this is the code I am trying to debug based on this definition of bug as Bug Definition:

There are 10 bugs to be fixed.
A bug is defined as {incorrect, missing, unnecessary} lines of code.
You earn 1 point for each correctly identified and fixed bug.

Incorrect Code : def rotate_half(x):
    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=None, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

def repeat_kv(hidden_states, n_rep):
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)

class RotaryEmbedder(nn.Module):
    def __init__(self, dim, base):
        super().__init__()
        self.freq = 1/(base ** (torch.arange(0, dim, 2, dtype=torch.int64).float()/dim))

    @torch.no_grad()
    def forward(self,x):
        pos = torch.arange(x.shape[-2],dtype=torch.long)
        angles = torch.einsum('f,p->fp', self.freq, pos.float()).unsqueeze(dim=0)
        emb = torch.cat((angles, angles), dim=-1)
        return emb.cos(), emb.sin()


class MLP(nn.Module):
    def __init__(self, hidden_size, intermediate_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.W_gate = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.W_up = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.W_down = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = torch.nn.modules.activation.SiLU()

    def forward(self, x):
        down_proj = self.W_down(self.act_fn((self.W_gate(x)) * self.W_up(x)))
        return down_proj

class RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.sqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states


class RopeAttention(nn.Module):
    def __init__(self,config):
        super().__init__()
        self.hidden_size=config.hidden_size
        self.num_heads = config.num_heads
        self.head_dim = config.hidden_size//self.num_heads
        self.kv_heads = config.kv_heads
        self.rope_theta = 10000.0

        self.W_query = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.W_key = nn.Linear(config.hidden_size, self.kv_heads * self.head_dim, bias=False)
        self.W_value = nn.Linear(config.hidden_size, self.kv_heads * self.head_dim, bias=False)
        self.W_output = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.rotary_emb = RotaryEmbedder(base=self.rope_theta,
                                         dim=config.hidden_size//self.num_heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask= None,
    ):
        b, q, _ = hidden_states.size()

        q_states = self.W_query(hidden_states)
        k_states = self.W_key(hidden_states)
        v_states = self.W_value(hidden_states)

        q_states = q_states.view(b, q, self.num_heads, self.head_dim).transpose(1, 2)
        k_states = k_states.view(b, q, self.kv_heads, self.head_dim).transpose(1, 2)
        v_states = v_states.view(b, q, self.kv_heads, self.head_dim).transpose(1, 2)

        cos, sin = self.rotary_emb(v_states)
        q_states, k_states = apply_rotary_pos_emb(q_states, k_states, cos, sin)

        __kv_groups = self.num_heads / self.kv_heads
        k_states = repeat_kv(k_states, __kv_groups)
        v_states = repeat_kv(v_states, __kv_groups)

        attn_weights = torch.matmul(q_states, k_states.transpose(2, 3)) / math.sqrt(self.hidden_size)
        attn_weights = attn_weights + attention_mask
        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
        attn_weights = nn.functional.dropout(attn_weights)

        attn_output = torch.matmul(attn_weights, v_states)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(b, q, -1)

        return attn_output

class LlamaDecoder(nn.Module):
    def __init__(self,config):
        super().__init__()
        self.self_attn = RopeAttention(config)
        self.mlp = MLP(hidden_size=config.hidden_size, intermediate_size=config.intermediate_size)
        self.pre_attn_rmsnorm = RMSNorm(config.hidden_size, eps=1e-05)
        self.pre_mlp_rmsnorm = RMSNorm(config.hidden_size, eps=1e-05)

    def forward(self,hidden_states, attention_mask):
        residual = hidden_states
        hidden_states = self.pre_attn_rmsnorm(hidden_states)
        attention_mask = torch.triu(torch.full((attention_mask.shape[-1],attention_mask.shape[-1]), fill_value=float('-inf')),diagonal=1)

