+ MHA_shift

src/model.py

@@ -54,10 +54,10 @@ class RWKV_TimeMix(nn.Module):
         k = torch.exp(k)
         sum_k = torch.cumsum(k, dim=1)
 
-        k = k.view(B, T, self.n_head, self.head_size)
-        v = v.view(B, T, self.n_head, self.head_size)
+        kv = (k * v).view(B, T, self.n_head, self.head_size)
+
+        wkv = (torch.einsum('htu,buhc->bthc', w, kv)).contiguous().view(B, T, C)
 
-        wkv = (torch.einsum('htu,buhc->bthc', w, k * v)).contiguous().view(B, T, C)
         rwkv = torch.sigmoid(r) * wkv / sum_k
 
         return self.output(rwkv) * self.time_gamma[:T, :]
@@ -83,6 +83,7 @@ class RWKV_ChannelMix(nn.Module):
         r = self.receptance(x)
         
         wkv = self.weight(F.mish(k) * v) # seems mish is a bit better than gelu
+
         rwkv = torch.sigmoid(r) * wkv
 
         return rwkv
@@ -120,7 +121,7 @@ def apply_rotary_pos_emb(q, k, cos, sin):
     return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
 
 class MHA_rotary(nn.Module):
-    def __init__(self, config, layer_id):
+    def __init__(self, config, layer_id, time_shift = False):
         super().__init__()
         self.layer_id = layer_id
         assert config.n_embd % config.n_head == 0
@@ -128,6 +129,9 @@ class MHA_rotary(nn.Module):
         self.ctx_len = config.ctx_len
         self.head_size = config.n_embd // config.n_head
 
+        if time_shift:
+            self.time_shift = nn.ZeroPad2d((0,0,1,0))
+
         self.query = nn.Linear(config.n_embd, config.n_embd)
         self.key = nn.Linear(config.n_embd, config.n_embd)
         self.value = nn.Linear(config.n_embd, config.n_embd)
@@ -142,6 +146,9 @@ class MHA_rotary(nn.Module):
     def forward(self, x):
         B, T, C = x.size()
 
+        if hasattr(self, 'time_shift'):
+            x = torch.cat([self.time_shift(x)[:, :-1, :C//2], x[:, :, C//2:]], dim = -1)
+
         q = self.query(x).view(B, T, self.n_head, self.head_size).transpose(1, 2)       # (B, T, C) -> (B, nh, T, hs)
         k = self.key(x).view(B, T, self.n_head, self.head_size).transpose(1, 2)         # (B, T, C) -> (B, nh, T, hs)
         v = self.value(x).view(B, T, self.n_head, self.head_size).transpose(1, 2)       # (B, T, C) -> (B, nh, T, hs)
@@ -160,19 +167,27 @@ class MHA_rotary(nn.Module):
         x = att @ v                                                                     # (B, nh, T, T) * (B, nh, T, hs) -> (B, nh, T, hs)
         x = x.transpose(1, 2).contiguous().view(B, T, C)                                # (B, nh, T, hs) -> (B, T, nh, hs) -> (B, T, C)
 
-        x = self.output(x)                                                              # output projection
+        x = self.output(x)
         return x
 
 class GeGLU(torch.nn.Module):
-    def __init__(self, config, layer_id):
+    def __init__(self, config, layer_id, time_shift = False):
         super().__init__()
         self.layer_id = layer_id
+
+        if time_shift:
+            self.time_shift = nn.ZeroPad2d((0,0,1,0))
+
         hidden_sz = 3 * config.n_embd
         self.key = nn.Linear(config.n_embd, hidden_sz)
         self.value = nn.Linear(config.n_embd, hidden_sz)
         self.weight = nn.Linear(hidden_sz, config.n_embd)
 
     def forward(self, x):
+        B, T, C = x.size()
+        if hasattr(self, 'time_shift'):
+            x = torch.cat([self.time_shift(x)[:, :-1, :C//2], x[:, :, C//2:]], dim = -1)
+        
         k = self.key(x)
         v = self.value(x)        
         y = self.weight(F.gelu(k) * v)
@@ -205,7 +220,7 @@ class MHA_pro(nn.Module):
         self.rotary_ndims = int(self.head_size * 0.5)
         self.rotary_emb = RotaryEmbedding(self.rotary_ndims)
 
-        self.head_mix = nn.Conv2d(self.n_head, self.n_head, kernel_size=1, bias=False) # talking heads
+        self.head_mix = nn.Conv2d(self.n_head, self.n_head, kernel_size=1, bias=False)  # talking heads
 
         self.output = nn.Linear(config.n_embd, config.n_embd)
 
