Delete files modeling_grok1.py with huggingface_hub

modeling_grok1.py

@@ -1,946 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging
-
-try:
-    from transformers.modeling_attn_mask_utils import \
-        _prepare_4d_causal_attention_mask
-
-    HAS_MASK_UTILS = True
-except ImportError:
-    HAS_MASK_UTILS = False
-
-from .configuration_grok1 import Grok1Config
-from .modeling_grok1_outputs import (MoeCausalLMOutputWithPast,
-                                     MoeModelOutputWithPast)
-
-logger = logging.get_logger(__name__)
-
-
-# copied from https://github.com/huggingface/transformers/blob/v4.36.1/src/transformers/models/mixtral/modeling_mixtral.py
-def load_balancing_loss_func(
-    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2
-) -> float:
-    r"""
-    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
-
-    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
-    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
-    experts is too unbalanced.
-
-    Args:
-        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
-            Logits from the `gate`, should be a tuple of tensors. Shape: [batch_size, seqeunce_length, num_experts].
-        num_experts (`int`, *optional*):
-            Number of experts
-
-    Returns:
-        The auxiliary loss.
-    """
-    if gate_logits is None:
-        return 0
-
-    if isinstance(gate_logits, tuple):
-        # cat along the layers?
-        compute_device = gate_logits[0].device
-        gate_logits = torch.cat(
-            [gate.to(compute_device) for gate in gate_logits], dim=0
-        )
-
-    routing_weights, selected_experts = torch.topk(gate_logits, top_k, dim=-1)
-    routing_weights = routing_weights.softmax(dim=-1)
-
-    # cast the expert indices to int64, otherwise one-hot encoding will fail
-    if selected_experts.dtype != torch.int64:
-        selected_experts = selected_experts.to(torch.int64)
-
-    if len(selected_experts.shape) == 2:
-        selected_experts = selected_experts.unsqueeze(2)
-
-    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
-
-    # For a given token, determine if it was routed to a given expert.
-    expert_mask = torch.max(expert_mask, axis=-2).values
-
-    # cast to float32 otherwise mean will fail
-    expert_mask = expert_mask.to(torch.float32)
-    tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)
-
-    router_prob_per_group_and_expert = torch.mean(routing_weights, axis=-1)
-    return torch.mean(
-        tokens_per_group_and_expert * router_prob_per_group_and_expert.unsqueeze(-1)
-    ) * (num_experts**2)
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
-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)
-
-
-class RMSNorm(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        eps: float = 1e-5,
-        create_scale: bool = True,
-    ) -> None:
-        super().__init__()
-        self.variance_epsilon = eps
-        if create_scale:
-            self.weight = nn.Parameter(torch.zeros(hidden_size))
-        else:
-            self.weight = 1.0
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        hidden_states = self.weight * hidden_states
-        return hidden_states.to(input_dtype)
-
-
-class RotaryEmbedding(nn.Module):
-    def __init__(
-        self, dim: int, max_position_embeddings: int = 2048, base: int = 10000
-    ) -> None:
-        super().__init__()
-        assert dim % 2 == 0
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (
-            self.base ** (torch.arange(0, self.dim, 2).float() / self.dim)
-        )
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        self._set_cos_sin_cache(
-            seq_len=max_position_embeddings,
-            device=self.inv_freq.device,
-            dtype=torch.get_default_dtype(),
-        )
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
-
-        freqs = torch.outer(t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=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),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        num_key_value_heads: Optional[int] = None,
-        max_position_embeddings: int = 2048,
-        attn_output_multiplier: float = 1.0,
-        max_attn_val: float = 30.0,
-    ):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.num_heads = num_heads
-        self.head_dim = hidden_size // num_heads
-        if num_key_value_heads is None:
-            num_key_value_heads = num_heads
-        self.num_key_value_heads = num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.attn_output_multiplier = attn_output_multiplier
-        self.max_attn_val = max_attn_val
-
-        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(hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(
-            hidden_size, self.num_key_value_heads * self.head_dim, bias=False
-        )
-        self.v_proj = nn.Linear(
-            hidden_size, self.num_key_value_heads * self.head_dim, bias=False
-        )
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, hidden_size, bias=False)
-
-        self.rotary_emb = RotaryEmbedding(
-            self.head_dim,
-            max_position_embeddings=max_position_embeddings,
-        )
-
-    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,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(
-            bsz, q_len, self.num_heads, self.head_dim
-        ).transpose(1, 2)
-        key_states = key_states.view(
-            bsz, q_len, self.num_key_value_heads, self.head_dim
-        ).transpose(1, 2)
-        value_states = value_states.view(
-            bsz, q_len, 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)).to(
-            torch.float
-        )
-        attn_weights = attn_weights * self.attn_output_multiplier
-        attn_weights = self.max_attn_val * F.tanh(attn_weights / self.max_attn_val)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-
-            attn_weights = attn_weights + attention_mask
-
-        attn_weights = F.softmax(attn_weights, dim=-1).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        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 MoeMLP(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        ffn_dim: int,
-    ) -> None:
-        super().__init__()
-        self.w3 = nn.Linear(hidden_dim, ffn_dim, bias=False)
-        self.w2 = nn.Linear(ffn_dim, hidden_dim, bias=False)
-        self.w1 = nn.Linear(hidden_dim, ffn_dim, bias=False)
-        self.act_fn = nn.GELU()
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        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 MoeBlock(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        ffn_dim: int,
-        num_experts: int,
-        top_k: int,
-    ) -> None:
-        super().__init__()
-        self.num_experts = num_experts
-        self.top_k = top_k
-        self.gate = nn.Linear(hidden_dim, num_experts, bias=False)
-        self.experts = nn.ModuleList(
-            [MoeMLP(hidden_dim, ffn_dim) for _ in range(num_experts)]
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(
-            routing_weights, self.top_k, dim=-1
-        )
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim),
-            dtype=hidden_states.dtype,
-            device=hidden_states.device,
-        )
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(
-            selected_experts, num_classes=self.num_experts
-        ).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx])
-
-            if top_x.shape[0] == 0:
-                continue
-
-            # in torch it is faster to index using lists than torch tensors
-            top_x_list = top_x.tolist()
-            idx_list = idx.tolist()
-
-            # 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_list].reshape(-1, hidden_dim)
-            current_hidden_states = (
-                expert_layer(current_state)
-                * routing_weights[top_x_list, idx_list, None]
-            )
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(
-                0, top_x, current_hidden_states.to(hidden_states.dtype)
-            )
-        final_hidden_states = final_hidden_states.reshape(
-            batch_size, sequence_length, hidden_dim
-        )
-        return final_hidden_states, router_logits
-
-
-class DecoderLayer(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        num_heads: int,
-        num_key_value_heads: int,
-        num_experts: int,
-        top_k: int,
-        max_position_embeddings: int = 2048,
-        attn_output_multiplier: float = 1.0,
-        max_attn_val: float = 30.0,
-        rms_norm_eps: float = 1e-5,
-    ) -> None:
-        super().__init__()
-        self.self_attn = MultiHeadAttention(
-            hidden_size,
-            num_heads,
-            num_key_value_heads,
-            max_position_embeddings=max_position_embeddings,
-            attn_output_multiplier=attn_output_multiplier,
-            max_attn_val=max_attn_val,
-        )
-        self.block_sparse_moe = MoeBlock(hidden_size, intermediate_size, num_experts, top_k)
-        self.pre_attn_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
-        self.post_attn_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
-        self.pre_moe_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
-        self.post_moe_norm = RMSNorm(hidden_size, eps=rms_norm_eps)
-
-    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: Optional[bool] = False,
-        output_router_logits: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        **kwargs,
-    ) -> Tuple[
-        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
-    ]:
-        residual = hidden_states
-        hidden_states = self.pre_attn_norm(hidden_states)
-        hidden_states, attention_weights, present_key_value = self.self_attn(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-        )
-        hidden_states = self.post_attn_norm(hidden_states)
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.pre_moe_norm(hidden_states)
-        hidden_states, router_logits = self.block_sparse_moe(hidden_states)
-        hidden_states = self.post_moe_norm(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (attention_weights,)
-        if use_cache:
-            outputs += (present_key_value,)
-        if output_router_logits:
-            outputs += (router_logits,)
-        return outputs
-
-
-class Grok1PretrainedModel(PreTrainedModel):
-    config_class = Grok1Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = False
-    _supports_cache_class = False
-
-    def _init_weights(self, module) -> None:
-        if isinstance(module, nn.Linear):
-            module.weight.data.zero_()
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.zero_()
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat(
-            [
-                torch.zeros(
-                    tgt_len, past_key_values_length, dtype=dtype, device=device
-                ),
-                mask,
-            ],
-            dim=-1,
-        )
-    return mask[None, None, :, :].expand(
-        bsz, 1, tgt_len, tgt_len + past_key_values_length
-    )
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(
