Fix: vocab_size=32000 (BPE base from model.vocab); top-level weights; all compat fixes

config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "PicoDecoderHF"
+  ],
+  "model_type": "pico_decoder",
+  "auto_map": {
+    "AutoConfig": "pico_decoder.PicoDecoderHFConfig",
+    "AutoModelForCausalLM": "pico_decoder.PicoDecoderHF"
+  },
+  "n_layers": 14,
+  "d_model": 768,
+  "attention_n_heads": 12,
+  "attention_n_kv_heads": 1,
+  "max_seq_len": 512,
+  "batch_size": 64,
+  "position_emb_theta": 10000.0,
+  "activation_hidden_dim": 3072,
+  "norm_eps": 1e-05,
+  "dropout": 0.1,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "vocab_size": 32000
+}

pico_decoder.py

@@ -0,0 +1,281 @@
+from dataclasses import asdict
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutput, CausalLMOutputWithPast
+try:
+    if TYPE_CHECKING:
+        from src.config import ModelConfig
+except ImportError:
+    pass
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.eps    = config.norm_eps
+        self.weight = nn.Parameter(torch.ones(config.d_model))
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+    def forward(self, x):
+        return self._norm(x.float()).type_as(x) * self.weight
+
+
+class RoPE(nn.Module):
+    """
+    Rotary Position Embedding.
+    freqs_cis is computed lazily on first use and cached per-device,
+    avoiding meta-tensor issues when HF loads with low_cpu_mem_usage=True.
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.theta   = config.position_emb_theta
+        self.dim     = config.d_model // config.attention_n_heads
+        self.max_seq = config.max_seq_len
+        # NOT a buffer — plain dict so it never touches the meta device
+        self._cache: Dict[torch.device, torch.Tensor] = {}
+
+    def _get_freqs_cis(self, device: torch.device) -> torch.Tensor:
+        if device not in self._cache:
+            freqs = 1.0 / (
+                self.theta ** (
+                    torch.arange(0, self.dim, 2, device=device).float() / self.dim
+                )
+            )
+            t = torch.arange(self.max_seq, device=device)
+            freqs = torch.outer(t, freqs)
+            self._cache[device] = torch.polar(torch.ones_like(freqs), freqs)
+        return self._cache[device]
+
+    def get_freqs_cis(self, input_shape, start_pos, end_pos, device):
+        _f   = self._get_freqs_cis(device)[start_pos:end_pos]
+        ndim = len(input_shape)
+        assert 0 <= 1 < ndim and _f.shape == (input_shape[1], input_shape[-1])
+        return _f.view(*[d if i == 1 or i == ndim - 1 else 1
+                         for i, d in enumerate(input_shape)])
+
+    def forward(self, queries, keys, start_pos=0):
+        device = queries.device
+        q_ = torch.view_as_complex(queries.float().reshape(*queries.shape[:-1], -1, 2))
+        k_ = torch.view_as_complex(keys.float().reshape(*keys.shape[:-1], -1, 2))
+        fc  = self.get_freqs_cis(q_.shape, start_pos, start_pos + q_.shape[1], device)
+        return (torch.view_as_real(q_ * fc).flatten(3).type_as(queries),
+                torch.view_as_real(k_ * fc).flatten(3).type_as(keys))
+
+
+class Attention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.n_heads     = config.attention_n_heads
+        self.n_kv_heads  = config.attention_n_kv_heads
+        self.batch_size  = config.batch_size
+        self.max_seq_len = config.max_seq_len
+        d = config.d_model
+        self.head_dim = d // self.n_heads
+        self.n_rep    = self.n_heads // self.n_kv_heads
+        self.q_proj = nn.Linear(d, self.n_heads    * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(d, self.n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(d, self.n_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.n_heads * self.head_dim, d,    bias=False)
+        self.rope   = RoPE(config)
+    def forward(self, input, mask=None, past_key_values=None, use_cache=False):
+        bsz, seq_len, _ = input.shape
+        queries = self.q_proj(input).view(bsz, seq_len, self.n_heads,    self.head_dim)
+        keys    = self.k_proj(input).view(bsz, seq_len, self.n_kv_heads, self.head_dim)
+        values  = self.v_proj(input).view(bsz, seq_len, self.n_kv_heads, self.head_dim)
+        start_pos = past_key_values[0].shape[1] if past_key_values is not None else 0
+        queries, keys = self.rope(queries, keys, start_pos)
+        if past_key_values is not None:
+            keys   = torch.cat([past_key_values[0], keys],   dim=1)
+            values = torch.cat([past_key_values[1], values], dim=1)
+        cached_keys   = keys   if use_cache else None
+        cached_values = values if use_cache else None
+        queries = queries.transpose(1, 2)
+        keys    = keys.transpose(1, 2)
+        values  = values.transpose(1, 2)
+        apply_gqa = self.n_rep > 1
+        if apply_gqa and queries.device.type == "mps":
+            keys   = keys.repeat_interleave(self.n_rep, dim=-3)
+            values = values.repeat_interleave(self.n_rep, dim=-3)
+            apply_gqa = False
+        attn_mask = mask.to(queries.dtype) if mask is not None else None
+        with sdpa_kernel(backends=[SDPBackend.CUDNN_ATTENTION, SDPBackend.MATH]):
+            attn_output = F.scaled_dot_product_attention(
+                queries.contiguous(), keys.contiguous(), values.contiguous(),
+                attn_mask=attn_mask, enable_gqa=apply_gqa,
+            )
+        attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
+        return self.o_proj(attn_output), (cached_keys, cached_values)
+
+
+class SwiGLU(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w_0 = nn.Linear(config.d_model, config.activation_hidden_dim, bias=False)
+        self.w_1 = nn.Linear(config.d_model, config.activation_hidden_dim, bias=False)
+        self.w_2 = nn.Linear(config.activation_hidden_dim, config.d_model, bias=False)
+    def forward(self, x):
+        return self.w_2(F.silu(self.w_0(x)) * self.w_1(x))
+
+
+class PicoDecoderBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attention      = Attention(config)
+        self.swiglu         = SwiGLU(config)
+        self.attention_norm = RMSNorm(config)
+        self.swiglu_norm    = RMSNorm(config)
+    def forward(self, input, mask=None, past_key_values=None, use_cache=False):
+        attention_output, cached_key_values = self.attention(
+            self.attention_norm(input), mask=mask,
+            past_key_values=past_key_values, use_cache=use_cache)
+        h = input + attention_output
+        return h + self.swiglu(self.swiglu_norm(h)), cached_key_values
+
+
+class PicoDecoder(nn.Module):
+    def __init__(self, model_config):
+        super().__init__()
+        self.config            = model_config
+        self.embedding_proj    = nn.Embedding(model_config.vocab_size, model_config.d_model)
+        self.layers            = nn.ModuleList(
+            [PicoDecoderBlock(model_config) for _ in range(model_config.n_layers)])
+        self.output_norm       = RMSNorm(model_config)
+        self.de_embedding_proj = nn.Linear(
+            model_config.d_model, model_config.vocab_size, bias=False)
+    def convert_to_hf_model(self):
+        hf = PicoDecoderHF(PicoDecoderHFConfig.from_dataclass(self.config))
+        hf.load_state_dict(self.state_dict())
+        return hf
+    def forward(self, input_ids, past_key_values=None, use_cache=False):
+        seq_len   = input_ids.shape[-1]
+        h         = self.embedding_proj(input_ids)
+        start_pos = 0 if past_key_values is None else past_key_values[0][0].shape[1]
+        mask = None
+        if seq_len > 1:
+            mask = torch.full((seq_len, seq_len), float("-inf"))
+            mask = torch.triu(mask, diagonal=1)
+            if past_key_values is not None:
+                mask = torch.hstack([torch.zeros((seq_len, start_pos)), mask])
+            mask = mask.to(h.device)
+        cached_key_values = () if use_cache else None
+        for idx, layer in enumerate(self.layers):
+            layer_past = past_key_values[idx] if past_key_values is not None else None
+            h, layer_cached = layer(
+                h, mask=mask, past_key_values=layer_past, use_cache=use_cache)
+            if use_cache:
+                cached_key_values += (layer_cached,)
+        return self.de_embedding_proj(self.output_norm(h)).float(), cached_key_values
+
+
+class PicoDecoderHFConfig(PretrainedConfig):
+    model_type = "pico_decoder"
+    def __init__(self,
+                 n_layers=14, d_model=768, vocab_size=32768,
+                 attention_n_heads=12, attention_n_kv_heads=1,
+                 max_seq_len=512, batch_size=64, position_emb_theta=10000.0,
+                 activation_hidden_dim=3072, norm_eps=1e-5, dropout=0.1,
