CosmicFish Initial commit

LICENSE

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config.json

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+{
+  "model_type": "cosmicfish",
+  "architectures": [
+    "CosmicFishForCausalLM"
+  ],
+  "vocab_size": 50257,
+  "n_embd": 704,
+  "n_layer": 12,
+  "n_head": 16,
+  "block_size": 512,
+  "bias": true,
+  "dropout": 0.1,
+  "eps": 1e-06,
+  "use_rotary": true,
+  "use_swiglu": true,
+  "use_gqa": true,
+  "use_qk_norm": false,
+  "n_query_groups": 4,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.0",
+  "use_cache": true,
+  "pad_token_id": 50256,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256
+}

example_usage.py

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+"""
+Example usage of CosmicFish model
+"""
+import torch
+from transformers import GPT2Tokenizer
+from modeling_cosmicfish import CosmicFish, CosmicConfig
+import json
+
+def load_cosmicfish(model_dir):
+    """Load CosmicFish model and tokenizer"""
+    # Load config
+    with open(f"{model_dir}/config.json", "r") as f:
+        config_dict = json.load(f)
+
+    # Create CosmicConfig
+    config = CosmicConfig(
+        vocab_size=config_dict["vocab_size"],
+        block_size=config_dict["block_size"],
+        n_layer=config_dict["n_layer"],
+        n_head=config_dict["n_head"],
+        n_embd=config_dict["n_embd"],
+        bias=config_dict["bias"],
+        dropout=0.0,  # Set to 0 for inference
+        use_rotary=config_dict["use_rotary"],
+        use_swiglu=config_dict["use_swiglu"],
+        use_gqa=config_dict["use_gqa"],
+        n_query_groups=config_dict["n_query_groups"],
+        use_qk_norm=config_dict["use_qk_norm"]
+    )
+
+    # Create model
+    model = CosmicFish(config)
+
+    # Load weights
+    state_dict = torch.load(f"{model_dir}/pytorch_model.bin", map_location="cpu")
+    model.load_state_dict(state_dict)
+    model.eval()
+
+    # Load tokenizer
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+    return model, tokenizer
+
+# Example usage:
+# model, tokenizer = load_cosmicfish("./")
+# input_text = "The future of AI is"
+# inputs = tokenizer.encode(input_text, return_tensors="pt")
+# outputs = model.generate(inputs, max_length=50, temperature=0.7, do_sample=True)
+# response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# print(response)

