Upload modeling_quark.py

modeling_quark.py

@@ -0,0 +1,204 @@
+"""
+Quark model implementation for HuggingFace Transformers.
+
+Usage:
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    model = AutoModelForCausalLM.from_pretrained("ThingAI/Quark-135m-v0.2", trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained("ThingAI/Quark-135m-v0.2")
+
+    inputs = tokenizer("Ciao, come stai?", return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7, do_sample=True)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from .configuration_quark import QuarkConfig
+
+
+class QuarkRMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.scale = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        rms = x.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
+        return (x.float() * rms).to(x.dtype) * self.scale
+
+
+class QuarkRotaryEmbedding(nn.Module):
+    def __init__(self, head_dim: int, max_seq_len: int, theta: float = 10000.0):
+        super().__init__()
+        assert head_dim % 2 == 0
+        inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._build_cache(max_seq_len)
+
+    def _build_cache(self, seq_len: int):
+        t = torch.arange(seq_len, device=self.inv_freq.device).float()
+        freqs = torch.outer(t, self.inv_freq)
+        emb = torch.cat([freqs, freqs], dim=-1)
+        self.register_buffer("cos_cache", emb.cos()[None, None], persistent=False)
+        self.register_buffer("sin_cache", emb.sin()[None, None], persistent=False)
+        self._max_cached = seq_len
+
+    @staticmethod
+    def _rotate_half(x):
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat([-x2, x1], dim=-1)
+
+    def forward(self, q, k):
+        T = q.size(2)
+        if T > self._max_cached:
+            self._build_cache(T)
+        cos = self.cos_cache[:, :, :T, :]
+        sin = self.sin_cache[:, :, :T, :]
+        q = q * cos + self._rotate_half(q) * sin
+        k = k * cos + self._rotate_half(k) * sin
+        return q, k
+
+
+class QuarkAttention(nn.Module):
+    """Grouped Query Attention (GQA)."""
+
+    def __init__(self, config: QuarkConfig):
+        super().__init__()
+        self.n_heads = config.n_heads
+        self.n_kv_heads = config.n_kv_heads
+        self.n_groups = config.n_heads // config.n_kv_heads
+        self.head_dim = config.head_dim
+
+        self.q_proj = nn.Linear(config.d_model, config.n_heads * config.head_dim, bias=config.qkv_bias)
+        self.k_proj = nn.Linear(config.d_model, config.n_kv_heads * config.head_dim, bias=config.qkv_bias)
+        self.v_proj = nn.Linear(config.d_model, config.n_kv_heads * config.head_dim, bias=config.qkv_bias)
+        self.o_proj = nn.Linear(config.n_heads * config.head_dim, config.d_model, bias=False)
+        self.rope = QuarkRotaryEmbedding(config.head_dim, config.max_seq_len, config.rope_theta)
+
+    def forward(self, x):
+        B, T, _ = x.shape
+        q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
+
+        q, k = self.rope(q, k)
+
+        if self.n_groups > 1:
+            k = k.repeat_interleave(self.n_groups, dim=1)
+            v = v.repeat_interleave(self.n_groups, dim=1)
+
+        out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        out = out.transpose(1, 2).contiguous().view(B, T, self.n_heads * self.head_dim)
+        return self.o_proj(out)
+
+
+class QuarkFFN(nn.Module):
+    """SwiGLU Feed-Forward Network."""
+
+    def __init__(self, config: QuarkConfig):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.up_proj = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.down_proj = nn.Linear(config.d_ff, config.d_model, bias=False)
+
+    def forward(self, x):
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class QuarkBlock(nn.Module):
+    """Transformer block with pre-norm."""
+
+    def __init__(self, config: QuarkConfig):
+        super().__init__()
+        self.norm_attn = QuarkRMSNorm(config.d_model, config.rms_eps)
+        self.attn = QuarkAttention(config)
+        self.norm_ffn = QuarkRMSNorm(config.d_model, config.rms_eps)
+        self.ffn = QuarkFFN(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.norm_attn(x))
+        x = x + self.ffn(self.norm_ffn(x))
+        return x
+
+
+class QuarkPreTrainedModel(PreTrainedModel):
+    config_class = QuarkConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = False
+
+    def _init_weights(self, module):
+        std = 0.02
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+
+
+class QuarkForCausalLM(QuarkPreTrainedModel):
+    """Quark model for causal language modeling."""
+
+    def __init__(self, config: QuarkConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
+        self.layers = nn.ModuleList([QuarkBlock(config) for _ in range(config.n_layers)])
+        self.norm = QuarkRMSNorm(config.d_model, config.rms_eps)
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        # Weight tying
+        self.lm_head.weight = self.embed_tokens.weight
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> CausalLMOutputWithPast:
+        h = self.embed_tokens(input_ids)
+
+        for layer in self.layers:
+            h = layer(h)
+
+        h = self.norm(h)
+        logits = self.lm_head(h)
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss = F.cross_entropy(
+                shift_logits.view(-1, self.config.vocab_size),
+                shift_labels.view(-1),
+                ignore_index=-100,
+            )
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids}