Initial commit

ktransformers/models/custom_cache.py

+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+'''
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/cache_utils.py
+# Copyright 2018- The Hugging Face team. All rights reserved.
+# Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+import torch
+import transformers
+from transformers import Cache, PretrainedConfig
+from typing import List, Optional, Dict, Any, Tuple
+class StaticCache(transformers.StaticCache):
+    """
+    Static Cache class to be used with `torch.compile(model)`.
+
+    Parameters:
+        config (`PretrainedConfig):
+            The configuration file defining the shape-related attributes required to initialize the static cache.
+        max_batch_size (`int`):
+            The maximum batch size with which the model will be used.
+        max_cache_len (`int`):
+            The maximum sequence length with which the model will be used.
+        device (`torch.device`):
+            The device on which the cache should be initialized. Should be the same as the layer.
+        dtype (*optional*, defaults to `torch.float32`):
+            The default `dtype` to use when initializing the layer.
+    """
+
+    def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
+        Cache.__init__(self)
+        self.max_batch_size = max_batch_size
+        self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
+        # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
+        self.head_dim = (
+            config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
+        )
+
+        self.dtype = dtype if dtype is not None else torch.float32
+        self.num_key_value_heads = (
+            config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
+        )
+
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+        cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
+        if config.architectures[0] == "DeepseekV2ForCausalLM":
+            # key_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.qk_rope_head_dim + config.qk_nope_head_dim)
+            # value_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.v_head_dim)
+            key_shape = (max_batch_size, 1, self.max_cache_len, config.qk_rope_head_dim)
+            value_shape = (max_batch_size, 1, self.max_cache_len, config.kv_lora_rank)
+        else:
+            key_shape = cache_shape
+            value_shape = cache_shape
+
+        self.past_tokens = []
+        self.num_hidden_layers = config.num_hidden_layers
+        for _ in range(self.num_hidden_layers):
+            # Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
+            # breaks when updating the cache.
+            new_layer_key_cache = torch.zeros(key_shape, dtype=self.dtype, device=device)
+            new_layer_value_cache = torch.zeros(value_shape, dtype=self.dtype, device=device)
+            torch._dynamo.mark_static_address(new_layer_key_cache)
+            torch._dynamo.mark_static_address(new_layer_value_cache)
+            self.key_cache.append(new_layer_key_cache)
+            self.value_cache.append(new_layer_value_cache)
+            self.past_tokens.append(0)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+        It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
+                to know how where to write in the cache.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        cache_position = cache_kwargs.get("cache_position")
+        k_out = self.key_cache[layer_idx]
+        v_out = self.value_cache[layer_idx]
+        #print(cache_position)
+        k_out[:, :, cache_position] = key_states
+        v_out[:, :, cache_position] = value_states
+        self.past_tokens[layer_idx] += cache_position.size(0)
+        return k_out, v_out
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states that were seen by the model."""
+        # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
+        # limit the check to the first batch member and head dimension.
+        # TODO: deprecate this function in favor of `cache_position`
+        return self.past_tokens[layer_idx]
+    
+    def change_seq_length(self, bias: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states that were seen by the model."""
+        # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
+        # limit the check to the first batch member and head dimension.
+        # TODO: deprecate this function in favor of `cache_position`
+        for layer_idx in range(self.num_hidden_layers):
+            self.past_tokens[layer_idx] += bias
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states."""
+        return self.max_cache_len
+
+    def reset(self):
+        """Resets the cache values while preserving the objects"""
+        for layer_idx in range(len(self.key_cache)):
+            # In-place ops prevent breaking the static address
+            self.key_cache[layer_idx].zero_()
+            self.value_cache[layer_idx].zero_()

ktransformers/models/modeling_deepseek.py

+# coding=utf-8
+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+'''
+# Adapted from
+# https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628/blob/main/modeling_deepseek.py
+# Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+# 
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch DeepSeek model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+    _prepare_4d_attention_mask,
+    _prepare_4d_causal_attention_mask,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import (
+    ALL_LAYERNORM_LAYERS,
+    is_torch_greater_or_equal_than_1_13,
+)
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_deepseek import DeepseekV2Config
+import torch.distributed as dist
+import numpy as np
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func, flash_attn_with_kvcache
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeepseekV2Config"
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(
+        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
+    )
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class DeepseekV2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DeepseekV2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        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)
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->DeepseekV2
+class DeepseekV2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        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, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)   
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
+class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
+    """DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        raise NotImplementedError("LinearScalingRotaryEmbedding is not supported now.")
+        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=self.inv_freq.dtype
+        )
+        t = t / self.scaling_factor
+
+        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)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
+class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
+    """DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        raise NotImplementedError("DynamicNTKScalingRotaryEmbedding is not supported now.")
+        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_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings)
+                - (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, persistent=False)
+
+        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)
+
+
+# Inverse dim formula to find dim based on number of rotations
+def yarn_find_correction_dim(
+    num_rotations, dim, base=10000, max_position_embeddings=2048
+):
+    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+        2 * math.log(base)
+    )
+
+
+# Find dim range bounds based on rotations
+def yarn_find_correction_range(
+    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
+):
+    low = math.floor(
+        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
+    )
+    high = math.ceil(
+        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
+    )
+    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
+
+
+def yarn_get_mscale(scale=1, mscale=1):
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
+def yarn_linear_ramp_mask(min, max, dim):
+    if min == max:
+        max += 0.001  # Prevent singularity
+
+    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+        original_max_position_embeddings=4096,
+        beta_fast=32,
+        beta_slow=1,
+        mscale=1,
+        mscale_all_dim=0,
+    ):
+        nn.Module.__init__(self)
+        self.scaling_factor = scaling_factor
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.beta_fast = beta_fast
+        self.beta_slow = beta_slow
+        self.mscale = mscale
+        self.mscale_all_dim = mscale_all_dim
+        self.scaling_factor = scaling_factor
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+
+        freq_extra = 1.0 / (
+            self.base
+            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        )
+        freq_inter = 1.0 / (
+            self.scaling_factor
+            * self.base
+            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        )
+
+        low, high = yarn_find_correction_range(
+            self.beta_fast,
+            self.beta_slow,
+            dim,
+            self.base,
+            self.original_max_position_embeddings,
+        )
+        inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
+            device=device, dtype=torch.float32
+        )
+        inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._mscale = float(
+            yarn_get_mscale(self.scaling_factor, self.mscale)
+            / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
+        )
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()* self._mscale
+            sin = emb.sin()* self._mscale
+        return cos.to(dtype=x.dtype), sin.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.mistral.modeling_mistral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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`):
+            Deprecated and unused.
+        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.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    b, h, s, d = q.shape
+    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+    b, h, s, d = k.shape
+    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class DeepseekV2MLP(nn.Module):
+    def __init__(self, config, hidden_size=None, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = (
+            config.intermediate_size if intermediate_size is None else intermediate_size
+        )
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        act = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
+        down_proj = self.down_proj(act)
+        return down_proj
+
+class MoEGate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+        self.topk_method = config.topk_method
+        self.n_group = config.n_group
+        self.topk_group = config.topk_group
+
+        # topk selection algorithm
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.hidden_size
+        self.weight = nn.Parameter(
+            torch.empty((self.n_routed_experts, self.gating_dim))
+        )
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        ### compute gating score
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(
+            hidden_states.type(torch.float32), self.weight.type(torch.float32), None
+        )
+        if self.scoring_func == "softmax":
+            scores = logits.softmax(dim=-1, dtype=torch.float32)
+        else:
+            raise NotImplementedError(
+                f"insupportable scoring function for MoE gating: {self.scoring_func}"
+            )
+
+        ### select top-k experts
+        if self.topk_method == "greedy":
+            topk_weight, topk_idx = torch.topk(
+                scores, k=self.top_k, dim=-1, sorted=False
+            )
+        elif self.topk_method == "group_limited_greedy":
+            group_scores = (
+                scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
+            )  # [n, n_group]
+            group_idx = torch.topk(
+                group_scores, k=self.topk_group, dim=-1, sorted=False
+            )[
+                1
+            ]  # [n, top_k_group]
+            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+            score_mask = (
+                group_mask.unsqueeze(-1)
+                .expand(
+                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
+                )
+                .reshape(bsz * seq_len, -1)
+            )  # [n, e]
+            tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+            topk_weight, topk_idx = torch.topk(
+                tmp_scores, k=self.top_k, dim=-1, sorted=False
+            )
+
+        ### norm gate to sum 1
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+        else:
+            topk_weight = topk_weight * self.routed_scaling_factor
+        ### expert-level computation auxiliary loss
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            # always compute aux loss based on the naive greedy topk method
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(
