Remove registration of NeuroBLAST model in registration.py

neuroblast_model/__init__.py

@@ -1,2 +0,0 @@
-from neuroblast_model.configuration_neuroblast import NeuroBLASTConfig
-from neuroblast_model.modeling_neuroblast import NeuroBLASTForCausalLM

neuroblast_model/configuration_neuroblast.py

@@ -1,85 +0,0 @@
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_rope_utils import rope_config_validation
-from typing import Optional
-
-
-class NeuroBLASTConfig(PretrainedConfig):
-    model_type = "neuroblast"
-
-    def __init__(
-        self,
-        vocab_size=28886,
-        hidden_size=2048,
-        kv_dim=2048,
-        intermediate_size=None,
-        num_attention_heads=32,
-        num_sensory_cortex_layers=6,
-        num_motor_cortex_layers=6,
-        num_association_cortex_layers=6,
-        dropout=0.1,
-        layer_norm_epsilon=1e-6,
-        pad_token_id=None,
-        use_cache=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        use_flash_attn=True,
-        num_experts=None,
-        num_experts_per_tok=None,
-        norm_topk_prob=False,
-        hidden_act="silu",
-        use_zero_memory=False,
-        zero_memory_alpha=1.0,
-        zero_memory_layers=None,
-        gradient_scaling_enabled=True,
-        association_gradient_scale=0.9,
-        sensory_gradient_scale=0.95,
-        cross_attention_gradient_scale=0.95,
-        clamp_value=1e5,
-        _attn_implementation='sdpa',
-        **kwargs
-    ):
-        # Calculate intermediate_size if not provided
-        if intermediate_size is None:
-            intermediate_size = int(hidden_size * 4 * 2 / 3)
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            **kwargs
-        )
-        
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.kv_dim = kv_dim
-        self.intermediate_size = intermediate_size
-        self.num_attention_heads = num_attention_heads
-        self.num_sensory_cortex_layers = num_sensory_cortex_layers
-        self.num_motor_cortex_layers = num_motor_cortex_layers
-        self.num_association_cortex_layers = num_association_cortex_layers
-        self.dropout = dropout
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.rms_norm_eps = layer_norm_epsilon
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.max_position_embeddings = max_position_embeddings
-        self.initializer_range = initializer_range
-        self.use_flash_attn = use_flash_attn
-        self.num_experts = num_experts
-        self.num_experts_per_tok = num_experts_per_tok
-        self.norm_topk_prob = norm_topk_prob
-        self.hidden_act = hidden_act
-        self.use_zero_memory = use_zero_memory
-        self.zero_memory_alpha = zero_memory_alpha
-        self.zero_memory_layers = zero_memory_layers
-        self.gradient_scaling_enabled = gradient_scaling_enabled
-        self.association_gradient_scale = association_gradient_scale
-        self.sensory_gradient_scale = sensory_gradient_scale
-        self.cross_attention_gradient_scale = cross_attention_gradient_scale
-        self._attn_implementation = _attn_implementation
-        self.clamp_value = clamp_value
-
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)

neuroblast_model/modeling_neuroblast.py

@@ -1,1961 +0,0 @@
-import torch
-import math
-from torch import nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel, GenerationMixin
-from transformers.cache_utils import DynamicCache, Cache
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.utils import logging
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.activations import ACT2FN
-from typing import Optional, Tuple, Union, List
-from neuroblast_model.configuration_neuroblast import NeuroBLASTConfig
-
-CLAMP_VALUE = 1e5
-
-logger = logging.get_logger(__name__)
-
-def apply_gradient_scaling(
-    tensor: torch.Tensor, scale: float, enabled: bool = True
-) -> torch.Tensor:
-    """
-    Apply gradient scaling to a tensor.
-    This scales the gradients during backward pass while keeping forward pass unchanged.
-    """
-    if not enabled or scale == 1.0 or not tensor.requires_grad:
-        return tensor
-
-    # Use a custom autograd function for gradient scaling
-    class GradientScale(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input_tensor, scale_factor):
-            ctx.scale = scale_factor
-            return input_tensor.clone()
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            if grad_output is None:
-                return None, None
-            return grad_output * ctx.scale, None
-
-    return GradientScale.apply(tensor, scale)
-
-
-def _prepare_4d_causal_attention_mask_with_cache_position(
-    attention_mask: torch.Tensor,
-    sequence_length: int,
-    target_length: int,
-    dtype: torch.dtype,
-    device: torch.device,
-    min_dtype: float,
-    cache_position: torch.Tensor,
-    batch_size: int,
-):
-    if attention_mask is not None and attention_mask.dim() == 4:
-        causal_mask = attention_mask
-    else:
-        causal_mask = torch.full(
-            (sequence_length, target_length),
-            fill_value=min_dtype,
-            dtype=dtype,
-            device=device,
-        )
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(
-            target_length, device=device
-        ) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-        if attention_mask is not None:
-            causal_mask = (
-                causal_mask.clone()
-            )
-            mask_length = attention_mask.shape[-1]
-            padding_mask = (
-                causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-            )
-            padding_mask = padding_mask == 0
-            causal_mask[:, :, :, :mask_length] = causal_mask[
-                :, :, :, :mask_length
-            ].masked_fill(padding_mask, min_dtype)
-
-    return causal_mask
-
-
-# --- RoPE Implementation (using HF LlamaRotaryEmbedding) ---
-
-
-class LlamaRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class NeuroBLASTRotaryEmbedding(nn.Module):
-    """
-        Rotary Positional Embedding for NeuroBLAST model.        
-        Source: LlamaRotaryEmbedding
-    """
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (
-            self.base
-            ** (torch.arange(0, self.dim, 2, dtype=torch.float32).to(device) / self.dim)
-        )
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self._set_cos_sin_cache(
-            seq_len=max_position_embeddings, device="cpu", dtype=torch.float32
-        )
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
-        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-    def forward(self, x, seq_len=None):
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-        elif self.cos_cached.device != x.device or self.cos_cached.dtype != x.dtype:
-            self.cos_cached = self.cos_cached.to(device=x.device, dtype=x.dtype)
-            self.sin_cached = self.sin_cached.to(device=x.device, dtype=x.dtype)
-
-        return (
-            self.cos_cached[:seq_len],
-            self.sin_cached[:seq_len],
-        )
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    """ Applies rotary positional embeddings to query and key tensors."""
