llm-plugin

llm_chargpt.py

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+import llm
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+from dataclasses import dataclass
+import pickle
+
+import os
+
+@llm.hookimpl
+def register_models(register):
+    register(CharGPT())
+    
+# @torch.jit.script # good to enable when not using torch.compile, disable when using (our default)
+def new_gelu(x):
+    """
+    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
+    Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
+    """
+    return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+
+class LayerNorm(nn.Module):
+    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
+
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+
+    def forward(self, input):
+        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
+    
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and self.dropout == 0.0
+        if not self.flash:
+           # print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
+            # causal mask to ensure that attention is only applied to the left in the input sequence
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                        .view(1, 1, config.block_size, config.block_size))
+
+    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
+        q, k ,v  = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        if self.flash:
+            # efficient attention using Flash Attention CUDA kernels
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=True)
+        else:
+            # manual implementation of attention
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+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.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 = new_gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+class Block(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
+        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
+
+@dataclass
+class GPTConfig:
+    block_size: int = 2048
+    vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
+    n_layer: int = 12
+    n_head: int = 12
+    n_embd: int = 768
+    dropout: float = 0.0
+    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.vocab_size is not None
+        assert config.block_size is not None
+        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),
+            drop = nn.Dropout(config.dropout),
+            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f = LayerNorm(config.n_embd, bias=config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        # with weight tying when using torch.compile() some warnings get generated:
+        # "UserWarning: functional_call was passed multiple values for tied weights.
+        # This behavior is deprecated and will be an error in future versions"
+        # not 100% sure what this is, so far seems to be harmless. TODO investigate
+        self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying
+
+        # init all weights
+        self.apply(self._init_weights)
+        # apply special scaled init to the residual projections, per GPT-2 paper
+        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))
+
+        # report number of parameters
+        print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding:
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+        
+    def reset_parameters(self):
+      # Initialize weights using Glorot initialization
+      for param in self.parameters():
+          if param.dim() > 1:
+              torch.nn.init.xavier_uniform_(param)
+
+    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, idx, targets=None):
+        device = idx.device
+        b, t = idx.size()
+        assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
+
+        # forward the GPT model itself
+        tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
+        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, n_embd)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+
+        if targets is not None:
+            # if we are given some desired targets also calculate the loss
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+        else:
+            # inference-time mini-optimization: only forward the lm_head on the very last position
+            logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
+            loss = None
+
+        return logits, loss
+
+
+    @torch.no_grad()
+    def generate_streaming(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        """
+        Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
+        the sequence max_new_tokens times, feeding the predictions back into the model each time.
+        Yield the generated indices one at a time rather than concatenating them into a single tensor.
+        Most likely you'll want to make sure to be in model.eval() mode of operation for this.
+        """
+        max_idx_length = self.config.block_size
+        for _ in range(max_new_tokens):
+            # if the sequence context is growing too long we must crop it at block_size
+            idx_cond = idx if idx.size(1) <= max_idx_length else idx[:, -max_idx_length:]
+            # forward the model to get the logits for the index in the sequence
+            logits, _ = self(idx_cond)
+            # pluck the logits at the final step and scale by desired temperature
+            logits = logits[:, -1, :] / temperature
+            # optionally crop the logits to only the top k options
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            # apply softmax to convert logits to (normalized) probabilities
+            probs = F.softmax(logits, dim=-1)
+            # sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1)
+            # yield the next index
+            # append sampled index to the running sequence and continue
+            idx = torch.cat((idx, idx_next), dim=1)
+            yield idx_next.item()
+
+
+
+def remove_caseifer(text):
+    new_text = ""
+    i = 0
+    while i < len(text):
+        if text[i] == "↨":
+            if i+1 < len(text):
+                new_text += text[i+1].upper()
+                i += 1
+            else:
+                pass  # skip this index
+        else:
+            new_text += text[i]
+        i += 1
+    return new_text
+    
+def add_caseifer(text):
+   
+    # Define your set of acceptable characters (original + keys from replace_map + replace_values)
+    #chars = "\n\"\t' &@!$#,/\\+=-<>*%.…_:;[]}{()^?0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz§↨©®™¶¥¼°½¾«»£βθ♪ƒ~±¤º·\x8f€¢"
+    tokenlist = "\n\t\x8f !#$%&()*+,-./:;<=>?@[\]^_{|}~§↨©®™¶¥¼°½¾«»£βθ♪ƒ±¤º·€¢\"'…0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
+
+    upperlist = set("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
+    new_text = []
+    for char in text:
+        if char in tokenlist:
+            if char in upperlist:
+                new_text.append("↨" + char.lower())
+            else:
+                new_text.append(char)
+        else:
+            pass
+    return "".join(new_text)
+
+    
+
+model_dir = '16bit'
+device = 'cuda'
+dtype = 'bfloat16'
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+device_type = 'cuda' if 'cuda' in device else 'cpu'
+ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
+ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
+max_new_tokens = 2048 # number of tokens generated in each sample
+temperature = 0.8 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
+top_k = 24 # retain only the top_k most likely tokens, clamp others to have 0 probability
+
+ckpt_path = os.path.join(model_dir, 'ckpt.pt')
+checkpoint = torch.load(ckpt_path, map_location=device)
+gptconf = GPTConfig(**checkpoint['model_args'])
+model = GPT(gptconf)
+state_dict = checkpoint['model']
+unwanted_prefix = '_orig_mod.'
+for k,v in list(state_dict.items()):
+    if k.startswith(unwanted_prefix):
+        state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+model.load_state_dict(state_dict)
+
+model.eval()
+model.to(device)
+meta_path = os.path.join(model_dir, 'meta.pkl')
+load_meta = os.path.exists(meta_path)
+with open(meta_path, 'rb') as f:
+    meta = pickle.load(f)
+# TODO want to make this more general to arbitrary encoder/decoder schemes
+stoi, itos = meta['stoi'], meta['itos']
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+class CharGPT(llm.Model):
+    model_id = "chargpt"
+      
+    def execute(self, prompt, stream, response, conversation):
+        text = prompt.prompt
+        shift = False  
+       # generated_text = ''
+        start_ids = encode(add_caseifer(text))
+        x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
+        for idx_next in model.generate_streaming(x, max_new_tokens, temperature=temperature, top_k=top_k):
+            # convert the index to a character and print it to the screen
+            char = decode([idx_next])
+            # check for newline character
+            if char == '§':
+                # append the completed line to the list or print it to the screen
+            #     generated_sequences.append(generated_text)
+                # reset the generated text for the next line
+               # generated_data = generated_text
+               # generated_text = ''
+                break
+
+            # append the character to the generated text
+            if shift:
+             #   generated_text += char.upper()
+                yield char.upper()# + ''
+            #  print(char.upper(), end='', flush=True)
+                shift = False
+            elif char == '↨':
+                shift = True
+            else:
+              #  generated_text += char
+                yield char# + ''
+                #print(char, end='', flush=True)

pyproject.toml

@@ -0,0 +1,6 @@
+[project]
+name = "chargpt"
+version = "0.1"
+
+[project.entry-points.llm]
+chargpt = "llm_chargpt"