+                    bsz, self.n_routed_experts, device=hidden_states.device
+                )
+                ce.scatter_add_(
+                    1,
+                    topk_idx_for_aux_loss,
+                    torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
+                ).div_(seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
+                    dim=1
+                ).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(
+                    topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
+                )
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = None
+        return topk_idx, topk_weight, aux_loss
+
+
+class AddAuxiliaryLoss(torch.autograd.Function):
+    """
+    The trick function of adding auxiliary (aux) loss,
+    which includes the gradient of the aux loss during backpropagation.
+    """
+
+    @staticmethod
+    def forward(ctx, x, loss):
+        assert loss.numel() == 1
+        ctx.dtype = loss.dtype
+        ctx.required_aux_loss = loss.requires_grad
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_loss = None
+        if ctx.required_aux_loss:
+            grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
+        return grad_output, grad_loss
+
+class DeepseekV2MoE(nn.Module):
+    """
+    A mixed expert module containing shared experts.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        if hasattr(config, "ep_size") and config.ep_size > 1:
+            assert config.ep_size == dist.get_world_size()
+            self.ep_size = config.ep_size
+            self.experts_per_rank = config.n_routed_experts // config.ep_size
+            self.ep_rank = dist.get_rank()
+            self.experts = nn.ModuleList(
+                [
+                    (
+                        DeepseekV2MLP(
+                            config, intermediate_size=config.moe_intermediate_size
+                        )
+                        if i >= self.ep_rank * self.experts_per_rank
+                        and i < (self.ep_rank + 1) * self.experts_per_rank
+                        else None
+                    )
+                    for i in range(config.n_routed_experts)
+                ]
+            )
+        else:
+            self.ep_size = 1
+            self.experts_per_rank = config.n_routed_experts
+            self.ep_rank = 0
+            self.experts = nn.ModuleList(
+                [
+                    DeepseekV2MLP(config, intermediate_size=config.moe_intermediate_size)
+                    for i in range(config.n_routed_experts)
+                ]
+            )
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = DeepseekV2MLP(
+                config=config, intermediate_size=intermediate_size
+            )
+
+    def forward(self, hidden_states):
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            hidden_states = hidden_states.repeat_interleave(
+                self.num_experts_per_tok, dim=0
+            )
+            y = torch.empty_like(hidden_states)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+            y = AddAuxiliaryLoss.apply(y, aux_loss)
+        else:
+            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, topk_ids, topk_weight):
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        sorted_tokens_shape = sorted_tokens.shape
+        if self.ep_size > 1:
+            tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
+            tokens_per_expert_group = tokens_per_expert.new_empty(
+                tokens_per_expert.shape[0]
+            )
+            dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
+            output_splits = (
+                tokens_per_expert_group.view(self.ep_size, -1)
+                .sum(1)
+                .cpu()
+                .numpy()
+                .tolist()
+            )
+            gathered_tokens = sorted_tokens.new_empty(
+                tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
+            )
+            input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
+            dist.all_to_all(
+                list(gathered_tokens.split(output_splits)),
+                list(sorted_tokens.split(input_split_sizes)),
+            )
+            tokens_per_expert_post_gather = tokens_per_expert_group.view(
+                self.ep_size, self.experts_per_rank
+            ).sum(dim=0)
+            gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
+            s = 0
+            for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
+                gatherd_idxs[s : s + k] = i % self.experts_per_rank
+                s += k
+            gatherd_idxs = gatherd_idxs.argsort()
+            sorted_tokens = gathered_tokens[gatherd_idxs]
+            tokens_per_expert = tokens_per_expert_post_gather
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            expert_out = expert(tokens_for_this_expert)
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+        if self.ep_size > 1:
+            new_x = torch.empty_like(outs)
+            new_x[gatherd_idxs] = outs
+            gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
+            dist.all_to_all(
+                list(gathered_tokens.split(input_split_sizes)),
+                list(new_x.split(output_splits)),
+            )
+            outs = gathered_tokens
+
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            new_x.view(*topk_ids.shape, -1)
+            .type(topk_weight.dtype)
+            .mul_(topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+        return final_out
+
+# 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)
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
+class DeepseekV2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.q_lora_rank = config.q_lora_rank
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.kv_lora_rank = config.kv_lora_rank
+        self.v_head_dim = config.v_head_dim
+        self.qk_nope_head_dim = config.qk_nope_head_dim
+        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
+
+        self.is_causal = True
+
+        if self.q_lora_rank is None:
+            self.q_proj = nn.Linear(
+                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
+            )
+        else:
+            self.q_a_proj = nn.Linear(
+                self.hidden_size, config.q_lora_rank, bias=config.attention_bias
+            )
+            self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
+            self.q_b_proj = nn.Linear(
