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app.py

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+import torch
+from transformers import GPT2Tokenizer
+import gradio as gr
+import tiktoken
+import model_file
+from dataclasses import dataclass
+import time
+import os
+import torch.nn.functional as F
+
+num_return_sequences = 1
+max_length = 100
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024
+    vocab_size: int = 50304
+    n_layer: int = 12
+    n_head: int = 12
+    n_embd: int = 768
+
+
+# tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+tokenizer = tiktoken.get_encoding("gpt2")
+
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+    device = "mps"
+
+device = torch.device(device)
+
+try:
+    model = model_file.get_model().to(device)
+    checkpoint = torch.load(os.path.join(os.path.dirname(__file__), "model_00350.pt"), map_location=device)
+    state_dict = {key.replace("_orig_mod.", ""): value for key, value in checkpoint['model'].items()}
+    model.load_state_dict(state_dict=state_dict)
+    model.eval()
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise e
+
+examples = [
+    "Who are you?",
+    "Write a Shakespeare short poem.",
+    "Tell me a joke.",
+    "What is the meaning of life?",
+]
+
+
+def chat_fn(message, history):
+    # Tokenize
+    print(f"message: {message}")
+    tokens = tokenizer.encode(message)
+    tokens = torch.tensor(tokens, dtype=torch.int32)
+    tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1)
+    x = tokens.to(device)
+    while x.size(1) < max_length:
+        # forward pass through model to get logits
+        with torch.no_grad():
+            logits = model(x)[0]  # batch_size, T, vocab_size
+            logits = logits[:, -1, :]  # get last position logits B, vocab_size
+
+            # calculate probabilities
+            probs = F.softmax(logits, dim=-1)
+
+            # doing topk here, HF defafult is 50
+            # topk is (5, 50), top_indices is (5, 50) too
+            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+
+            # sampling a token from topk
+            ix = torch.multinomial(input=topk_probs, num_samples=1)  # (B, 1) (5, 1)
+
+            # gather corresponding indices
+            xcol = torch.gather(input=topk_indices, dim=-1, index=ix)
+            # append to the seq
+            x = torch.cat([x, xcol], dim=1)
+
+    for i in range(num_return_sequences):
+        tokens = x[i, :max_length].tolist()
+        decoded = tokenizer.decode(tokens)
+
+        yield decoded
+
+
+gr.ChatInterface(chat_fn, examples=examples).launch()
+# interface.launch()

model_00350.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1e55d3d3c97b288aeb1659b766d405b8fcee33051dac8281869a0063c77a3d0
+size 548296450

model_file.py

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+import torch
+import tiktoken
+import inspect
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024  # this is max sequence len
+    vocab_size: int = 50304  # 50257 # total vocab including 256 bytes + 1 special token (<|endoftext|>) and 1000-257 BPE merges
+    n_layer: int = 12  # number of layers
+    n_head: int = 12  # total number of attention heads
+    n_embd: int = 768  # embedding dimension
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        n_head = config.n_head
+        n_embd = config.n_embd
+
+        assert n_embd % n_head == 0
+
+        # query, key, value prjections all combined
+        self.c_attn = nn.Linear(n_embd, 3 * n_embd)
+
+        # output projection, after `v` is already multiplied with attention_scores
+        self.c_proj = nn.Linear(n_embd, n_embd)
+
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+        block_size = config.block_size
+
+        self.register_buffer('bias', torch.tril(torch.ones(block_size, block_size)).view(1, 1, block_size, block_size))
+
+        self.n_embd = n_embd
+        self.n_head = n_head
+
+    def forward(self, x):
+        B, T, C = x.size()  # batch_size, sequence_len, embedding_dim (n_embd)
+        # total dim = n_head * head_size
+        # example GPT2 has 12 heads with each hs = 64 thus C= 12*64 = 768
+
+        qkv = self.c_attn(x)  # get combined qkv matix B, T, n_embd * 3(768*3=2304)
+
+        q, k, v = qkv.split(self.n_embd, dim=2)  # each item gets n_embd size, dimension against two
+
+        # b, seq, n_embd -> b, seq, n_heads, head_size -> b, n_heads, seq_len, head_size
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        # final-> bs, n_heads, seq_len, mini-n_head_embd
+
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+
+        # # print(f"shape of q: {q.shape}... shape of k : {k.shape}")
+
+        # attn = (q @ k.transpose(-2, -1))/(math.sqrt(k.shape[-1]))
+
+        # # apply masked fill at places where mask ==0, remember tril is lower triangle
+        # attn = attn.masked_fill(mask = self.bias[ : , : , :T, :T] == 0, value=float('-inf'))
+
+        # attn = F.softmax(attn, dim=-1)
+
+        # y = attn @ v # B, n_heads, T/seq, T @ B, n_heads, T/Seq, head_size) -> B, n_heads, T, head_size
+
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)  # flash attention
+
+        # transpose y to merge all n_heads. B, n_heads, T, head_size -> transpose B, T, n_heads, head_size -> view B, T, Channel_size/n_emb 768 
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+
+        # out projection, B, T, C -> B, T, C
+        y = self.c_proj(y)
+
+        return y
+
+    def generate(self, prompt):
+        if not isinstance(prompt, str) or len(prompt) == 0:
+            return "Say something!"
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
+        self.gelu = nn.GELU(approximate='tanh')
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=nn.Embedding(config.vocab_size, config.n_embd),
+                wpe=nn.Embedding(config.block_size, config.n_embd),
+                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+                ln_f=nn.LayerNorm(config.n_embd)
+            ))
+
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # weight sharing
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # weight initialization
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            if hasattr(module, 'NANOGPT_SCALE_INIT'):
+                std *= (2 * self.config.n_layer) ** -0.5
+
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.size()  # batch , seq_len
+
+        # check if incoming seq_len of idx is within limits
+        assert T <= self.config.block_size, f"Cannot proceed as your Sequence len : {T} is more than {self.config.block_size}"
+
+        # forward for token and position encodings
+        # shape (T)
+        pos = torch.arange(0, T, dtype=torch.int32, device=idx.device)
+        pos_emb = self.transformer.wpe(pos)  # position embds of shape (T, n_embd)
+        token_emb = self.transformer.wte(idx)  # token embds of shape (Batch, T/seq_len, n_embd)
+
+        x = pos_emb + token_emb
+
+        # now forward through transformer blocks
+        for block in self.transformer.h:
+            x = block(x)
+
+        # pass through final layernorm
+        x = self.transformer.ln_f(x)
+
+        # pass through final LM_HEAD
+        logits = self.lm_head(x)  # shape (Batch_size, T, vocab_size)
+
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+
+        return logits, loss
+
+    def configure_optimizers(self, weight_decay, learning_rate, device_type):
+        # start with all of the candidate parameters (that require grad)
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
+        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
+        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
+        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
+        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
+        optim_groups = [
+            {'params': decay_params, 'weight_decay': weight_decay},
+            {'params': nodecay_params, 'weight_decay': 0.0}
+        ]
+        num_decay_params = sum(p.numel() for p in decay_params)
+        num_nodecay_params = sum(p.numel() for p in nodecay_params)
+
+        print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
+        print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
+        # Create AdamW optimizer and use the fused version if it is available
+        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
+        use_fused = fused_available and device_type == "cuda"
+
+        print(f"using fused AdamW: {use_fused}")
+        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
+        return optimizer
+
+
+class DataLoaderLite:
+    def __init__(self, B, T, process_rank, num_processes):
+        self.B = B
+        self.T = T
+        self.process_rank = process_rank
+        self.num_processes = num_processes
+
+        with open('input.txt', 'r') as f:
+            text = f.read()
+        enc = tiktoken.get_encoding('gpt2')
+        tokens = enc.encode(text)
+        self.tokens = torch.tensor(tokens)
+        print(f'loaded len : {len(self.tokens)}')
+        # print(f'1 epoch = {len(self.tokens)//(B*T)} batches ')
+        self.current_position = self.B * self.T * self.process_rank
+
+    def next_batch(self):
+        B, T = self.B, self.T
+        buf = self.tokens[self.current_position: self.current_position + (B * T) + 1]
+        y = buf[1:].view(B, T)
+        x = buf[:-1].view(B, T)
+
+        self.current_position += (B * T * self.num_processes)
+
+        if self.current_position + (B * T * self.num_processes + 1) > len(self.tokens):
+            self.current_position = self.B * self.T * self.process_rank
+        return x, y
+
+
+def get_model():
+    model = GPT(GPTConfig())
+    return model
+
+# cuda = torch.cuda.is_available()
+# torch.set_float32_matmul_precision('high')
+
+# max_lr = 6e-4
+# min_lr = 0.1 * max_lr
+# warmup_steps = 10
+# max_steps = 5000
+
+# def get_lr(iteration):
+#     if iteration < warmup_steps:
+#         return max_lr * (iteration + 1) / warmup_steps
+#     if iteration > max_steps:
+#         return min_lr
+
+#     decay_ratio = (iteration - warmup_steps) / (max_steps - warmup_steps)
+
+#     assert 0<= decay_ratio <= 1
+
+#     coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
+#     return min_lr + coeff * (max_lr - min_lr)
+
+
+# model = GPT(GPTConfig()).to(device=device)
+
+# model = torch.compile(model, mode='default')
+
+# if ddp:
+#     print("\n\n====================================\nDDP")
+#     model = DDP(module=model,device_ids=[ddp_local_rank])
+
+# raw_model = model.module if ddp else model
+
+# # optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, betas=(0.9, 0.95), eps=1e-8)
+# optimizer = raw_model.configure_optimizers(weight_decay=0.1, learning_rate=6e-4, device_type=device)
+
+
+# total_batch_size = 524288
+
+# B = 16
+# T = 1024
+
+# assert total_batch_size % (B * T * ddp_world_size) == 0, "just to make sure total batch size is divisible by B*T"
+
+# grad_accumulation_steps = total_batch_size // (B * T * ddp_world_size)
+
+# if master_process:
+#     print(f"\nGradient accumulation steps needed with B: {B} and T: {T} for total batch size: {total_batch_size} = {grad_accumulation_steps}")
+#     print(f"total params: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
+
+
+# train_loader = DataLoaderLite(B=B, T=T, process_rank=ddp_rank, num_processes=ddp_world_size)
+
+# # torch.cuda.amp.autocast(enabled=True)
+# torch.backends.cuda.matmul.allow_tf32 = True
+# torch.backends.cudnn.allow_tf32 = True
+
+# log_dir = "logs"
+# os.makedirs(log_dir, exist_ok=True)
+
+# start= time.time()
+
+# for step in range(max_steps):
+#     t0 = time.time()
+#     optimizer.zero_grad()
+
+#     loss_mini = 0.0
+#     for micro_step in range(grad_accumulation_steps):
+#         x, y = train_loader.next_batch()
+#         x, y = x.to(device=device), y.to(device)
+#         with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
+#             logits, loss = model(x, y)
+#             # if i == 0:
+#             #     assert logits.dtype == torch.bfloat16
+#             #     assert loss.dtype == torch.float32
+#             #     assert model.transformer.wte.weight.dtype == torch.float32
+
+#         loss = loss/grad_accumulation_steps
+#         loss_mini += loss.detach()
+#         if ddp:
+#             model.require_backward_grad_sync = (micro_step == grad_accumulation_steps - 1)
+#         loss.backward()
+#     if ddp:
+#         dist.all_reduce(loss_mini, op=dist.ReduceOp.AVG)
+#     if master_process and step%50==0 and step > 100:
+#             print(f"saving at: {step}")            
+#             checkpoint_path = os.path.join(log_dir, f"model_{step:05d}.pt")
+#             checkpoint = {
+#                 'model': raw_model.state_dict(),
+#                 'config': raw_model.config,
+#                 'step': step
+#             }
+#             torch.save(checkpoint, checkpoint_path)
+#     # grad clip
+#     norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+
+#     lr = get_lr(step)
+#     for param_group in optimizer.param_groups:
+#         param_group['lr'] = lr 
+
+#     optimizer.step()
+#     torch.cuda.synchronize()
+
+#     t1 = time.time() 
+#     dt = (t1 - t0) 
+#     tokens_per_sec = (train_loader.B * train_loader.T * grad_accumulation_steps * ddp_world_size) / (dt)
+#     if master_process:
+#         # print happens via CPU, hence wait (synchronize GPU)
+#         print(f'step : {step+1} | loss: {loss_mini.item()} | lr: {lr:.7f} | dt: {dt* 1000:.2f} ms | tokens/sec: {tokens_per_sec:_.6f} | norm: {norm:.2f}')
+
+
+# end = time.time()
+# print("final loss: ", loss*grad_accumulation_steps)
+# print(f"total time: {end - start} seconds")
+# torch.save(model.state_dict(), "5k-run-new-DDP.pt")
+
+# if ddp:
+#     destroy_process_group()

requirements.txt

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+torch
+tiktoken
+transformers
+gradio