+                 **kwargs):
+        if not attention_n_kv_heads:
+            attention_n_kv_heads = attention_n_heads
+        super().__init__(**kwargs)
+        self.n_layers              = n_layers
+        self.d_model               = d_model
+        self.vocab_size            = vocab_size
+        self.attention_n_heads     = attention_n_heads
+        self.attention_n_kv_heads  = attention_n_kv_heads
+        self.max_seq_len           = max_seq_len
+        self.batch_size            = batch_size
+        self.position_emb_theta    = position_emb_theta
+        self.activation_hidden_dim = activation_hidden_dim
+        self.norm_eps              = norm_eps
+        self.dropout               = dropout
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PicoDecoderHFConfig":
+        pico_config = cls(**config_dict)
+        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
+        unused_kwargs = {k: v for k, v in kwargs.items() if not hasattr(pico_config, k)}
+        if return_unused_kwargs:
+            return pico_config, unused_kwargs
+        return pico_config
+    @classmethod
+    def from_dataclass(cls, model_config):
+        return cls.from_dict(asdict(model_config))
+
+
+class PicoDecoderHF(PreTrainedModel):
+    """
+    HuggingFace wrapper for BeetleLM PicoDecoder.
+    Usage: AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)
+    Works with CPU, CUDA (A100, etc.), and MPS out of the box.
+    """
+    config_class       = PicoDecoderHFConfig
+    _no_split_modules  = ["PicoDecoderBlock"]
+    _tied_weights_keys = []
+
+    def __init__(self, config: PicoDecoderHFConfig):
+        super().__init__(config)
+        self.embedding_proj    = nn.Embedding(config.vocab_size, config.d_model)
+        self.layers            = nn.ModuleList(
+            [PicoDecoderBlock(config) for _ in range(config.n_layers)])
+        self.output_norm       = RMSNorm(config)
+        self.de_embedding_proj = nn.Linear(config.d_model, config.vocab_size, bias=False)
+        # Required: lets HF finalize weight init and meta-device materialization
+        self.post_init()
+
+    # Required for low_cpu_mem_usage / Accelerate device-dispatch to work
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, RMSNorm):
+            nn.init.ones_(module.weight)
+
+    def get_input_embeddings(self):        return self.embedding_proj
+    def set_input_embeddings(self, value): self.embedding_proj = value
+
+    def forward(self, input_ids=None, past_key_values=None,
+                use_cache=False, labels=None, **kwargs):
+        seq_len   = input_ids.shape[-1]
+        h         = self.embedding_proj(input_ids)
+        start_pos = 0 if past_key_values is None else past_key_values[0][0].shape[1]
+        mask = None
+        if seq_len > 1:
+            mask = torch.full((seq_len, seq_len), float("-inf"), device=h.device)
+            mask = torch.triu(mask, diagonal=1)
+            if past_key_values is not None:
+                mask = torch.hstack([torch.zeros((seq_len, start_pos), device=h.device), mask])
+        cached_key_values = () if use_cache else None
+        for idx, layer in enumerate(self.layers):
+            layer_past = past_key_values[idx] if past_key_values is not None else None
+            h, layer_cached = layer(
+                h, mask=mask, past_key_values=layer_past, use_cache=use_cache)
+            if use_cache:
+                cached_key_values += (layer_cached,)
+        logits = self.de_embedding_proj(self.output_norm(h)).float()
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(
+                logits[:, :-1].contiguous().view(-1, self.config.vocab_size),
+                labels[:, 1:].contiguous().clamp(0, self.config.vocab_size - 1).view(-1),
+            )
+        if use_cache:
+            return CausalLMOutputWithPast(
+                loss=loss, logits=logits, past_key_values=cached_key_values)
+        return CausalLMOutput(loss=loss, logits=logits)
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        return {"input_ids": input_ids,
+                "past_key_values": past_key_values,
+                "use_cache": True}
+
+
+PicoDecoderHFConfig.register_for_auto_class()
+PicoDecoderHF.register_for_auto_class("AutoModel")
+PicoDecoderHF.register_for_auto_class("AutoModelForCausalLM")