+                config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
+            )
+
+        self.kv_a_proj_with_mqa = nn.Linear(
+            self.hidden_size,
+            config.kv_lora_rank + config.qk_rope_head_dim,
+            bias=config.attention_bias,
+        )
+        self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
+        self.kv_b_proj = nn.Linear(
+            config.kv_lora_rank,
+            self.num_heads
+            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
+            bias=False,
+        )
+
+        self.o_proj = nn.Linear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=config.attention_bias,
+        )
+        self._init_rope()
+
+        self.softmax_scale = self.q_head_dim ** (-0.5)
+        if self.config.rope_scaling is not None:
+            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
+            scaling_factor = self.config.rope_scaling["factor"]
+            if mscale_all_dim:
+                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+                self.softmax_scale = self.softmax_scale * mscale * mscale
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = DeepseekV2RotaryEmbedding(
+                self.qk_rope_head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "yarn":
+                kwargs = {
+                    key: self.config.rope_scaling[key]
+                    for key in [
+                        "original_max_position_embeddings",
+                        "beta_fast",
+                        "beta_slow",
+                        "mscale",
+                        "mscale_all_dim",
+                    ]
+                    if key in self.config.rope_scaling
+                }
+                self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
+                    self.qk_rope_head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                    **kwargs,
+                )
+            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.v_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[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.q_lora_rank is None:
+            q = self.q_proj(hidden_states)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+        q_nope, q_pe = torch.split(
+            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+        )
+
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        compressed_kv, k_pe = torch.split(
+            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+        )
+        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+        kv = (
+            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+            .transpose(1, 2)
+        )
+
+        k_nope, value_states = torch.split(
+            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
+        )
+        kv_seq_len = value_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(q_pe, position_ids)
+        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin)
+
+        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        attn_weights = (
+            torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
+        )
+
+        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)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_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.num_heads * self.v_head_dim)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
+class DeepseekV2FlashAttention2(DeepseekV2Attention):
+    """
+    DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # DeepseekV2FlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+        q_nope, q_pe = torch.split(
+            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+        )
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        compressed_kv, k_pe = torch.split(
+            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+        )
+        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+        kv = (
+            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+            .transpose(1, 2)
+        )
+
+        k_nope, value_states = torch.split(
+            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
+        )
+        kv_seq_len = value_states.shape[-2]
+
+        kv_seq_len = value_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        cos, sin = self.rotary_emb(q_pe, position_ids)
+        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin)
+
+        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+
+        if self.q_head_dim != self.v_head_dim:
+            value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (DeepseekV2RMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            else:
+                target_dtype = self.q_a_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            position_ids=position_ids,
+            dropout=dropout_rate,
+            softmax_scale=self.softmax_scale,
+        )
+        if self.q_head_dim != self.v_head_dim:
+            attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+        attn_output = attn_output.reshape(
+            bsz, q_len, self.num_heads * self.v_head_dim
+        ).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        position_ids,
+        dropout=0.0,
+        softmax_scale=None,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV2FlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            (
+                query_states,
+                key_states,
+                value_states,
+                indices_q,
+                cu_seq_lens,
+                max_seq_lens,
+            ) = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(
+                attn_output_unpad, indices_q, batch_size, query_length
+            )
+        else:
+            if query_length == 1:
+                position_ids = position_ids.to(dtype=torch.int32).squeeze(1)
+                attn_output = flash_attn_with_kvcache(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cache_seqlens=position_ids,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )   
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+
+        return attn_output
+
+    def _upad_input(
+        self, query_layer, key_layer, value_layer, attention_mask, query_length
+    ):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
+                indices_k,
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
+                query_layer, attention_mask
+            )
