = nn.Parameter(torch.zeros(self.n_head, self.head_size, self.head_size))

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.receptance = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.key = nn.Linear(args.n_embd, args.dim_att, bias=False)

        self.value = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
        self.gate = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.ln_x = nn.GroupNorm(self.n_head, args.dim_att, eps=(1e-5)*(args.head_size_divisor**2))

    @MyFunction
    def jit_func(self, x):
        B, T, C = x.size()

        xx = self.time_shift(x) - x

        xxx = x + xx * self.time_maa_x
        xxx = torch.tanh(xxx @ self.time_maa_w1).view(B*T, 5, -1).transpose(0, 1)
        xxx = torch.bmm(xxx, self.time_maa_w2).view(5, B, T, -1)
        mw, mk, mv, mr, mg = xxx.unbind(dim=0)

        xw = x + xx * (self.time_maa_w + mw)
        xk = x + xx * (self.time_maa_k + mk)
        xv = x + xx * (self.time_maa_v + mv)
        xr = x + xx * (self.time_maa_r + mr)
        xg = x + xx * (self.time_maa_g + mg)

        r = self.receptance(xr)
        k = self.key(xk)
        v = self.value(xv)
        g = F.silu(self.gate(xg))

        ww = torch.tanh(xw @ self.time_decay_w1) @ self.time_decay_w2
        w = self.time_decay + ww

        return r, k, v, g, w

    @MyFunction
    def jit_func_2(self, x, g):
        B, T, C = x.size()
        x = x.view(B * T, C)
        
        x = self.ln_x(x).view(B, T, C)
        x = self.output(x * g)
        return x

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

        r, k, v, g, w = self.jit_func(x)
        x = RUN_CUDA_RWKV6_STATE(B, T, C, H, r, k, v, w, u=self.time_faaaa, s=self.time_state)

        return self.jit_func_2(x, g)

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

class RWKV_Tmix_x060a(MyModule):
    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

        with torch.no_grad():
            ratio_0_to_1 = layer_id / (args.n_layer - 1)  # 0 to 1
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd

            # fancy time_mix
            self.time_maa_x = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_w = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_v = nn.Parameter(1.0 - (torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1))
            self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))
            self.time_maa_g = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))

            D_MIX_LORA = 32 # generate TIME_MIX for w,k,v,r,g
            self.time_maa_w1 = nn.Parameter(torch.zeros(args.n_embd, D_MIX_LORA*5))
            self.time_maa_w2 = nn.Parameter(torch.zeros(5, D_MIX_LORA, args.n_embd).uniform_(-0.01, 0.01))

            # fancy time_decay
            decay_speed = torch.ones(args.dim_att)
            for n in range(args.dim_att):
                decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
            self.time_decay = nn.Parameter(decay_speed.reshape(1,1,args.dim_att))

            D_DECAY_LORA = 64
            self.time_decay_w1 = nn.Parameter(torch.zeros(args.n_embd, D_DECAY_LORA))
            self.time_decay_w2 = nn.Parameter(torch.zeros(D_DECAY_LORA, args.dim_att).uniform_(-0.01, 0.01))

            tmp = torch.zeros(args.dim_att)
            for n in range(args.dim_att):
                zigzag = ((n + 1) % 3 - 1) * 0.1
                tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag

            self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))

            D_GATE_LORA = 64
            self.gate_w1 = nn.Parameter(torch.empty(args.n_embd, D_GATE_LORA).uniform_(-0.01, 0.01))
            self.gate_w2 = nn.Parameter(torch.zeros(D_GATE_LORA, args.n_embd).uniform_(-0.01, 0.01))

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.receptance = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.key = nn.Linear(args.n_embd, args.dim_att, bias=False)

        self.value = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
        self.ln_x = nn.GroupNorm(self.n_head, args.dim_att, eps=(1e-5)*(args.head_size_divisor**2))

