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modeling_rwkv.py

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+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+from typing import Optional
+import types, gc, os, time, re
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+torch.backends.cudnn.benchmark = True
+torch.backends.cudnn.allow_tf32 = True
+torch.backends.cuda.matmul.allow_tf32 = True
+current_path = os.path.dirname(os.path.abspath(__file__))
+
+########################################################################################################
+
+if os.environ.get('RWKV_JIT_ON') != '0':
+    os.environ["RWKV_JIT_ON"] = '1'
+    MyModule = torch.jit.ScriptModule
+    MyFunction = torch.jit.script_method
+    MyStatic = torch.jit.script
+else:
+    MyModule = torch.nn.Module
+    def __nop(ob):
+        return ob
+    MyFunction = __nop
+    MyStatic = __nop
+
+if os.environ.get('RWKV_CUDA_ON') == '1':
+    from torch.utils.cpp_extension import load
+    try:
+        load(
+            name=f"wkv_cuda",
+            sources=[f"{current_path}/cuda/wrapper.cpp", f"{current_path}/cuda/operators.cu", f"{current_path}/cuda/gemm_fp16_cublas.cpp"],
+            verbose=True,
+            extra_ldflags=["cublas.lib" if os.name == "nt" else ""],
+            extra_cuda_cflags=["--use_fast_math", "-O3", "--extra-device-vectorization"],
+            is_python_module=False)
+        DISABLE_CUBLAS_GEMM = False
+    except:
+        print("Failed to build cuBLAS matmul, falling back to torch.matmul. Small model with fp16 will overflow.")
+        load(
+            name=f"wkv_cuda",
+            sources=[f"{current_path}/cuda/wrapper.cpp", f"{current_path}/cuda/operators.cu"],
+            verbose=True,
+            extra_cuda_cflags=["--use_fast_math", "-O3", "--extra-device-vectorization"],
+            extra_cflags=["-DDISABLE_CUBLAS_GEMM"],
+            is_python_module=False)
+        DISABLE_CUBLAS_GEMM = True
+
+    @MyStatic
+    def cuda_wkv(T: int, C: int, w, u, k, v, aa, bb, pp):
+        assert 1 * C % min(C, 32) == 0
+        assert k.dtype == v.dtype == torch.float16 or k.dtype == v.dtype == torch.float32
+        assert w.dtype == u.dtype == aa.dtype == bb.dtype == pp.dtype == torch.float32
+        w = w.contiguous()
+        u = u.contiguous()
+        k = k.contiguous()
+        v = v.contiguous()
+        y = torch.empty((T, C), device=w.device, memory_format=torch.contiguous_format, dtype=k.dtype)
+        torch.ops.rwkv.wkv_forward(1, T, C, w, u, k, v, y, aa, bb, pp)
+        return y, aa, bb, pp
+    @MyStatic
+    def cuda_mm8_seq(B: int, N: int, M: int, x, w, mx, rx, my, ry):
+        assert x.dtype == mx.dtype == rx.dtype == my.dtype == ry.dtype
+        assert x.dtype == torch.float32 or x.dtype == torch.float16
+        assert w.dtype == torch.uint8
+        assert x.shape == (B, N)
+        assert w.shape == (N, M)
+        assert rx.shape == mx.shape == (M,)
+        assert ry.shape == my.shape == (N, 1)
+        y = torch.empty((B, M), device=w.device, dtype=x.dtype)
+        torch.ops.rwkv.mm8_seq(B, N, M, x, w, mx, rx, my, ry, y)
+        return y
+    @MyStatic
+    def cuda_mm8_one(N: int, M: int, x, w, mx, rx, my, ry):
+        assert x.dtype == mx.dtype == rx.dtype == my.dtype == ry.dtype
+        assert x.dtype == torch.float32 or x.dtype == torch.float16
+        assert w.dtype == torch.uint8
+        assert x.shape == (N,)
+        assert w.shape == (N, M)
+        assert rx.shape == mx.shape == (M,)
+        assert ry.shape == my.shape == (N, 1)
+        y = torch.zeros((M,), device=w.device, dtype=torch.float32)
+        torch.ops.rwkv.mm8_one(N, M, x, w, mx, rx, my, ry, y)
+        return y.to(dtype=x.dtype)
+else:
+    os.environ["RWKV_CUDA_ON"] = '0'
+
+
+@MyStatic
+def torch_mm8_seq(x, w, mx, rx, my, ry):
+    return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
+
+@MyStatic
+def torch_mm8_one(x, w, mx, rx, my, ry):
+    return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
+
+if os.environ.get('RWKV_CUDA_ON') == '1':
+    @MyStatic
+    def mm8_seq(x, w, mx, rx, my, ry):
+        if w.device.type == 'cuda' and x.dtype == torch.float16:
+            B, N, M = x.shape[0], w.shape[0], w.shape[1]
+            return cuda_mm8_seq(B, N, M, x, w, mx, rx, my, ry)
+        else:
+            return torch_mm8_seq(x, w, mx, rx, my, ry)
+    @MyStatic
+    def mm8_one(x, w, mx, rx, my, ry):
+        if w.device.type == 'cuda':
+            N, M = w.shape[0], w.shape[1]
+            return cuda_mm8_one(N, M, x, w, mx, rx, my, ry)
+        else:
+            return torch_mm8_one(x, w, mx, rx, my, ry)
+else:
+    @MyStatic
+    def mm8_seq(x, w, mx, rx, my, ry):
+        return torch_mm8_seq(x, w, mx, rx, my, ry)
+    @MyStatic
+    def mm8_one(x, w, mx, rx, my, ry):
+        return torch_mm8_one(x, w, mx, rx, my, ry)
+
+def mm8(x: torch.Tensor, w: torch.Tensor, mx: torch.Tensor, rx: torch.Tensor, my: torch.Tensor, ry: torch.Tensor):
+    if len(x.shape) == 1:
+        return mm8_one(x, w, mx, rx, my, ry)
+    return mm8_seq(x, w, mx, rx, my, ry)
+
+def matmul(a, b, mx: Optional[torch.Tensor]=None, rx: Optional[torch.Tensor]=None, my: Optional[torch.Tensor]=None, ry: Optional[torch.Tensor]=None, output_dtype: Optional[torch.dtype]=None) -> torch.Tensor:
+    if output_dtype is None:
+        output_dtype = a.dtype
+    if b.dtype in [torch.float16, torch.bfloat16, torch.float32]:
+        assert a.dtype == b.dtype
+        return matmul_float(a, b, output_dtype=output_dtype)
+    elif b.dtype == torch.uint8:
+        assert mx is not None
+        assert rx is not None
+        assert my is not None
+        assert ry is not None
+        return mm8(a, b, mx, rx, my, ry).to(output_dtype)
+    else:
+        raise ValueError("Unsupported dtype")
+
+
+if os.environ.get('RWKV_CUDA_ON') == '1' and not DISABLE_CUBLAS_GEMM:
+    def matmul_float(a, b, output_dtype: Optional[torch.dtype]=None):
+        if output_dtype is None:
+            output_dtype = a.dtype
+        if a.dtype == b.dtype == torch.float16 and a.device.type == 'cuda':
+            if len(a.shape) == 1:
+                assert len(b.shape) == 2
+                c = torch.empty((b.shape[-1],), dtype=output_dtype, device=a.device)
+                a = a.unsqueeze(0)
+            else:
+                assert len(a.shape) == len(b.shape)
+                assert len(a.shape) == 2 or len(a.shape) == 3
+                # torch.empty((*a.shape[:-1], b.shape[-1])) doesn't work with jit
+                if len(a.shape) == 2:
+                    c = torch.empty((a.shape[0], b.shape[-1]), dtype=output_dtype, device=a.device)
+                else:
+                    c = torch.empty((a.shape[0], a.shape[1], b.shape[-1]), dtype=output_dtype, device=a.device)
+            torch.ops.rwkv.gemm_fp16_cublas(a, b, c)