modeling_cosmicfish.py

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+"""
+CosmicFish Model - Inference Only Version
+Minimal implementation for loading and running inference with CosmicFish.
+Removes all training-specific code and optimizations.
+"""
+
+import math
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class CosmicConfig:
+    """Configuration class for CosmicFish."""
+
+    def __init__(self,
+                 vocab_size=50257,
+                 block_size=512,
+                 n_layer=12,
+                 n_head=16,
+                 n_embd=704,
+                 bias=True,
+                 dropout=0.0,  # Always 0 for inference
+                 n_query_groups=4,
+                 eps=1e-6,
+                 use_rotary=True,
+                 use_swiglu=True,
+                 use_qk_norm=False,
+                 use_gqa=True):
+        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.bias = bias
+        self.dropout = dropout
+        self.eps = eps
+        self.use_rotary = use_rotary
+        self.use_swiglu = use_swiglu
+        self.use_qk_norm = use_qk_norm
+        self.use_gqa = use_gqa
+        self.n_query_groups = n_query_groups if use_gqa else n_head
+        # Ensure n_head is divisible by n_query_groups
+        assert n_head % self.n_query_groups == 0, "n_head must be divisible by n_query_groups"
+
+
+class RMSNorm(nn.Module):
+    """Root Mean Square Normalization"""
+
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+        return self.weight * (x / rms)
+
+
+def precompute_freqs_cis(dim, end, theta=10000.0):
+    """Precompute the frequency tensor for complex exponentials (cis)"""
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    return freqs_cis
+
+
+def apply_rotary_emb(xq, xk, freqs_cis):
+    """Apply rotary embeddings to input tensors"""
+    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))
+
+    seq_len = xq_.size(2)
+    if freqs_cis.size(0) < seq_len:
+        raise ValueError(f"freqs_cis has only {freqs_cis.size(0)} values but sequence length is {seq_len}")
+
+    freqs_cis_seq = freqs_cis[:seq_len]
+    xq_out = torch.view_as_real(xq_ * freqs_cis_seq.unsqueeze(0)).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis_seq.unsqueeze(0)).flatten(3)
+
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+class GroupedQueryAttention(nn.Module):
+    """Grouped Query Attention (GQA) implementation"""
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+
+        head_dim = config.n_embd // config.n_head
+        self.head_dim = head_dim
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.n_query_groups = config.n_query_groups
+
+        self.kv_heads = config.n_head // config.n_query_groups if config.use_gqa else config.n_head
+        qkv_proj_size = (config.n_head + 2 * self.kv_heads) * head_dim
+
+        self.c_attn = nn.Linear(config.n_embd, qkv_proj_size, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+
+        # Flash attention support
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        if not self.flash:
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                 .view(1, 1, config.block_size, config.block_size))
+
+        # Query-key normalization
+        self.qk_norm = getattr(config, 'use_qk_norm', False)
+        if self.qk_norm:
+            self.q_norm = RMSNorm(head_dim, eps=getattr(config, 'eps', 1e-6))
+            self.k_norm = RMSNorm(head_dim, eps=getattr(config, 'eps', 1e-6))
+
+    def forward(self, x, freqs_cis=None):
+        B, T, C = x.size()
+        qkv = self.c_attn(x)
+        head_dim = C // self.n_head
+
+        q_size = self.n_head * head_dim
+        k_size = self.kv_heads * head_dim
+        v_size = self.kv_heads * head_dim
+
+        q, k, v = qkv.split([q_size, k_size, v_size], dim=2)
+
+        q = q.view(B, T, self.n_head, head_dim).transpose(1, 2)
+        k = k.view(B, T, self.kv_heads, head_dim).transpose(1, 2)
+        v = v.view(B, T, self.kv_heads, head_dim).transpose(1, 2)
+
+        # Repeat k and v if needed for GQA
+        if self.kv_heads < self.n_head:
+            repeats = self.n_head // self.kv_heads
+            k = k.repeat_interleave(repeats, dim=1)
+            v = v.repeat_interleave(repeats, dim=1)
+
+        # Apply rotary embeddings
+        if freqs_cis is not None:
+            q, k = apply_rotary_emb(q, k, freqs_cis)
+
+        # Apply query-key normalization
+        if self.qk_norm:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+
+        # Compute attention
+        if self.flash:
+            y = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True
+            )
+        else:
+            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)
+            y = att @ v
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.c_proj(y)
+        return y
+
+
+class Block(nn.Module):
+    """Transformer block"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = RMSNorm(config.n_embd, eps=config.eps)
+        self.ln_2 = RMSNorm(config.n_embd, eps=config.eps)
+        self.attn = GroupedQueryAttention(config)
+
+        # MLP implementation based on configuration
+        if config.use_swiglu:
+            # SwiGLU MLP
+            self.mlp = nn.ModuleDict(dict(
+                gate=nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias),
+                up=nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias),
+                down=nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias),
+                act=nn.SiLU(),
+            ))
+            m = self.mlp
+            self.mlpf = lambda x: m.down(m.act(m.up(x)) * m.gate(x))
+        else:
+            # Traditional MLP
+            self.mlp = nn.ModuleDict(dict(
+                c_fc=nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias),
+                c_proj=nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias),
+                act=nn.GELU(),
+            ))
+            m = self.mlp
+            self.mlpf = lambda x: m.c_proj(m.act(m.c_fc(x)))
+
+    def forward(self, x, freqs_cis=None):
+        x = x + self.attn(self.ln_1(x), freqs_cis)
+        x = x + self.mlpf(self.ln_2(x))
+        return x
+
+
+class CosmicFish(nn.Module):
+    """
+    CosmicFish model for inference only.
+    Features: Rotary Positional Embeddings, Grouped-Query Attention, SwiGLU, RMSNorm
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(dict(
+            wte=nn.Embedding(config.vocab_size, config.n_embd),
+            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f=RMSNorm(config.n_embd, eps=config.eps),
+        ))
+
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # Share weights between embedding and output
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # Precompute rotary embedding frequencies
+        if config.use_rotary:
+            head_dim = config.n_embd // config.n_head
+            self.freqs_cis = precompute_freqs_cis(head_dim, config.block_size)
+        else:
+            self.freqs_cis = None
+            self.transformer.wpe = nn.Embedding(config.block_size, config.n_embd)
+
+    def get_num_params(self, non_embedding=True):
+        """Return the number of parameters in the model."""
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding and hasattr(self.transformer, 'wpe'):
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+
+    def forward(self, idx, targets=None):
+        """Forward pass through the model."""
+        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}"
+
+        # Get token embeddings
+        tok_emb = self.transformer.wte(idx)
+
+        # Handle positional embeddings
+        if self.config.use_rotary:
+            x = tok_emb
+            freqs_cis = self.freqs_cis.to(device) if self.freqs_cis is not None else None
+        else:
+            pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)
+            pos_emb = self.transformer.wpe(pos)
+            x = tok_emb + pos_emb
+            freqs_cis = None
+
+        # Apply transformer blocks
+        for block in self.transformer.h:
+            x = block(x, freqs_cis)
+
+        # Apply final normalization
+        x = self.transformer.ln_f(x)
+
+        # Calculate outputs
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+        else:
+            # For inference, only compute logits for the last token
+            logits = self.lm_head(x[:, [-1], :])
+            loss = None
+
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        """
+        Generate text by sampling from the model, token by token.
+        """
+        for _ in range(max_new_tokens):
+            # Crop sequence to block size if needed
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+
+            # Forward pass
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+
+            # Apply top-k sampling
+            if top_k is not None:
+                v, _ = torch.topk(logits, top_k)
+                logits[logits < v[:, [-1]]] = -float('Inf')
+
+            # Sample next token
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+
+            # Append to sequence
+            idx = torch.cat((idx, idx_next), dim=1)
+
+        return idx

pytorch_model.bin

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+oid sha256:f8fdc674e0b6392940633e05bb6dbf6cab45da20ae94af719de462ca50cbbf7d
+size 243512043

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "tokenizer_class": "GPT2Tokenizer",
+  "vocab_size": 50257,
+  "model_max_length": 512,
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "add_prefix_space": false,
+  "do_lower_case": false
+}

vocab_info.json

@@ -0,0 +1,5 @@
+{
+  "note": "This model uses GPT-2 tokenizer. Please use: tokenizer = GPT2Tokenizer.from_pretrained('gpt2')",
+  "vocab_size": 50257,
+  "encoding": "gpt2"
+}