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-# Overload for Cross Attention where only query is rotated
-def apply_rotary_pos_emb_single(q, cos, sin, position_ids):
-    """ Applies rotary positional embeddings to query tensor. """
-    cos = cos[position_ids].unsqueeze(1)  # [1, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [1, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    return q_embed
-
-
-class SwiGLUMLP(nn.Module):
-    """SwiGLU MLP block"""
-
-    def __init__(self, hidden_size, config: NeuroBLASTConfig, dropout):
-        super().__init__()
-        intermediate_size = getattr(config, "intermediate_size", int(hidden_size * 2.5))
-        self.init_std = getattr(config, "initializer_range", 0.02)
-        self.clamp_value = config.clamp_value
-
-        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=True)
-        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
-        self.act_fn = nn.SiLU()
-        self.dropout = nn.Dropout(dropout)
-
-        if config.num_experts is not None:
-            self.experts = NeuroBLASTSparseMoeBlock(config)
-
-        # Re-enable scaled initialization
-        with torch.no_grad():
-            # Scale down initial weights in the up/gate projections
-            self.gate_proj.weight.data.normal_(
-                mean=0.0,
-                std=self.init_std / math.sqrt(hidden_size),  # Scale by input dim
-            )
-            if self.gate_proj.bias is not None:
-                self.gate_proj.bias.data.zero_()
-
-            self.up_proj.weight.data.normal_(
-                mean=0.0, std=self.init_std / math.sqrt(hidden_size)
-            )  # Scale by input dim
-            # Scale down initial weights in the down projection even further
-            self.down_proj.weight.data.normal_(
-                mean=0.0,
-                std=self.init_std
-                / math.sqrt(intermediate_size),  # Scale by intermediate dim
-            )
-
-    def forward(self, x):
-        gated_x = self.gate_proj(x)
-        activated_x = self.act_fn(gated_x)
-        up_projected_x = self.up_proj(x)
-
-        intermediate_activation = activated_x * up_projected_x
-
-        # Clamp the intermediate activation before down_proj
-        clamp_value = self.clamp_value
-
-        intermediate_activation = torch.clamp(
-            intermediate_activation, min=-clamp_value, max=clamp_value
-        )
-        intermediate_activation = torch.nan_to_num(
-            intermediate_activation
-        )  # Safeguard against NaNs
-
-        y = self.down_proj(intermediate_activation)
-        y = self.dropout(y)
-
-        if hasattr(self, "experts"):
-            z = self.experts(y)
-
-            y = y + z
-
-        return y
-
-
-class NeuroBLASTMoeMLP(nn.Module):
-    """ Source: Qwen3MoeMLP """
-    def __init__(self, config, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.clamp_value = config.clamp_value
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = (
-            intermediate_size
-            if intermediate_size is not None
-            else config.intermediate_size
-        )
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-        down_proj = torch.clamp(down_proj, min=-self.clamp_value, max=self.clamp_value)
-        down_proj = torch.nan_to_num(down_proj)  # Safeguard against NaNs
-
-        return down_proj
-
-
-class NeuroBLASTSparseMoeBlock(nn.Module):
-    """ Source: Qwen3SparseMoeBlock """
-    def __init__(self, config):
-        super().__init__()
-        self.num_experts = config.num_experts
-        self.top_k = config.num_experts_per_tok
-        self.norm_topk_prob = config.norm_topk_prob
-
-        # gating
-        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
-        self.experts = nn.ModuleList(
-            [NeuroBLASTMoeMLP(config) for _ in range(self.num_experts)]
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """ """
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(
-            routing_weights, self.top_k, dim=-1
-        )
-        if self.norm_topk_prob:  # only diff with mixtral sparse moe block!
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim),
-            dtype=hidden_states.dtype,
-            device=hidden_states.device,
-        )
-
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(
-            selected_experts, num_classes=self.num_experts
-        ).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx])
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_hidden_states = (
-                expert_layer(current_state) * routing_weights[top_x, idx, None]
-            )
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(
-                0, top_x, current_hidden_states.to(hidden_states.dtype)
-            )
-        final_hidden_states = final_hidden_states.reshape(
-            batch_size, sequence_length, hidden_dim
-        )
-        return final_hidden_states
-
-
-class NeuroBLASTRouterBlock(nn.Module):
-    """ Memory router; overcomplicated due to backward compatibility """
-    def __init__(
-        self,
-        config,
-        hidden_size,
-    ):
-        super().__init__()
-        self.num_experts = 2
-        self.top_k = 1
-        self.norm_topk_prob = config.norm_topk_prob
-
-        # gating
-        self.gate = nn.Linear(hidden_size, self.num_experts, bias=False)
-
-    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """ """
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(
-            routing_weights, self.top_k, dim=-1
-        )
-        if self.norm_topk_prob:  # only diff with mixtral sparse moe block!
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        return routing_weights, selected_experts
-
-
-class SelfAttention(torch.nn.Module):
-    def __init__(
-        self,
-        config: NeuroBLASTConfig,
-        hidden_size: int,
-        is_causal: bool = False,
-        layer_idx: Optional[int] = None,
-    ):
-        super().__init__()
-        self.is_causal = is_causal
-        self.dropout_p = config.dropout  # Will apply based on self.training
-        self.layer_idx = layer_idx
-        self.hidden_size = hidden_size
-        self.intermediate_size = config.kv_dim
-        self.use_flash_attn = config.use_flash_attn
-        # Allow overriding num_heads, default to config.num_attention_heads
-        self.num_heads = getattr(
-            config, f"num_heads_{layer_idx}", config.num_attention_heads
-        )
-        self.head_dim = self.intermediate_size // self.num_heads
-
-        if (self.head_dim * self.num_heads) != self.intermediate_size:
-            raise ValueError(
-                f"Layer {self.layer_idx}: hidden_size ({self.intermediate_size}) must be divisible by num_heads ({self.num_heads})"
-            )
-
-        self.qkv_proj = nn.Linear(
-            self.hidden_size, self.intermediate_size * 3, bias=True
-        )
-        self.o_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.dropout = nn.Dropout(config.dropout)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],  # (cos, sin)
-        attention_mask: Optional[
-            torch.Tensor
-        ],  # Not directly used by flash_attn causal
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        position_ids: Optional[torch.LongTensor] = None,
-    ):
-        batch_size, seq_len, _ = hidden_states.shape
-        dropout_p = self.dropout_p if self.training else 0.0
-
-        qkv = self.qkv_proj(hidden_states)
-        query_states, key_states, value_states = qkv.chunk(3, dim=-1)
-
-        query_states = query_states.view(
-            batch_size, seq_len, self.num_heads, self.head_dim
-        ).transpose(1, 2)
-        key_states = key_states.view(
-            batch_size, seq_len, self.num_heads, self.head_dim
-        ).transpose(1, 2)
-        value_states = value_states.view(
-            batch_size, seq_len, self.num_heads, self.head_dim
-        ).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, position_ids
-        )
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            # When using Cache object, updating happens in-place.
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.layer_idx, cache_kwargs
-            )
-
-        if self.use_flash_attn:
-            causal_mask = attention_mask
-            if attention_mask is not None:
-                if attention_mask.dim() == 4:
-                    causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-                elif attention_mask.dim() == 2:
-                    causal_mask = attention_mask
-
-                if causal_mask.dtype not in [
-                    torch.bool,
-                    torch.float16,
-                    torch.float32,
-                    torch.bfloat16,
-                ]:
-                    causal_mask = causal_mask.to(query_states.dtype)
-
-            is_causal = (
-                True if causal_mask is None and query_states.shape[-2] > 1 else False
-            )
-
-            attn_output = F.scaled_dot_product_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_mask=causal_mask,
-                dropout_p=dropout_p,
-                enable_gqa=False,
-                scale=self.head_dim**-0.5,
-                is_causal=is_causal,
-            )
-            attn_weights = None
-        else:
-            attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2)) / (
-                self.head_dim**0.5
-            )
-
-            if attention_mask is not None:
-                attn_weights = attn_weights + attention_mask
-            elif self.is_causal and seq_len > 1:
-                causal_mask = torch.triu(
-                    torch.ones(
-                        (seq_len, key_states.shape[2]),
-                        dtype=torch.bool,
-                        device=query_states.device,
-                    ),
-                    diagonal=1,
-                )
-                attn_weights = attn_weights.masked_fill(
-                    causal_mask.unsqueeze(0).unsqueeze(0), float("-inf")
-                )
-
-            attn_weights = F.softmax(attn_weights, dim=-1)
-            attn_weights_dropped = F.dropout(
-                attn_weights, p=dropout_p, training=self.training
-            )
-            attn_output = torch.matmul(attn_weights_dropped, value_states)
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(batch_size, seq_len, -1)
-        attn_output = self.o_proj(attn_output)
-        attn_output = self.dropout(attn_output)
-
-        if output_attentions:
-            logger.warning(
-                f"Layer {self.layer_idx}: Flash Attention does not return attention weights."
