735 lines
34 KiB
Python
735 lines
34 KiB
Python
########################################################################################################
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# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
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########################################################################################################
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import types, gc, os, time, re
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import torch
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from torch.nn import functional as F
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.allow_tf32 = True
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torch.backends.cuda.matmul.allow_tf32 = True
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current_path = os.path.dirname(os.path.abspath(__file__))
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# https://zhuanlan.zhihu.com/p/612879065
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def LoadPreCompileLibrary(file):
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import importlib
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import os
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import torch
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# load the custom_op_library and register the custom ops
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lib_dir = os.path.dirname(__file__)
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if os.name == "nt":
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# Register the main torchvision library location on the default DLL path
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import ctypes
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import sys
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kernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True)
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with_load_library_flags = hasattr(kernel32, "AddDllDirectory")
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prev_error_mode = kernel32.SetErrorMode(0x0001)
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if with_load_library_flags:
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kernel32.AddDllDirectory.restype = ctypes.c_void_p
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if sys.version_info >= (3, 8):
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os.add_dll_directory(lib_dir)
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elif with_load_library_flags:
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res = kernel32.AddDllDirectory(lib_dir)
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if res is None:
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err = ctypes.WinError(ctypes.get_last_error())
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err.strerror += f' Error adding "{lib_dir}" to the DLL directories.'
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raise ValueError(err)
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kernel32.SetErrorMode(prev_error_mode)
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loader_details = (
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importlib.machinery.ExtensionFileLoader,
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importlib.machinery.EXTENSION_SUFFIXES,
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)
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extfinder = importlib.machinery.FileFinder(lib_dir, loader_details)
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ext_specs = extfinder.find_spec(file)
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if ext_specs is None:
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return False
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try:
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torch.ops.load_library(ext_specs.origin)
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except OSError as exc:
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return False
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return True
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########################################################################################################
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if os.environ.get('RWKV_JIT_ON') != '0':
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os.environ["RWKV_JIT_ON"] = '1'
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MyModule = torch.jit.ScriptModule
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MyFunction = torch.jit.script_method
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MyStatic = torch.jit.script
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else:
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MyModule = torch.nn.Module
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def __nop(ob):
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return ob
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MyFunction = __nop
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MyStatic = __nop
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if os.environ.get('RWKV_CUDA_ON') == '1':
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if LoadPreCompileLibrary('wkv_cuda') is False:
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from torch.utils.cpp_extension import load
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load(
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name=f"wkv_cuda",
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sources=[f"{current_path}/cuda/wrapper.cpp", f"{current_path}/cuda/operators.cu"],
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verbose=True,
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extra_cuda_cflags=["-t 4", "-std=c++17", "--use_fast_math", "-O3", "--extra-device-vectorization"],
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is_python_module=False)
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@MyStatic
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def cuda_wkv(T: int, C: int, w, u, k, v, aa, bb, pp):
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assert 1 * C % min(C, 32) == 0
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assert k.dtype == v.dtype == torch.float16 or k.dtype == v.dtype == torch.float32
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assert w.dtype == u.dtype == aa.dtype == bb.dtype == pp.dtype == torch.float32
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w = w.contiguous()
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u = u.contiguous()
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k = k.contiguous()
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v = v.contiguous()
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y = torch.empty((T, C), device=w.device, memory_format=torch.contiguous_format, dtype=k.dtype)
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torch.ops.rwkv.wkv_forward(1, T, C, w, u, k, v, y, aa, bb, pp)
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return y, aa, bb, pp
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@MyStatic
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def cuda_mm8_seq(B: int, N: int, M: int, x, w, mx, rx, my, ry):
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assert x.dtype == mx.dtype == rx.dtype == my.dtype == ry.dtype
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assert x.dtype == torch.float32 or x.dtype == torch.float16
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assert w.dtype == torch.uint8
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assert x.shape == [B, N]
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assert w.shape == [N, M]
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assert rx.shape == mx.shape == [M]
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assert ry.shape == my.shape == [N, 1]
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y = torch.empty((B, M), device=w.device, dtype=x.dtype)
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torch.ops.rwkv.mm8_seq(B, N, M, x, w, mx, rx, my, ry, y)
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return y
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@MyStatic
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def cuda_mm8_one(N: int, M: int, x, w, mx, rx, my, ry):
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assert x.dtype == mx.dtype == rx.dtype == my.dtype == ry.dtype
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assert x.dtype == torch.float32 or x.dtype == torch.float16
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assert w.dtype == torch.uint8
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assert x.shape == [N]
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assert w.shape == [N, M]
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assert rx.shape == mx.shape == [M]
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assert ry.shape == my.shape == [N, 1]
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y = torch.zeros((M,), device=w.device, dtype=torch.float32)
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torch.ops.rwkv.mm8_one(N, M, x, w, mx, rx, my, ry, y)
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return y.to(dtype=x.dtype)
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else:
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os.environ["RWKV_CUDA_ON"] = '0'
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########################################################################################################
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class RWKV(MyModule):
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def __init__(self, model, strategy, verbose = True, convert_and_save_and_exit = None):
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super().__init__()
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if verbose:
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prxxx = lambda *args, **kwargs: print(*args, **kwargs)
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else:
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prxxx = lambda *args, **kwargs: None
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STRATEGY_REGEX = r"^(?:(?:^|->) *(?:cuda(?::[\d]+)?|cpu|mps) (?:fp(?:16|32)|bf16)(?:i8|i4|i3)?(?: \*[\d]+\+?)? *)+$"
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if not re.match(STRATEGY_REGEX, strategy):
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raise ValueError("Invalid strategy. Please read https://pypi.org/project/rwkv/")
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strategy = ('->'.join([x.strip() for x in strategy.split('->')])).replace('->', ' -> ')
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self.args = types.SimpleNamespace()
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args = self.args
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args.MODEL_NAME = model
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args.strategy_string = strategy
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# Rescale for fp16 mode: set x = x/2 every X layer (to avoid fp16 overflow)
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self.RESCALE_LAYER = 6 if 'fp16' in strategy else 0
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prxxx(f'RWKV_JIT_ON {os.environ["RWKV_JIT_ON"]} RWKV_CUDA_ON {os.environ["RWKV_CUDA_ON"]} RESCALE_LAYER {self.RESCALE_LAYER}\n')
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args.MODEL_NAME = args.MODEL_NAME.strip()
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if not args.MODEL_NAME.endswith('.pth'):
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args.MODEL_NAME += '.pth'
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prxxx(f'Loading {args.MODEL_NAME} ...')
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with torch.no_grad():
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self.w = torch.load(args.MODEL_NAME, map_location='cpu') # load model to CPU first
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gc.collect()
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w = self.w
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ALREADY_CONVERTED = False
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if '_strategy' in w:
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ALREADY_CONVERTED = True
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assert convert_and_save_and_exit == None # you should only convert a raw model
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prxxx(f"Converted model: strategy {w['_strategy']}, version {w['_version']}\n")
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assert w['_strategy'] == args.strategy_string # if you are using a new strategy, re-convert the model
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assert float(w['_version']) >= 0.7 # sometimes you should re-convert using latest convert_model.py
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assert w['_rescale_layer'] == self.RESCALE_LAYER
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del w['_strategy']
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del w['_version']
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del w['_rescale_layer']
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args.n_embd = w['emb.weight'].shape[1]
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args.n_layer = 0
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keys = list(w.keys())
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for x in keys:
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layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
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args.n_layer = max(args.n_layer, layer_id+1)
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####################### Compute strategy
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s = [x.strip().split(' ') for x in strategy.split('->')]
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plan = [0] * len(s)
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stream_i = -1
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stream_count = 0
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to_allocate = args.n_layer + 1
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allocated = 0
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free_slots = 0
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for i in range(len(s)):
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si = s[i]
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si1 = si[1]
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if si1.startswith('fp32'): si[1] = [torch.float]
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elif si1.startswith('fp16'): si[1] = [torch.float16]
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elif si1.startswith('bf16'): si[1] = [torch.bfloat16]
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if si1.endswith('i8'): si[1] += [torch.uint8]
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else: si[1] += [si[1][0]]
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if len(si) > 2:
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ss = si[2]
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assert ss.startswith('*')
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if ss.endswith('+'):
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plan[i] = int(ss[1:-1])
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stream_i = i
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else:
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plan[i] = int(ss[1:])
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allocated += plan[i]
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if allocated >= to_allocate:
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plan[i] += to_allocate - allocated
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break
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else:
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free_slots += 1
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if stream_i < 0:
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if free_slots > 0 and to_allocate > allocated:
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for i in range(len(s)):
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if plan[i] == 0:
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plan[i] = (to_allocate - allocated) // free_slots
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allocated += plan[i]
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free_slots -= 1
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if to_allocate > allocated:
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plan[len(s)-1] += to_allocate - allocated
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else:
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if to_allocate > allocated:
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stream_count = to_allocate - allocated
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plan[stream_i] += stream_count
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prxxx(f'Strategy: (total {args.n_layer}+1={args.n_layer+1} layers)')
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for i in range(len(s)):
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ss = s[i]
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if i != stream_i:
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prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]} layers')
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else:
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prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]-stream_count} layers, stream {stream_count} layers')
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plan[i] += (0 if i == 0 else plan[i-1])
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self.strategy = [None] * (args.n_layer + 1)
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strategy = self.strategy
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for n in range(args.n_layer + 1):
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for i in range(len(s)):
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if n < plan[i]:
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strategy[n] = types.SimpleNamespace()
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strategy[n].device = s[i][0]
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strategy[n].atype = s[i][1][0]
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strategy[n].wtype = s[i][1][1]
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strategy[n].stream = False
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if i == stream_i and n >= (plan[i] - stream_count):
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strategy[n].stream = True
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break
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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=' ')
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prxxx()
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####################### Load weights to self.w
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if not ALREADY_CONVERTED:
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try: # precompute embedding
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w['emb.weight'] = F.layer_norm(w['emb.weight'], (args.n_embd,), weight=w['blocks.0.ln0.weight'], bias=w['blocks.0.ln0.bias'])
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except:
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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())
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del w['blocks.0.ln0.weight']
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del w['blocks.0.ln0.bias']
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print_need_newline = False
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keys = list(w.keys())
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for x in keys:
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w[x].requires_grad = False
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layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
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if ('ln_out.' in x) or ('head.' in x):
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layer_id = args.n_layer
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dd = strategy[layer_id]
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DEVICE = dd.device
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ATYPE = dd.atype
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WTYPE = dd.wtype
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if not ALREADY_CONVERTED:
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if self.RESCALE_LAYER > 0:
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if 'att.output.weight' in x:
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w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
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if 'ffn.value.weight' in x:
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w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
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if '.time_' in x:
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w[x] = w[x].squeeze()
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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:
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w[x] = w[x].t()
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if '.time_decay' in x: # need fp32 for this
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w[x] = -torch.exp(w[x].float())
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elif '.time_first' in x: # need fp32 for this
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w[x] = w[x].float()
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else:
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if (len(w[x].shape) == 2) and ('emb' not in x):
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if WTYPE != torch.uint8:
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w[x] = w[x].to(dtype=WTYPE)
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else:
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w[x] = w[x].float()
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if w[x].shape[0] > w[x].shape[1]:
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w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
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w[x] = w[x] - w[x+'_my']
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w[x+'_mx'] = torch.amin(w[x], dim=0)
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w[x] = w[x] - w[x+'_mx']
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w[x+'_rx'] = torch.amax(w[x], dim=0)
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w[x] = w[x] / w[x+'_rx']
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w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
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w[x] = w[x] / w[x+'_ry']
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else:
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w[x+'_mx'] = torch.amin(w[x], dim=0)
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w[x] = w[x] - w[x+'_mx']
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w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
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w[x] = w[x] - w[x+'_my']
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w[x+'_rx'] = torch.amax(w[x], dim=0)
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w[x] = w[x] / w[x+'_rx']
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w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
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w[x] = w[x] / w[x+'_ry']
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w[x] = torch.clip(torch.floor(w[x] * 256), min=0, max=255).to(dtype=torch.uint8)
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w[x+'_mx'] = w[x+'_mx'].to(dtype=ATYPE).contiguous()
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w[x+'_rx'] = (w[x+'_rx'] / 16).to(dtype=ATYPE).contiguous()
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w[x+'_my'] = w[x+'_my'].to(dtype=ATYPE).contiguous()
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w[x+'_ry'] = (w[x+'_ry'] / 16).to(dtype=ATYPE).contiguous()
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else:
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w[x] = w[x].to(dtype=ATYPE)
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if convert_and_save_and_exit == None:
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if 'emb.' in x:
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w[x] = w[x].contiguous()
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elif (dd.stream) and (x.endswith('key.weight') or x.endswith('value.weight') or x.endswith('receptance.weight') or x.endswith('output.weight')):
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try:
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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 :)
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except:
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print('Note: You are running out of RAM. Get more CPU RAM. Now this will run much slower.')
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elif DEVICE != 'cpu':
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w[x] = w[x].to(device=DEVICE).contiguous()
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if (dd.stream) or (DEVICE != 'cpu'):
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try:
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w[x+'_mx'] = w[x+'_mx'].to(device=DEVICE).contiguous()
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w[x+'_rx'] = w[x+'_rx'].to(device=DEVICE).contiguous()
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w[x+'_my'] = w[x+'_my'].to(device=DEVICE).contiguous()
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w[x+'_ry'] = w[x+'_ry'].to(device=DEVICE).contiguous()
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except:
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pass
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if 'ffn.value.weight' in x:
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gc.collect()
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if 'cuda' in args.strategy_string:
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torch.cuda.empty_cache()
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shape = [i for i in w[x].shape if i != 1]
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if len(shape) > 1:
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shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}"
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else:
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shape = f" {str(shape[0]).rjust(5)} "
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if layer_id == 0 or layer_id >= args.n_layer-1:
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if print_need_newline:
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prxxx('\n', end = '')
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print_need_newline = False
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dt = str(w[x].dtype).replace('torch.', '')
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dt = dt.replace('float32', 'f32').replace('bfloat16', 'bf16').replace('float16', 'f16').replace('uint8', 'i8')
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prxxx(x.ljust(32), dt.rjust(4), str(w[x].device).rjust(8), shape, ' (pinned)' if w[x].is_pinned() else '')
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else:
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print_need_newline = True
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prxxx('.', end = '', flush = True)
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if convert_and_save_and_exit:
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w['_strategy'] = args.strategy_string
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w['_rescale_layer'] = self.RESCALE_LAYER
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w['_version'] = '0.7'
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if not convert_and_save_and_exit.endswith('.pth'):
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convert_and_save_and_exit += '.pth'
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prxxx(f'Saving to {convert_and_save_and_exit}...')
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torch.save(w, convert_and_save_and_exit)
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prxxx(f'Converted and saved. Now this will exit.')
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exit(0)
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gc.collect()
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if 'cuda' in args.strategy_string:
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torch.cuda.empty_cache()
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@MyFunction
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def torch_mm8_seq(self, x, w, mx, rx, my, ry):
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return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
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@MyFunction
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def torch_mm8_one(self, x, w, mx, rx, my, ry):
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return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
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if os.environ.get('RWKV_CUDA_ON') == '1':
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@MyFunction
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def mm8_seq(self, x, w, mx, rx, my, ry):
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if w.device.type == 'cuda' and x.dtype == torch.float16:
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B, N, M = x.shape[0], w.shape[0], w.shape[1]
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return cuda_mm8_seq(B, N, M, x, w, mx, rx, my, ry)
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else:
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return self.torch_mm8_seq(x, w, mx, rx, my, ry)
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@MyFunction
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def mm8_one(self, x, w, mx, rx, my, ry):
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if w.device.type == 'cuda':
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N, M = w.shape[0], w.shape[1]
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return cuda_mm8_one(N, M, x, w, mx, rx, my, ry)
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else:
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return self.torch_mm8_one(x, w, mx, rx, my, ry)
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else:
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@MyFunction
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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
|