add training code for hf jobs

code/model.py

@@ -0,0 +1,117 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class KairoGPTConfig:
+    def __init__(self, vocab_size, block_size=4096, n_layer=14, n_head=14, n_embd=896, dropout=0.1):
+        self.vocab_size = vocab_size
+        self.block_size = block_size
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_embd = n_embd
+        self.dropout = dropout
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        assert cfg.n_embd % cfg.n_head == 0
+        self.qkv = nn.Linear(cfg.n_embd, 3 * cfg.n_embd)
+        self.proj = nn.Linear(cfg.n_embd, cfg.n_embd)
+        self.attn_drop_p = cfg.dropout
+        self.resid_drop = nn.Dropout(cfg.dropout)
+        self.n_head = cfg.n_head
+
+    def forward(self, x):
+        B, T, C = x.shape
+        q, k, v = self.qkv(x).split(C, dim=2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        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)
+
+        y = F.scaled_dot_product_attention(
+            q, k, v,
+            dropout_p=self.attn_drop_p if self.training else 0.0,
+            is_causal=True,
+        )
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        return self.resid_drop(self.proj(y))
+
+
+class Block(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(cfg.n_embd)
+        self.attn = CausalSelfAttention(cfg)
+        self.ln2 = nn.LayerNorm(cfg.n_embd)
+        self.mlp = nn.Sequential(
+            nn.Linear(cfg.n_embd, 4 * cfg.n_embd),
+            nn.GELU(),
+            nn.Linear(4 * cfg.n_embd, cfg.n_embd),
+            nn.Dropout(cfg.dropout),
+        )
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class KairoGPT(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.n_embd)
+        self.pos_emb = nn.Parameter(torch.zeros(1, cfg.block_size, cfg.n_embd))
+        self.drop = nn.Dropout(cfg.dropout)
+        self.blocks = nn.Sequential(*[Block(cfg) for _ in range(cfg.n_layer)])
+        self.ln_f = nn.LayerNorm(cfg.n_embd)
+        self.head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                nn.init.zeros_(module.bias)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        x = self.drop(self.tok_emb(idx) + self.pos_emb[:, :T, :])
+        x = self.blocks(x)
+        x = self.ln_f(x)
+        logits = self.head(x)
+
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=0.8, top_k=40):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.cfg.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, top_k)
+                logits[logits < v[:, [-1]]] = float("-inf")
+            probs = F.softmax(logits, dim=-1)
+            next_id = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, next_id), dim=1)
+        return idx
+
+    @torch.no_grad()
+    def generate_stream(self, idx, max_new_tokens, temperature=0.8, top_k=40):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.cfg.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, top_k)
+                logits[logits < v[:, [-1]]] = float("-inf")
+            probs = F.softmax(logits, dim=-1)
+            next_id = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, next_id), dim=1)
+            yield next_id.item()