-            )
-
-        outputs = (attn_output,)
-        if output_attentions:
-            outputs += (attn_weights,)
-        if use_cache:
-            outputs += (past_key_value,)  # Return the cache object
-
-        return outputs
-
-
-class CrossAttention(torch.nn.Module):
-    def __init__(
-        self,
-        config: NeuroBLASTConfig,
-        query_dim: int,
-        kv_dim: int,
-        layer_idx: int,
-        is_causal: bool = True,
-    ):
-        super().__init__()
-        self.dropout_p = config.dropout
-        self.layer_idx = layer_idx
-        self.query_dim = query_dim
-        self.kv_dim = kv_dim
-        self.is_causal = is_causal
-
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.kv_dim // self.num_heads
-        self.kv_head_dim = (
-            self.kv_dim // self.num_heads
-        )
-
-        if (self.head_dim * self.num_heads) != self.kv_dim:
-            raise ValueError(
-                f"CrossAttn {layer_idx}: query_dim ({self.kv_dim}) must be divisible by num_heads ({self.num_heads})"
-            )
-        if (self.kv_head_dim * self.num_heads) != self.kv_dim:
-            raise ValueError(
-                f"CrossAttn {layer_idx}: kv_dim ({kv_dim}) must be divisible by num_heads ({self.num_heads})"
-            )
-
-        self.q_proj = nn.Linear(self.query_dim, self.kv_dim, bias=True)
-        self.k_proj = nn.Linear(self.query_dim, self.kv_dim, bias=True)
-        self.v_proj = nn.Linear(self.query_dim, self.kv_dim, bias=True)
-        self.o_proj = nn.Linear(self.kv_dim, self.query_dim, bias=False)
-        self.dropout = nn.Dropout(config.dropout)
-
-        self.use_flash_attn = hasattr(
-            F, "scaled_dot_product_attention"
-        )
-
-    def forward(
-        self,
-        query_states: torch.Tensor,
-        kv_states: torch.Tensor,
-        position_embeddings: Tuple[
-            torch.Tensor, torch.Tensor
-        ],
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-        position_ids: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = False,
-    ):
-        batch_size, q_seq_len, _ = query_states.shape
-        kv_seq_len = kv_states.shape[1]
-        dropout_p = self.dropout_p if self.training else 0.0
-
-        query = self.q_proj(query_states)
-        key = self.k_proj(kv_states)
-        value = self.v_proj(kv_states)
-
-        query = query.view(
-            batch_size, q_seq_len, self.num_heads, self.head_dim
-        ).transpose(1, 2)
-        
-        cos, sin = position_embeddings
-        query = apply_rotary_pos_emb_single(query, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key, value = past_key_value.update(
-                key, value, self.layer_idx or 0, cache_kwargs
-            )
-            kv_seq_len = key.shape[1]
-
-        key = key.view(
-            batch_size, kv_seq_len, self.num_heads, self.kv_head_dim
-        ).transpose(1, 2)
-        value = value.view(
-            batch_size, kv_seq_len, self.num_heads, self.kv_head_dim
-        ).transpose(1, 2)
-
-        sdpa_attn_mask = attention_mask
-
-        if self.use_flash_attn:
-            is_causal = True if sdpa_attn_mask is None and q_seq_len > 1 else False
-            attn_output = F.scaled_dot_product_attention(
-                query,
-                key,
-                value,
-                attn_mask=sdpa_attn_mask if not is_causal else None,
-                dropout_p=dropout_p,
-                is_causal=is_causal,
-                enable_gqa=False,
-                scale=self.head_dim**-0.5,
-            )
-            attn_weights = None
-        else:
-            attn_weights = torch.matmul(query, key.transpose(-1, -2)) / (
-                self.head_dim**0.5
-            )
-
-            if sdpa_attn_mask is not None:
-                attn_weights = attn_weights + sdpa_attn_mask
-            elif self.is_causal and q_seq_len > 1:
-                causal_mask = torch.triu(
-                    torch.ones(
-                        (q_seq_len, kv_seq_len), dtype=torch.bool, device=query.device
-                    ),
-                    diagonal=1,
-                )
-                attn_weights = attn_weights.masked_fill(
-                    causal_mask.unsqueeze(0).unsqueeze(0), float("-inf")
-                )
-
-            attn_weights = F.softmax(attn_weights, dim=-1)
-            attn_weights_dropped = F.dropout(
-                attn_weights, p=dropout_p, training=self.training
-            )
-            attn_output = torch.matmul(attn_weights_dropped, value)
-
-        attn_output = attn_output.transpose(
-            1, 2
-        ).contiguous()
-        attn_output = attn_output.reshape(batch_size, q_seq_len, self.kv_dim)
-
-        attn_output = self.o_proj(attn_output)
-        attn_output = self.dropout(attn_output)
-
-        outputs = (attn_output,)
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        if use_cache:
-            outputs += (past_key_value,)
-
-        return outputs
-
-
-class AttentionBlock(torch.nn.Module):
-    """Modified Attention Block with Pre-Norm and choice of Self/Cross Attention & MLP"""
-
-    def __init__(
-        self,
-        config: NeuroBLASTConfig,
-        hidden_size: int,
-        attention_module: nn.Module,
-        mlp_module: nn.Module,
-        is_cross_attention: bool = False,
-        layer_idx: int = 0,
-        precomputed_total_layers: Optional[int] = None,
-    ):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.config = config
-        self.layer_idx = layer_idx
-        self.is_cross_attention = is_cross_attention
-
-        self.input_layernorm = nn.LayerNorm(
-            hidden_size, eps=getattr(config, "rms_norm_eps", 1e-5)
-        )
-        self.attention = attention_module
-
-        self.post_attention_layernorm = nn.LayerNorm(
-            hidden_size, eps=getattr(config, "rms_norm_eps", 1e-5)
-        )
-        self.mlp = mlp_module
-
-        if self.config.use_zero_memory and (
-            self.config.zero_memory_layers is None
-            or self.layer_idx in self.config.zero_memory_layers
-        ):
-            self.router = NeuroBLASTRouterBlock(config, hidden_size)
-            self.memory = NeuroBLASTMemory(
-                config,
-                hidden_size=hidden_size,
-                layer_idx=layer_idx,
-                precomputed_total_layers=precomputed_total_layers,
-            )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        position_ids: Optional[torch.LongTensor],
-        kv_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        previous_states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ):
-        residual = hidden_states
-        hidden_states = torch.nan_to_num(hidden_states)
-        normed_hidden_states = self.input_layernorm(hidden_states)
-
-        if self.is_cross_attention:
-            if kv_states is None:
-                raise ValueError("kv_states must be provided for CrossAttention")
-            attn_outputs = self.attention(
-                query_states=normed_hidden_states,
-                kv_states=kv_states,
-                past_key_value=past_key_value,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                attention_mask=attention_mask,
-                output_attentions=output_attentions,
-                position_ids=position_ids,
-                use_cache=use_cache,
-            )
-        else:
-            attn_outputs = self.attention(
-                normed_hidden_states,
-                position_embeddings=position_embeddings,
-                attention_mask=attention_mask,
-                past_key_value=past_key_value,
-                cache_position=cache_position,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                position_ids=position_ids,
-            )
-        attn_output = attn_outputs[0]
-        past_key_value = attn_outputs[-1] if use_cache else None
-
-        hidden_states = residual + attn_output
-        hidden_states = torch.nan_to_num(hidden_states)
-
-        residual = hidden_states
-
-        normed_hidden_states = self.post_attention_layernorm(hidden_states)
-
-        mlp_output = self.mlp(normed_hidden_states)
-
-        hidden_states = residual + mlp_output
-
-        hidden_states = torch.nan_to_num(hidden_states)
-
-        if self.config.use_zero_memory and (
-            self.config.zero_memory_layers is None
-            or self.layer_idx in self.config.zero_memory_layers
-        ):
-            routing_weights, selected_experts = self.router(hidden_states)
-
-            residual = hidden_states
-
-            hidden_states, (hx, cx), past_key_value = self.memory(
-                hidden_states,
-                previous_states,
-                past_key_value=past_key_value,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                attention_mask=attention_mask,
-                output_attentions=output_attentions,
-                position_ids=position_ids,
-                use_cache=use_cache,
-            )
-
-            hidden_states = torch.nan_to_num(hidden_states)
-            hx = torch.nan_to_num(hx)
-            cx = torch.nan_to_num(cx)
-
-            hidden_states = hidden_states * routing_weights.reshape(
-                hidden_states.shape[:-1]
-            ).unsqueeze(-1)
-
-            hidden_states = residual + self.config.zero_memory_alpha * hidden_states
-            hidden_states = torch.nan_to_num(hidden_states)
-
-        outputs = (hidden_states,) + attn_outputs[1:]
-
-        if self.config.use_zero_memory and (
-            self.config.zero_memory_layers is None
-            or self.layer_idx in self.config.zero_memory_layers
-        ):
-            outputs += ((hx, cx),)
-        else:
-            outputs += (previous_states,)
-
-        if use_cache:
-            outputs += (past_key_value,)
-
-        return outputs
-
-
-class NeuroBLASTMemory(nn.Module):
-    def __init__(
-        self,
-        config: NeuroBLASTConfig,
-        hidden_size: int = 256,
-        num_heads: int = 4,
-        scale_factor: int = 4,
-        layer_idx: int = 0,
-        with_hx: bool = True,
-        precomputed_total_layers: Optional[int] = None,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-
-        self.hidden_size = hidden_size
-        self.num_heads = num_heads
-        self.scale_factor = scale_factor
-        self.clamp_value = config.clamp_value
-        # Use precomputed_total_layers instead of hardcoded 100 for layer index shift
-        layer_shift = (
-            precomputed_total_layers if precomputed_total_layers is not None else 100
-        )
-        self.layer_idx = layer_idx + layer_shift
-        self.with_hx = with_hx
-        self.kv_dim = (
-            config.kv_dim
-        )
-
-        self.scaled_dim = hidden_size * scale_factor
-        self.head_dim = self.kv_dim // config.num_attention_heads
-        self.num_heads = self.hidden_size // self.head_dim
-
-        self.norm1 = nn.LayerNorm(hidden_size)
-
-        if self.with_hx:
-            self.lin1 = nn.Linear(self.hidden_size, self.scaled_dim)
-        self.lin2 = nn.Linear(self.hidden_size, self.scaled_dim)
-        self.lin3 = nn.Linear(self.scaled_dim, self.hidden_size)
-
-        self.lin4 = nn.Linear(self.hidden_size, self.scaled_dim)
-        self.lin5 = nn.Linear(self.scaled_dim, self.hidden_size)
-
-        if self.with_hx:
-            self.lin6 = nn.Linear(self.scaled_dim, self.hidden_size)
-
-        self.gate1 = nn.Linear(self.scaled_dim, self.scaled_dim)
-        self.act1 = nn.SiLU()
-
-        self.gate2 = nn.Linear(self.scaled_dim, self.scaled_dim)
-        self.act2 = nn.SiLU()
-
-        self.last_token_reg = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
-        self.prev_tokens_reg = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
-
-        self.norm2 = nn.LayerNorm(hidden_size)
-        self.dropout = nn.Dropout(config.dropout)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        previous_state: tuple[torch.Tensor, torch.Tensor],
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        position_ids: Optional[torch.LongTensor],
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ):
-        hx, cx = previous_state
-
-        b, s, d = x.size()
-
-        x = torch.nan_to_num(x)
-        norm_x = self.norm1(x)
-
-        norm_x = norm_x.view(b, s, self.num_heads, self.head_dim).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        norm_x = apply_rotary_pos_emb_single(norm_x, cos, sin, position_ids)
-
-        kv_seq_len = s
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            norm_x, _ = past_key_value.update(
-                norm_x,
-                torch.zeros((b, 1, kv_seq_len, d)),
-                self.layer_idx,
-                cache_kwargs,
-            )
-            kv_seq_len = norm_x.shape[2]
-
-        norm_x = norm_x.transpose(1, 2).contiguous()
-        norm_x = norm_x.view(b, kv_seq_len, d)
-
-        norm_x = torch.nan_to_num(norm_x)
-
-        expanded_x = None
-
-        shifted_x = torch.cat(
-            [
-                torch.zeros((b, 1, d), device=x.device, dtype=x.dtype),
-                (norm_x[:, :-1].contiguous()),
-            ],
-            dim=1,
-        ).contiguous()
-
-        shifted_x = torch.nan_to_num(shifted_x)  # Replace NaNs with zeros
-
-        prev_tokens_x = norm_x.cumsum(dim=1)
-
-        prev_tokens_x = prev_tokens_x - shifted_x
-        prev_tokens_x = torch.nan_to_num(prev_tokens_x)[:, -s:].contiguous()
-
-        if self.with_hx:
-            expanded_x = self.lin1(norm_x[:, -s:].contiguous())
-            expanded_x = torch.nan_to_num(expanded_x)
-
-        expanded_shifted_x = self.lin2(shifted_x[:, -s:].contiguous())
-
-        expanded_shifted_x = torch.nan_to_num(expanded_shifted_x)
-
-        gated_shifted_x = self.gate1(expanded_shifted_x)
-        gated_shifted_x = self.act1(gated_shifted_x)
-        gated_shifted_x = torch.clamp(
-            gated_shifted_x, min=-self.clamp_value, max=self.clamp_value
-        )
-
-        gated_shifted_x = torch.nan_to_num(gated_shifted_x)
-
-        collapsed_shifted_x = self.lin3(gated_shifted_x)
-        collapsed_shifted_x = torch.nan_to_num(collapsed_shifted_x)
-
-        prev_tokens_x = torch.nan_to_num(prev_tokens_x)
-
-        expanded_prev_tokens_x = self.lin4(prev_tokens_x)
-        expanded_prev_tokens_x = torch.nan_to_num(expanded_prev_tokens_x)
-
-        gated_prev_tokens_x = self.gate2(expanded_prev_tokens_x)
-        gated_prev_tokens_x = self.act2(gated_prev_tokens_x)
-        gated_prev_tokens_x = torch.clamp(
-            gated_prev_tokens_x, min=-self.clamp_value, max=self.clamp_value
-        )
-
-        gated_prev_tokens_x = torch.nan_to_num(gated_prev_tokens_x)
-
-        collapsed_prev_tokens_x = self.lin5(gated_prev_tokens_x)
-        collapsed_prev_tokens_x = torch.nan_to_num(collapsed_prev_tokens_x)
-
-        if self.with_hx:
-            weights = torch.softmax(expanded_x * expanded_shifted_x, dim=-1)
-
-            expanded_x_attn = weights * expanded_x
-
-            expanded_x_attn = torch.nan_to_num(expanded_x_attn)
-            hx = hx + self.lin6(expanded_x_attn)
-            hx = torch.nan_to_num(hx)
-
-        if self.with_hx:
-            x = torch.nan_to_num(x)
-            hx = torch.nan_to_num(hx)
-            collapsed_shifted_x = torch.nan_to_num(collapsed_shifted_x)
-            collapsed_prev_tokens_x = torch.nan_to_num(collapsed_prev_tokens_x)
-            output = x + (
-                hx
-                * (
-                    self.last_token_reg(collapsed_shifted_x)
-                    + self.prev_tokens_reg(collapsed_prev_tokens_x)
-                )
-            )
-            output = torch.nan_to_num(output)
-
-        else:
-            output = (
-                x
-                + (
-                    self.last_token_reg(collapsed_shifted_x)
-                    + self.prev_tokens_reg(collapsed_prev_tokens_x)
-                )[:, -s:].contiguous()
-            )
-
-        output = self.norm2(output)
-        output = self.dropout(output)
-        output = torch.nan_to_num(output)
