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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 | |
| 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 | |
| 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 | |
| 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' | |
| def torch_mm8_seq(x, w, mx, rx, my, ry): | |
| return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx) | |
| 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': | |
| 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) | |
| 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: | |
| def mm8_seq(x, w, mx, rx, my, ry): | |
| return torch_mm8_seq(x, w, mx, rx, my, ry) | |
| 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): | |
| 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): | |
| 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) | |
| ######################################################################################################## | |
| 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 | |
| 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,:] | |
| 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 | |
| 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,:] | |
| ######################################################################################################## | |
| 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 | |
| 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 | |
| ######################################################################################################## | |
| 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 | |
| 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 | |
| ######################################################################################################## | |
| 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 | |
| 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 | |
| ######################################################################################################## | |
| 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 | |
| ######################################################################################################## | |
| 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 | |
| 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': | |
| 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 | |
| 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() | |
| 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) | |
| 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 | |