+            return c
+        else:
+            return (a @ b).to(output_dtype)
+
+else:
+    def matmul_float(a, b, output_dtype: Optional[torch.dtype]=None):
+        return (a @ b).to(output_dtype)
+
+
+if os.environ.get('RWKV_DML_ON') == '1':
+    import torch_directml
+    print("PyTorch with DirectML Enabled")
+
+########################################################################################################
+
+class RWKV(MyModule):
+    def __init__(self, model, strategy, verbose = True, convert_and_save_and_exit = None):
+        super().__init__()
+        if verbose:
+            prxxx = lambda *args, **kwargs: print(*args, **kwargs)
+        else:
+            prxxx = lambda *args, **kwargs: None
+
+        STRATEGY_REGEX = r"^(?:(?:^|->) *(?:cuda(?::[\d]+)?|cpu|mps|dml) (?:fp(?:16|32)|bf16)(?:i8|i4|i3)?(?: \*[\d]+\+?)? *)+$"
+        if not re.match(STRATEGY_REGEX, strategy):
+            raise ValueError("Invalid strategy. Please read https://pypi.org/project/rwkv/")
+
+        strategy = ('->'.join([x.strip() for x in strategy.split('->')])).replace('->', ' -> ')
+        self.args = types.SimpleNamespace()
+        args = self.args
+        args.MODEL_NAME = model
+        args.strategy_string = strategy
+
+        # Rescale for fp16 mode: set x = x/2 every X layer (to avoid fp16 overflow)
+        try:
+            self.RESCALE_LAYER = int(os.environ["RWKV_RESCALE_LAYER"]) # !!! NOTE: SEEMS YOU SHOULD SET IT TO 999 (disable) FOR RWKV-MUSIC MODELS !!!
+        except:
+            self.RESCALE_LAYER = 6 if 'fp16' in strategy else 0
+        prxxx(f'RWKV_JIT_ON {os.environ["RWKV_JIT_ON"]} RWKV_CUDA_ON {os.environ["RWKV_CUDA_ON"]} RESCALE_LAYER {self.RESCALE_LAYER}\n')
+
+        args.MODEL_NAME = args.MODEL_NAME.strip()
+        if not args.MODEL_NAME.endswith('.pth'):
+            args.MODEL_NAME += '.pth'
+        prxxx(f'Loading {args.MODEL_NAME} ...')
+        with torch.no_grad():
+            self.w = torch.load(args.MODEL_NAME, map_location='cpu') # load model to CPU first
+            gc.collect()
+            w = self.w
+
+            ALREADY_CONVERTED = False
+            if '_strategy' in w:
+                ALREADY_CONVERTED = True
+                assert convert_and_save_and_exit == None # you should only convert a raw model
+                prxxx(f"Converted model: strategy {w['_strategy']}, version {w['_version']}\n")
+                assert w['_strategy'] == args.strategy_string # if you are using a new strategy, re-convert the model
+                assert float(w['_version']) >= 0.7 # sometimes you should re-convert using latest convert_model.py
+                assert w['_rescale_layer'] == self.RESCALE_LAYER # must use same RESCALE_LAYER to avoid mistakes
+                del w['_strategy']
+                del w['_version']
+                del w['_rescale_layer']
+            
+            args.n_embd = w['emb.weight'].shape[1]
+            args.n_att = w['blocks.0.att.key.weight'].shape[0] # note: transposed matrix
+            args.n_ffn = w['blocks.0.ffn.key.weight'].shape[0] # note: transposed matrix
+            args.n_layer = 0
+            keys = list(w.keys())
+            self.version = 4
+            for x in keys:
+                layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
+                args.n_layer = max(args.n_layer, layer_id+1)
+                if 'ln_x' in x:
+                    self.version = max(5, self.version)
+                if 'gate.weight' in x:
+                    self.version = max(5.1, self.version)
+                if int(self.version) == 5 and 'att.time_decay' in x:
+                    args.n_head = w[x].shape[0]
+                    if len(w[x].shape) > 1:
+                        if w[x].shape[1] > 1:
+                            self.version = max(5.2, self.version)
+                if 'time_maa' in x:
+                    self.version = max(6, self.version)
+                if int(self.version) == 6 and 'time_faaaa' in x:
+                    args.n_head = w[x].shape[0]
+            prxxx(f'Model detected: v{self.version:.1f}')
+
+            ####################### Compute strategy
+
+            s = [x.strip().split(' ') for x in strategy.split('->')]
+            plan = [0] * len(s)
+            stream_i = -1
+            stream_count = 0
+            to_allocate = args.n_layer + 1
+            allocated = 0
+            free_slots = 0
+            for i in range(len(s)):
+                si = s[i]
+                si1 = si[1]
+                if si1.startswith('fp32'): si[1] = [torch.float]
+                elif si1.startswith('fp16'): si[1] = [torch.float16]
+                elif si1.startswith('bf16'): si[1] = [torch.bfloat16]
+                if si1.endswith('i8'): si[1] += [torch.uint8]
+                else: si[1] += [si[1][0]]
+                if len(si) > 2:
+                    ss = si[2]
+                    assert ss.startswith('*')
+                    if ss.endswith('+'):
+                        plan[i] = int(ss[1:-1])
+                        stream_i = i
+                    else:
+                        plan[i] = int(ss[1:])
+                    allocated += plan[i]
+                    if allocated >= to_allocate:
+                        plan[i] += to_allocate - allocated
+                        break
+                else:
+                    free_slots += 1
+            if stream_i < 0:
+                if free_slots > 0 and to_allocate > allocated:
+                    for i in range(len(s)):
+                        if plan[i] == 0:
+                            plan[i] = (to_allocate - allocated) // free_slots
+                            allocated += plan[i]
+                            free_slots -= 1
+                if to_allocate > allocated:
+                    plan[len(s)-1] += to_allocate - allocated
+            else:
+                if to_allocate > allocated:
+                    stream_count = to_allocate - allocated
+                    plan[stream_i] += stream_count
+            prxxx(f'Strategy: (total {args.n_layer}+1={args.n_layer+1} layers)')
+            for i in range(len(s)):
+                ss = s[i]
+                if i != stream_i:
+                    prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]} layers')
+                else:
+                    prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]-stream_count} layers, stream {stream_count} layers')
+                plan[i] += (0 if i == 0 else plan[i-1])
+            self.strategy = [None] * (args.n_layer + 1)
+            strategy = self.strategy
+            for n in range(args.n_layer + 1):
+                for i in range(len(s)):
+                    if n < plan[i]:
+                        strategy[n] = types.SimpleNamespace()
+                        strategy[n].device = s[i][0]
