Update modelling_custom.py

modelling_custom.py

@@ -1,7 +1,357 @@
 import torch
 from transformers import PreTrainedModel
-from model import Moose
+from dataclasses import dataclass
 
+@dataclass 
+class ModelArgs:
+    dim: int = 768 
+    n_layers: int = 16 
+    n_heads: int = 16 
+    n_kv_heads: Optional[int] = 4
+    vocab_size: int = 50304
+    multiple_of: int = 256 
+    ffn_dim_multiplier: Optional[float] = None
+    norm_eps: float = 1e-5
+    rope_theta: float = 50000 
+    max_batch_size: int = 4
+    max_seq_len: int = 1024 
+    device: str = 'cuda' if torch.cuda.is_available() else 'cpu'
+    dropout_rate: float = 0.1 
+
+params = ModelArgs()
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    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)
+        return output * self.weight
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device, dtype=torch.float32)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return freqs_cis.to(params.device)
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis.shape {freqs_cis.shape} != (x.shape[1], x.shape[-1]) {(x.shape[1], x.shape[-1])}'
+    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+    return freqs_cis.view(*shape)
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    bs, seqlen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, seqlen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, seqlen, n_kv_heads * n_rep, head_dim)
+    ) 
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.n_heads = args.n_heads
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        self.n_rep = args.n_heads // self.n_kv_heads
+        self.head_dim = args.dim // args.n_heads
+
+        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+
+        self.cache_k = torch.zeros(
+            (args.max_batch_size, args.max_seq_len, self.n_kv_heads, self.head_dim),
+            requires_grad = False
+        ).to(args.device)
+        self.cache_v = torch.zeros(
+            (args.max_batch_size, args.max_seq_len, self.n_kv_heads, self.head_dim),
+            requires_grad = False
+        ).to(args.device)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        start_pos: int = None,
+    ):
+        bsz, seqlen, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+
+        xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
+
+        if start_pos is not None: # if we're performing inference, use kv caching 
+            self.cache_k = self.cache_k.to(xq)
+            self.cache_v = self.cache_v.to(xq)
+
+            # set the values in our cache according to the current input
+            self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
+            self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
+
+            # grab our key and value matrixes which have a longer sequence length than our queries
+            keys = self.cache_k[:bsz, : start_pos + seqlen]
+            values = self.cache_v[:bsz, : start_pos + seqlen]
+        else:
+            # if we're training, do full sequence length 
+            keys, values = xk, xv
+
+        keys = repeat_kv(keys, self.n_rep) 
+        values = repeat_kv(values, self.n_rep) 
+
+        xq = xq.transpose(1, 2)  
+        keys = keys.transpose(1, 2)  
+        values = values.transpose(1, 2) 
+
+        scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if mask is not None:
+            scores = scores + mask 
+        scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+
+        output = torch.matmul(scores, values) 
+        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+        return self.wo(output)
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+    ):
+        super().__init__()
+        # custom dim factor multiplier that ensures we're using a multiple of "multiple_of" for hardware efficiency reasons
+        hidden_dim = int(2 * hidden_dim / 3)
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.n_heads = args.n_heads
+        self.dim = args.dim
+        self.head_dim = args.dim // args.n_heads
+        self.attention = Attention(args)
+        self.feed_forward = FeedForward(
+            dim=args.dim,
+            hidden_dim=4 * args.dim,
+            multiple_of=args.multiple_of,
+            ffn_dim_multiplier=args.ffn_dim_multiplier,
+        )
+        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.dropout_rate = args.dropout_rate
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        start_pos: int = None,
+        training = False,
+    ):
+        h = x + F.dropout(self.attention(self.attention_norm(x), freqs_cis, mask, start_pos), p=self.dropout_rate, training=training)
+        out = h + F.dropout(self.feed_forward(self.ffn_norm(h)), p=self.dropout_rate, training=training)
+        return out
+
+class Moose(nn.Module):
+    def __init__(self, params: ModelArgs):
+        super().__init__()
+        self.params = params
+        self.vocab_size = params.vocab_size
+        self.n_layers = params.n_layers
+        self.max_seq_len = params.max_seq_len
+
+        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
+
+        self.layers = torch.nn.ModuleList()
+        for _ in range(params.n_layers):
+            self.layers.append(TransformerBlock(params))
+
+        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
+        self.output = nn.Linear(
+            params.dim,
+            params.vocab_size,
+            bias=False)
+
+        self.freqs_cis = precompute_freqs_cis(
+            params.dim // params.n_heads,
+            params.max_seq_len * 2,
+            params.rope_theta,)
+
+        # precompute the causal attention mask
+        mask = torch.full((params.max_seq_len, params.max_seq_len),
+                          float("-inf"),
+                          device=params.device)
+        mask = torch.triu(mask, diagonal=1)
+        self.register_buffer('mask', mask)
+
+        self.criterion = nn.CrossEntropyLoss()
+
+    def forward(self,
+                tokens: torch.Tensor,
+                targets: torch.Tensor):
+        bsz, seqlen = tokens.shape
+        assert tokens.shape == targets.shape
+        assert seqlen == self.max_seq_len
+
+        h = self.tok_embeddings(tokens)
+
+        freqs_cis = self.freqs_cis.to(h.device)
+        freqs_cis = self.freqs_cis[:seqlen]
+
+        for layer in self.layers:
+            h = layer(
+                h,
+                freqs_cis,
+                self.mask,
+                start_pos = None,
+                training = True
+            )
+
+        h = self.norm(h)
+        logits = self.output(h).float()
+
+        loss = self.criterion(
+            logits.view(bsz * seqlen, self.vocab_size),
+            targets.reshape(bsz * seqlen))
+
+        return logits, loss
+
+    @torch.inference_mode()
+    def forward_inference(self,
+                          tokens: torch.Tensor,
+                          start_pos: int,
+                          max_context_window: int,
+                         ):
+        _bsz, seqlen = tokens.shape
+        h = self.tok_embeddings(tokens)
+        self.freqs_cis = self.freqs_cis.to(h.device)
+        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
+
+        mask = self.mask[:seqlen, :seqlen]
+        mask = torch.hstack(
+            [torch.zeros((seqlen, start_pos), device=tokens.device), mask]
+        ).type_as(h)
+
+        for layer in self.layers:
+            h = layer(
+                h,
+                freqs_cis,
+                mask,
+                start_pos = start_pos
+            )
+        h = self.norm(h)
+        logits = self.output(h).float()
+        return logits
+
+    @torch.inference_mode() 
+    def Sampler(
+        self,
+        logits: torch.Tensor, 
+        temperature: float,
+        top_p: float
+    ) -> torch.Tensor:
+        logits = logits[:,-1,:]
+
+        logits.div_(temperature) 
+
+        probs = torch.softmax(logits, dim=-1, dtype=torch.float) 
+
+        probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+
+        probs_sum = torch.cumsum(probs_sort, dim=-1)
+        top_ps_mask = (probs_sum - probs_sort) > top_p
+        probs_sort = torch.where(top_ps_mask, 0, probs_sort)
+        probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+        probs = torch.gather(probs_sort, dim=-1, index=torch.argsort(probs_idx, dim=-1))
+        next_token_id = torch.multinomial(probs, num_samples=1)
+
+        return next_token_id
+
+    @torch.inference_mode()
+    def generate(
+        self,
+        prompt: str,
+        max_gen_len: int = 100,
+        memory_saver_div: int = 1, 
+        temperature: float = 0.6,
+        top_p: float = 0.9, 
+    ) -> str:
+        assert ((memory_saver_div & (memory_saver_div-1)) == 0) & (memory_saver_div > 0), f'memory_saver_div {memory_saver_div} must be power of 2'
+        max_context_window = self.max_seq_len // memory_saver_div
+
+        enc = tiktoken.get_encoding('gpt2')
+        tokens = enc.encode(prompt)
+
+        tokens = torch.tensor(tokens, device=self.params.device)
+        tokens = tokens.unsqueeze(0) if len(tokens.shape)==1 else tokens
+
+        start_pos = max(tokens.shape[1] - max_context_window, 0)
+        eot = enc._special_tokens['<|endoftext|>'] # end of text token
+
+        while True:
+            logits = self.forward_inference(
+                tokens[:,-max_context_window:],
+                start_pos = start_pos,
+                max_context_window = max_context_window
+            )
+
+            next_token = self.Sampler(
+                logits = logits,
+                temperature = temperature,
+                top_p = top_p,
+            )
+
+            tokens = torch.cat((tokens, next_token), dim=1)
+
+            if next_token.item() == eot:
+                break
+
+            if tokens.shape[1] >= max_context_window:
+                start_pos += 1
+
+        output = enc.decode(tokens.squeeze(0).tolist())
+
+        return output
+        
 class MooseModel(PreTrainedModel):
     def __init__(self, config):
         super().__init__(config)