-        inverted_mask.to(torch.bool), torch.finfo(dtype).min
-    )
-
-
-class Grok1Model(Grok1PretrainedModel):
-    def __init__(self, config: Grok1Config, **kwargs) -> None:
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.embedding_multiplier_scale = config.embedding_multiplier_scale
-
-        self.embed_tokens = nn.Embedding(
-            config.vocab_size, config.hidden_size, self.padding_idx
-        )
-        self.layers = nn.ModuleList(
-            [
-                DecoderLayer(
-                    hidden_size=config.hidden_size,
-                    intermediate_size=config.intermediate_size,
-                    num_heads=config.num_attention_heads,
-                    num_key_value_heads=config.num_key_value_heads,
-                    num_experts=config.num_experts,
-                    top_k=config.num_experts_per_tok,
-                    max_position_embeddings=config.max_position_embeddings,
-                    attn_output_multiplier=config.attn_output_multiplier,
-                    max_attn_val=config.max_attn_value,
-                    rms_norm_eps=config.rms_norm_eps,
-                )
-                for layer_idx in range(config.num_hidden_layers)
-            ]
-        )
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.gradient_checkpointing = False
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(
-        self, attention_mask, input_shape, inputs_embeds, past_key_values_length
-    ):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(
-                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
-            ).to(inputs_embeds.device)
-            combined_attention_mask = (
-                expanded_attn_mask
-                if combined_attention_mask is None
-                else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
-    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_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 input_ids and inputs_embeds at the same time"
-            )
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
-            )
-            position_ids = position_ids.unsqueeze(0)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-            inputs_embeds = inputs_embeds * self.embedding_multiplier_scale
-
-        if HAS_MASK_UTILS:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-        else:
-            if attention_mask is None:
-                attention_mask = torch.ones(
-                    (batch_size, seq_length_with_past),
-                    dtype=torch.bool,
-                    device=inputs_embeds.device,
-                )
-            attention_mask = self._prepare_decoder_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_router_logits = () if output_router_logits else None
-        next_decoder_cache = () if use_cache else None
-
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            past_key_value = (
-                past_key_values[idx] if past_key_values is not None else None
-            )
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-            if output_router_logits:
-                all_router_logits += (layer_outputs[-1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-        next_cache = next_decoder_cache if use_cache else None
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_cache,
-                    all_hidden_states,
-                    all_self_attns,
-                    all_router_logits,
-                ]
-                if v is not None
-            )
-        return MoeModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            router_logits=all_router_logits,
-        )
-
-
-class Grok1ModelForCausalLM(Grok1PretrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: Grok1Config, **kwargs):
-        super().__init__(config)
-        self.model = Grok1Model(config)
-        self.vocab_size = config.vocab_size
-        self.output_multiplier_scale = config.output_multiplier_scale
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.router_aux_loss_coef = config.router_aux_loss_coef
-        self.num_experts = config.num_experts
-        self.num_experts_per_tok = config.num_experts_per_tok
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    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,
-        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]:
-        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
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            output_router_logits=output_router_logits,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        logits = logits * self.output_multiplier_scale
-        logits = logits.float()
-
-        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 = nn.CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        aux_loss = None
-        if output_router_logits:
-            aux_loss = load_balancing_loss_func(
-                outputs.router_logits if return_dict else outputs[-1],
-                self.num_experts,
-                self.num_experts_per_tok,
-            )
-            if labels is not None:
-                loss += self.router_aux_loss_coef * aux_loss
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            if output_router_logits:
-                output = (aux_loss,) + output
-            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,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        **kwargs,
-    ):
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-
-        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)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs