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neuroblast_model/__init__.py

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+from neuroblast_model.configuration_neuroblast import NeuroBLASTConfig
+from neuroblast_model.modeling_neuroblast import NeuroBLASTForCausalLM

neuroblast_model/configuration_neuroblast.py

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+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

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+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

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