refactoring
BlinkDL
4 years ago
parent
5817d265c3
commit
71538e44a9
@ -0,0 +1,143 @@
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import types
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import copy
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import torch
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from torch.nn import functional as F
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RWKV_K_CLAMP = 60
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RWKV_K_EPS = 1e-16
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RWKV_HEAD_QK_DIM = 256
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DEBUG_TIME = False # True False - show trained time-coeffs
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class RWKV_RNN():
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def __init__(self, MODEL_NAME, RUN_DEVICE, model_type, n_layer, n_embd, ctx_len):
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self.RUN_DEVICE = RUN_DEVICE
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self.model_type = model_type
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self.n_layer = n_layer
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self.n_embd = n_embd
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self.ctx_len = ctx_len
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self.w = types.SimpleNamespace()
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w = torch.load(MODEL_NAME + '.pth',
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map_location=torch.device(RUN_DEVICE))
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for x in w.keys():
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if '.time_' in x:
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w[x] = w[x].squeeze()
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if '.time_decay' in x:
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w[x] = torch.exp(-torch.exp(w[x]))
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if '.time_first' in x:
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w[x] = torch.exp(w[x])
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if DEBUG_TIME and '.time_' in x:
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print(x, w[x].squeeze().cpu().numpy())
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xx = x.split('.')
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here = self.w
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for i in range(len(xx)):
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if xx[i].isdigit():
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ii = int(xx[i])
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if ii not in here:
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here[ii] = types.SimpleNamespace()
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here = here[ii]
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else:
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if i == len(xx) - 1:
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setattr(here, xx[i], w[x])
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elif not hasattr(here, xx[i]):
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if xx[i+1].isdigit():
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setattr(here, xx[i], {})
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else:
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setattr(here, xx[i], types.SimpleNamespace())
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here = getattr(here, xx[i])
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self.clear()
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def clear(self):
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self.xx = {}
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self.aa = {}
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self.bb = {}
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self.hk = None
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def save(self, target):
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target.xx = copy.deepcopy(self.xx)
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target.aa = copy.deepcopy(self.aa)
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target.bb = copy.deepcopy(self.bb)
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target.hk = copy.deepcopy(self.hk)
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def load(self, target):
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self.xx = copy.deepcopy(target.xx)
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self.aa = copy.deepcopy(target.aa)
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self.bb = copy.deepcopy(target.bb)
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self.hk = copy.deepcopy(target.hk)
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def LN(self, xx, w):
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return F.layer_norm(xx, (self.n_embd,), weight=w.weight, bias=w.bias)
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def FF(self, xx, w, name):
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if name not in self.xx:
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self.xx[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
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x = xx * w.time_mix + self.xx[name] * (1 - w.time_mix)
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self.xx[name] = xx
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r = torch.sigmoid(w.receptance.weight @ x)
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k = torch.square(torch.relu(w.key.weight @ x))
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kv = w.value.weight @ k
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return r * kv
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def SA(self, xx, w, name):
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if name not in self.xx:
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self.xx[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
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self.aa[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
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self.bb[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
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x = xx * w.time_mix + self.xx[name] * (1 - w.time_mix)
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self.xx[name] = xx
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r = torch.sigmoid(w.receptance.weight @ x)
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k = torch.exp(torch.clamp(w.key.weight @ x, max=RWKV_K_CLAMP))
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v = w.value.weight @ x
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kv = k * v
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a = self.aa[name] + w.time_first * kv
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b = self.bb[name] + w.time_first * k
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self.aa[name] = w.time_decay * self.aa[name] + kv
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self.bb[name] = w.time_decay * self.bb[name] + k
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rwkv = r * a / (b + RWKV_K_EPS)
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return w.output.weight @ rwkv
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def run(self, ctx):
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w = self.w
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x = w.emb.weight[ctx[-1]]
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for i in range(self.n_layer):
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x = self.LN(x, w.blocks[i].ln1)
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if i == 0 and self.model_type == 'RWKV-ffnPre':
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x = x + self.FF(x, w.blocks[i].ffnPre, f'ffnPre.{i}')
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else:
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x = x + self.SA(x, w.blocks[i].att, f'att.{i}')
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x = self.LN(x, w.blocks[i].ln2)
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x = x + self.FF(x, w.blocks[i].ffn, f'ffn.{i}')
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x = self.LN(x, w.ln_out)
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if self.hk == None:
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self.hk = (w.head_k.weight @ x).unsqueeze(0)
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else:
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self.hk = torch.cat(
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[self.hk, (w.head_k.weight @ x).unsqueeze(0)], dim=0)
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if self.hk.shape[0] > self.ctx_len:
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self.hk = self.hk[-self.ctx_len:, :]
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q = w.head_q.weight @ x
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x = w.head.weight @ x
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x = x.cpu().numpy().tolist()
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c = (self.hk @ q) / RWKV_HEAD_QK_DIM
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for i in range(len(c)):
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x[ctx[i]] += c[i]
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return x
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