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+ATTENTION_CLASSES = {
+    "eager": DeepseekV2Attention,
+    "flash_attention_2": DeepseekV2FlashAttention2,
+}
+
+class DeepseekV2DecoderLayer(nn.Module):
+    def __init__(self, config: DeepseekV2Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
+            config=config, layer_idx=layer_idx
+        )
+
+        self.mlp = (
+            DeepseekV2MoE(config)
+            if (
+                config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0
+            )
+            else DeepseekV2MLP(config)
+        )
+        self.input_layernorm = DeepseekV2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = DeepseekV2RMSNorm(
+            config.hidden_size, eps=config.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,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+DeepseekV2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`DeepseekV2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2PreTrainedModel(PreTrainedModel):
+    config_class = DeepseekV2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeepseekV2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+    _supports_static_cache = True
+
+    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_()
+
+
+DeepseekV2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2Model(DeepseekV2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
+
+    Args:
+        config: DeepseekV2Config
+    """
+
+    def __init__(self, config: DeepseekV2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [
+                DeepseekV2DecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+    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,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        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")
+
+        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`transformers."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            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],)
+
+        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 = None
+        if use_cache:
+            next_cache = (
+                next_decoder_cache.to_legacy_cache()
+                if use_legacy_cache
+                else next_decoder_cache
+            )
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+    
+    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+
+class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DeepseekV2Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        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
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
+    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,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
+
+        >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        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
+        )
+        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,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits[:,-1,:].unsqueeze(0).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 = 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)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        past_length = 0
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
+                max_cache_length = (
+                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
+                    if past_key_values.get_max_length() is not None
+                    else None
+                )
+                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        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[:, -input_ids.shape[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_length == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
+        elif use_cache:
+            cache_position = cache_position[-input_length:]
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DeepseekV2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+    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,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        transformer_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,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (
+                    torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                ).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[
+            torch.arange(batch_size, device=logits.device), sequence_lengths
+        ]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

ktransformers/models/modeling_qwen2_moe.py

+# coding=utf-8
+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+''' 
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.42.3/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
+# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+# 
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Qwen2MoE model."""
+
+import inspect
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+)
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
+
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func, flash_attn_with_kvcache
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "Qwen/Qwen1.5-MoE-A2.7B"
+_CONFIG_FOR_DOC = "Qwen2MoeConfig"
+
+
+# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
+) -> 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 model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        attention_mask (`torch.Tensor`, None):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+        num_experts (`int`, *optional*):
+            Number of experts
+
+    Returns:
+        The auxiliary loss.
+    """
+    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 = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    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
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2Moe
+class Qwen2MoeRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Qwen2MoeRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        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)
+        return self.weight * hidden_states.to(input_dtype)
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Qwen2Moe
+class Qwen2MoeRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        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, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.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.mistral.modeling_mistral.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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`):
+            Deprecated and unused.