+                        strategy[n].atype = s[i][1][0]
+                        strategy[n].wtype = s[i][1][1]
+                        strategy[n].stream = False
+                        if strategy[n].device == 'dml':
+                            strategy[n].device = torch_directml.device()
+                        if i == stream_i and n >= (plan[i] - stream_count):
+                            strategy[n].stream = True
+                        break
+                prxxx(f"{n}-{strategy[n].device}-{str(strategy[n].atype).replace('torch.','')}-{str(strategy[n].wtype).replace('torch.','')}{'-stream' if strategy[n].stream else ''}",end=' ')
+            prxxx()
+
+            ####################### Load weights to self.w
+
+            if not ALREADY_CONVERTED:
+                try: # precompute embedding
+                    w['emb.weight'] = F.layer_norm(w['emb.weight'], (args.n_embd,), weight=w['blocks.0.ln0.weight'], bias=w['blocks.0.ln0.bias'])
+                except:
+                    w['emb.weight'] = F.layer_norm(w['emb.weight'].float(), (args.n_embd,), weight=w['blocks.0.ln0.weight'].float(), bias=w['blocks.0.ln0.bias'].float())
+                del w['blocks.0.ln0.weight']
+                del w['blocks.0.ln0.bias']
+
+            print_need_newline = False
+
+            REAL_TIME_FIRST = False
+            for x in list(w.keys()):
+                if '.time_faaaa' in x: REAL_TIME_FIRST = True
+            if REAL_TIME_FIRST:
+                w = {k.replace('.time_faaaa','.time_first') if '.time_faaaa' in k else k: v for k, v in w.items()}
+                self.w = w
+            
+            keys = list(w.keys())
+            for x in keys:
+                w[x].requires_grad = False
+                layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
+                if ('ln_out.' in x) or ('head.' in x):
+                    layer_id = args.n_layer
+                dd = strategy[layer_id]
+                DEVICE = dd.device
+                ATYPE = dd.atype
+                WTYPE = dd.wtype
+
+                if not ALREADY_CONVERTED:
+                    if self.RESCALE_LAYER > 0:
+                        if 'att.output.weight' in x:
+                            w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
+                        if 'ffn.value.weight' in x:
+                            w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
+
+                    if '.time_' in x:
+                        w[x] = w[x].squeeze()
+                    if 'key.weight' in x or 'value.weight' in x or 'receptance.weight' in x or 'gate.weight' in x or 'output.weight' in x or 'head.weight' in x:
+                        w[x] = w[x].t()
+
+                    if '.time_decay' in x and '_w' not in x: # need fp32 for this
+                        if self.version == 4:
+                            w[x] = -torch.exp(w[x].float())
+                        elif int(self.version) == 5:
+                            w[x] = torch.exp(-torch.exp(w[x].float())).reshape(-1,1,1)
+                            if self.version == 5.2:
+                                w[x] = w[x].reshape(args.n_head, -1, 1)
+                        elif self.version == 6.0:
+                            w[x] = w[x].float().reshape(args.n_head, -1, 1)
+                    elif '.time_first' in x: # need fp32 for this
+                        if self.version == 4:
+                            w[x] = w[x].float()
+                        elif int(self.version) in [5, 6]:
+                            if REAL_TIME_FIRST:
+                                w[x] = w[x].float().reshape(-1,1,1)
+                            else:
+                                w[x] = torch.exp(w[x].float()).reshape(-1,1,1)
+                            if self.version in [5.2, 6.0]:
+                                w[x] = w[x].reshape(args.n_head, -1, 1)
+                    elif '.ln_x' in x: # need fp32 for group_norm
+                        w[x] = w[x].float()
+                    else:
+                        if (len(w[x].shape) == 2) and ('emb' not in x):
+                            if WTYPE != torch.uint8:
+                                w[x] = w[x].to(dtype=WTYPE)
+                            else:
+                                w[x] = w[x].float()
+
+                                if w[x].shape[0] > w[x].shape[1]:
+                                    w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
+                                    w[x] = w[x] - w[x+'_my']
+                                    w[x+'_mx'] = torch.amin(w[x], dim=0)
+                                    w[x] = w[x] - w[x+'_mx']
+                                    w[x+'_rx'] = torch.amax(w[x], dim=0)
+                                    w[x] = w[x] / w[x+'_rx']
+                                    w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
+                                    w[x] = w[x] / w[x+'_ry']
+                                else:
+                                    w[x+'_mx'] = torch.amin(w[x], dim=0)
+                                    w[x] = w[x] - w[x+'_mx']
+                                    w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
+                                    w[x] = w[x] - w[x+'_my']
+                                    w[x+'_rx'] = torch.amax(w[x], dim=0)
+                                    w[x] = w[x] / w[x+'_rx']
+                                    w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
+                                    w[x] = w[x] / w[x+'_ry']
+
+                                w[x] = torch.clip(torch.floor(w[x] * 256), min=0, max=255).to(dtype=torch.uint8)
+                                w[x+'_mx'] = w[x+'_mx'].to(dtype=ATYPE).contiguous()
+                                w[x+'_rx'] = (w[x+'_rx'] / 16).to(dtype=ATYPE).contiguous()
+                                w[x+'_my'] = w[x+'_my'].to(dtype=ATYPE).contiguous()
+                                w[x+'_ry'] = (w[x+'_ry'] / 16).to(dtype=ATYPE).contiguous()
+                        else:
+                            w[x] = w[x].to(dtype=ATYPE)
+                
+                if convert_and_save_and_exit == None:
+                    if 'emb.' in x:
+                        w[x] = w[x].contiguous()
+                    elif (dd.stream) and (x.endswith('key.weight') or x.endswith('value.weight') or x.endswith('receptance.weight') or x.endswith('output.weight')):
+                        try:
+                            w[x] = w[x].contiguous().pin_memory() # if you see "CUDA error: out of memory" here, that's out of CPU RAM, not VRAM. Get more RAM :)
+                        except:
+                            print('Note: You are running out of RAM. Get more CPU RAM. Now this will run much slower.')
