Fixed embeding mistake

.gitignore

@@ -0,0 +1,2 @@
+*.safetensors
+*.csv

data.py

@@ -2,33 +2,42 @@ import numpy as np
 import threading
 import queue
 
-def startDataWorker(dataset,encoding,batch_size,block_size):
+def startDataWorker(dataset,encoding,batch_size,block_size,chat):
     data_q = queue.Queue(maxsize=100)
-    t = threading.Thread(target=data_worker, args=(data_q, dataset, encoding, batch_size, block_size), daemon=True)
+    t = threading.Thread(target=dataWorker, args=(data_q, dataset, encoding, batch_size, block_size,chat), daemon=True)
     t.start()
     while (1):
         try:
             bx, by = data_q.get(timeout=30)
         except queue.Empty:
+            print("queue empty ...")
             continue
         yield (bx,by)
 
-def dataWorker(q, dataset, encoding, batch_size, block_size):
+def dataWorker(q, dataset, encoding, batch_size, block_size,chat):
     batch_x, batch_y = [], []
-    while(1):
-        for text in dataset["text"]:
-            tokens = encoding.encode(text)
-            for i in range(0, len(tokens)-block_size-1,block_size):
-                x = [encoding.bos_token_id] + tokens[i:i+block_size-1]
-                y = tokens[i:i+block_size]
+    while True:
+        for text in dataset:
+            tokens = []
+            if(chat):
+                for msg in text['messages']:
+                    role = msg['role']
+                    content = msg['content']
+                    txt = f"<|{role}|>{content}<|end|> "
+                    tokens += encoding.encode(txt) + [encoding.eos_token_id]
+            else:
+                tokens = encoding.encode(text["text"])
+            for i in range(0, len(tokens)-block_size+1,block_size):
+                x = tokens[i:i+block_size]
+                y = tokens[i+1:i+block_size+1]
 
                 if len(x) < block_size:
-                    pad = len(x)-(block_size-1)
-                    x = x + [encoding.eos_token_id] + [encoding.pad_token_id] * pad
+                    pad = len(x)-(block_size)
+                    x = x + [encoding.eos_token_id] * pad
 
                 if len(y) < block_size:
-                    pad = len(y)-(block_size-1)
-                    y = y + [encoding.eos_token_id] + [encoding.pad_token_id] * pad
+                    pad = len(y)-(block_size)
+                    y = y + [encoding.eos_token_id] * pad
 
                 batch_x.append(x)
                 batch_y.append(y)

model.py

@@ -1,12 +1,13 @@
 from tinygrad import Tensor,nn,TinyJit
 
 class MultiHeadAttention:
-    def __init__(self,embed_size,n_heads):
+    def __init__(self,embed_size,n_heads,lin):
         assert embed_size % n_heads == 0
         self.head_size = embed_size//n_heads
         self.n_heads = n_heads
         self.qkv = nn.Linear(embed_size, embed_size*3,bias=False)
         self.projection = nn.Linear(embed_size, embed_size,bias=False)
+        self.lin = lin
     def __call__(self,x):
         B,T,C=x.shape
 
@@ -15,10 +16,16 @@ class MultiHeadAttention:
         k = k.view(B, T, self.n_heads, self.head_size).transpose(1, 2)
         v = v.view(B, T, self.n_heads, self.head_size).transpose(1, 2)
         #B H T S
-        #TODO attention free transformer
 
-        out = q.scaled_dot_product_attention(k,v,is_causal=True,dropout_p=0.01)
+        out = None
+        if self.lin:
+            q = q.sigmoid()
+            k = k.sigmoid()
+            out = ((q*k).exp()/(q*k)) * v
+        else:
+            out = q.scaled_dot_product_attention(k,v,is_causal=True)
         out = out.transpose(1,2).view(B,T,C)
+
         return self.projection(out)
     def cast(self,dtype):
         self.qkv.weight = self.qkv.weight.cast(dtype)
@@ -43,8 +50,8 @@ class FeedForwardNetwork:
         return self
 
 class Block:
-    def __init__(self,embed_size,n_heads):
-        self.mha = MultiHeadAttention(embed_size,n_heads)
+    def __init__(self,embed_size,n_heads,lin):
+        self.mha = MultiHeadAttention(embed_size,n_heads,lin)
         self.ffn = FeedForwardNetwork(embed_size)
         self.mhaNorm = nn.RMSNorm(embed_size)
         self.ffnNorm = nn.RMSNorm(embed_size)
@@ -58,12 +65,12 @@ class Block:
         return self
 
 class Transformer():
-    def __init__(self,vocab_size,embed_size,n_heads,n_blocks,max_len):
+    def __init__(self,vocab_size,embed_size,n_heads,n_blocks,block_size):
         self.tok_embed = nn.Embedding(vocab_size,embed_size)
         self.pos_embed = nn.Embedding(block_size,embed_size)
-        self.pos_idx = Tensor.arange(max_len, requires_grad=False)
+        self.pos_idx = Tensor.arange(block_size, requires_grad=False)
 