+        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.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
+
+
+# Modified from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2Moe
+class Qwen2MoeMLP(nn.Module):
+    def __init__(self, config, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# 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)
+
+
+# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2Attention with Qwen2->Qwen2Moe
+class Qwen2MoeAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: Qwen2MoeConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.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=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = Qwen2MoeRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> 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:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # 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 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:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_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)
+
+        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
+
+
+# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2FlashAttention2 with Qwen2->Qwen2Moe
+class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
+    """
+    Qwen2Moe flash attention module, following Qwen2Moe attention module. This module inherits from `Qwen2MoeAttention`
+    as the weights of the module stays untouched. The only required change would be on the forward pass
+    where it needs to correctly call the public API of flash attention and deal with padding tokens
+    in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
+    config.max_window_layers layers.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+        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:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+            and self.config.use_sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+            # we slice the states for static kv cache to be supported in FA2. Not sure it's a must as compile fails
+            # for bsz == 1, avoid using slice to capture cuda graph
+            if cache_position is not None and q_len > 1:
+                key_states = key_states[:, :, : cache_position[-1] + 1, :]
+                value_states = value_states[:, :, : cache_position[-1] + 1, :]
+
+        # 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)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            position_ids=position_ids,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        position_ids,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Decide whether to use SWA or not by layer index.
+        if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
+            use_sliding_windows = False
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                if query_length == 1:
+                    position_ids = position_ids.to(dtype=torch.int32).squeeze(1)
+                    attn_output = flash_attn_with_kvcache(
+                        query_states,
+                        key_states,
+                        value_states,
+                        cache_seqlens=position_ids,
+                        softmax_scale=softmax_scale,
+                        causal=causal,
+                    )   
+                else:
+                    attn_output = flash_attn_func(
+                        query_states,
+                        key_states,
+                        value_states,
+                        dropout,
+                        softmax_scale=softmax_scale,
+                        causal=causal,
+                    )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralSdpaAttention with Mixtral->Qwen2Moe
+class Qwen2MoeSdpaAttention(Qwen2MoeAttention):
+    """
+    Qwen2Moe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Qwen2MoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from Qwen2MoeAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Qwen2MoeModel is using Qwen2MoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        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.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+        is_causal = True if causal_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+QWEN2MOE_ATTENTION_CLASSES = {
+    "eager": Qwen2MoeAttention,
+    "flash_attention_2": Qwen2MoeFlashAttention2,
+    "sdpa": Qwen2MoeSdpaAttention,
+}
+
+
+class Qwen2MoeSparseMoeBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+        self.norm_topk_prob = config.norm_topk_prob
+
+        # gating
+        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+        self.experts = nn.ModuleList(
+            [Qwen2MoeMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)]
+        )
+
+        self.shared_expert = Qwen2MoeMLP(config, intermediate_size=config.shared_expert_intermediate_size)
+        self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> 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)
+        if self.norm_topk_prob:
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # 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])
+
+            # 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].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, 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))
+
+        shared_expert_output = self.shared_expert(hidden_states)
+        shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
+
+        final_hidden_states = final_hidden_states + shared_expert_output
+
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+
+class Qwen2MoeDecoderLayer(nn.Module):
+    def __init__(self, config: Qwen2MoeConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = QWEN2MOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+        if (layer_idx not in config.mlp_only_layers) and (
+            config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
+        ):
+            self.mlp = Qwen2MoeSparseMoeBlock(config)
+        else:
+            self.mlp = Qwen2MoeMLP(config, intermediate_size=config.intermediate_size)
+
+        self.input_layernorm = Qwen2MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2MoeRMSNorm(config.hidden_size, eps=config.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,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
+                and should not be returned during inference.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states = self.mlp(hidden_states)
+        if isinstance(hidden_states, tuple):
+            hidden_states, router_logits = hidden_states
+        else:
+            router_logits = None
+
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+
+QWEN2MOE_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`Qwen2MoeConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2MoE Model outputting raw hidden-states without any specific head on top.",
+    QWEN2MOE_START_DOCSTRING,
+)
+class Qwen2MoePreTrainedModel(PreTrainedModel):
+    config_class = Qwen2MoeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2MoeDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_static_cache = True
+
+    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_()
+
+
+QWEN2MOE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        output_router_logits (`bool`, *optional*):
+            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+            should not be returned during inference.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2MoE Model outputting raw hidden-states without any specific head on top.",
+    QWEN2MOE_START_DOCSTRING,
+)
+class Qwen2MoeModel(Qwen2MoePreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2MoeDecoderLayer`]
+
+    Args:
+        config: Qwen2MoeConfig
+    """
+
+    def __init__(self, config: Qwen2MoeConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [Qwen2MoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = Qwen2MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+    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,
+        cache_position: Optional[torch.LongTensor] = 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
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        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
+
+        use_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            use_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            logger.warning_once(
+                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # 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 = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_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,
+                    cache_position=cache_position,
+                )
+
+            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 and layer_outputs[-1] is not None:
+                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 = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        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,
+        )
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+
+class Qwen2MoeForCausalLM(Qwen2MoePreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen2MoeModel(config)
+        self.vocab_size = config.vocab_size
+        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
+        # Initialize weights and apply final processing
+        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
+
+    @add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    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,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen2MoeForCausalLM
+
+        >>> model = Qwen2MoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        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,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        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 = 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,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+        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,
+        cache_position=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        past_length = 0
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
+                max_cache_length = (
+                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
+                    if past_key_values.get_max_length() is not None
+                    else None
+                )
+                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        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[:, -input_ids.shape[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_length == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
+        elif use_cache:
+            cache_position = cache_position[-input_length:]
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Qwen2MoE Model transformer with a sequence classification head on top (linear layer).
+
+    [`Qwen2MoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    QWEN2MOE_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Qwen2Moe, LLAMA->QWEN2MOE
+class Qwen2MoeForSequenceClassification(Qwen2MoePreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Qwen2MoeModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, 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,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_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,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The Qwen2MoE Model transformer with a token classification head on top (a linear layer on top of the hidden-states
+    output) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    QWEN2MOE_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->Qwen2Moe, LLAMA->QWEN2MOE
+class Qwen2MoeForTokenClassification(Qwen2MoePreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Qwen2MoeModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[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,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        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,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

ktransformers/operators/RoPE.py

+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+from torch import nn
+from ktransformers.models.modeling_deepseek import DeepseekV2YarnRotaryEmbedding, DeepseekV2RotaryEmbedding
+from ktransformers.operators.base_operator import BaseInjectedModule
+from ktransformers.util.custom_gguf import GGUFLoader
+from ktransformers.util.utils import InferenceState
+from transformers.configuration_utils import PretrainedConfig
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2Moe
+class RotaryEmbedding(BaseInjectedModule, DeepseekV2RotaryEmbedding):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        self.orig_module.__init__(orig_module.dim,
+            orig_module.max_position_embeddings,
+            orig_module.base)
+    
+    def load(self):
+        self.orig_module.__init__(self.orig_module.dim,
+            self.orig_module.max_position_embeddings,
+            self.orig_module.base,
+            self.device)
+    