+                    elif DEVICE != 'cpu':
+                        w[x] = w[x].to(device=DEVICE).contiguous()
+                    
+                    if (dd.stream) or (DEVICE != 'cpu'):
+                        try:
+                            w[x+'_mx'] = w[x+'_mx'].to(device=DEVICE).contiguous()
+                            w[x+'_rx'] = w[x+'_rx'].to(device=DEVICE).contiguous()
+                            w[x+'_my'] = w[x+'_my'].to(device=DEVICE).contiguous()
+                            w[x+'_ry'] = w[x+'_ry'].to(device=DEVICE).contiguous()
+                        except:
+                            pass
+
+                if 'ffn.value.weight' in x:
+                    gc.collect()
+                    if 'cuda' in args.strategy_string:
+                        torch.cuda.empty_cache()
+
+                shape = [i for i in w[x].shape if i != 1]
+                if len(shape) > 1:
+                    shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}"
+                else:
+                    shape = f" {str(shape[0]).rjust(5)}      "
+                if layer_id == 0 or layer_id >= args.n_layer-1:
+                    if print_need_newline:
+                        prxxx('\n', end = '')
+                        print_need_newline = False
+                    dt = str(w[x].dtype).replace('torch.', '')
+                    dt = dt.replace('float32', 'f32').replace('bfloat16', 'bf16').replace('float16', 'f16').replace('uint8', 'i8')
+                    prxxx(x.ljust(32), dt.rjust(4), str(w[x].device).rjust(8), shape, ' (pinned)' if w[x].is_pinned() else '')
+                else:
+                    print_need_newline = True
+                    prxxx('.', end = '', flush = True)
+            
+            if convert_and_save_and_exit:
+                w['_strategy'] = args.strategy_string
+                w['_rescale_layer'] = self.RESCALE_LAYER
+                w['_version'] = '0.7'
+                if not convert_and_save_and_exit.endswith('.pth'):
+                    convert_and_save_and_exit += '.pth'
+                prxxx(f'Saving to {convert_and_save_and_exit}...')
+                torch.save(w, convert_and_save_and_exit)
+                prxxx(f'Converted and saved. Now this will exit.')
+                exit(0)
+            
+            if self.version == 5.2 and os.environ["RWKV_CUDA_ON"] == '1':
+                HEAD_SIZE = args.n_att // args.n_head
+                rwkv5 = load(name="rwkv5", sources=[f"{current_path}/cuda/rwkv5_op.cpp", f"{current_path}/cuda/rwkv5.cu"],
+                                verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3" if os.name != "nt" else "", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}"])
+
+                class RWKV_5(torch.autograd.Function):
+                    @staticmethod
+                    def forward(ctx, B, T, C, H, state, r, k, v, w, u):
+                        with torch.no_grad():
+                            assert HEAD_SIZE == C // H
+                            ctx.B = B
+                            ctx.T = T
+                            ctx.C = C
+                            ctx.H = H
+                            assert state.dtype == torch.float32
+                            assert w.dtype == torch.float32
+                            assert r.is_contiguous()
+                            assert k.is_contiguous()
+                            assert v.is_contiguous()
+                            assert w.is_contiguous()                            
+                            assert u.is_contiguous()                            
+                            assert state.is_contiguous()
+
+                            y = torch.empty((B, T, C), device=w.device, dtype=r.dtype, memory_format=torch.contiguous_format)
+                            if r.dtype == torch.bfloat16:
+                                rwkv5.forward_bf16(B, T, C, H, state, r, k, v, w, u, y)
+                            elif r.dtype == torch.float16:
+                                rwkv5.forward_fp16(B, T, C, H, state, r, k, v, w, u, y)
+                            elif r.dtype == torch.float32:
+                                rwkv5.forward_fp32(B, T, C, H, state, r, k, v, w, u, y)
+                            return y, state
+                self.RWKV_5 = RWKV_5
+
+            if self.version == 6.0 and os.environ["RWKV_CUDA_ON"] == '1':
+                HEAD_SIZE = args.n_att // args.n_head
+                rwkv6 = load(name="rwkv6", sources=[f"{current_path}/cuda/rwkv6_op.cpp", f"{current_path}/cuda/rwkv6.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_={4096}"])
+                    
+                class RWKV_6(torch.autograd.Function):
+                    @staticmethod
+                    def forward(ctx, B, T, C, H, state, r, k, v, w, u):
+                        with torch.no_grad():
+                            assert HEAD_SIZE == C // H
+                            ctx.B = B
+                            ctx.T = T
+                            ctx.C = C
+                            ctx.H = H
+                            assert state.dtype == torch.float32
+                            assert w.dtype == torch.float32
+                            assert r.is_contiguous()
+                            assert k.is_contiguous()
+                            assert v.is_contiguous()
+                            assert w.is_contiguous()
+                            assert u.is_contiguous()
+                            eew = torch.exp(-torch.exp(w.float())).contiguous()
+
+                            y = torch.empty((B, T, C), device=w.device, dtype=r.dtype, memory_format=torch.contiguous_format)
+                            if r.dtype == torch.bfloat16:
+                                rwkv6.forward_bf16(B, T, C, H, state, r, k, v, eew, u, y)
+                            elif r.dtype == torch.float16:
+                                rwkv6.forward_fp16(B, T, C, H, state, r, k, v, eew, u, y)
+                            elif r.dtype == torch.float32:
+                                rwkv6.forward_fp32(B, T, C, H, state, r, k, v, eew, u, y)
+                            return y, state
+                self.RWKV_6 = RWKV_6
+        
+            gc.collect()
+            if 'cuda' in args.strategy_string:
+                torch.cuda.empty_cache()
+
+    def RUN_RWKV_5(self, B, T, C, H, state, r, k, v, w, u):
+        return self.RWKV_5.apply(B, T, C, H, state, r, k, v, w, u)
+
+    def RUN_RWKV_6(self, B, T, C, H, state, r, k, v, w, u):
+        return self.RWKV_6.apply(B, T, C, H, state, r, k, v, w, u)
+
+    ########################################################################################################
+
+    @MyFunction
+    def ffn_one(self, x, sx, ln_w, ln_b, k_mix, r_mix, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        kx = xx * k_mix + sx * (1 - k_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+
+        r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+        vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
+        out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
+        return x + out, xx
+
+    @MyFunction
+    def ffn_seq(self, x, sx, ln_w, ln_b, k_mix, r_mix, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+        kx = xx * k_mix + sx * (1 - k_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+
+        r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+        vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
+        out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
+        return x + out, xx[-1,:]
+
+    @MyFunction
+    def ffn_one_v6(self, x, sx, ln_w, ln_b, k_maa, r_maa, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = sx - xx
+        kx = xx + sx * k_maa
+        rx = xx + sx * r_maa
+
+        r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+        vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
+        out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
+        return x + out, xx
+
+    @MyFunction
+    def ffn_seq_v6(self, x, sx, ln_w, ln_b, k_maa, r_maa, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+        sx = sx - xx
+        kx = xx + sx * k_maa
+        rx = xx + sx * r_maa
+
+        r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+        vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
+        out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
+        return x + out, xx[-1,:]
+
+    ########################################################################################################
+
+    @MyFunction
+    def att_one(self, x, sx, aa, bb, pp, ln_w, ln_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+
+        r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
+
+        ww = t_first + k
+        p = torch.maximum(pp, ww)
+        e1 = torch.exp(pp - p)
+        e2 = torch.exp(ww - p)
+        wkv = ((e1 * aa + e2 * v) / (e1 * bb + e2)).to(dtype=x.dtype)
+        ww = t_decay + pp
+        p = torch.maximum(ww, k)
+        e1 = torch.exp(ww - p)
+        e2 = torch.exp(k - p)
+
+        out = matmul(r * wkv, ow, omx, orx, omy, ory)
+        return x + out, xx, e1 * aa + e2 * v, e1 * bb + e2, p
+
+    @MyFunction
+    def att_seq(self, x, sx, aa, bb, pp, ln_w, ln_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+
+        r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
+
+        T = x.shape[0]
+        for t in range(T):
+            kk = k[t]
+            vv = v[t]
+            ww = t_first + kk
+            p = torch.maximum(pp, ww)
+            e1 = torch.exp(pp - p)
+            e2 = torch.exp(ww - p)
+            sx[t] = ((e1 * aa + e2 * vv) / (e1 * bb + e2)).to(dtype=x.dtype)
+            ww = t_decay + pp
+            p = torch.maximum(ww, kk)
+            e1 = torch.exp(ww - p)
+            e2 = torch.exp(kk - p)
+            aa = e1 * aa + e2 * vv
+            bb = e1 * bb + e2
+            pp = p
+        out = matmul(r * sx, ow, omx, orx, omy, ory)
+        return x + out, xx[-1,:], aa, bb, pp
+
+    ########################################################################################################
+
+    @MyFunction
+    def att_one_v5(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(H, 1, N)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(H, N, 1)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(H, 1, N)
+        
+        a = matmul(k, v)
+        out = r @ (t_first * a + s)
+        s = a + t_decay * s
+
+        out = out.flatten()
+        out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5).squeeze(0)
+        out = out.to(dtype=x.dtype)
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx, s
+
+    @MyFunction
+    def att_seq_v5(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+        T = x.shape[0]
+
+        w = t_decay.reshape(-1, 1)
+        u = t_first.reshape(-1, 1)
+        ws = w.pow(T).reshape(H, 1, 1)
+        ind = torch.arange(T-1, -1, -1, device=w.device).unsqueeze(0).repeat(H, 1)
+        w = w.repeat(1, T).pow(ind)
+        wk = w.reshape(H, 1, T)
+        wb = wk.transpose(-2, -1).flip(1)
+        w = torch.cat([w[:, 1:], u], dim=1)
+        w = F.pad(w, (0, T))
+        w = torch.tile(w, [T])
+        w = w[:, :-T].reshape(-1, T, 2 * T - 1)
+        w = w[:, :, T-1:].reshape(H, T, T)
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+
+        out = ((r @ k) * w) @ v + (r @ s) * wb
+        s = ws * s + (k * wk) @ v
+        
+        out = out.transpose(0, 1).contiguous().reshape(T, H*N)
+        out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
+        out = out.to(dtype=x.dtype)
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx[-1,:], s
+
+    ########################################################################################################
+
+    @MyFunction
+    def att_one_v5_1(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+        gx = xx * g_mix + sx * (1 - g_mix)
+
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(H, 1, N)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(H, N, 1)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(H, 1, N)
+        g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+        
+        a = matmul(k, v)
+        out = r @ (t_first * a + s)
+        s = a + t_decay * s
+
+        out = out.flatten()
+        out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5).squeeze(0)
+        out = out.to(dtype=x.dtype) * g
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx, s
+
+    @MyFunction
+    def att_seq_v5_1(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+        gx = xx * g_mix + sx * (1 - g_mix)
+
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+        T = x.shape[0]
+
+        w = t_decay.reshape(-1, 1)
+        u = t_first.reshape(-1, 1)
+        ws = w.pow(T).reshape(H, 1, 1)
+        ind = torch.arange(T-1, -1, -1, device=w.device).unsqueeze(0).repeat(H, 1)
+        w = w.repeat(1, T).pow(ind)
+        wk = w.reshape(H, 1, T)
+        wb = wk.transpose(-2, -1).flip(1)
+        w = torch.cat([w[:, 1:], u], dim=1)
+        w = F.pad(w, (0, T))
+        w = torch.tile(w, [T])
+        w = w[:, :-T].reshape(-1, T, 2 * T - 1)
+        w = w[:, :, T-1:].reshape(H, T, T)
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+
+        out = ((r @ k) * w) @ v + (r @ s) * wb
+        s = ws * s + (k * wk) @ v
+        
+        out = out.transpose(0, 1).contiguous().reshape(T, H*N)
+        out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
+        out = out.to(dtype=x.dtype) * g
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx[-1,:], s
+
+    ########################################################################################################
+
+    @MyFunction
+    def att_seq_v5_2(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+        kx = xx * k_mix + sx * (1 - k_mix)
+        vx = xx * v_mix + sx * (1 - v_mix)
+        rx = xx * r_mix + sx * (1 - r_mix)
+        gx = xx * g_mix + sx * (1 - g_mix)
+
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+        T = x.shape[0]
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+
+        out = torch.empty((T, H, N), dtype=r.dtype, device=r.device)
+        for t in range(T):
+            rt = r[:,t:t+1,:]
+            kt = k[:,:,t:t+1]
+            vt = v[:,t:t+1,:]
+            at = matmul(kt, vt)
+            out[t] = (rt @ (t_first * at + s)).squeeze(1)
+            s = at + t_decay * s
+
+        out = out.reshape(T, H*N)
+        out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
+        out = out.to(dtype=x.dtype) * g
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx[-1,:], s
+
+    ########################################################################################################
+
+    @MyFunction
+    def att_one_v6_0(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        
+        sx = sx - xx
+        xxx = xx + sx * x_maa
+        xxx = torch.tanh(xxx @ tm_w1).view(5, 1, -1)
+        xxx = torch.bmm(xxx, tm_w2).view(5, -1)
+        mw, mk, mv, mr, mg = xxx.unbind(dim=0)
+
+        wx = xx + sx * (w_maa + mw)
+        kx = xx + sx * (k_maa + mk)
+        vx = xx + sx * (v_maa + mv)
+        rx = xx + sx * (r_maa + mr)
+        gx = xx + sx * (g_maa + mg)
+
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(H, 1, N)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(H, N, 1)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(H, 1, N)
+        g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+        
+        w = t_decay + (torch.tanh(wx @ td_w1) @ td_w2).float().view(H, N, 1)
+        w = torch.exp(-torch.exp(w.float()))
+
+        a = matmul(k, v)
+        out = r @ (t_first * a + s)
+        s = a + w * s
+
+        out = out.flatten()
+        out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5).squeeze(0)
+        out = out.to(dtype=x.dtype) * g
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx, s
+
+    @MyFunction
+    def att_seq_v6_0(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+        H = t_decay.shape[0]
+        N = x.shape[-1] // H