-
-        return (
-            output,
-            (hx, cx),
-            past_key_value,
-        )
-
-
-class NeuroBLASTPreTrainedModel(PreTrainedModel):
-    config_class = NeuroBLASTConfig
-    base_model_prefix = "brain"
-    supports_gradient_checkpointing = True
-    _no_split_modules = []
-    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-    _supports_flash_attn_2 = False
-    _supports_sdpa = True
-
-    def _init_weights(self, module):
-        std = getattr(self.config, "initializer_range", 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)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            if module.bias is not None:
-                module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-
-class NeuroBLASTModel(NeuroBLASTPreTrainedModel):
-    def __init__(self, config: NeuroBLASTConfig):
-        super(NeuroBLASTModel, self).__init__(config)
-        self.config = config
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(
-            config.vocab_size,
-            config.hidden_size,  # Using main hidden_size for embeddings now
-            padding_idx=self.padding_idx,
-        )
-        self.rotary_emb = NeuroBLASTRotaryEmbedding(
-            config.kv_dim // config.num_attention_heads,
-            max_position_embeddings=config.max_position_embeddings,
-            base=config.rope_theta,
-        )
-        self.dropout = nn.Dropout(config.dropout)
-
-        self.assoc_to_sensory_pooler = nn.Sequential(
-            nn.Linear(config.hidden_size, config.hidden_size),
-            nn.Identity(), # Backward compatibility - previously LayerNorm, but we found that removing it improve generalization
-            nn.GELU(),
-            nn.LayerNorm(config.hidden_size),
-        )
-        self.assoc_to_motor_pooler = nn.Sequential(
-            nn.Linear(config.hidden_size, config.hidden_size),
-            nn.Identity(), # Backward compatibility
-            nn.GELU(),
-            nn.LayerNorm(config.hidden_size),
-        )
-        self.sensory_to_motor_pooler = nn.Sequential(
-            nn.Linear(config.hidden_size, config.hidden_size),
-            nn.Identity(),  # Backward compatibility
-            nn.GELU(),
-            nn.LayerNorm(config.hidden_size),
-        )
-
-        # --- Cortex Layers ---
-        # Using generic AttentionBlock with specific Self/Cross Attention modules passed in
-        total_layers = 0
-
-        # Precompute total layers for Memory layer indexing before creating any layers
-        precomputed_total_layers = (
-            config.num_association_cortex_layers
-            + config.num_sensory_cortex_layers * 2
-            + config.num_motor_cortex_layers
-            * 3
-        )
-        self.precomputed_total_layers = precomputed_total_layers
-        config.precomputed_total_layers = precomputed_total_layers
-
-        # 1. Association Cortex (Self-Attention)
-        self.association_cortex = nn.ModuleList()
-        for i in range(config.num_association_cortex_layers):
-            layer_idx = total_layers + i
-            print(f"Adding layer {layer_idx} to association cortex")
-            self.association_cortex.append(
-                AttentionBlock(
-                    config,
-                    config.hidden_size,  # Use main hidden_size
-                    attention_module=SelfAttention(
-                        config, config.hidden_size, is_causal=True, layer_idx=layer_idx
-                    ),
-                    mlp_module=(
-                        NeuroBLASTSparseMoeBlock(
-                            config,
-                        )
-                        if config.num_experts
-                        else NeuroBLASTMoeMLP(config)
-                    ),
-                    is_cross_attention=False,
-                    layer_idx=layer_idx,
-                    precomputed_total_layers=precomputed_total_layers,
-                )
-            )
-
-        total_layers += config.num_association_cortex_layers
-        # 2. Sensory Cortex (Self-Attention + Cross-Attention to Association)
-        self.sensory_self_attn_layers = nn.ModuleList()
-        self.sensory_cross_attn_layers = (
-            nn.ModuleList()
-        )  # One cross-attn per self-attn layer
-        for i in range(config.num_sensory_cortex_layers):
-            layer_idx = total_layers + i
-            print(f"Adding layer {layer_idx} to sensory cortex")
-            self.sensory_self_attn_layers.append(
-                AttentionBlock(
-                    config,
-                    config.hidden_size,
-                    attention_module=SelfAttention(
-                        config,
-                        config.hidden_size,
-                        is_causal=True,
-                        layer_idx=layer_idx,
-                    ),
-                    mlp_module=(
-                        NeuroBLASTSparseMoeBlock(
-                            config,
-                        )
-                        if config.num_experts
-                        else NeuroBLASTMoeMLP(config)
-                    ),
-                    is_cross_attention=False,
-                    layer_idx=layer_idx,
-                    precomputed_total_layers=precomputed_total_layers,
-                )
-            )
-
-        total_layers += config.num_sensory_cortex_layers
-        for i in range(config.num_sensory_cortex_layers):
-            layer_idx = total_layers + i
-            print(f"Adding layer {layer_idx} to sensory cross-attention")
-            # Add Cross-Attention layer: Sensory queries, Association is K/V source
-            self.sensory_cross_attn_layers.append(
-                AttentionBlock(
-                    config,
-                    config.hidden_size,  # Query Dim
-                    attention_module=CrossAttention(
-                        config,
-                        query_dim=config.hidden_size,
-                        kv_dim=config.kv_dim,  # Assoc output dim
-                        layer_idx=layer_idx,
-                    ),
-                    mlp_module=(
-                        NeuroBLASTSparseMoeBlock(
-                            config,
-                        )
-                        if config.num_experts
-                        else NeuroBLASTMoeMLP(config)
-                    ),
-                    is_cross_attention=True,
-                    layer_idx=layer_idx,
-                    precomputed_total_layers=precomputed_total_layers,
-                )
-            )
-
-        total_layers += config.num_sensory_cortex_layers
-
-        # 3. Motor Cortex (Self-Attention + Cross-Attention to Sensory + Cross-Attention to Association)
-        self.motor_self_attn_layers = nn.ModuleList()
-        self.motor_cross_sensory_layers = nn.ModuleList()
-        self.motor_cross_assoc_layers = nn.ModuleList()
-        for i in range(config.num_motor_cortex_layers):
-            layer_idx = total_layers + i
-            print(f"Adding layer {layer_idx} to motor cortex")
-            self.motor_self_attn_layers.append(
-                AttentionBlock(
-                    config,
-                    config.hidden_size,
-                    attention_module=SelfAttention(
-                        config,
-                        config.hidden_size,
-                        is_causal=True,
-                        layer_idx=layer_idx,
-                    ),
-                    mlp_module=(
-                        NeuroBLASTSparseMoeBlock(
-                            config,
-                        )
-                        if config.num_experts
-                        else NeuroBLASTMoeMLP(config)
-                    ),
-                    is_cross_attention=False,
-                    layer_idx=layer_idx,
-                    precomputed_total_layers=precomputed_total_layers,
-                )
-            )
-
-        total_layers += config.num_motor_cortex_layers
-        for i in range(config.num_motor_cortex_layers):
-            layer_idx = total_layers + i
