Upload code to run Rasphi

configuration_rasphi.py

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+from transformers.modeling_utils import PretrainedConfig
+
+class RasphiConfig(PretrainedConfig):
+    model_type = "rasphi"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=4096,
+        intermediate_size=6400,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        rope_scaling=None,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=16,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        router_jitter_noise=0.01,
+        input_jitter_noise=0.0,
+        attention_bias=False,
+        lm_head_bias=False,
+        # Rasphi specific configurations
+        num_reasoning_experts=8,  # Number of experts dedicated to reasoning stream
+        num_content_experts=8,    # Number of experts dedicated to content stream
+        reasoning_hidden_size=2048,  # Hidden size for reasoning stream
+        content_hidden_size=2048,    # Hidden size for content stream
+        stream_interaction="attention",  # How the two streams interact: "attention", "mlp", or "both"
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+        self.attention_bias = attention_bias
+        self.lm_head_bias = lm_head_bias
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.router_jitter_noise = router_jitter_noise
+        self.input_jitter_noise = input_jitter_noise
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        # Rasphi specific configurations
+        self.num_reasoning_experts = num_reasoning_experts
+        self.num_content_experts = num_content_experts
+        self.reasoning_hidden_size = reasoning_hidden_size
+        self.content_hidden_size = content_hidden_size
+        self.stream_interaction = stream_interaction
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, "
+                f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
+        rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
+        original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )
+        if not isinstance(rope_scaling_short_mscale, (int, float)):
+            raise ValueError(
+                f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
+            )
+        if not isinstance(rope_scaling_long_mscale, (int, float)):
+            raise ValueError(
+                f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
+            )
+        if not isinstance(original_max_position_embeddings, int):
+            raise ValueError(
+                f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}"
+            )

modeling_rasphi.py

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+import torch
+import torch.nn as nn
+from transformers.modeling_outputs import MoeModelOutputWithPast, MoeCausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel, PretrainedConfig
+from torch.nn import CrossEntropyLoss
+from typing import Optional, Tuple, Union, List
+import torch.nn.functional as F
+import math
+
+ACT2FN = {
+    "relu": F.relu,
+    "silu": F.silu,
+    "gelu": F.gelu,
+    "tanh": torch.tanh,
+    "sigmoid": torch.sigmoid,
+}
+
+class RasphiDecoderLayer(nn.Module):
+    def __init__(self, config: RasphiConfig, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.reasoning_hidden_size = config.reasoning_hidden_size
+        self.content_hidden_size = config.content_hidden_size
+
+        # Attention layers
+        self.reasoning_self_attn = RasphiAttention(config, self.reasoning_hidden_size, layer_idx)
+        self.content_self_attn = RasphiAttention(config, self.content_hidden_size, layer_idx)
+
+        # MoE layers
+        self.reasoning_moe = RasphiSparseMoeBlock(config, is_reasoning=True)
+        self.content_moe = RasphiSparseMoeBlock(config, is_reasoning=False)
+
+        # Layer norms
+        self.reasoning_input_layernorm = nn.LayerNorm(self.reasoning_hidden_size, eps=config.rms_norm_eps)
+        self.reasoning_post_attention_layernorm = nn.LayerNorm(self.reasoning_hidden_size, eps=config.rms_norm_eps)
+        self.content_input_layernorm = nn.LayerNorm(self.content_hidden_size, eps=config.rms_norm_eps)
+        self.content_post_attention_layernorm = nn.LayerNorm(self.content_hidden_size, eps=config.rms_norm_eps)
+
+        # Stream interaction
+        self.stream_interaction = config.stream_interaction
+        if self.stream_interaction in ["attention", "both"]:
+            self.reasoning_to_content_attn = RasphiAttention(config, self.content_hidden_size, layer_idx)
+            self.content_to_reasoning_attn = RasphiAttention(config, self.reasoning_hidden_size, layer_idx)
+        if self.stream_interaction in ["mlp", "both"]:
+            self.reasoning_to_content_mlp = nn.Linear(self.reasoning_hidden_size, self.content_hidden_size)
+            self.content_to_reasoning_mlp = nn.Linear(self.content_hidden_size, self.reasoning_hidden_size)
+
+    def forward(
+        self,
+        reasoning_hidden_states: torch.Tensor,
+        content_hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, ...]:
