Create model.py

model.py

@@ -0,0 +1,206 @@
+import math
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from transformers import PreTrainedModel
+from .config import GPTConfig
+
+
+################################
+###         Layers           ###
+################################
+
+class Rotary(torch.nn.Module):
+
+    def __init__(self, dim, base=10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.seq_len_cached = None
+        self.cos_cached = None
+        self.sin_cached = None
+
+    def forward(self, x):
+        seq_len = x.shape[1]
+        if seq_len != self.seq_len_cached:
+            self.seq_len_cached = seq_len
+            t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+            freqs = torch.outer(t, self.inv_freq).to(x.device)
+            self.cos_cached = freqs.cos()
+            self.sin_cached = freqs.sin()
+        return self.cos_cached[None, :, None, :], self.sin_cached[None, :, None, :]
+
+def apply_rotary_emb(x, cos, sin):
+    assert x.ndim == 4 # multihead attention
+    d = x.shape[3]//2
+    x1 = x[..., :d]
+    x2 = x[..., d:]
+    y1 = x1 * cos + x2 * sin
+    y2 = x1 * (-sin) + x2 * cos
+    return torch.cat([y1, y2], 3)
+
+def rmsnorm(x0, eps=1e-6):
+    x = x0.float()
+    x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
+    return x.type_as(x0)
+
+
+class RMSNorm(nn.Module):
+    """ Root Mean Square Normalization """
+    def __init__(self, dim: int, weight: bool = False, bias: bool = False, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        
+        if weight:
+            self.weight = nn.Parameter(torch.ones(dim))
+        else:
+            self.register_parameter("weight", None)
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(dim))
+        else:
+            self.register_parameter("bias", None)
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        if self.weight is not None:
+            output = output * self.weight
+        if self.bias is not None:
+            output = output + self.bias
+        return output
+
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.head_dim = self.n_embd // self.n_head
+        assert self.n_embd % self.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(self.n_embd, 3 * self.n_embd, bias=False)
+        # output projection
+        self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
+        self.rotary = Rotary(self.head_dim)
+
+    def forward(self, x):
+        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, self.head_dim)
+        q = q.view(B, T, self.n_head, self.head_dim)
+        v = v.view(B, T, self.n_head, self.head_dim)
+        cos, sin = self.rotary(q)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        y = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True)
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+        # output projection
+        y = self.c_proj(y)
+        return y
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True)
+        return self.weight * x / (norm + self.eps)
+
+class Block(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.attn = CausalSelfAttention(config)
+        self.mlp = MLP(config)
+        self.attn_scale = (1 / (2 * config.n_layer)**0.5)
+
+    def forward(self, x):
+        x = x + self.attn_scale * self.attn(rmsnorm(x))
+        x = x + self.mlp(rmsnorm(x))
+        return x
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.gelu    = nn.GELU()
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+################################
+###          Model           ###
+################################
+
+class GPT(PreTrainedModel):
+    config_class = GPTConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = nn.ModuleDict(dict(
+            wte=nn.Embedding(config.vocab_size, config.n_embd),
+            drop=nn.Dropout(config.dropout),
+            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        self.apply(self._init_weights)
+
+        # GPT-2 style scaled init
+        for pn, p in self.named_parameters():
+            if pn.endswith('c_proj.weight'):
+                torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            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, input_ids, labels=None):
+        tok_emb = self.transformer.wte(input_ids)
+        x = self.transformer.drop(tok_emb)
+
+        for block in self.transformer.h:
+            x = block(x)
+        x = rmsnorm(x)
+
+        logits = self.lm_head(x)
+
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-1)
+
+        return {'loss': loss, 'logits': logits} if loss is not None else {'logits': logits}
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits = self(idx_cond)['logits']
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx