claudeson.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import faiss
import math
import numpy as np
from typing import Optional, Tuple, Literal
from dataclasses import dataclass

# Global configuration
world_size = 1
rank = 0
block_size = 128
gemm_impl: Literal["bf16", "fp8"] = "bf16"
attn_impl: Literal["naive", "absorb"] = "absorb"

@dataclass
class ModelArgs:
    dim: int = 4096
    n_layers: int = 32
    n_heads: int = 32
    n_kv_heads: int = 8
    vocab_size: int = 32000
    multiple_of: int = 256
    ffn_dim_multiplier: Optional[float] = None
    max_seq_len: int = 4096
    original_seq_len: int = 4096
    rope_theta: float = 10000.0
    rope_factor: float = 1.0
    beta_fast: float = 32.0
    beta_slow: float = 1.0
    mscale: float = 0.707
    q_lora_rank: int = 0
    kv_lora_rank: int = 0
    qk_nope_head_dim: int = 128
    qk_rope_head_dim: int = 64
    v_head_dim: int = 128
    n_routed_experts: int = 8
    n_activated_experts: int = 2
    n_expert_groups: int = 1
    n_limited_groups: int = 1
    score_func: str = "softmax"
    route_scale: float = 1.0
    n_dense_layers: int = 0
    moe_inter_dim: int = None
    n_shared_experts: int = 1
    max_batch_size: int = 32
    dtype: str = "bf16"

    def __post_init__(self):
        if self.ffn_dim_multiplier is None:
            self.inter_dim = int(2 * self.dim / 3)
            self.inter_dim = self.multiple_of * ((self.inter_dim + self.multiple_of - 1) // self.multiple_of)
        else:
            self.inter_dim = int(2 * self.dim * self.ffn_dim_multiplier)
        
        if self.moe_inter_dim is None:
            self.moe_inter_dim = int(2 * self.dim / 3)
            self.moe_inter_dim = self.multiple_of * ((self.moe_inter_dim + self.multiple_of - 1) // self.multiple_of)

# Embedding layer
class ParallelEmbedding(nn.Module):
    def __init__(self, vocab_size: int, dim: int):
        super().__init__()
        self.vocab_size = vocab_size
        self.dim = dim
        assert vocab_size % world_size == 0
        self.part_vocab_size = (vocab_size // world_size)
        self.vocab_start_idx = rank * self.part_vocab_size
        self.vocab_end_idx = self.vocab_start_idx + self.part_vocab_size
        self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if world_size > 1:
            mask = (x < self.vocab_start_idx) | (x >= self.vocab_end_idx)
            x = x - self.vocab_start_idx
            x[mask] = 0
        y = F.embedding(x, self.weight)
        if world_size > 1:
            y[mask] = 0
            torch.distributed.all_reduce(y)
        return y

# Linear layer
def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
    if weight.element_size() > 1:
        return F.linear(x, weight, bias)
    elif gemm_impl == "bf16":
        weight = weight_dequant(weight, weight.scale)
        return F.linear(x, weight, bias)
    else:
        x, scale = act_quant(x, block_size)
        y = fp8_gemm(x, scale, weight, weight.scale)
        if bias is not None:
            y += bias
        return y

class Linear(nn.Module):
    dtype = torch.bfloat16

    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype=None):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype or Linear.dtype))
        if self.weight.element_size() == 1:
            scale_out_features = (out_features + block_size - 1) // block_size
            scale_in_features = (in_features + block_size - 1) // block_size
            self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float32))
        else:
            self.register_parameter("scale", None)
        if bias:
            self.bias = nn.Parameter(torch.empty(out_features))
        else:
            self.register_parameter("bias", None)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return linear(x, self.weight, self.bias)

class ColumnParallelLinear(Linear):
    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype=None):
        assert out_features % world_size == 0
        self.part_out_features = out_features // world_size
        super().__init__(in_features, self.part_out_features, bias, dtype)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return linear(x, self.weight, self.bias)

class RowParallelLinear(Linear):
    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype=None):
        assert in_features % world_size == 0
        self.part_in_features = in_features // world_size
        super().__init__(self.part_in_features, out_features, bias, dtype)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        y = linear(x, self.weight)
        if world_size > 1:
            torch.distributed.all_reduce(y)
        if self.bias is not None:
            y += self.bias
        return y

# Normalization layer
class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor):
        x = x.float()
        y = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
        return y.type_as(self.weight) * self.weight

# Positional encoding
def precompute_freqs_cis(args: ModelArgs) -> torch.Tensor:
    dim = args.qk_rope_head_dim
    seqlen = args.max_seq_len
    beta_fast = args.beta_fast
    beta_slow = args.beta_slow
    base = args.rope_theta
    factor = args.rope_factor

    def find_correction_dim(num_rotations, dim, base, max_seq_len):
        return dim * math.log(max_seq_len / (num_rotations * 2 * math.pi)) / (2 * math.log(base))

    def find_correction_range(low_rot, high_rot, dim, base, max_seq_len):
        low = math.floor(find_correction_dim(low_rot, dim, base, max_seq_len))
        high = math.ceil(find_correction_dim(high_rot, dim, base, max_seq_len))
        return max(low, 0), min(high, dim-1)

    def linear_ramp_factor(min, max, dim):
        if min == max:
            max += 0.001
        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
        ramp_func = torch.clamp(linear_func, 0, 1)
        return ramp_func

    freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
    if seqlen > args.original_seq_len:
        low, high = find_correction_range(beta_fast, beta_slow, dim, base, args.original_seq_len)
        smooth = 1 - linear_ramp_factor(low, high, dim // 2)
        freqs = freqs / factor * (1 - smooth) + freqs * smooth

    t = torch.arange(seqlen)
    freqs = torch.outer(t, freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
    return freqs_cis

def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
    dtype = x.dtype
    x = torch.view_as_complex(x.float().view(*x.shape[:-1], -1, 2))
    freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
    y = torch.view_as_real(x * freqs_cis).flatten(3)
    return y.to(dtype)

# Multi-Head Latent Attention (MLA)
class MLA(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.dim = args.dim
        self.n_heads = args.n_heads
        self.n_local_heads = args.n_heads // world_size
        self.q_lora_rank = args.q_lora_rank
        self.kv_lora_rank = args.kv_lora_rank
        self.qk_nope_head_dim = args.qk_nope_head_dim
        self.qk_rope_head_dim = args.qk_rope_head_dim
        self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
        self.v_head_dim = args.v_head_dim

        if self.q_lora_rank == 0:
            self.wq = ColumnParallelLinear(self.dim, self.n_heads * self.qk_head_dim)
        else:
            self.wq_a = Linear(self.dim, self.q_lora_rank)
            self.q_norm = RMSNorm(self.q_lora_rank)
            self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
        self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
        self.kv_norm = RMSNorm(self.kv_lora_rank)
        self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
        self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
        self.softmax_scale = self.qk_head_dim ** -0.5
        if args.max_seq_len > args.original_seq_len:
            mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
            self.softmax_scale = self.softmax_scale * mscale * mscale

        if attn_impl == "naive":
            self.register_buffer("k_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.qk_head_dim), persistent=False)
            self.register_buffer("v_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.v_head_dim), persistent=False)
        else:
            self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.kv_lora_rank), persistent=False)
            self.register_buffer("pe_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.qk_rope_head_dim), persistent=False)

    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
        bsz, seqlen, _ = x.size()
        end_pos = start_pos + seqlen
        if self.q_lora_rank == 0:
            q = self.wq(x)
        else:
            q = self.wq_b(self.q_norm(self.wq_a(x)))
        q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
        q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        q_pe = apply_rotary_emb(q_pe, freqs_cis)
        kv = self.wkv_a(x)
        kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis)
        if attn_impl == "naive":
            q = torch.cat([q_nope, q_pe], dim=-1)
            kv = self.wkv_b(self.kv_norm(kv))
            kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
            k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
            k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
            self.k_cache[:bsz, start_pos:end_pos] = k
            self.v_cache[:bsz, start_pos:end_pos] = v
            scores = torch.einsum("bshd,bthd->bsht", q, self.k_cache[:bsz, :end_pos]) * self.softmax_scale
        else:
            wkv_b = self.wkv_b.weight if self.wkv_b.scale is None else weight_dequant(self.wkv_b.weight, self.wkv_b.scale, block_size) 
            wkv_b = wkv_b.view(self.n_local_heads, -1, self.kv_lora_rank)
            q_nope = torch.einsum("bshd,hdc->bshc", q_nope, wkv_b[:, :self.qk_nope_head_dim])
            self.kv_cache[:bsz, start_pos:end_pos] = self.kv_norm(kv)
            self.pe_cache[:bsz, start_pos:end_pos] = k_pe.squeeze(2)
            scores = (torch.einsum("bshc,btc->bsht", q_nope, self.kv_cache[:bsz, :end_pos]) +
                      torch.einsum("bshr,btr->bsht", q_pe, self.pe_cache[:bsz, :end_pos])) * self.softmax_scale
        if mask is not None:
            scores += mask.unsqueeze(1)
        scores = scores.softmax(dim=-1, dtype=torch.float32).type_as(x)
        if attn_impl == "naive":
            x = torch.einsum("bsht,bthd->bshd", scores, self.v_cache[:bsz, :end_pos])
        else:
            x = torch.einsum("bsht,btc->bshc", scores, self.kv_cache[:bsz, :end_pos])
            x = torch.einsum("bshc,hdc->bshd", x, wkv_b[:, -self.v_head_dim:])
        x = self.wo(x.flatten(2))
        return x