    @MyFunction
    def jit_func(self, x):
        B, T, C = x.size()

        xx = self.time_shift(x) - x

        xxx = x + xx * self.time_maa_x
        xxx = torch.tanh(xxx @ self.time_maa_w1).view(B*T, 5, -1).transpose(0, 1)
        xxx = torch.bmm(xxx, self.time_maa_w2).view(5, B, T, -1)
        mw, mk, mv, mr, mg = xxx.unbind(dim=0)

        xw = x + xx * (self.time_maa_w + mw)
        xk = x + xx * (self.time_maa_k + mk)
        xv = x + xx * (self.time_maa_v + mv)
        xr = x + xx * (self.time_maa_r + mr)
        xg = x + xx * (self.time_maa_g + mg)

        r = self.receptance(xr)
        k = self.key(xk)
        v = self.value(xv)
        g = torch.tanh(xg @ self.gate_w1) @ self.gate_w2

        ww = torch.tanh(xw @ self.time_decay_w1) @ self.time_decay_w2
        w = self.time_decay + ww

        return r, k, v, g, w

    @MyFunction
    def jit_func_2(self, x, g):
        B, T, C = x.size()
        x = x.view(B * T, C)
        
        x = self.ln_x(x).view(B, T, C)
        x = self.output(x * g)
        return x

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

        r, k, v, g, w = self.jit_func(x)
        x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)

        return self.jit_func_2(x, g)
########################################################################################################

class RWKV_Tmix_x060b(MyModule):
    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

        with torch.no_grad():
            ratio_0_to_1 = layer_id / (args.n_layer - 1)  # 0 to 1
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd

            self.time_maa_x = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, 0.5 * ratio_1_to_almost0))
            self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_v = nn.Parameter(1.0 - (torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1))
            self.time_maa_w = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            D_MIX_LORA = 32
            self.time_maa_rkvw_w1 = nn.Parameter(torch.zeros(args.n_embd, D_MIX_LORA*4))
            self.time_maa_rkvw_w2 = nn.Parameter(torch.zeros(4, D_MIX_LORA, args.n_embd).uniform_(-0.01, 0.01))

            decay_speed = torch.ones(args.dim_att)
            for n in range(args.dim_att):
                decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
            self.time_decay = nn.Parameter(decay_speed.reshape(1,1,args.dim_att))
            D_DECAY_LORA = 64
            self.time_decay_w1 = nn.Parameter(torch.zeros(args.n_embd, D_DECAY_LORA))
            self.time_decay_w2 = nn.Parameter(torch.zeros(D_DECAY_LORA, args.dim_att).uniform_(-0.01, 0.01))

            tmp = torch.zeros(args.dim_att)
            for n in range(args.dim_att):
                zigzag = ((n + 1) % 3 - 1) * 0.1
                tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag
            self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.receptance = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.key = nn.Linear(args.n_embd, args.dim_att, bias=False)

        self.value = nn.Linear(args.n_embd, args.dim_att, bias=False)
        self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
        self.ln_x = nn.LayerNorm(args.dim_att)

    @MyFunction
    def jit_func(self, x):
        B, T, C = x.size()

        xx = self.time_shift(x) - x

        xxx = x + xx * self.time_maa_x
        xxx = torch.tanh(xxx @ self.time_maa_rkvw_w1).view(B*T, 4, -1).transpose(0, 1)
        xxx = torch.bmm(xxx, self.time_maa_rkvw_w2).view(4, B, T, C)

        r, k, v, w = xxx.unbind(dim=0)
        r = x + xx * (self.time_maa_r + r)
        k = x + xx * (self.time_maa_k + k)
        v = x + xx * (self.time_maa_v + v)
        w = x + xx * (self.time_maa_w + w)
        
        r = self.receptance(r)
        k = self.key(k)
        v = self.value(v)
        w = self.time_decay + torch.tanh(w @ self.time_decay_w1) @ self.time_decay_w2
        return r, k, v, w

    @MyFunction
    def jit_func_2(self, x):
        x = self.ln_x(x)
        x = self.output(x)
        return x

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

        r, k, v, w = self.jit_func(x)
        x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)

        return self.jit_func_2(x)
    
########################################################################################################


class RWKV_CMix_x052(MyModule):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))

        with torch.no_grad():  # fancy init of time_mix
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd
            self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
            self.time_mix_r = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
        
        self.key = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
        self.receptance = nn.Linear(args.n_embd, args.n_embd, bias=False)
        self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)

    @MyFunction
    def forward(self, x):
        xx = self.time_shift(x)
        xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
        xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
        k = self.key(xk)
        k = torch.relu(k) ** 2
        kv = self.value(k)
        return torch.sigmoid(self.receptance(xr)) * kv

class RWKV_CMix_x060(MyModule):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))

        with torch.no_grad():  # fancy init of time_mix
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
            ddd = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                ddd[0, 0, i] = i / args.n_embd
            self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
            self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))

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

    @MyFunction
    def forward(self, x):
        xx = self.time_shift(x) - x
        xk = x + xx * self.time_maa_k
        xr = x + xx * self.time_maa_r

        k = self.key(xk)
        k = torch.relu(k) ** 2
        kv = self.value(k)
        return torch.sigmoid(self.receptance(xr)) * kv

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

class MishGLU(MyModule):
    def __init__(self, args, layer_id):
        super().__init__()
        self.args = args
        self.layer_id = layer_id
        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))

        with torch.no_grad():
            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)

            x = torch.ones(1, 1, args.n_embd)
            for i in range(args.n_embd):
                x[0, 0, i] = i / args.n_embd

            self.time_mix_k = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
            self.time_mix_r = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
            self.aa = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
            self.bb = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
            self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)

    @MyFunction
    def forward(self, x):
        xx = self.time_shift(x)
        xa = x * self.time_mix_k + xx * (1 - self.time_mix_k)
        xb = x * self.time_mix_r + xx * (1 - self.time_mix_r)
        a = self.aa(xa)
        b = self.bb(xb)
        return self.value(a * F.mish(b))

########################################################################################################
# The RWKV Model with our blocks
########################################################################################################


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

        self.ln1 = nn.LayerNorm(args.n_embd)
        self.ln2 = nn.LayerNorm(args.n_embd)

        if self.layer_id == 0:
            self.ln0 = nn.LayerNorm(args.n_embd)
            if args.my_pos_emb > 0:
                self.pos_emb_x = nn.Parameter(torch.zeros((1,args.my_pos_emb,args.n_embd)))
                self.pos_emb_y = nn.Parameter(torch.zeros((args.my_pos_emb,1,args.n_embd)))

        if self.layer_id == 0 and self.args.pre_ffn > 0:
            self.ffnPre = RWKV_ChannelMix(args, 0)
        else:
            if 'x060a' in os.environ["RWKV_MY_TESTING"]:
                self.att = RWKV_Tmix_x060a(args, layer_id)
            elif 'x060b' in os.environ["RWKV_MY_TESTING"]:
                self.att = RWKV_Tmix_x060b(args, layer_id)
            elif 'x060' in os.environ["RWKV_MY_TESTING"]:
                if os.environ["RWKV_TRAIN_TYPE"] == 'states':
                    self.att = RWKV_Tmix_x060_state(args, layer_id)
                else:
                    self.att = RWKV_Tmix_x060(args, layer_id)
            elif 'x052' in os.environ["RWKV_MY_TESTING"]:
                self.att = RWKV_Tmix_x052(args, layer_id)
            elif 'mamba' in os.environ["RWKV_MY_TESTING"]:
                self.att = Mamba(d_model=args.n_embd, d_state=16, d_conv=4, expand=2.125) # match rwkv6 #params

        if 'g' in os.environ["RWKV_MY_TESTING"]:
            self.ffn = MishGLU(args, layer_id)
        elif 'x060' in os.environ["RWKV_MY_TESTING"]:
            self.ffn = RWKV_CMix_x060(args, layer_id)
        elif 'x052' in os.environ["RWKV_MY_TESTING"]:
            self.ffn = RWKV_CMix_x052(args, layer_id)
        elif 'mamba' in os.environ["RWKV_MY_TESTING"]:
            self.ffn = Mamba(d_model=args.n_embd, d_state=16, d_conv=4, expand=2.125) # match rwkv6 #params
        
        if args.tiny_att_dim > 0 and self.layer_id == args.tiny_att_layer:
            self.tiny_ln = nn.LayerNorm(args.n_embd)
            self.tiny_q = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
            self.tiny_k = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
            self.tiny_v = nn.Linear(args.n_embd, args.n_embd, bias=False)
            self.register_buffer("tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len)))

        if args.dropout > 0:
            self.drop0 = nn.Dropout(p = args.dropout)
            self.drop1 = nn.Dropout(p = args.dropout)
        