-            print(f"Adding layer {layer_idx} to motor cross-sensory")
-            # Cross-Attend to Sensory Output
-            self.motor_cross_sensory_layers.append(
-                AttentionBlock(
-                    config,
-                    config.hidden_size,  # Query Dim
-                    attention_module=CrossAttention(
-                        config,
-                        query_dim=config.hidden_size,
-                        kv_dim=config.kv_dim,  # Sensory output dim
-                        layer_idx=layer_idx,
-                    ),
-                    mlp_module=(
-                        NeuroBLASTSparseMoeBlock(
-                            config,
-                        )
-                        if config.num_experts
-                        else NeuroBLASTMoeMLP(config)
-                    ),
-                    is_cross_attention=True,
-                    layer_idx=layer_idx,
-                    precomputed_total_layers=precomputed_total_layers,
-                )
-            )
-
-        total_layers += config.num_motor_cortex_layers
-        for i in range(config.num_motor_cortex_layers):
-            layer_idx = total_layers + i
-            print(f"Adding layer {layer_idx} to motor cross-association")
-            # Cross-Attend to Association Output
-            self.motor_cross_assoc_layers.append(
-                AttentionBlock(
-                    config,
-                    config.hidden_size,  # Query Dim
-                    attention_module=CrossAttention(
-                        config,
-                        query_dim=config.hidden_size,
-                        kv_dim=config.kv_dim,  # Assoc output dim
-                        layer_idx=layer_idx,
-                    ),
-                    mlp_module=(
-                        NeuroBLASTSparseMoeBlock(
-                            config,
-                        )
-                        if config.num_experts
-                        else NeuroBLASTMoeMLP(config)
-                    ),
-                    is_cross_attention=True,
-                    layer_idx=layer_idx,
-                    precomputed_total_layers=precomputed_total_layers,
-                )
-            )
-
-        total_layers += config.num_motor_cortex_layers
-
-        # Initialize more conservatively to prevent strong gradient flow initially
-        self.sensory_cross_assoc_gate = NeuroBLASTMoeMLP(
-            config,
-        )
-        self.motor_cross_sensory_gate = NeuroBLASTMoeMLP(
-            config,
-        )
-        self.motor_cross_assoc_gate = NeuroBLASTMoeMLP(
-            config,
-        )
-
-        # Final normalization before output head
-        self.norm = nn.LayerNorm(
-            config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-5)
-        )
-
-        self.gradient_checkpointing = False
-        self.post_init()
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[
-            Cache
-        ] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ):
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        # use_cache = False
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("Specify either input_ids or inputs_embeds")
-        batch_size, seq_length = (
-            input_ids.shape if input_ids is not None else inputs_embeds.shape[:2]
-        )
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` incompatible with gradient checkpointing. Setting `use_cache=False`"
-                )
-                use_cache = False
-
-            if not any(param.requires_grad for param in self.parameters()):
-                logger.warning_once(
-                    "No parameters require gradients. Disabling gradient checkpointing to avoid warnings."
-                )
-                self.gradient_checkpointing = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        past_key_values_length = 0
-        if use_cache:
-            if not isinstance(past_key_values, Cache):
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_seq_length()
-
-        if cache_position is None:
-            past_seen_tokens = (
-                past_key_values.get_seq_length() if past_key_values is not None else 0
-            )
-            cache_position = torch.arange(
-                past_seen_tokens,
-                past_seen_tokens + inputs_embeds.shape[1],
-                device=inputs_embeds.device,
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask,
-            inputs_embeds,
-            cache_position,
-            past_key_values,
-            output_attentions,
-        )
-
-        hidden_states = inputs_embeds
-
-        cos, sin = self.rotary_emb(
-            hidden_states, seq_len=seq_length + past_key_values_length
-        )
-        position_embeddings = (cos, sin)
-
-        all_hidden_states = () if output_hidden_states else None
-        all_attentions = (
-            () if output_attentions else None
-        )
-        next_decoder_cache = (
-            past_key_values if use_cache else None
-        )
-
-        if self.config.use_zero_memory:
-            hx = torch.ones(
-                (batch_size, seq_length, hidden_states.size(-1)),
-                device=hidden_states.device,
-                dtype=hidden_states.dtype,
-            )
-            cx = torch.ones(
-                (batch_size, seq_length, hidden_states.size(-1)),
-                device=hidden_states.device,
-                dtype=hidden_states.dtype,
-            )
-
-            if self.training:
-                hx.requires_grad_()
-                cx.requires_grad_()
-        else:
-            hx = None
-            cx = None
-
-        # 1. Association Cortex (Self-Attention)
-        assoc_output = hidden_states
-        for i, layer in enumerate(self.association_cortex):
-            if output_hidden_states:
-                all_hidden_states += (assoc_output,)
-            if self.gradient_checkpointing and self.training:
-                outputs = torch.utils.checkpoint.checkpoint(
-                    layer,
-                    assoc_output,
-                    position_embeddings,
-                    causal_mask,
-                    position_ids,
-                    None,
-                    next_decoder_cache,
-                    cache_position,
-                    output_attentions,
-                    use_cache,
-                    (hx, cx),
-                )
-            else:
-                outputs = layer(
-                    assoc_output,
-                    position_embeddings,
-                    causal_mask,
-                    position_ids,
-                    kv_states=None,
-                    past_key_value=next_decoder_cache,
-                    cache_position=cache_position,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    previous_states=(hx, cx),
-                )
-            assoc_output = outputs[0]
-
-            hx, cx = outputs[-1 if not use_cache else -2]
-
-            if output_attentions:
-                all_attentions += (outputs[1],)
-
-            if use_cache:
-                next_decoder_cache = outputs[-1]
-            else:
-                next_decoder_cache = None
-
-        sensory_state = self.assoc_to_sensory_pooler(assoc_output)
-
-        sensory_state = apply_gradient_scaling(
-            sensory_state,
-            self.config.association_gradient_scale,
-            self.config.gradient_scaling_enabled,
-        )
-
-        # 2. Sensory Cortex (Self-Attention + Cross-Attention to Association)
-        for i in range(self.config.num_sensory_cortex_layers):
-            if output_hidden_states:
-                all_hidden_states += (sensory_state,)
-
-            self_attn_layer = self.sensory_self_attn_layers[i]
-            if self.gradient_checkpointing and self.training:
-                outputs_self = torch.utils.checkpoint.checkpoint(
-                    self_attn_layer,
-                    sensory_state,
-                    position_embeddings,
-                    causal_mask,
-                    position_ids,
-                    None,
