fixed torch version; CUDA acceleration utils

.gitignore

@@ -13,4 +13,7 @@ __pycache__
 /backend-python/get-pip.py
 /py310
 *.zip
-/cmd-helper.bat
+/cmd-helper.bat
+/backend-python/wkv_cuda
+*.exe
+*.old

backend-golang/rwkv.go

@@ -48,7 +48,7 @@ func (a *App) InstallPyDep(cnMirror bool) (string, error) {
 		return "", err
 	}
 	ChangeFileLine("./py310/python310._pth", 3, "Lib\\site-packages")
-	_, err = Cmd(python, "-m", "pip", "install", "torch", "torchvision", "torchaudio", "--index-url", "https://download.pytorch.org/whl/cu117")
+	_, err = Cmd(python, "-m", "pip", "install", "torch==1.13.1", "torchvision==0.14.1", "torchaudio==0.13.1", "--index-url", "https://download.pytorch.org/whl/cu117")
 	if err != nil {
 		return "", err
 	}

backend-python/utils/rwkv.py

@@ -1,3 +1,5 @@
+import os
+import pathlib
 from typing import Dict
 from langchain.llms import RWKV
 from pydantic import BaseModel
@@ -34,6 +36,10 @@ def get_rwkv_config(model: RWKV) -> ModelConfigBody:
     )
 
 
+# os.environ["RWKV_CUDA_ON"] = '1'
+# os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
+
+
 def rwkv_generate(model: RWKV, prompt: str, stop: str = None):
     model.model_state = None
     model.model_tokens = []