+        # Self Attention for both streams
+        reasoning_residual = reasoning_hidden_states
+        content_residual = content_hidden_states
+
+        reasoning_hidden_states = self.reasoning_input_layernorm(reasoning_hidden_states)
+        content_hidden_states = self.content_input_layernorm(content_hidden_states)
+
+        reasoning_self_attn_output, reasoning_self_attn_weights, reasoning_present_key_value = self.reasoning_self_attn(
+            hidden_states=reasoning_hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value[0] if past_key_value is not None else None,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+        content_self_attn_output, content_self_attn_weights, content_present_key_value = self.content_self_attn(
+            hidden_states=content_hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value[1] if past_key_value is not None else None,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+        reasoning_hidden_states = reasoning_residual + reasoning_self_attn_output
+        content_hidden_states = content_residual + content_self_attn_output
+
+        # Stream Interaction
+        if self.stream_interaction in ["attention", "both"]:
+            reasoning_to_content, _, _ = self.reasoning_to_content_attn(
+                hidden_states=content_hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=None,
+                output_attentions=False,
+                use_cache=False,
+                key_value_states=reasoning_hidden_states,
+            )
+            content_to_reasoning, _, _ = self.content_to_reasoning_attn(
+                hidden_states=reasoning_hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=None,
+                output_attentions=False,
+                use_cache=False,
+                key_value_states=content_hidden_states,
+            )
+            reasoning_hidden_states = reasoning_hidden_states + content_to_reasoning
+            content_hidden_states = content_hidden_states + reasoning_to_content
+
+        if self.stream_interaction in ["mlp", "both"]:
+            reasoning_to_content = self.reasoning_to_content_mlp(reasoning_hidden_states)
+            content_to_reasoning = self.content_to_reasoning_mlp(content_hidden_states)
+            reasoning_hidden_states = reasoning_hidden_states + content_to_reasoning
+            content_hidden_states = content_hidden_states + reasoning_to_content
+
+        # MoE for both streams
+        reasoning_residual = reasoning_hidden_states
+        content_residual = content_hidden_states
+
+        reasoning_hidden_states = self.reasoning_post_attention_layernorm(reasoning_hidden_states)
+        content_hidden_states = self.content_post_attention_layernorm(content_hidden_states)
+
+        reasoning_moe_output, reasoning_router_logits = self.reasoning_moe(reasoning_hidden_states)
+        content_moe_output, content_router_logits = self.content_moe(content_hidden_states)
+
+        reasoning_hidden_states = reasoning_residual + reasoning_moe_output
+        content_hidden_states = content_residual + content_moe_output
+
+        outputs = (reasoning_hidden_states, content_hidden_states)
+
+        if use_cache:
+            outputs += ((reasoning_present_key_value, content_present_key_value),)
+        if output_attentions:
+            outputs += (reasoning_self_attn_weights, content_self_attn_weights)
+        if output_router_logits:
+            outputs += (reasoning_router_logits, content_router_logits)
+
+        return outputs
+
+class RasphiModel(PreTrainedModel):
+    config_class = RasphiConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RasphiDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def __init__(self, config: RasphiConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.reasoning_embed_tokens = nn.Embedding(config.vocab_size, config.reasoning_hidden_size, self.padding_idx)
+        self.content_embed_tokens = nn.Embedding(config.vocab_size, config.content_hidden_size, self.padding_idx)
+        
+        self.layers = nn.ModuleList([RasphiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+        
+        self.reasoning_norm = nn.LayerNorm(config.reasoning_hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
+        self.content_norm = nn.LayerNorm(config.content_hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
+
+        self.gradient_checkpointing = False
+        
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return (self.reasoning_embed_tokens, self.content_embed_tokens)
+
+    def set_input_embeddings(self, value):
+        self.reasoning_embed_tokens = value[0]
+        self.content_embed_tokens = value[1]
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        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
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if inputs_embeds is None:
+            reasoning_inputs_embeds = self.reasoning_embed_tokens(input_ids)
+            content_inputs_embeds = self.content_embed_tokens(input_ids)