@@ -218,7 +233,7 @@ class MHA_pro(nn.Module):
         w = w[:, :, TT-1:] # w is now a circulant matrix
         w = w[:, :T, :T] * self.time_alpha[:, :, :T] * self.time_beta[:, :T, :]
 
-        x = torch.cat([self.time_shift(x)[:, :-1, :C//2], x[:, :, C//2:]], dim = -1)    # time-mixing
+        x = torch.cat([self.time_shift(x)[:, :-1, :C//2], x[:, :, C//2:]], dim = -1)    # time-shift mixing
         q = self.query(x).view(B, T, self.n_head, self.head_size).transpose(1, 2)       # (B, T, C) -> (B, nh, T, hs)
         k = self.key(x).view(B, T, self.n_head, self.head_size).transpose(1, 2)         # (B, T, C) -> (B, nh, T, hs)
         v = self.value(x).view(B, T, self.n_head, self.head_size).transpose(1, 2)       # (B, T, C) -> (B, nh, T, hs)
@@ -300,9 +315,15 @@ class Block(nn.Module):
         if config.model_type == 'RWKV':
             self.attn = RWKV_TimeMix(config, layer_id)
             self.mlp = RWKV_ChannelMix(config, layer_id)
+
         elif config.model_type == 'MHA_rotary':
             self.attn = MHA_rotary(config, layer_id)
             self.mlp = GeGLU(config, layer_id)
+        
+        elif config.model_type == 'MHA_shift':
+            self.attn = MHA_rotary(config, layer_id, time_shift=True)
+            self.mlp = GeGLU(config, layer_id, time_shift=True)
+        
         elif config.model_type == 'MHA_pro':
             self.attn = MHA_pro(config, layer_id)
             self.mlp = RWKV_ChannelMix(config, layer_id)

src/trainer.py

@@ -40,8 +40,10 @@ class Trainer:
 
         if 'wandb' in sys.modules:
             cfg = model.config
+            for k in config.__dict__:
+                setattr(cfg, k, config.__dict__[k]) # combine cfg
             run_name = str(cfg.vocab_size) + '-' + str(cfg.ctx_len) + '-' + cfg.model_type + '-' + str(cfg.n_layer) + '-' + str(cfg.n_embd)
-            wandb.init(project="RWKV-LM", name=run_name + '-' + wandb.util.generate_id(), config=config, save_code=False)
+            wandb.init(project="RWKV-LM", name=run_name + '-' + wandb.util.generate_id(), config=cfg, save_code=False)
 
         # take over whatever gpus are on the system
         self.device = 'cpu'

train.py

@@ -15,14 +15,15 @@ set_seed(42)
 np.set_printoptions(precision=4, suppress=True, linewidth=200)
 logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO,)
 
-# RWKV - our new model - fastest when ctx_len is long - VRAM friendly - good performance
-# MHA_rotary - usual Multi-head Attention+Rotary+GeGLU - not as good
-# MHA_pro - slow (lots of tricks) - VRAM hungry - good performance
-model_type = 'RWKV' # 'RWKV' or 'MHA_rotary' or 'MHA_pro'
+# RWKV       : our new model - fastest when ctx_len is long - VRAM friendly - good performance
+# MHA_rotary : usual Multi-head Attention+Rotary+GeGLU - not as good
+# MHA_shift  : with time-shift - good performance
+# MHA_pro    : slow (lots of tricks) - VRAM hungry - very good performance
+model_type = 'RWKV'
 
 # datafile = u"V:\\NLP\\text8"
 # datafile = u"V:\\NLP\\enwik8"
-datafile = u"V:\\NLP\\simplebooks\\simplebooks-92-raw\\train.txt" # https://dldata-public.s3.us-east-2.amazonaws.com/simplebooks.zip
+datafile = u"V:\\NLP\\simplebooks\\simplebooks-92-raw\\train.txt"
 datafile_encoding = 'utf-8'
 # datafile = u"Y:\\BlinkNLP\\_txt_\\txt\\_all.txt"
 # datafile_encoding = 'utf-16'
@@ -60,8 +61,8 @@ class Dataset(Dataset):
             print('splitting token...')
             data = data.lower().split(' ')
         unique = sorted(list(set(data)))
-        for u in unique:
-            print(u, end=' ')
+        # for u in unique:
+        #     print(u, end=' ')
         data_size, vocab_size = len(data), len(unique)
         print('\n\ndata has %d %ss, %d unique.' % (data_size, model_level, vocab_size))
         self.stoi = { ch:i for i,ch in enumerate(unique) }