# MLP layer
class MLP(nn.Module):
    def __init__(self, dim: int, inter_dim: int):
        super().__init__()
        self.w1 = ColumnParallelLinear(dim, inter_dim)
        self.w2 = RowParallelLinear(inter_dim, dim)
        self.w3 = ColumnParallelLinear(dim, inter_dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))

# Mixture of Experts (MoE) components
class Gate(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.dim = args.dim
        self.topk = args.n_activated_experts
        self.n_groups = args.n_expert_groups
        self.topk_groups = args.n_limited_groups
        self.score_func = args.score_func
        self.route_scale = args.route_scale
        self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
        self.bias = nn.Parameter(torch.empty(args.n_routed_experts)) if self.dim == 7168 else None

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        scores = linear(x, self.weight)
        if self.score_func == "softmax":
            scores = scores.softmax(dim=-1, dtype=torch.float32)
        else:
            scores = scores.sigmoid()
        original_scores = scores
        if self.bias is not None:
            scores = scores + self.bias
        if self.n_groups > 1:
            scores = scores.view(x.size(0), self.n_groups, -1)
            if self.bias is None:
                group_scores = scores.amax(dim=-1)
            else:
                group_scores = scores.topk(2, dim=-1)[0].sum(dim=-1)
            indices = group_scores.topk(self.topk_groups, dim=-1)[1]
            mask = torch.zeros_like(scores[..., 0]).scatter_(1, indices, True)
            scores = (scores * mask.unsqueeze(-1)).flatten(1)
        indices = torch.topk(scores, self.topk, dim=-1)[1]
        weights = original_scores.gather(1, indices)
        if self.score_func == "sigmoid":
            weights /= weights.sum(dim=-1, keepdim=True)
        weights *= self.route_scale
        return weights.type_as(x), indices

class Expert(nn.Module):
    def __init__(self, dim: int, inter_dim: int):
        super().__init__()
        self.w1 = Linear(dim, inter_dim)
        self.w2 = Linear(inter_dim, dim)
        self.w3 = Linear(dim, inter_dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))

class MoE(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.dim = args.dim
        assert args.n_routed_experts % world_size == 0
        self.n_routed_experts = args.n_routed_experts
        self.n_local_experts = args.n_routed_experts // world_size
        self.n_activated_experts = args.n_activated_experts
        self.experts_start_idx = rank * self.n_local_experts
        self.experts_end_idx = self.experts_start_idx + self.n_local_experts
        self.gate = Gate(args)
        self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim) if self.experts_start_idx <= i < self.experts_end_idx else None
                                      for i in range(self.n_routed_experts)])
        self.shared_experts = MLP(args.dim, args.n_shared_experts * args.moe_inter_dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shape = x.size()
        x = x.view(-1, self.dim)
        weights, indices = self.gate(x)
        y = torch.zeros_like(x)
        counts = torch.bincount(indices.flatten(), minlength=self.n_routed_experts).tolist()
        for i in range(self.experts_start_idx, self.experts_end_idx):
            if counts[i] == 0:
                continue
            expert = self.experts[i]
            idx, top = torch.where(indices == i)
            y[idx] += expert(x[idx]) * weights[idx, top, None]
        z = self.shared_experts(x)
        if world_size > 1:
            torch.distributed.all_reduce(y)
        return (y + z).view(shape)

# Transformer block
class Block(nn.Module):
    def __init__(self, layer_id: int, args: ModelArgs):
        super().__init__()
        self.attn = MLA(args)
        self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
        self.attn_norm = RMSNorm(args.dim)
        self.ffn_norm = RMSNorm(args.dim)

    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
        x = x + self.attn(self.attn_norm(x), start_pos, freqs_cis, mask)
        x = x + self.ffn(self.ffn_norm(x))
        return x

# Transformer model
class Transformer(nn.Module):
    def __init__(self, args: ModelArgs):
        global world_size, rank
        world_size = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1
        rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
        Linear.dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
        super().__init__()
        self.max_seq_len = args.max_seq_len
        self.embed = ParallelEmbedding(args.vocab_size, args.dim)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(args.n_layers):
            self.layers.append(Block(layer_id, args))
        self.norm = RMSNorm(args.dim)
        self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.get_default_dtype())
        self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)

    @torch.inference_mode()
    def forward(self, tokens: torch.Tensor, start_pos: int = 0):
        seqlen = tokens.size(1)
        h = self.embed(tokens)
        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
        mask = None
        if seqlen > 1:
            mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1)
        for layer in self.layers:
            h = layer(h, start_pos, freqs_cis, mask)
        h = self.norm(h)[:, -1]
        logits = self.head(h)
        if world_size > 1:
            all_logits = [torch.empty_like(logits) for _ in range(world_size)]
            torch.distributed.all_gather(all_logits, logits)
            logits = torch.cat(all_logits, dim=-1)
        return logits

# FAISS Retriever
class FAISSRetriever:
    def __init__(self, knowledge_base: faiss.Index, dim: int = 768, num_results: int = 5):
        self.index = knowledge_base
        self.dim = dim
        self.num_results = num_results

    def search(self, query_embedding: torch.Tensor, k: int = None) -> torch.Tensor:
        if k is None:
            k = self.num_results
        query_np = query_embedding.detach().cpu().numpy()
        distances, indices = self.index.search(query_np, k)
        return torch.tensor(indices, device=query_embedding.device)

# Complete Multi-Modal LLM
class CombinedMultiModalTransformer(nn.Module):
    def __init__(self, args: ModelArgs, knowledge_base: faiss.Index):
        super(CombinedMultiModalTransformer, self).__init__()
        self.args = args
        self.transformer = Transformer(args)
        
        # Multi-modal components
        self.audio_encoder = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=11, stride=2, padding='same'),
            nn.ReLU(),
            nn.Conv1d(256, 256, kernel_size=11, stride=2, padding='same'),
            nn.ReLU(),
            nn.Conv1d(256, args.dim, kernel_size=11, stride=2, padding='same'),
            nn.ReLU()
        )
        
        self.image_encoder = nn.Sequential(
            # Simplified ResNet50 implementation
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(2048, args.dim)
        )
        
        # Music generation components
        self.pitch_embedding = nn.Embedding(128, args.dim)
        self.duration_embedding = nn.Embedding(32, args.dim)
        self.velocity_embedding = nn.Embedding(128, args.dim)
        
        # Anomaly detection components
        self.anomaly_detector = nn.Sequential(
            nn.Linear(args.dim, args.dim),
            nn.ReLU(),
            nn.Linear(args.dim, 1),
            nn.Sigmoid()
        )
        
        # RAG components
        self.knowledge_base = FAISSRetriever(knowledge_base)
        self.query_encoder = nn.Sequential(
            nn.Linear(args.dim, args.dim),
            nn.ReLU(),
            nn.Linear(args.dim, args.dim)
        )
        
    def forward(self, inputs, task, start_pos=0):
        if task == 'text_generation':
            # RAG component
            query_embedding = self.query_encoder(self.transformer.embed(inputs))
            retrieved_indices = self.knowledge_base.search(query_embedding, k=5)
            
            # Concatenate retrieved docs with input
            # In practice, you would convert indices to actual embeddings
            retrieved_embeddings = torch.zeros_like(inputs[:, :5, :])  # Placeholder
            inputs = torch.cat([retrieved_embeddings, inputs], dim=1)
            
            # Pass through transformer
            logits = self.transformer(inputs, start_pos)
            return logits
        
        elif task == 'speech_recognition':
            x = self.audio_encoder(inputs)
            # Convert audio encoder output to transformer format
            batch_size, seq_len = x.shape[0], x.shape[1]
            tokens = torch.zeros(batch_size, seq_len, dtype=torch.long, device=x.device)
            logits = self.transformer(tokens, start_pos)
            return logits
        
        elif task == 'image_captioning':
            image_features = self.image_encoder(inputs)
            # Convert image features to transformer format
            batch_size = image_features.shape[0]
            tokens = torch.zeros(batch_size, 1, dtype=torch.long, device=image_features.device)
            logits = self.transformer(tokens, start_pos)
            return logits
        
        elif task == 'music_generation':
            pitch, duration, velocity = inputs
            x = self.pitch_embedding(pitch) + self.duration_embedding(duration) + self.velocity_embedding(velocity)
            # Convert music features to transformer format
            batch_size, seq_len = x.shape[0], x.shape[1]
            tokens = torch.zeros(batch_size, seq_len, dtype=torch.long, device=x.device)
            logits = self.transformer(tokens, start_pos)
            return logits
        
        elif task == 'anomaly_detection':
            x = self.transformer.embed(inputs)
            anomaly_scores = self.anomaly_detector(x)
            return anomaly_scores
        
        else:
            raise ValueError(f"Unknown task: {task}")