    def forward(self, x, x_emb=None):
        args = self.args
        B, T, C = x.size()
        if self.layer_id == 0:
            x = self.ln0(x)
            if args.my_pos_emb > 0:
                pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T+1, -1)[:-1,:]
                x = x + pos_emb

        if self.args.dropout == 0:
            if self.layer_id == 0 and args.pre_ffn > 0:
                x = x + self.ffnPre(self.ln1(x))
            else:
                x = x + self.att(self.ln1(x))
            x = x + self.ffn(self.ln2(x))
        else:
            if self.layer_id == 0 and args.pre_ffn > 0:
                x = self.drop0(x + self.ffnPre(self.ln1(x)))
            else:
                x = self.drop0(x + self.att(self.ln1(x)))
            x = self.drop1(x + self.ffn(self.ln2(x)))

        if args.tiny_att_dim > 0 and self.layer_id == args.tiny_att_layer:
            xx = self.tiny_ln(x)
            q = self.tiny_q(xx)[:, :T, :]
            k = self.tiny_k(xx)[:, :T, :]
            c = (q @ k.transpose(-2, -1)) * (args.tiny_att_dim ** (-0.5))
            c = c.masked_fill(self.tiny_mask[:T, :T] == 0, 0)
            x = x + c @ self.tiny_v(x_emb)
        return x


class L2Wrap(torch.autograd.Function):
    @staticmethod
    def forward(ctx, loss, y):
        ctx.save_for_backward(y)
        return loss

    @staticmethod
    def backward(ctx, grad_output):
        y = ctx.saved_tensors[0]
        # to encourage the logits to be close to 0
        factor = 1e-4 / (y.shape[0] * y.shape[1])
        maxx, ids = torch.max(y, -1, keepdim=True)
        gy = torch.zeros_like(y)
        gy.scatter_(-1, ids, maxx * factor)
        return (grad_output, gy)


class RWKV(pl.LightningModule):
    def __init__(self, args):
        super().__init__()
        self.args = args
        if not hasattr(args, 'dim_att'):
            args.dim_att = args.n_embd
        if not hasattr(args, 'dim_ffn'):
            if '-f4' in os.environ["RWKV_MY_TESTING"]:
                args.dim_ffn = int((args.n_embd * 4) // 32 * 32)
            else:
                args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32) # default = 3.5x emb size            
        if not hasattr(args, 'tiny_att_layer'):
            args.tiny_att_layer = -1
        if not hasattr(args, 'tiny_att_dim'):
            args.tiny_att_dim = -1
        assert args.n_embd % 32 == 0
        assert args.dim_att % 32 == 0
        assert args.dim_ffn % 32 == 0

        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)

        if args.head_qk > 0:
            self.head_q = nn.Linear(args.n_embd, args.head_qk, bias=False)
            self.head_k = nn.Linear(args.n_embd, args.head_qk, bias=False)
            self.register_buffer("copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len)))
        if args.dropout > 0:
            self.drop0 = nn.Dropout(p = args.dropout)

    def configure_optimizers(self):
        args = self.args
        
        lr_decay = set()
        lr_1x = set()
        lr_2x = set()
        lr_3x = set()
        for n, p in self.named_parameters():

            # if not p.requires_grad:
            #     continue
            if args.train_type == 'states':
                if 'time_sta' not in n:
                    continue

            if (("_w1" in n) or ("_w2" in n)) and (args.layerwise_lr > 0):
                lr_1x.add(n)
            elif (("time_sta" in n) and (args.weight_decay > 0)):
                lr_decay.add(n)
            elif (("time_mix" in n) or ("time_maa" in n)) and (args.layerwise_lr > 0):
                if args.my_pile_stage == 2:
                    lr_2x.add(n)
                else:
                    lr_1x.add(n)
            elif (("time_decay" in n) or ("time_daaaa" in n)) and (args.layerwise_lr > 0):
                if args.my_pile_stage == 2:
                    lr_3x.add(n)
                else:
                    lr_2x.add(n)
            elif ("time_faaaa" in n) and (args.layerwise_lr > 0):
                if args.my_pile_stage == 2:
                    lr_2x.add(n)
                else:
                    lr_1x.add(n)
            elif ("time_first" in n) and (args.layerwise_lr > 0):
                lr_3x.add(n)
            elif (len(p.squeeze().shape) >= 2) and (args.weight_decay > 0):
                lr_decay.add(n)
            else:
                lr_1x.add(n)

