core/data_architecture/attention_mechanism.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from typing import Optional, Tuple
import numpy as np

# Framework-specific imports
try:
    from flash_attn import flash_attn_func
    use_flash_attention = True
except ImportError:
    use_flash_attention = False

try:
    import jax
    import jax.numpy as jnp
    from jax import jit
    use_jax = True
except ImportError:
    use_jax = False

try:
    import tensorflow as tf
    use_tensorflow = True
except ImportError:
    use_tensorflow = False

try:
    import nemo.collections.nlp as nemo_nlp
    use_nemo = True
except ImportError:
    use_nemo = False

try:
    import onnx
    import onnxruntime as ort
    use_onnx = True
except ImportError:
    use_onnx = False

class MultiFrameworkAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int, dropout: float = 0.1,
                 causal: bool = False, bias: bool = False,
                 framework: str = "pytorch"):
        super().__init__()
        self.framework = framework.lower()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        assert dim % num_heads == 0, "dim must be divisible by num_heads"
        self.dropout = dropout
        self.causal = causal
        self.scale = self.head_dim ** -0.5

        # Framework-specific initialization
        if self.framework == "pytorch":
            self.qkv_proj = nn.Linear(dim, dim * 3, bias=bias)
            self.out_proj = nn.Linear(dim, dim, bias=bias)
            self.dropout_layer = nn.Dropout(dropout)
        elif self.framework == "tensorflow" and use_tensorflow:
            self.qkv_proj = tf.keras.layers.Dense(dim * 3, use_bias=bias)
            self.out_proj = tf.keras.layers.Dense(dim, use_bias=bias)
            self.dropout_layer = tf.keras.layers.Dropout(dropout)
        elif self.framework == "jax" and use_jax:
            # JAX uses functional paradigm, weights initialized in  later
            pass
        elif self.framework == "nemo" and use_nemo:
            # NeMo-specific initialization
            pass

    def forward(self, x, mask: Optional = None) -> any:
        if self.framework == "pytorch":
            return self._pytorch_forward(x, mask)
        elif self.framework == "tensorflow" and use_tensorflow:
            return self._tensorflow_forward(x, mask)
        elif self.framework == "jax" and use_jax:
            return self._jax_forward(x, mask)
        elif self.framework == "nemo" and use_nemo:
            return self._nemo_forward(x, mask)
        else:
            raise ValueError(f"Unsupported framework: {self.framework}")

    def _pytorch_forward(self, x: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
        B, S, D = x.size()
        qkv = self.qkv_proj(x).reshape(B, S, 3, self.num_heads, self.head_dim)
        q, k, v = qkv.unbind(dim=2)
        q, k, v = [rearrange(t, "b s h d -> b h s d") for t in (q, k, v)]

        if use_flash_attention and mask is None:
            out = flash_attn_func(
                q, k, v,
                dropout_p=self.dropout if self.training else 0,
                causal=self.causal
            )
        else:
            scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale
            if mask is not None:
                scores = scores.masked_fill(mask == 0, float('-inf'))
            attn = F.softmax(scores, dim=-1)
            attn = self.dropout_layer(attn)
            out = torch.matmul(attn, v)

        out = rearrange(out, "b h s d -> b s (h d)")
        return self.out_proj(out)

    def _tensorflow_forward(self, x, mask=None):
        B, S, D = x.shape
        qkv = self.qkv_proj(x)
        qkv = tf.reshape(qkv, [B, S, 3, self.num_heads, self.head_dim])
        q, k, v = tf.unstack(qkv, axis=2)
        q = tf.transpose(q, [0, 2, 1, 3])
        k = tf.transpose(k, [0, 2, 3, 1])
        v = tf.transpose(v, [0, 2, 1, 3])