-        self.blocks = [Block(embed_size,n_heads) for _ in range(n_blocks)]
+        self.blocks = [Block(embed_size,n_heads,i%4==0) for i in range(n_blocks)]
         self.norm = nn.RMSNorm(embed_size)
         self.output = nn.Linear(embed_size,vocab_size,bias=False)
     def __call__(self,x):

optm.py

@@ -1,16 +1,18 @@
-from tinygrad import Tensor
+from tinygrad import Tensor,nn
 import math
 
 class CosineLR:
-    def __init__(self,optm,totalSteps,minlr):
+    def __init__(self,optm,totalSteps,maxlr,minlr):
         self.optm = optm
-        self.maxlr = optm.lr
+        self.maxlr = maxlr
         self.minlr = minlr
         self.totalSteps = totalSteps
         self.steps = 0
 
     def step(self):
-        self.optm.lr = self.minlr + 0.5 * (self.maxlr - self.minlr) * (1 + math.cos((step / self.totalSteps) * math.pi))
+        lr = self.minlr + 0.5 * (self.maxlr - self.minlr) * (1 + math.cos((self.steps / self.totalSteps) * math.pi))
+        for o in self.optm:
+            o.lr = lr
         self.optm.step()
         self.steps += 1
 
@@ -18,11 +20,11 @@ class CosineLR:
         self.optm.zero_grad()
 
 
-def llmOptimizer(params,steps,minlr):
+def llmOptimizer(params,steps,maxlr,minlr):
     muon_params = [p for p in params if len(p.shape) >= 2]
     adamw_params = [p for p in params if len(p.shape) < 2]
 
-    o1 = nn.optim.Muon(muon_params, lr=hypr["starting_lr"])
-    o2 = nn.optim.AdamW(adamw_params, lr=hypr["starting_lr"])
-    optimizer = nn.optim.OptimizerGroup([o1,o2])
-    return CosineLR(optimizer,steps,minlr)
+    o1 = nn.optim.Muon(muon_params, lr=maxlr)
+    o2 = nn.optim.AdamW(adamw_params, lr=maxlr)
+    optimizer = nn.optim.OptimizerGroup(o1,o2)
+    return CosineLR(optimizer,steps,maxlr,minlr)

train.py

@@ -0,0 +1,96 @@
+from tinygrad.nn.state import get_state_dict,safe_load, load_state_dict
+from concurrent.futures import ThreadPoolExecutor
+from tinygrad import Tensor,TinyJit,Device,nn
+from transformers import AutoTokenizer
+from datasets import load_dataset
+from model import Transformer
+from tqdm import tqdm
+import optm
+import data
+import log
+import sys
+
+hypr = {
+    "embed_size":  768,
+    "n_heads":     8,
+    "n_blocks":    12,
+    "block_size":  512,
+    "batch_size":  8,
+    "starting_lr": 6e-4,
+    "minimum_lr":  6e-5,
+    "warmup":      5_000,
+    "steps":       535_000,
+    "encoding":    "TinyLlama/TinyLlama_v1.1",
+    "dataset":     "HuggingFaceTB/smollm-corpus",
+    "subset":      "cosmopedia-v2",
+    "chat_dataset": "HuggingFaceTB/smoltalk",
+    "chat_subset":  "all",
+    "half":         True,
+}
+
+print(Device.DEFAULT)
+chat = len(sys.argv) > 1
+if(chat):
+    hypr["dataset"] = hypr["chat_dataset"]
+    hypr["subset"] = hypr["chat_subset"]
+    hypr["starting_lr"] *= 0.1
+    hypr["minimum_lr"] *= 0.1
+
+#for loging
+loger = ThreadPoolExecutor(max_workers=2)
+
+dataset = load_dataset(hypr["dataset"],
+                       hypr["subset"],
+                       split="train",
+                       streaming=True)
+encoding = AutoTokenizer.from_pretrained(hypr["encoding"])
+if encoding.pad_token_id == None:
+    encoding.pad_token_id=encoding.eos_token_id
+hypr["vocab_size"] = encoding.vocab_size
+batch = data.startDataWorker(dataset,encoding,hypr["batch_size"],hypr["block_size"],chat)
+
+model = Transformer(hypr["vocab_size"],hypr["embed_size"],hypr["n_heads"],hypr["n_blocks"],hypr["block_size"])
+if (chat):
+    load_state_dict(model,safe_load(sys.argv[1]))
+if hypr["half"]:
+    from tinygrad import dtypes
+    model = model.cast(dtypes.float16)
+params = nn.state.get_parameters(model)
+optimizer = optm.llmOptimizer(params,hypr["steps"],hypr["starting_lr"],hypr["minimum_lr"])
+
+@TinyJit
+def step(x,y):
+    optimizer.zero_grad()
+
+    logits = model(x)
+    B,T,C = logits.shape
+    logits = logits.view(B*T,C)
+    y = y.view(B*T)
+    loss = logits.cross_entropy(y)
+
+    loss.backward()
+    optimizer.step()
+    return loss
+
+Tensor.training=True
+bar = tqdm(range(hypr["steps"]))
+
+for steps in bar:
+    nx, ny = next(batch)
+    x = Tensor(nx, device=Device.DEFAULT).realize()
+    y = Tensor(ny, device=Device.DEFAULT).realize()
+    loss = step(x, y)
+    if steps % 10 == 0:
+        l = loss.numpy()
+        loger.submit(log.logLoss, steps, l)
+        bar.set_postfix(loss= f"{l:.4f}")
+    if steps % 500 == 0:
+        loss.realize()
+        m = get_state_dict(model)
+        log.logModel(steps,m)
+        #TODO non sycronus safetensor loging
+        #loger.submit(log.logModel,steps,m)
+
+m = get_state_dict(model)
+log.logModel("final",m)
+loger.shutdown(wait=True)