        lr_decay = sorted(list(lr_decay))
        lr_1x = sorted(list(lr_1x))
        lr_2x = sorted(list(lr_2x))
        lr_3x = sorted(list(lr_3x))

        if self.trainer.is_global_zero:
            print('decay', lr_decay, '\n')
            print('1x', lr_1x, '\n')
            print('2x', lr_2x, '\n')
            print('3x', lr_3x, '\n')

        param_dict = {n: p for n, p in self.named_parameters()}
        
        if args.layerwise_lr > 0:
            if args.my_pile_stage == 2:
                optim_groups = [
                    {"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0},
                    {"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 2e-3 / args.lr_init},
                    {"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 3e-3 / args.lr_init},
                ]
            else:
                optim_groups = [
                    {"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0},
                    {"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 2.0},
                    {"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 3.0},
                ]
        else:
            optim_groups = [{"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0}]

        if args.weight_decay > 0:
            optim_groups += [{"params": [param_dict[n] for n in lr_decay], "weight_decay": args.weight_decay, "my_lr_scale": 1.0}]
            if self.deepspeed_offload:
                return DeepSpeedCPUAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adamw_mode=True, amsgrad=False)
            return FusedAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adam_w_mode=True, amsgrad=False)
        else:
            if self.deepspeed_offload:
                return DeepSpeedCPUAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adamw_mode=False, weight_decay=0, amsgrad=False)
            return FusedAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adam_w_mode=False, weight_decay=0, amsgrad=False)
        # return ZeroOneAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, weight_decay=0, amsgrad=False, cuda_aware=False)

    @property
    def deepspeed_offload(self) -> bool:
        strategy = self.trainer.strategy
        if isinstance(strategy, DeepSpeedStrategy):
            cfg = strategy.config["zero_optimization"]
            return cfg.get("offload_optimizer") or cfg.get("offload_param")
        return False

    def forward(self, idx):
        args = self.args
        B, T = idx.size()
        assert T <= args.ctx_len, "Cannot forward, model ctx_len is exhausted."

        x = self.emb(idx)
        x_emb = x

        if args.dropout > 0:
            x = self.drop0(x)
        if args.tiny_att_dim > 0:
            for block in self.blocks:
                if args.grad_cp == 1:
                    x = deepspeed.checkpointing.checkpoint(block, x, x_emb)
                else:
                    x = block(x, x_emb)
        else:
            for block in self.blocks:
                if args.grad_cp == 1:
                    x = deepspeed.checkpointing.checkpoint(block, x)
                else:
                    x = block(x)

        x = self.ln_out(x)

        if args.head_qk > 0:
            q = self.head_q(x)[:, :T, :]
            k = self.head_k(x)[:, :T, :]
            c = (q @ k.transpose(-2, -1)) * (1.0 / args.head_qk)
            c = c.masked_fill(self.copy_mask[:T, :T] == 0, 0)

            if "32" in os.environ["RWKV_FLOAT_MODE"]:
                c = c @ F.one_hot(idx, num_classes=args.vocab_size)
            elif os.environ["RWKV_FLOAT_MODE"] == "fp16":
                c = c @ F.one_hot(idx, num_classes=args.vocab_size).half()
            elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
                c = c @ F.one_hot(idx, num_classes=args.vocab_size).bfloat16()

            x = self.head(x) + c
        else:
            x = self.head(x)

        return x

    def training_step(self, batch, batch_idx):
        args = self.args
        if args.my_qa_mask != 1:
            idx, targets = batch
            logits = self(idx)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
            # if '0' in os.environ["RWKV_MY_TESTING"]:
            #     print('logits', logits)
            #     torch.set_printoptions(threshold=10000)
            #     print('idx', idx)
            #     exit(0)
        else:
            idx, targets, mask = batch
            mask = mask.view(-1)
            sum_mask = torch.sum(mask).item()
            # if sum_mask == 0:
            #     return torch.tensor([0.0], requires_grad=True)

            logits = self(idx)
            if sum_mask == mask.shape[0]:
                loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
                # print('rank', self.global_rank, 'loss', loss.item())
            else:
                loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), reduction='none')
                # loss_raw = loss
                loss = torch.sum(loss * mask) / sum_mask