+        T = x.shape[0]
+
+        xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+        sx = torch.cat((sx.unsqueeze(0), xx[:-1,:])) - xx
+        xxx = xx + sx * x_maa
+        xxx = torch.tanh(xxx @ tm_w1).view(T, 5, -1).transpose(0, 1)
+        xxx = torch.bmm(xxx, tm_w2).view(5, T, -1)
+        mw, mk, mv, mr, mg = xxx.unbind(dim=0)
+
+        wx = xx + sx * (w_maa + mw)
+        kx = xx + sx * (k_maa + mk)
+        vx = xx + sx * (v_maa + mv)
+        rx = xx + sx * (r_maa + mr)
+        gx = xx + sx * (g_maa + mg)
+
+        r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
+        v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
+        g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+
+        w = t_decay.view(1, H, N, 1) + (torch.tanh(wx @ td_w1) @ td_w2).float().view(T, H, N, 1)
+        w = torch.exp(-torch.exp(w.float()))
+        out = torch.empty((T, H, N), dtype=r.dtype, device=r.device)
+        for t in range(T):
+            rt = r[:,t:t+1,:]
+            kt = k[:,:,t:t+1]
+            vt = v[:,t:t+1,:]
+            at = matmul(kt, vt)
+            out[t] = (rt @ (t_first * at + s)).squeeze(1)
+            s = at + w[t] * s
+
+        out = out.reshape(T, H*N)
+        out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
+        out = out.to(dtype=x.dtype) * g
+        out = matmul(out, ow, omx, orx, omy, ory)
+
+        return x + out, xx[-1,:], s
+
+    ########################################################################################################
+
+    if os.environ["RWKV_CUDA_ON"] == '1':
+        @MyFunction
+        def cuda_att_seq(self, x, sx, aa, bb, pp, ln_w, ln_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
+            T, C = x.shape
+            xx = F.layer_norm(x, (C,), weight=ln_w, bias=ln_b)
+            sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+            kx = xx * k_mix + sx * (1 - k_mix)
+            vx = xx * v_mix + sx * (1 - v_mix)
+            rx = xx * r_mix + sx * (1 - r_mix)
+
+            r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
+            k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
+            v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
+            y, aa, bb, pp = cuda_wkv(T, C, t_decay, t_first, k, v, aa, bb, pp)
+
+            out = matmul(r * y.to(x.dtype), ow, omx, orx, omy, ory)
+            return x + out, xx[-1,:], aa, bb, pp
+
+        @MyFunction
+        def v5_2_before(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+            xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+            sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+            kx = xx * k_mix + sx * (1 - k_mix)
+            vx = xx * v_mix + sx * (1 - v_mix)
+            rx = xx * r_mix + sx * (1 - r_mix)
+            gx = xx * g_mix + sx * (1 - g_mix)
+
+            r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32)
+            k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
+            v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
+            g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+
+            return r, k, v, g, xx[-1,:], s.transpose(-1,-2).contiguous()
+
+        @MyFunction
+        def v5_2_after(self, t_decay, out, s, x, xxx, g, lx_w, lx_b, ow, omx, orx, omy, ory):
+            H = t_decay.shape[0]
+            N = x.shape[-1] // H
+            T = x.shape[0]
+
+            s = s.transpose(-1,-2)
+            out = out.reshape(T, H*N)
+            out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
+            out = out.to(dtype=x.dtype) * g
+            out = matmul(out, ow, omx, orx, omy, ory)
+
+            return x + out, xxx, s
+
+        def cuda_att_seq_v5_2(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+            H = t_decay.shape[0]
+            N = x.shape[-1] // H
+            T = x.shape[0]
+
+            r, k, v, g, xxx, ss = self.v5_2_before(x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory)
+
+            out, s = self.RUN_RWKV_5(1, T, self.args.n_att, H, ss, r, k, v, w=t_decay, u=t_first)
+
+            return self.v5_2_after(t_decay, out, s, x, xxx, g, lx_w, lx_b, ow, omx, orx, omy, ory)
+
+        @MyFunction
+        def v6_0_before(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+            H = t_decay.shape[0]
+            N = x.shape[-1] // H
+            T = x.shape[0]
+
+            xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+            sx = torch.cat((sx.unsqueeze(0), xx[:-1,:])) - xx
+            xxx = xx + sx * x_maa
+            xxx = torch.tanh(xxx @ tm_w1).view(T, 5, -1).transpose(0, 1)
+            xxx = torch.bmm(xxx, tm_w2).view(5, T, -1)
+            mw, mk, mv, mr, mg = xxx.unbind(dim=0)
+
+            wx = xx + sx * (w_maa + mw)
+            kx = xx + sx * (k_maa + mk)
+            vx = xx + sx * (v_maa + mv)
+            rx = xx + sx * (r_maa + mr)
+            gx = xx + sx * (g_maa + mg)
+
+            r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32)
+            k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
+            v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
+            g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
+
+            w = t_decay.view(1, H, N, 1) + (torch.tanh(wx @ td_w1) @ td_w2).float().view(T, H, N, 1)
+
+            return r, k, v, g, w, xx[-1,:], s.transpose(-1,-2).contiguous()
+
+        def cuda_att_seq_v6_0(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
+            H = t_decay.shape[0]
+            N = x.shape[-1] // H
+            T = x.shape[0]
+
+            r, k, v, g, w, xxx, ss = self.v6_0_before(x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory)
+
+            out, s = self.RUN_RWKV_6(1, T, self.args.n_att, H, ss, r, k, v, w=w, u=t_first)
+            return self.v5_2_after(t_decay, out, s, x, xxx, g, lx_w, lx_b, ow, omx, orx, omy, ory)
+
+    ########################################################################################################
+
+    def forward(self, tokens, state, full_output=False, embs=None):
+        with torch.no_grad():
+            w = self.w
+            args = self.args
+
+            if state == None:
+                if self.version == 4:
+                    state = [None] * args.n_layer * 5
+                    for i in range(args.n_layer): # state: 0=att_xx 1=att_aa 2=att_bb 3=att_pp 4=ffn_xx
+                        dd = self.strategy[i]
+                        dev = dd.device
+                        atype = dd.atype
+                        state[i*5+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+                        state[i*5+1] = torch.zeros(args.n_att, dtype=torch.float, requires_grad=False, device=dev).contiguous()
+                        state[i*5+2] = torch.zeros(args.n_att, dtype=torch.float, requires_grad=False, device=dev).contiguous()
+                        state[i*5+3] = torch.zeros(args.n_att, dtype=torch.float, requires_grad=False, device=dev).contiguous() - 1e30
+                        state[i*5+4] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+                elif int(self.version) in [5,6]:
+                    state = [None] * args.n_layer * 3
+                    for i in range(args.n_layer): # state: 0=att_xx 1=att_kv 2=ffn_xx
+                        dd = self.strategy[i]
+                        dev = dd.device
+                        atype = dd.atype
+                        state[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+                        state[i*3+1] = torch.zeros((args.n_head, args.n_att//args.n_head, args.n_att//args.n_head), dtype=torch.float, requires_grad=False, device=dev).contiguous()
+                        state[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+
+            if embs is None:
+                seq_mode = len(tokens) > 1
+                x = w['emb.weight'][tokens if seq_mode else tokens[0]]
+            else:
+                x = embs
+
+            for i in range(args.n_layer):
+                bbb = f'blocks.{i}.'
+                att = f'blocks.{i}.att.'
+                ffn = f'blocks.{i}.ffn.'
+                dd = self.strategy[i]
+                dev = dd.device
+                atype = dd.atype
+                wtype = dd.wtype
+                if seq_mode:
+                    cuda_applicable = os.environ["RWKV_CUDA_ON"] == '1' and 'cuda' in str(dev)
+                    if cuda_applicable:
+                        ATT = self.cuda_att_seq
+                    else:
+                        ATT = self.att_seq
+                    if self.version == 5:
+                        ATT = self.att_seq_v5
+                    elif self.version == 5.1:
+                        ATT = self.att_seq_v5_1
+                    elif self.version == 5.2:
+                        ATT = self.att_seq_v5_2
+                        if cuda_applicable:
+                            ATT = self.cuda_att_seq_v5_2
+                    elif self.version == 6.0:
+                        ATT = self.att_seq_v6_0
+                        if cuda_applicable:
+                            ATT = self.cuda_att_seq_v6_0
+                    FFN = self.ffn_seq
+                    if self.version >= 6.0:
+                        FFN = self.ffn_seq_v6
+                else:
+                    ATT = self.att_one
+                    if self.version == 5:
+                        ATT = self.att_one_v5
+                    elif self.version == 5.1:
+                        ATT = self.att_one_v5_1
+                    elif self.version == 5.2:
+                        ATT = self.att_one_v5_1 # same as v5.1
+                    elif self.version == 6.0:
+                        ATT = self.att_one_v6_0
+                    FFN = self.ffn_one
+                    if self.version >= 6.0:
+                        FFN = self.ffn_one_v6
+
+                x = x.to(dtype=atype, device=dev)
+
+                kw = w[f'{att}key.weight']
+                vw = w[f'{att}value.weight']
+                rw = w[f'{att}receptance.weight']
+                ow = w[f'{att}output.weight']
+                if dd.stream:
+                    kw = kw.to(device=dev, non_blocking=True)
+                    vw = vw.to(device=dev, non_blocking=True)
+                    rw = rw.to(device=dev, non_blocking=True)
+                    ow = ow.to(device=dev, non_blocking=True)
+                kmx = w[f'{att}key.weight_mx'] if wtype == torch.uint8 else x
+                krx = w[f'{att}key.weight_rx'] if wtype == torch.uint8 else x
+                kmy = w[f'{att}key.weight_my'] if wtype == torch.uint8 else x
+                kry = w[f'{att}key.weight_ry'] if wtype == torch.uint8 else x
+                vmx = w[f'{att}value.weight_mx'] if wtype == torch.uint8 else x
+                vrx = w[f'{att}value.weight_rx'] if wtype == torch.uint8 else x
+                vmy = w[f'{att}value.weight_my'] if wtype == torch.uint8 else x
+                vry = w[f'{att}value.weight_ry'] if wtype == torch.uint8 else x
+                rmx = w[f'{att}receptance.weight_mx'] if wtype == torch.uint8 else x
+                rrx = w[f'{att}receptance.weight_rx'] if wtype == torch.uint8 else x
+                rmy = w[f'{att}receptance.weight_my'] if wtype == torch.uint8 else x
+                rry = w[f'{att}receptance.weight_ry'] if wtype == torch.uint8 else x
+                omx = w[f'{att}output.weight_mx'] if wtype == torch.uint8 else x
+                orx = w[f'{att}output.weight_rx'] if wtype == torch.uint8 else x
+                omy = w[f'{att}output.weight_my'] if wtype == torch.uint8 else x
+                ory = w[f'{att}output.weight_ry'] if wtype == torch.uint8 else x
+                if self.version in [5.1, 5.2, 6.0]:
+                    gw = w[f'{att}gate.weight']
+                    if dd.stream:
+                        gw = gw.to(device=dev, non_blocking=True)
+                    gmx = w[f'{att}gate.weight_mx'] if wtype == torch.uint8 else x
+                    grx = w[f'{att}gate.weight_rx'] if wtype == torch.uint8 else x
+                    gmy = w[f'{att}gate.weight_my'] if wtype == torch.uint8 else x
+                    gry = w[f'{att}gate.weight_ry'] if wtype == torch.uint8 else x
+                if self.version == 4:
+                    x, state[i*5+0], state[i*5+1], state[i*5+2], state[i*5+3] = ATT(
+                        x, state[i*5+0], state[i*5+1], state[i*5+2], state[i*5+3],
+                        w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
+                        w[f'{att}time_mix_k'], w[f'{att}time_mix_v'], w[f'{att}time_mix_r'],
+                        w[f'{att}time_decay'], w[f'{att}time_first'],
+                        kw, vw, rw, ow,
+                        kmx, krx, kmy, kry,
+                        vmx, vrx, vmy, vry,
+                        rmx, rrx, rmy, rry,
+                        omx, orx, omy, ory,
+                        )
+                elif self.version == 5:
+                    x, state[i*3+0], state[i*3+1] = ATT(
+                        x, state[i*3+0], state[i*3+1],
+                        w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
+                        w[f'{att}ln_x.weight'], w[f'{att}ln_x.bias'],
+                        w[f'{att}time_mix_k'], w[f'{att}time_mix_v'], w[f'{att}time_mix_r'],
+                        w[f'{att}time_decay'], w[f'{att}time_first'],
+                        kw, vw, rw, ow,
+                        kmx, krx, kmy, kry,
+                        vmx, vrx, vmy, vry,
+                        rmx, rrx, rmy, rry,
+                        omx, orx, omy, ory,
+                        )
+                elif self.version in [5.1, 5.2]:
+                    x, state[i*3+0], state[i*3+1] = ATT(
+                        x, state[i*3+0], state[i*3+1],
+                        w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
+                        w[f'{att}ln_x.weight'], w[f'{att}ln_x.bias'],
+                        w[f'{att}time_mix_k'], w[f'{att}time_mix_v'], w[f'{att}time_mix_r'], w[f'{att}time_mix_g'],
+                        w[f'{att}time_decay'], w[f'{att}time_first'],
+                        kw, vw, rw, gw, ow,
+                        kmx, krx, kmy, kry,
+                        vmx, vrx, vmy, vry,
+                        rmx, rrx, rmy, rry,
+                        gmx, grx, gmy, gry,
+                        omx, orx, omy, ory,
+                        )
+                elif self.version == 6.0:
+                    x, state[i*3+0], state[i*3+1] = ATT(
+                        x, state[i*3+0], state[i*3+1],