# Helper functions
def act_quant(x: torch.Tensor, block_size: int = 128):
    # Simplified activation quantization function
    return x, torch.ones(1, device=x.device)

def weight_dequant(weight: torch.Tensor, scale: torch.Tensor, block_size: int = 128):
    # Simplified weight dequantization function
    return weight * scale

def fp8_gemm(x: torch.Tensor, x_scale: torch.Tensor, weight: torch.Tensor, weight_scale: torch.Tensor):
    # Simplified FP8 GEMM function
    return torch.matmul(x, weight.t()) * x_scale * weight_scale

# Training function
def train_model(model, dataloader, optimizer, criterion, device, num_epochs=10):
    model.train()
    model.to(device)
    
    for epoch in range(num_epochs):
        total_loss = 0.0
        for batch_idx, (inputs, targets, tasks) in enumerate(dataloader):
            inputs, targets = inputs.to(device), targets.to(device)
            
            optimizer.zero_grad()
            outputs = model(inputs, tasks)
            
            if isinstance(outputs, dict):
                # Handle multi-task outputs
                loss = 0.0
                for task, output in outputs.items():
                    task_targets = targets[task]
                    loss += criterion(output, task_targets)
            else:
                loss = criterion(outputs, targets)
            
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
            
            if batch_idx % 100 == 0:
                print(f'Epoch {epoch+1}/{num_epochs}, Batch {batch_idx}/{len(dataloader)}, Loss: {loss.item():.4f}')
        
        avg_loss = total_loss / len(dataloader)
        print(f'Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}')

# Inference function
def generate_text(model, prompt, max_length=100, temperature=1.0, device='cpu'):
    model.eval()
    model.to(device)
    
    # Convert prompt to tokens
    tokens = torch.tensor([prompt], dtype=torch.long, device=device)
    
    with torch.no_grad():
        for _ in range(max_length):
            logits = model(tokens, 'text_generation')
            
            # Apply temperature scaling
            logits = logits[:, -1, :] / temperature
            
            # Get probabilities for next token
            probs = F.softmax(logits, dim=-1)
            
            # Sample next token
            next_token = torch.multinomial(probs, num_samples=1)
            
            # Append new token to sequence
            tokens = torch.cat([tokens, next_token], dim=1)
    
    return tokens[0].tolist()

# Example usage
if __name__ == "__main__":
    # Initialize model parameters
    args = ModelArgs()
    
    # Create a dummy knowledge base for testing
    dim = args.dim
    knowledge_base = faiss.IndexFlatL2(dim)
    # Add some dummy vectors
    vectors = np.random.rand(100, dim).astype('float32')
    knowledge_base.add(vectors)
    
    # Initialize model
    model = CombinedMultiModalTransformer(args, knowledge_base)
    
    # Print model structure
    print(model)
    
    # Test text generation
    prompt = [1, 2, 3, 4, 5]  # Example token sequence
    generated_tokens = generate_text(model, prompt, max_length=20)
    print(f"Generated tokens: {generated_tokens}")
    
    # Test other tasks
    # Note: In practice, you would provide appropriate input data
    try:
        # Speech recognition
        audio_input = torch.randn(1, 256, 160)  # Example audio input
        speech_output = model(audio_input, 'speech_recognition')
        print(f"Speech recognition output shape: {speech_output.shape}")
        
        # Image captioning
        image_input = torch.randn(1, 3, 224, 224)  # Example image input
        caption_output = model(image_input, 'image_captioning')
        print(f"Image captioning output shape: {caption_output.shape}")
        
        # Music generation
        pitch = torch.randint(0, 128, (1, 100))
        duration = torch.randint(0, 32, (1, 100))
        velocity = torch.randint(0, 128, (1, 100))
        music_output = model((pitch, duration, velocity), 'music_generation')
        print(f"Music generation output shape: {music_output.shape}")
        
        # Anomaly detection
        anomaly_input = torch.randn(1, 100, args.dim)
        anomaly_output = model(anomaly_input, 'anomaly_detection')
        print(f"Anomaly detection output shape: {anomaly_output.shape}")
    except Exception as e:
        print(f"Error during testing: {e}")