backend-python/wkv_cuda_utils/wkv_cuda_model.py

@@ -0,0 +1,734 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+import types, gc, os, time, re
+import torch
+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__))
+
+# https://zhuanlan.zhihu.com/p/612879065
+def LoadPreCompileLibrary(file):
+    import importlib
+    import os
+
+    import torch
+
+    # load the custom_op_library and register the custom ops
+    lib_dir = os.path.dirname(__file__)
+    if os.name == "nt":
+        # Register the main torchvision library location on the default DLL path
+        import ctypes
+        import sys
+
+        kernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True)
+        with_load_library_flags = hasattr(kernel32, "AddDllDirectory")
+        prev_error_mode = kernel32.SetErrorMode(0x0001)
+
+        if with_load_library_flags:
+            kernel32.AddDllDirectory.restype = ctypes.c_void_p
+
+        if sys.version_info >= (3, 8):
+            os.add_dll_directory(lib_dir)
+        elif with_load_library_flags:
+            res = kernel32.AddDllDirectory(lib_dir)
+            if res is None:
+                err = ctypes.WinError(ctypes.get_last_error())
+                err.strerror += f' Error adding "{lib_dir}" to the DLL directories.'
+                raise ValueError(err)
+
+        kernel32.SetErrorMode(prev_error_mode)
+
+    loader_details = (
+        importlib.machinery.ExtensionFileLoader,
+        importlib.machinery.EXTENSION_SUFFIXES,
+    )
+
+    extfinder = importlib.machinery.FileFinder(lib_dir, loader_details)
+    ext_specs = extfinder.find_spec(file)
+    if ext_specs is None:
+        return False
+
+    try:
+        torch.ops.load_library(ext_specs.origin)
+    except OSError as exc:
+        return False
+    return True
+
+########################################################################################################
+
+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':
+    if LoadPreCompileLibrary('wkv_cuda') is False:
+        from torch.utils.cpp_extension import load
+        load(
+            name=f"wkv_cuda",
+            sources=[f"{current_path}/cuda/wrapper.cpp", f"{current_path}/cuda/operators.cu"],
+            verbose=True,
+            extra_cuda_cflags=["-t 4", "-std=c++17", "--use_fast_math", "-O3", "--extra-device-vectorization"],
+            is_python_module=False)
+
+    @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'
+
+########################################################################################################
+
+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) (?: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)
+        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
+                del w['_strategy']
+                del w['_version']
+                del w['_rescale_layer']
+            
+            args.n_embd = w['emb.weight'].shape[1]
+            args.n_layer = 0
+            keys = list(w.keys())
+            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)
+
+            ####################### 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 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
+            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 'output.weight' in x or 'head.weight' in x:
+                        w[x] = w[x].t()
+
+                    if '.time_decay' in x: # need fp32 for this
+                        w[x] = -torch.exp(w[x].float())
+                    elif '.time_first' in x: # need fp32 for this
+                        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)
+            
+            gc.collect()
+            if 'cuda' in args.strategy_string:
+                torch.cuda.empty_cache()
+
+    @MyFunction
+    def torch_mm8_seq(self, x, w, mx, rx, my, ry):
+        return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
+
+    @MyFunction
+    def torch_mm8_one(self, 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':
+        @MyFunction
+        def mm8_seq(self, 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 self.torch_mm8_seq(x, w, mx, rx, my, ry)
+        @MyFunction
+        def mm8_one(self, 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 self.torch_mm8_one(x, w, mx, rx, my, ry)
+    else:
+        @MyFunction
+        def mm8_seq(self, x, w, mx, rx, my, ry):
+            return self.torch_mm8_seq(x, w, mx, rx, my, ry)
+        @MyFunction
+        def mm8_one(self, x, w, mx, rx, my, ry):
+            return self.torch_mm8_one(x, w, mx, rx, my, ry)
+
+    ########################################################################################################
+
+    @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(rx @ rw)
+        vx = torch.square(torch.relu(kx @ kw))
+        out = r * (vx @ vw)
+        return x + out, xx
+
+    @MyFunction
+    def ffn_one_i8(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(self.mm8_one(rx, rw, rmx, rrx, rmy, rry))
+        vx = torch.square(torch.relu(self.mm8_one(kx, kw, kmx, krx, kmy, kry)))
+        out = r * (self.mm8_one(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(rx @ rw)
+        vx = torch.square(torch.relu(kx @ kw))
+        out = r * (vx @ vw)
+        return x + out, xx[-1,:]
+
+    @MyFunction
+    def ffn_seq_i8(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(self.mm8_seq(rx, rw, rmx, rrx, rmy, rry))
+        vx = torch.square(torch.relu(self.mm8_seq(kx, kw, kmx, krx, kmy, kry)))
+        out = r * (self.mm8_seq(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(rx @ rw)
+        k = (kx @ kw).float()
+        v = (vx @ vw).float()
+
+        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 = (r * wkv) @ ow
+        return x + out, xx, e1 * aa + e2 * v, e1 * bb + e2, p
+
+    @MyFunction
+    def att_one_i8(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(self.mm8_one(rx, rw, rmx, rrx, rmy, rry))
+        k = (self.mm8_one(kx, kw, kmx, krx, kmy, kry)).float()
+        v = (self.mm8_one(vx, vw, vmx, vrx, vmy, vry)).float()
+
+        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 = self.mm8_one(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(rx @ rw)
+        k = (kx @ kw).float()
+        v = (vx @ vw).float()
+
+        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 = (r * sx) @ ow
+        return x + out, xx[-1,:], aa, bb, pp
+
+    @MyFunction
+    def att_seq_i8(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(self.mm8_seq(rx, rw, rmx, rrx, rmy, rry))
+        k = self.mm8_seq(kx, kw, kmx, krx, kmy, kry).float()
+        v = self.mm8_seq(vx, vw, vmx, vrx, vmy, vry).float()
+
+        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 = self.mm8_seq(r * sx, ow, omx, orx, omy, ory)
+        return x + out, xx[-1,:], aa, bb, pp
+
+    ########################################################################################################
+
+    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.size()
+            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(rx @ rw)
+            k = kx @ kw
+            v = vx @ vw
+            y, aa, bb, pp = cuda_wkv(T, C, t_decay, t_first, k, v, aa, bb, pp)
+            
+            out = (r * y) @ ow
+            return x + out, xx[-1,:], aa, bb, pp
+
+        @MyFunction
+        def cuda_att_seq_i8(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.size()
+            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(self.mm8_seq(rx, rw, rmx, rrx, rmy, rry))
+            k = self.mm8_seq(kx, kw, kmx, krx, kmy, kry)
+            v = self.mm8_seq(vx, vw, vmx, vrx, vmy, vry)
+            y, aa, bb, pp = cuda_wkv(T, C, t_decay, t_first, k, v, aa, bb, pp)
+
+            out = self.mm8_seq(r * y, ow, omx, orx, omy, ory)
+            return x + out, xx[-1,:], aa, bb, pp
+
+    ########################################################################################################
+
+    def forward(self, tokens, state, full_output=False):
+        with torch.no_grad():
+            w = self.w
+            args = self.args
+
+            if state == None:
+                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_embd, dtype=torch.float, requires_grad=False, device=dev).contiguous()
+                    state[i*5+2] = torch.zeros(args.n_embd, dtype=torch.float, requires_grad=False, device=dev).contiguous()
+                    state[i*5+3] = torch.zeros(args.n_embd, 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()
+
+            seq_mode = len(tokens) > 1
+
+            x = w['emb.weight'][tokens if seq_mode else tokens[0]]
+
+            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:
+                    if 'cuda' in str(dev) and os.environ["RWKV_CUDA_ON"] == '1':
+                        ATT = self.cuda_att_seq if wtype != torch.uint8 else self.cuda_att_seq_i8
+                    else:
+                        ATT = self.att_seq if wtype != torch.uint8 else self.att_seq_i8
+                    FFN = self.ffn_seq if wtype != torch.uint8 else self.ffn_seq_i8
+                else:
+                    ATT = self.att_one if wtype != torch.uint8 else self.att_one_i8
+                    FFN = self.ffn_one if wtype != torch.uint8 else self.ffn_one_i8
+
+                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
+                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,
+                    )
+                if dd.stream:
+                    del kw, vw, rw, ow
+
+                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                    
+                x, state[i*5+4] = FFN(
+                    x, state[i*5+4],
+                    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,                    
+                    )
+                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 = self.mm8_seq(x, w['head.weight'], w['head.weight_mx'], w['head.weight_rx'], w['head.weight_my'], w['head.weight_ry'])
+                else:
+                    x = self.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