                # torch.set_printoptions(threshold=10000)
                # if True: #self.global_rank == 1:
                #     tmp = ''
                #     sss = 0
                #     ccc = 0
                #     for i in range(mask.shape[0]):
                #         if mask[i] > 0:
                #             tmp += str(idx.view(-1)[i].item()) + ','
                #             sss += loss_raw.view(-1)[i].float().item()
                #             ccc += 1
                #     print('rank', self.global_rank, 'loss', loss.item(), 'lavg', sss / ccc)#, 'tmp', tmp, 'input', idx)

        return L2Wrap.apply(loss, logits)

    def training_step_end(self, batch_parts):
        if pl.__version__[0]!='2':
            all = self.all_gather(batch_parts)
            if self.trainer.is_global_zero:
                self.trainer.my_loss_all = all

    def generate_init_weight(self):
        print(
            f"""
############################################################################
#
# Init model weight (slow for large models)...
#
############################################################################
"""
        )
        m = {}
        n_params = 0
        for n in self.state_dict():
            p = self.state_dict()[n]
            shape = p.shape

            s0 = str(shape[0]) if len(shape) > 0 else ""
            s1 = str(shape[1]) if len(shape) > 1 else ""
            s2 = str(shape[2]) if len(shape) > 2 else ""
            print(f"{s0.ljust(5)} {s1.ljust(5)} {s2.ljust(5)} {n}", end="")

            scale = 1.0
            if "ln_" in n or ".ln" in n or "time_" in n or "_mask" in n or "pos_emb" in n or '.mask.' in n or n.endswith('_w') or n.endswith('_w1') or n.endswith('_w2') or n.endswith('_bias'):
                if 'ln_x.weight' in n:
                    layer_scale = (1+int(n.split('.')[1])) / self.args.n_layer
                    m[n] = (p * 0.0) + (layer_scale ** 0.7)
                else:
                    m[n] = p
                print()
            elif n == "emb.weight":
                m[n] = p
                scale = -1e-4
                nn.init.uniform_(m[n], a=scale, b=-scale)
                print(f" [scale {scale}]")
            elif n == "head.weight":
                m[n] = p
                if self.args.vocab_size > self.args.n_embd:
                    scale = 0.5 * math.sqrt(self.args.vocab_size / self.args.n_embd)
                else:
                    scale = 0.5
                nn.init.orthogonal_(m[n], gain=scale)
                print(f" [scale {scale}]")
            else:
                if 'mamba' in os.environ["RWKV_MY_TESTING"]:
                    m[n] = p
                    if '.out_proj.weight' in n:
                        scale = 0
                        nn.init.zeros_(m[n])
                        print(f" [scale {scale}]")
                    elif '.bias' in n:
                        scale = 0
                        nn.init.zeros_(m[n])
                        print(f" [scale {scale}]")
                    else:
                        print()
                else:
                    assert n.endswith('.weight') # should always be true

                    zero = [".att.output.", ".ffn.value.", ".ffn.receptance.", ".ffnPre.value.", ".ffnPre.receptance.", "head_q.", '.oo.', '.rr.']

                    for kk in zero:
                        if kk in n:
                            scale = 0
                    if "head_k." in n:
                        scale = 0.1
                    if "head_q." in n:
                        scale = 0

                    for kk in [".att.key."]:
                        if kk in n:
                            scale = 0.1
                    for kk in [".att.gate."]:
                        if kk in n:
                            scale = 0.1

                    print(f" [scale {scale}]")

                    if self.args.accelerator.upper() == "GPU":
                        m[n] = torch.empty((shape[0], shape[1]), device="cuda")
                    else:
                        m[n] = torch.empty((shape[0], shape[1]))

                    if scale == 0:
                        nn.init.zeros_(m[n])
                    elif scale < 0:
                        nn.init.uniform_(m[n], a=scale, b=-scale)
                    else:
                        nn.init.orthogonal_(m[n], gain=scale)

            m[n] = m[n].cpu()
            if os.environ["RWKV_FLOAT_MODE"] == "fp16":
                m[n] = m[n].half()
            elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
                m[n] = m[n].bfloat16()
            n_params += m[n].numel()

            # if n == "emb.weight":
            #     print(m[n])

        print('model params', n_params)
        gc.collect()
        torch.cuda.empty_cache()
        return m