+                        w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
+                        w[f'{att}ln_x.weight'], w[f'{att}ln_x.bias'],
+                        w[f'{att}time_maa_x'], w[f'{att}time_maa_w'], w[f'{att}time_maa_k'], w[f'{att}time_maa_v'], w[f'{att}time_maa_r'], w[f'{att}time_maa_g'],
+                        w[f'{att}time_maa_w1'], w[f'{att}time_maa_w2'], w[f'{att}time_decay_w1'], w[f'{att}time_decay_w2'],
+                        w[f'{att}time_decay'], w[f'{att}time_first'],
+                        kw, vw, rw, gw, ow,
+                        kmx, krx, kmy, kry,
+                        vmx, vrx, vmy, vry,
+                        rmx, rrx, rmy, rry,
+                        gmx, grx, gmy, gry,
+                        omx, orx, omy, ory,
+                        )
+                if dd.stream:
+                    del kw, vw, rw, ow
+                    if self.version in [5.1, 5.2, 6.0]:
+                        del gw
+
+                kw = w[f'{ffn}key.weight']
+                vw = w[f'{ffn}value.weight']
+                rw = w[f'{ffn}receptance.weight']
+                if dd.stream:
+                    kw = kw.to(device=dev, non_blocking=True)
+                    vw = vw.to(device=dev, non_blocking=True)
+                    rw = rw.to(device=dev, non_blocking=True)
+                kmx = w[f'{ffn}key.weight_mx'] if wtype == torch.uint8 else x
+                krx = w[f'{ffn}key.weight_rx'] if wtype == torch.uint8 else x
+                kmy = w[f'{ffn}key.weight_my'] if wtype == torch.uint8 else x
+                kry = w[f'{ffn}key.weight_ry'] if wtype == torch.uint8 else x
+                vmx = w[f'{ffn}value.weight_mx'] if wtype == torch.uint8 else x
+                vrx = w[f'{ffn}value.weight_rx'] if wtype == torch.uint8 else x
+                vmy = w[f'{ffn}value.weight_my'] if wtype == torch.uint8 else x
+                vry = w[f'{ffn}value.weight_ry'] if wtype == torch.uint8 else x
+                rmx = w[f'{ffn}receptance.weight_mx'] if wtype == torch.uint8 else x
+                rrx = w[f'{ffn}receptance.weight_rx'] if wtype == torch.uint8 else x
+                rmy = w[f'{ffn}receptance.weight_my'] if wtype == torch.uint8 else x
+                rry = w[f'{ffn}receptance.weight_ry'] if wtype == torch.uint8 else x
+                if self.version == 4:
+                    offset = i*5+4
+                elif int(self.version) in [5,6]:
+                    offset = i*3+2
+                if self.version < 6.0:
+                    x, state[offset] = FFN(
+                        x, state[offset],
+                        w[f'{bbb}ln2.weight'], w[f'{bbb}ln2.bias'],
+                        w[f'{ffn}time_mix_k'], w[f'{ffn}time_mix_r'],
+                        kw, vw, rw,
+                        kmx, krx, kmy, kry,
+                        vmx, vrx, vmy, vry,
+                        rmx, rrx, rmy, rry,                    
+                        )
+                else:
+                    x, state[offset] = FFN(
+                        x, state[offset],
+                        w[f'{bbb}ln2.weight'], w[f'{bbb}ln2.bias'],
+                        w[f'{ffn}time_maa_k'], w[f'{ffn}time_maa_r'],
+                        kw, vw, rw,
+                        kmx, krx, kmy, kry,
+                        vmx, vrx, vmy, vry,
+                        rmx, rrx, rmy, rry,                    
+                        )
+                if dd.stream:                
+                    del kw, vw, rw
+                
+                if self.RESCALE_LAYER > 0:
+                    if (i+1) % self.RESCALE_LAYER == 0:
+                        x = x / 2
+            
+            dd = self.strategy[args.n_layer]
+            x = x[-1,:] if (seq_mode and (not full_output)) else x
+            x = x.to(dtype=dd.atype, device=dd.device)
+            
+            x = F.layer_norm(x, (args.n_embd,), weight=w['ln_out.weight'], bias=w['ln_out.bias'])
+            if w['head.weight'].dtype != torch.uint8:
+                x = x @ w['head.weight']
+            else:
+                if seq_mode and full_output:
+                    x = mm8_seq(x, w['head.weight'], w['head.weight_mx'], w['head.weight_rx'], w['head.weight_my'], w['head.weight_ry'])
+                else:
+                    x = mm8_one(x, w['head.weight'], w['head.weight_mx'], w['head.weight_rx'], w['head.weight_my'], w['head.weight_ry'])
+
+            return x.float(), state

modeling_vision.py

@@ -0,0 +1,48 @@
+from transformers import CLIPVisionModel
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass
+
+@dataclass
+class VisionEncoderConfig:
+    n_embd: int = 2048
+    vision_tower_name: str = 'openai/clip-vit-large-patch14-336'
+    grid_size: int = -1 # -1: no grid pooling, 0: take cls token, 1: global avg pooling, 2, 3, 4, ...: grid pooling
+
+class VisionEncoder(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        self.args = args
+        self.vit = CLIPVisionModel.from_pretrained(args.vision_tower_name)
+        self.proj = nn.Linear(self.vit.config.hidden_size, args.n_embd, bias=False)
+
+    def encode_images(self, images):
+        B, N, C, H, W = images.shape
+        images = images.view(B*N, C, H, W)
+        image_features = self.vit(images).last_hidden_state
+        L, D = image_features.shape[1], image_features.shape[2]
+        # rerange [B*N, L, D] -> [B, N, L, D]
+        image_features = image_features.view(B, N, L, D)[:, 0, :, :]
+        image_features = self.grid_pooling(image_features)
+        return self.proj(image_features)
+    
+    def grid_pooling(self, image_features):
+        if self.args.grid_size == -1: # no grid pooling
+            return image_features
+        if self.args.grid_size == 0: # take cls token
+            return image_features[:, 0:1, :]
+        if self.args.grid_size == 1: # global avg pooling
+            return image_features.mean(dim=1, keepdim=True)
+        cls_features = image_features[:, 0:1, :]
+        image_features = image_features[:, 1:, :] #drop cls token
+        B, L, D = image_features.shape
+        H_or_W = int(L**0.5)
+        image_features = image_features.view(B, H_or_W, H_or_W, D)
+        grid_stride = H_or_W // self.args.grid_size
+        image_features = F.avg_pool2d(image_features.permute(0, 3, 1, 2), 
+                                      padding=0,
+                                      kernel_size=grid_stride, 
+                                      stride=grid_stride)
+        image_features = image_features.permute(0, 2, 3, 1).view(B, -1, D)
+        return torch.cat((cls_features, image_features), dim=1)