        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
        )
        hidden_states += residual
        hidden_states = self.pre_mlp_rmsnorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states += residual

        outputs = (hidden_states,)

        return outputs

class smolModel(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.embed_tokens = nn.Embedding(num_embeddings=config.vocab_size,
                                         embedding_dim=config.hidden_size)
        self.layers = nn.ModuleList([LlamaDecoder(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, eps=1e-05)

    def forward(
        self,
        input_ids= None,
        attention_mask= None,
    ):
        inputs_embeds = self.embed_tokens(input_ids)
        hidden_states = inputs_embeds
        for decoder_layer in self.layers:
            layer_outputs = decoder_layer(
                hidden_states,
                attention_mask=attention_mask,
            )
            hidden_states = layer_outputs[0]
        hidden_states = self.norm(hidden_states)
        return [hidden_states]

class smolLM(nn.Module):
    def __init__(self,config):
        super().__init__()
        self.model = smolModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def forward(self,input_ids,attention_mask):
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
        )
        hidden_states = outputs[0].squeeze()
        logits = self.lm_head(hidden_states)
        logits = logits.float()
        return {'logits':logits}
These are some bugs I have found initially verify that these are correct bugs indeed if not find the correct ones Thank you for the clarification. I'll review the code more thoroughly and identify 10 bugs based on the definition you provided. Here are the bugs I've found, along with explanations:

1. Bug in `RMSNorm` class:
   ```python
   hidden_states = hidden_states * torch.sqrt(variance + self.variance_epsilon)
   ```
   Should be:
   ```python
   hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
   ```
   Reason: The correct normalization should use the reciprocal of the square root (rsqrt) instead of just the square root.

2. Bug in `RopeAttention` class:
   ```python
   attn_weights = torch.matmul(q_states, k_states.transpose(2, 3)) / math.sqrt(self.hidden_size)
   ```
   Should be:
   ```python
   attn_weights = torch.matmul(q_states, k_states.transpose(2, 3)) / math.sqrt(self.head_dim)
   ```
   Reason: The scaling factor should use the head dimension, not the hidden size.

3. Bug in `LlamaDecoder` class:
   ```python
   attention_mask = torch.triu(torch.full((attention_mask.shape[-1],attention_mask.shape[-1]), fill_value=float('-inf')),diagonal=1)
   ```
   Should be:
   ```python
   attention_mask = (1 - attention_mask.unsqueeze(1).unsqueeze(2)) * torch.finfo(hidden_states.dtype).min
   ```
   Reason: The attention mask creation is incorrect. It should create a causal mask based on the input attention mask.

4. Bug in `RopeAttention` class:
   ```python
   __kv_groups = self.num_heads / self.kv_heads
   ```
   Should be:
   ```python
   __kv_groups = self.num_heads // self.kv_heads
   ```
   Reason: Integer division should be used instead of float division.

5. Bug in `smolLM` class:
   ```python
   hidden_states = outputs[0].squeeze()
   ```
   Should be:
   ```python
   hidden_states = outputs[0]
   ```
   Reason: Squeezing the output may remove necessary dimensions.

6. Bug in `RotaryEmbedder` class:
   ```python
   self.freq = 1/(base ** (torch.arange(0, dim, 2, dtype=torch.int64).float()/dim))
   ```
   Should be:
   ```python
   self.freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
   ```
   Reason: The dtype specification is unnecessary, and we should use 1.0 for float division.

7. Missing line in `RopeAttention` class:
   Add after `self.rotary_emb = RotaryEmbedder(...)`:
   ```python
   self.scaling = self.head_dim ** -0.5
   ```
   Reason: This scaling factor is typically used in attention mechanisms and is missing here.

8. Bug in `apply_rotary_pos_emb` function:
   ```python
   def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
   ```
   Should be:
   ```python
   def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
   ```
   Reason: The `position_ids` parameter is unused, and `unsqueeze_dim` is not needed.

9. Unnecessary line in `smolModel` class:
   ```python
   hidden_states = inputs_embeds
   ```
   This line is redundant and can be removed.
   Reason: `inputs_embeds` can be used directly in the loop.

10. Bug in `MLP` class:
    ```python
    down_proj = self.W_down(self.act_fn((self.W_gate(x)) * self.W_up(x)))
    ```
    Should be:
    ```python
    down_proj = self.W_down(self.act_fn(self.W_gate(x)) * self.W_up(x))
    ```
    Reason: The activation function should only be applied to the gate output, not the entire product.

These are the 10 bugs I've identified based on the given definition.