-                    next_decoder_cache,
-                    cache_position,
-                    output_attentions,
-                    use_cache,
-                    (hx, cx),
-                )
-            else:
-                outputs_self = self_attn_layer(
-                    sensory_state,
-                    position_embeddings,
-                    causal_mask,
-                    position_ids,
-                    kv_states=None,
-                    past_key_value=next_decoder_cache,
-                    cache_position=cache_position,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    previous_states=(hx, cx),
-                )
-            sensory_state = outputs_self[0]
-            hx, cx = outputs_self[-1 if not use_cache else -2]
-
-            if output_attentions:
-                all_attentions += (outputs_self[1],)
-
-            if use_cache:
-                next_decoder_cache = outputs_self[-1]
-            else:
-                next_decoder_cache = None
-
-            cross_attn_layer = self.sensory_cross_attn_layers[i]
-            
-            cross_attn_causal_mask = None
-
-            if causal_mask is not None:
-                q_seq_len = sensory_state.size(1)
-                kv_seq_len = assoc_output.size(1)
-
-                cross_attn_causal_mask = torch.ones(
-                    (batch_size, 1, q_seq_len, kv_seq_len),
-                    device=hidden_states.device,
-                    dtype=hidden_states.dtype,
-                )
-
-                causal_mask_upper = torch.triu(
-                    torch.ones((q_seq_len, kv_seq_len), device=hidden_states.device),
-                    diagonal=1,
-                )
-
-                cross_attn_causal_mask = cross_attn_causal_mask.masked_fill(
-                    causal_mask_upper.unsqueeze(0).unsqueeze(0).bool(),
-                    torch.finfo(hidden_states.dtype).min,
-                )
-
-            if self.gradient_checkpointing and self.training:
-                outputs_cross = torch.utils.checkpoint.checkpoint(
-                    cross_attn_layer,
-                    sensory_state,
-                    position_embeddings,
-                    cross_attn_causal_mask,
-                    position_ids,
-                    assoc_output,
-                    next_decoder_cache,
-                    cache_position,
-                    output_attentions,
-                    use_cache,
-                    (hx, cx),
-                )
-            else:
-                outputs_cross = cross_attn_layer(
-                    sensory_state,
-                    position_embeddings,
-                    past_key_value=next_decoder_cache,
-                    cache_position=cache_position,
-                    attention_mask=cross_attn_causal_mask,
-                    position_ids=position_ids,
-                    kv_states=apply_gradient_scaling(
-                        assoc_output,
-                        self.config.cross_attention_gradient_scale,
-                        self.config.gradient_scaling_enabled,
-                    ),
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    previous_states=(hx, cx),
-                )
-                
-            cross_contribution = nn.functional.layer_norm(
-                outputs_cross[0],
-                normalized_shape=(self.config.hidden_size,),
-                eps=getattr(self.config, "rms_norm_eps", 1e-5),
-            )
-
-            sensory_state = sensory_state + self.sensory_cross_assoc_gate(
-                cross_contribution
-            )
-
-            hx, cx = outputs_cross[-1 if not use_cache else -2]
-            if output_attentions:
-                all_attentions += (outputs_cross[1],)
-
-            if use_cache:
-                next_decoder_cache = outputs_cross[-1]
-            else:
-                next_decoder_cache = None
-
-        motor_state = self.sensory_to_motor_pooler(sensory_state)
-        
-        motor_state = apply_gradient_scaling(
-            motor_state,
-            self.config.sensory_gradient_scale,
-            self.config.gradient_scaling_enabled,
-        )
-
-        motor_state_from_assoc = self.assoc_to_motor_pooler(assoc_output)
-        
-        motor_state_from_assoc = apply_gradient_scaling(
-            motor_state_from_assoc,
-            self.config.association_gradient_scale,
-            self.config.gradient_scaling_enabled,
-        )
-
-        motor_state = motor_state + motor_state_from_assoc  # Combine pooled inputs
-
-        # 3. Motor Cortex (Self + Cross-Sensory + Cross-Association)
-        for i in range(self.config.num_motor_cortex_layers):
-            if output_hidden_states:
-                all_hidden_states += (motor_state,)
-
-            self_attn_layer = self.motor_self_attn_layers[i]
-            if self.gradient_checkpointing and self.training:
-                outputs_self = torch.utils.checkpoint.checkpoint(
-                    self_attn_layer,
-                    motor_state,
-                    position_embeddings,
-                    causal_mask,
-                    position_ids,
-                    None,
-                    next_decoder_cache,
-                    cache_position,
-                    output_attentions,
-                    use_cache,
-                    (hx, cx),
-                )
-            else:
-                outputs_self = self_attn_layer(
-                    motor_state,
-                    position_embeddings,
-                    causal_mask,
-                    position_ids,
-                    kv_states=None,
-                    past_key_value=next_decoder_cache,
-                    cache_position=cache_position,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    previous_states=(hx, cx),
-                )
-            motor_state = outputs_self[0]
-            hx, cx = outputs_self[-1 if not use_cache else -2]
-            if output_attentions:
-                all_attentions += (outputs_self[1],)
-
-            if use_cache:
-                next_decoder_cache = outputs_self[-1]
-            else:
-                next_decoder_cache = None
-
-            cross_sensory_layer = self.motor_cross_sensory_layers[i]
-
-            motor_cross_sensory_mask = None
-
-            if causal_mask is not None:
-                motor_q_seq_len = motor_state.size(1)
-                sensory_kv_seq_len = sensory_state.size(
-                    1
-                ) 
-                
-                motor_cross_sensory_mask = torch.ones(
-                    (batch_size, 1, motor_q_seq_len, sensory_kv_seq_len),
-                    device=hidden_states.device,
-                    dtype=hidden_states.dtype,
-                )
-                
-                causal_mask_upper = torch.triu(
-                    torch.ones(
-                        (motor_q_seq_len, sensory_kv_seq_len),
-                        device=hidden_states.device,
-                    ),
-                    diagonal=1,
-                )
-
-                motor_cross_sensory_mask = motor_cross_sensory_mask.masked_fill(
-                    causal_mask_upper.unsqueeze(0).unsqueeze(0).bool(),
-                    torch.finfo(hidden_states.dtype).min,
-                )
-
-            if self.gradient_checkpointing and self.training:
-                outputs_cross_sensory = torch.utils.checkpoint.checkpoint(
-                    cross_sensory_layer,
-                    motor_state,
-                    position_embeddings,
-                    motor_cross_sensory_mask,
-                    position_ids,
-                    sensory_state,
-                    next_decoder_cache,
-                    cache_position,
-                    output_attentions,
-                    use_cache,
-                    (hx, cx),
-                )
-            else:
-                outputs_cross_sensory = cross_sensory_layer(
-                    motor_state,
-                    position_embeddings,
-                    attention_mask=motor_cross_sensory_mask,
-                    position_ids=position_ids,
-                    kv_states=apply_gradient_scaling(
-                        sensory_state,
-                        self.config.cross_attention_gradient_scale,
-                        self.config.gradient_scaling_enabled,
-                    ),
-                    output_attentions=output_attentions,
-                    past_key_value=next_decoder_cache,
-                    cache_position=cache_position,
-                    use_cache=use_cache,
-                    previous_states=(hx, cx),
-                )
-            motor_state = motor_state + self.motor_cross_sensory_gate(
-                outputs_cross_sensory[0]
-            )
-            hx, cx = outputs_cross_sensory[-1 if not use_cache else -2]
-            if output_attentions:
-                all_attentions += (outputs_cross_sensory[1],)
-
-            if use_cache:
-                next_decoder_cache = outputs_cross_sensory[-1]
-            else:
-                next_decoder_cache = None
-
-            cross_assoc_layer = self.motor_cross_assoc_layers[i]
-            
-            motor_cross_assoc_mask = None
-            if causal_mask is not None:
-                motor_q_seq_len = motor_state.size(1)
-                assoc_kv_seq_len = assoc_output.size(
-                    1
-                )
-                
-                motor_cross_assoc_mask = torch.ones(
-                    (batch_size, 1, motor_q_seq_len, assoc_kv_seq_len),
-                    device=hidden_states.device,
-                    dtype=hidden_states.dtype,
-                )
-
-                causal_mask_upper = torch.triu(
-                    torch.ones(
-                        (motor_q_seq_len, assoc_kv_seq_len), device=hidden_states.device
-                    ),
-                    diagonal=1,
-                )
-
-                motor_cross_assoc_mask = motor_cross_assoc_mask.masked_fill(
-                    causal_mask_upper.unsqueeze(0).unsqueeze(0).bool(),
-                    torch.finfo(hidden_states.dtype).min,
-                )
-
-            if self.gradient_checkpointing and self.training:
-                outputs_cross_assoc = torch.utils.checkpoint.checkpoint(
-                    cross_assoc_layer,
-                    motor_state,
-                    position_embeddings,
-                    motor_cross_assoc_mask,
-                    position_ids,
-                    assoc_output,
-                    next_decoder_cache,
-                    cache_position,
-                    output_attentions,
-                    use_cache,
-                    (hx, cx),
-                )
-            else:
-                outputs_cross_assoc = cross_assoc_layer(
-                    motor_state,
-                    position_embeddings,
-                    attention_mask=motor_cross_assoc_mask,
-                    position_ids=position_ids,
-                    kv_states=apply_gradient_scaling(
-                        assoc_output,
-                        self.config.cross_attention_gradient_scale,
-                        self.config.gradient_scaling_enabled,
-                    ),
-                    output_attentions=output_attentions,
-                    past_key_value=next_decoder_cache,
-                    cache_position=cache_position,
-                    use_cache=use_cache,
-                    previous_states=(hx, cx),
-                )
-            motor_state = motor_state + self.motor_cross_assoc_gate(
-                outputs_cross_assoc[0]
-            )
-            hx, cx = outputs_cross_assoc[-1 if not use_cache else -2]
-            if output_attentions:
-                all_attentions += (outputs_cross_assoc[1],)
-
-            if use_cache:
-                next_decoder_cache = outputs_cross_assoc[-1]
-            else:
-                next_decoder_cache = None
-
-        final_output = self.norm(motor_state)
-
-        if output_hidden_states:
-            all_hidden_states += (final_output,) 
-
-        if not return_dict:
-            outputs_tuple = (final_output,)
-            if use_cache:
-                outputs_tuple += (next_decoder_cache,)
-            if output_hidden_states:
-                outputs_tuple += (all_hidden_states,)
-            if output_attentions:
-                outputs_tuple += (all_attentions,)
-            return tuple(v for v in outputs_tuple if v is not None)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=final_output,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_attentions,
-        )
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
-    ):
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        past_seen_tokens = (
-            past_key_values.get_seq_length() if past_key_values is not None else 0
-        )
-        using_static_cache = isinstance(past_key_values, StaticCache)
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and not using_static_cache
-            and not output_attentions
-        ):
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                is_training=self.training,
-            ):
-                return None
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        if using_static_cache:
-            target_length = (
-                getattr(
-                    past_key_values,
-                    "get_max_length",
-                    lambda: past_key_values.get_seq_length(),
-                )()
-                if hasattr(past_key_values, "get_seq_length")
-                else sequence_length + past_seen_tokens
-            )
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            device=device,
-            min_dtype=min_dtype,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-        )
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type == "cuda"
-            and not output_attentions
-        ):
-            causal_mask = AttentionMaskConverter._unmask_unattended(
-                causal_mask, min_dtype
-            )
-
-        return causal_mask
-
-
-class NeuroBLASTForCausalLM(NeuroBLASTPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: NeuroBLASTConfig):
-        super().__init__(config)
-        self.config = config
-        self.model = NeuroBLASTModel(config)
-        self.vocab_size = config.vocab_size  # Ensure vocab_size is accessible
-        self.lm_head = torch.nn.Linear(config.hidden_size, self.vocab_size, bias=False)
-        self.loss_steps = 0
-        self.post_init()  # Initialize weights
-
-    def get_input_embeddings(self):
-        return self.model.get_input_embeddings()
-
-    def set_input_embeddings(self, value):
-        self.model.set_input_embeddings(value)
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def tie_weights(self):
-        if getattr(self.config, "tie_word_embeddings", False):
-            output_embeddings = self.get_output_embeddings()
-            input_embeddings = self.get_input_embeddings()
-            output_embeddings.weight = input_embeddings.weight
-            if getattr(output_embeddings, "bias", None) is not None:
-                output_embeddings.bias.data.zero_()
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **loss_kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-
-        hidden_states = outputs[0]
-
-        logits = self.lm_head(hidden_states)
-        logits = logits.float()
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **loss_kwargs,
-            )
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def get_input_embeddings(self):
-        return self.model.get_input_embeddings()
-
-    def set_input_embeddings(self, value):
-        self.model.set_input_embeddings(value)
-
-    def __str__(self):
-        return f"NeuroBLASTForCausalLM(config={self.config})"

neuroblast_model/registration.py

@@ -1,5 +0,0 @@
-from transformers import AutoConfig, AutoModel
-from neuroblast_model import NeuroBLASTConfig, NeuroBLASTForCausalLM
-
-AutoConfig.register("neuroblast", NeuroBLASTForCausalLM)
-AutoModel.register(NeuroBLASTConfig, NeuroBLASTForCausalLM)