+        else:
+            reasoning_inputs_embeds = inputs_embeds[:, :, :self.config.reasoning_hidden_size]
+            content_inputs_embeds = inputs_embeds[:, :, self.config.reasoning_hidden_size:]
+
+        reasoning_hidden_states = reasoning_inputs_embeds
+        content_hidden_states = content_inputs_embeds
+
+        # decoder layers
+        all_reasoning_hidden_states = () if output_hidden_states else None
+        all_content_hidden_states = () if output_hidden_states else None
+        all_reasoning_self_attns = () if output_attentions else None
+        all_content_self_attns = () if output_attentions else None
+        all_reasoning_router_logits = () if output_router_logits else None
+        all_content_router_logits = () if output_router_logits else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_reasoning_hidden_states += (reasoning_hidden_states,)
+                all_content_hidden_states += (content_hidden_states,)
+
+            layer_outputs = decoder_layer(
+                reasoning_hidden_states,
+                content_hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                output_router_logits=output_router_logits,
+                use_cache=use_cache,
+            )
+
+            reasoning_hidden_states = layer_outputs[0]
+            content_hidden_states = layer_outputs[1]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_reasoning_self_attns += (layer_outputs[2],)
+                all_content_self_attns += (layer_outputs[3],)
+
+            if output_router_logits:
+                all_reasoning_router_logits += (layer_outputs[-2],)
+                all_content_router_logits += (layer_outputs[-1],)
+
+        reasoning_hidden_states = self.reasoning_norm(reasoning_hidden_states)
+        content_hidden_states = self.content_norm(content_hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_reasoning_hidden_states += (reasoning_hidden_states,)
+            all_content_hidden_states += (content_hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [reasoning_hidden_states, content_hidden_states, next_cache, all_reasoning_hidden_states, 
+                          all_content_hidden_states, all_reasoning_self_attns, all_content_self_attns, 
+                          all_reasoning_router_logits, all_content_router_logits]
+                if v is not None
+            )
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=(reasoning_hidden_states, content_hidden_states),
+            past_key_values=next_cache,
+            hidden_states=(all_reasoning_hidden_states, all_content_hidden_states),
+            attentions=(all_reasoning_self_attns, all_content_self_attns),
+            router_logits=(all_reasoning_router_logits, all_content_router_logits),
+        )
+
+class RasphiSparseMoeBlock(nn.Module):
+    def __init__(self, config: RasphiConfig, is_reasoning: bool):
+        super().__init__()
+        self.hidden_dim = config.reasoning_hidden_size if is_reasoning else config.content_hidden_size
+        self.ffn_dim = config.intermediate_size
+        self.num_experts = config.num_reasoning_experts if is_reasoning else config.num_content_experts
+        self.top_k = config.num_experts_per_tok
+        
+        # gating
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+        self.experts = nn.ModuleList([RasphiBlockSparseTop2MLP(config, is_reasoning) for _ in range(self.num_experts)])
+
+        # Jitter parameters
+        self.router_jitter_noise = config.router_jitter_noise
+        self.input_jitter_noise = config.input_jitter_noise
+        
+    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        if self.training and self.input_jitter_noise > 0:
+            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise)
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        
+        router_logits = self.gate(hidden_states)
+
+        routing_weights, selected_experts = sparsemixer(
+            router_logits, 
+            top_k=self.top_k, 
+            jitter_eps=self.router_jitter_noise, 
+            training=self.training,
+        )
+
+        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
+        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])
+
+            if top_x.shape[0] == 0:
+                continue
+
+            # 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.tolist()].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x.tolist(), idx.tolist(), None]
+
+            # Add the expert output to the final hidden states
+            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, router_logits
+
+class RasphiBlockSparseTop2MLP(nn.Module):
+    def __init__(self, config: RasphiConfig, is_reasoning: bool):
+        super().__init__()
+        self.ffn_dim = config.intermediate_size
+        self.hidden_dim = config.reasoning_hidden_size if is_reasoning else config.content_hidden_size
+
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+class RasphiPreTrainedModel(PreTrainedModel):
+    config_class = RasphiConfig
+    base_model_prefix = "rasphi"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RasphiDecoderLayer"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        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_()
+
+class RasphiForCausalLM(RasphiPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RasphiModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.content_hidden_size, config.vocab_size, bias=config.lm_head_bias)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_content_experts  # We use content experts for language modeling
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()[1]  # Return content embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings((self.model.get_input_embeddings()[0], value))
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = 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,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            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,
+            output_router_logits=output_router_logits,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        content_hidden_states = hidden_states[1]  # Use content stream for language modeling
+        logits = self.lm_head(content_hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits[1] if return_dict else outputs[-1][1],  # Use content stream router logits
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        return tuple(
+            tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
+            for layer_past in past
+        )
+
+
+#—Model > Rasphi changes start—#
+class RasphiAttention(nn.Module):
+    def __init__(self, config: RasphiConfig, hidden_size: int, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+
+        if getattr(config, 'rope_scaling', None) is None:
+            self.rotary_emb = RasphiMoERotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            if scaling_type == "linear":
+                self.rotary_emb = LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=self.config.rope_scaling["factor"],
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=self.config.rope_scaling["factor"],
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        key_value_states: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+
+        if key_value_states is None:
+            # self-attention
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+        else:
+            # cross-attention
+            key_states = self.k_proj(key_value_states)
+            value_states = self.v_proj(key_value_states)
+            kv_len = key_value_states.size(1)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+class mp(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx, 
+        scores: torch.Tensor, 
+        multiplier: torch.Tensor, 
+        selected_experts: torch.Tensor,
+        masked_gates: torch.Tensor,
+        mask_for_one: torch.Tensor,
+    ):
+        ctx.save_for_backward(multiplier, selected_experts, masked_gates)
+        return multiplier * mask_for_one
+        
+    @staticmethod
+    def backward(
+        ctx, 
+        grad_at_output: torch.Tensor, 
+    ):
+        multiplier, selected_experts, masked_gates = ctx.saved_tensors
+        
+        grad_at_output = grad_at_output * multiplier
+        
+        grad_at_scores_expaned = masked_gates * grad_at_output.mul(-1)
+        grad_at_scores_expaned.scatter_add_(
+            dim=-1,
+            index=selected_experts,
+            src=grad_at_output,
+        )
+        
+        return (
+            grad_at_scores_expaned, 
+            None, 
+            None, 
+            None, 
+            None, 
+        )
+
+def sparsemixer(scores, top_k, jitter_eps, training):
+    assert top_k == 2
+    
+    ################ first expert ################
+    
+    with torch.no_grad():
+        # compute mask for sparsity
+        mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
+        factor = scores.abs().clamp(min=mask_logits_threshold)
+        mask_logits_threshold = (
+            (mask_logits_threshold - scores) / factor
+        ) > (2 * jitter_eps)
+
+    # apply mask 
+    masked_gates = scores.masked_fill(mask_logits_threshold, float('-inf'))
+    if training:
+        selected_experts = (
+            masked_gates - torch.empty_like(masked_gates, memory_format=torch.legacy_contiguous_format).exponential_().log()
+        ).max(dim=-1)[1].unsqueeze(-1) # gumbel sampling, more robust than than the multinomial method
+    else:
+        selected_experts = max_ind
+        
+    # compute scores for gradients
+    masked_gates = torch.softmax(masked_gates, dim=-1)
+    multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
+    
+    if training:
+        # compute midpoint mask 
+        max_scores, max_ind = masked_gates.max(dim=-1, keepdim=True)
+        mask_for_one = torch.logical_or(
+            selected_experts == max_ind,
+            torch.rand_like(max_scores) > 0.75 # Heun's third-order method: f(x) - f(0) = .25 f'(x) + .75 f'(x/3.)
+        ) 
+        # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
+        mask_for_one = torch.add(0.3333, mask_for_one, alpha=0.6667).type_as(masked_gates)
+
+        multiplier = mp.apply(
+            scores, 
+            multiplier_o, 
+            selected_experts, 
+            masked_gates, 
+            mask_for_one,
+        )
+    else:
+        multiplier = multiplier_o
+
+    # masked out first expert 
+    masked_scores = torch.scatter(
+        scores,
+        -1,
+        selected_experts,
+        float('-inf'),
+    )
+    with torch.no_grad():
+        # compute mask for sparsity
+        mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True)
+        factor = scores.abs().clamp(min=mask_logits_threshold)
+        mask_logits_threshold = (
+            (mask_logits_threshold - scores) / factor
+        ) > (2 * jitter_eps)
+
+    # apply mask 
+    masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float('-inf'))
+    if training:
+        selected_experts_top2 = (
+            masked_gates_top2 - torch.empty_like(masked_gates_top2, memory_format=torch.legacy_contiguous_format).exponential_().log()
+        ).max(dim=-1)[1].unsqueeze(-1) # gumbel sampling, more robust than than the multinomial method
+    else:
+        selected_experts_top2 = max_ind
+    # compute scores for gradients
+    masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
+    multiplier_top2_o = masked_gates_top2.gather(dim=-1, index=selected_experts_top2)
+    
+    if training: 
+        # compute midpoint mask 
+        max_scores, max_ind = masked_gates_top2.max(dim=-1, keepdim=True)
+        mask_for_one_top2 = torch.logical_or(
+            selected_experts_top2 == max_ind,
+            torch.rand_like(max_scores).uniform_() > 0.75 # Heun's third-order method: f(x) - f(0) = .25 f'(x) + .75 f'(x/3.)
+        ) 
+        # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
+        mask_for_one_top2 = torch.add(0.3333, mask_for_one_top2, alpha=0.6667).type_as(masked_gates_top2)
+
+        multiplier_top2 = mp.apply(
+            scores, 
+            multiplier_top2_o, 
+            selected_experts_top2, 
+            masked_gates_top2, 
+            mask_for_one_top2,
+        )
+    else:
+        multiplier_top2 = multiplier_top2_o
+    
+    multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
+    selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1)
+    
+    return (
+        multiplier, 
+        selected_experts,
+    )
+
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
+) -> float:
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = F.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = F.one_hot(selected_experts, num_experts).permute(2, 1, 0)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .reshape(-1, top_k, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+            .reshape(-1, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+    return overall_loss * num_experts
+
+class RasphiMoERotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.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()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def forward(self, x, seq_len=None):
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+class LinearScalingRotaryEmbedding(RasphiMoERotaryEmbedding):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    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=dtype)
+        t = t / self.scaling_factor
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1).to(device)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+class DynamicNTKScalingRotaryEmbedding(RasphiMoERotaryEmbedding):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_seq_len_cached:
+            base = self.base * ((self.scaling_factor * seq_len / self.max_seq_len_cached) - (self.scaling_factor - 1)) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1).to(device)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    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
+
+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 repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    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)
+
+from transformers import AutoModelForCausalLM
+
+AutoModelForCausalLM.register("rasphi", RasphiForCausalLM)