        scores = tf.matmul(q, k) * self.scale
        if mask is not None:
            scores = tf.where(mask == 0, float('-inf'), scores)
        attn = tf.nn.softmax(scores)
        attn = self.dropout_layer(attn)
        out = tf.matmul(attn, v)
        out = tf.transpose(out, [0, 2, 1, 3])
        out = tf.reshape(out, [B, S, D])
        return self.out_proj(out)

    def _jax_forward(self, x, mask=None):
        B, S, D = x.shape
        def attention_fn(params, x):
            qkv = jnp.dot(x, params['qkv_proj'])
            qkv = qkv.reshape(B, S, 3, self.num_heads, self.head_dim)
            q, k, v = jnp.split(qkv, 3, axis=2)
            q = jnp.transpose(q, (0, 2, 1, 3))
            k = jnp.transpose(k, (0, 2, 3, 1))
            v = jnp.transpose(v, (0, 2, 1, 3))
            
            scores = jnp.matmul(q, k) * self.scale
            if mask is not None:
                scores = jnp.where(mask == 0, float('-inf'), scores)
            attn = jax.nn.softmax(scores)
            out = jnp.matmul(attn, v)
            out = jnp.transpose(out, (0, 2, 1, 3))
            out = out.reshape(B, S, D)
            return jnp.dot(out, params['out_proj'])
        
        # Dummy params for JAX (would need proper initialization in practice)
        params = {
            'qkv_proj': jnp.zeros((D, D * 3)),
            'out_proj': jnp.zeros((D, D))
        }
        return jit(attention_fn)(params, x)

    def _nemo_forward(self, x, mask=None):
        # NeMo integration would typically use PyTorch backend
        # This is a placeholder showing how it could be structured
        if use_nemo:
            nemo_model = nemo_nlp.models.TransformerEncoderModel(
                hidden_size=D,
                num_attention_heads=self.num_heads
            )
            return nemo_model(input_ids=x, attention_mask=mask)
        return self._pytorch_forward(x, mask)

    def to_onnx(self, dummy_input, output_path: str):
        if not use_onnx:
            raise ImportError("ONNX support not available")
        
        if self.framework == "pytorch":
            torch.onnx.export(
                self,
                dummy_input,
                output_path,
                opset_version=13,
                input_names=['input'],
                output_names=['output']
            )
        elif self.framework == "tensorflow" and use_tensorflow:
            # Convert TF to ONNX would require tf2onnx
            pass
        elif self.framework == "jax" and use_jax:
            # JAX to ONNX would require jax2tf then tf2onnx
            pass

class TransformerBlock(nn.Module):
    def __init__(self, dim: int, num_heads: int, ff_dim: int,
                 num_experts: int = 16, top_k: int = 2, dropout: float = 0.1,
                 framework: str = "pytorch"):
        super().__init__()
        self.framework = framework
        self.attn_norm = nn.LayerNorm(dim) if framework == "pytorch" else None
        self.attn = MultiFrameworkAttention(dim, num_heads, dropout, framework=framework)
        self.ff_norm = nn.LayerNorm(dim) if framework == "pytorch" else None
        # Add MoE and other components similarly
        self.dropout = nn.Dropout(dropout) if framework == "pytorch" else None

    def forward(self, x, mask: Optional = None):
        if self.framework == "pytorch":
            x = x + self.dropout(self.attn(self.attn_norm(x), mask))
            # Add FF/MoE logic
            return x
        # Add other framework implementations
        return x

# Usage example
if __name__ == "__main__":
    # PyTorch example
    model = MultiFrameworkAttention(dim=512, num_heads=8, framework="pytorch")
    x = torch.randn(2, 64, 512)
    output = model(x)
    
    # Export to ONNX
    if use_onnx:
        model.to_onnx(x, "attention.onnx")
        
    # TensorFlow example
    if use_tensorflow:
        tf_model = MultiFrameworkAttention(dim=512, num_heads=8, framework="tensorflow")
        x_tf = tf.random.normal((2, 64, 512))
        output_tf = tf_model(x_tf)