+class YarnRotaryEmbedding(BaseInjectedModule, DeepseekV2YarnRotaryEmbedding):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        self.orig_module.__init__(orig_module.dim,
+            orig_module.max_position_embeddings,
+            orig_module.base,
+            None, #device
+            orig_module.scaling_factor,
+            orig_module.original_max_position_embeddings,
+            orig_module.beta_fast,
+            orig_module.beta_slow,
+            orig_module.mscale,
+            orig_module.mscale_all_dim)
+        
+    
+    def load(self):
+        self.orig_module.__init__(self.orig_module.dim,
+            self.orig_module.max_position_embeddings,
+            self.orig_module.base,
+            self.device,
+            self.orig_module.scaling_factor,
+            self.orig_module.original_max_position_embeddings,
+            self.orig_module.beta_fast,
+            self.orig_module.beta_slow,
+            self.orig_module.mscale,
+            self.orig_module.mscale_all_dim)
+    

ktransformers/operators/__init__.py

+

ktransformers/operators/attention.py

+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+import torch
+from torch import nn
+import warnings
+from ktransformers.models.configuration_deepseek import DeepseekV2Config
+from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb
+from typing import Optional, Tuple
+from ktransformers.operators.base_operator import BaseInjectedModule
+from ktransformers.util.custom_gguf import GGUFLoader
+from transformers.configuration_utils import PretrainedConfig
+from transformers.cache_utils import Cache
+
+class DeepseekV2AttentionInjected(BaseInjectedModule, DeepseekV2Attention):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        self.orig_module.__init__(orig_module.config,
+            orig_module.layer_idx)
+
+    def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
+            kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
+            q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
+            out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
+            self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim, 
+                                      bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
+            self.q_absorb.weight.data = q_absorb
+            self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim, 
+                                        bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
+            self.out_absorb.weight.data = out_absorb
+            del self.orig_module.kv_b_proj
+        q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
+        out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
+        return q_absorb, out_absorb
+
+    def forward_chunck(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+        if self.q_lora_rank is None:
+            q = self.q_proj(hidden_states)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+        q_nope, q_pe = torch.split(
+            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+        )
+
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        compressed_kv, k_pe = torch.split(
+            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+        )
+        compressed_kv = self.kv_a_layernorm(compressed_kv)
+        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+
+        kv_seq_len = k_pe.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        cos, sin = self.rotary_emb(q_pe, position_ids)
+        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            compressed_kv = compressed_kv.unsqueeze(1)
+            k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs)
+            compressed_kv = compressed_kv.squeeze(1)
+            #if cache_position is not None:  
+            #    compressed_kv = compressed_kv[:,: cache_position[-1] + 1,:]
+            #    k_pe = k_pe[:,:,: cache_position[-1] + 1,:]
+        q_absorb, out_absorb = self.get_absorbed()
+
+        q_nope = torch.matmul(q_nope, q_absorb)
+        attn_weights = (torch.matmul(q_pe, k_pe.mT) + torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale
+        """
+        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()}"
+            )
+        assert attention_mask is not None
+        """
+        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()}"
+                )
+            """
+            #causal_mask = attention_mask[:, :, :, : kv_seq_len]
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(q_pe.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+        attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv)
+
+        attn_output = torch.matmul(attn_output, out_absorb.mT) 
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_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.num_heads * self.v_head_dim)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+        chunck_size = 256 # TODO, generate chunck_size automatically.
+        
+        if q_len <= chunck_size:
+            return self.forward_chunck(
+                            hidden_states,
+                            attention_mask,
+                            position_ids,
+                            past_key_value,
+                            output_attentions,
+                            use_cache,
+                            cache_position,
+                            **kwargs
+                        )
+
+        assert output_attentions == False, "output_attentions is not supported when using chunked attention"
+        attn_output = None
+        cur_idx = 0
+        while cur_idx < q_len:
+            if attention_mask is not None:
+                chunk_mask = attention_mask[:, :, cur_idx:min(cur_idx + chunck_size, q_len), ...]
+            else:
+                chunk_mask = None
+
+            cur_output, _, _ = self.forward_chunck(
+                            hidden_states[:, cur_idx:min(cur_idx + chunck_size, q_len), ...],
+                            chunk_mask,
+                            position_ids[:, cur_idx:min(cur_idx + chunck_size, q_len)],
+                            past_key_value,
+                            output_attentions,
+                            use_cache,
+                            cache_position[cur_idx:min(cur_idx + chunck_size, q_len)],
+                            **kwargs
+                        )
+            cur_idx += chunck_size
+            if attn_output is None:
+                attn_output = cur_output
+            else:
+                attn_output = torch.cat((attn_output, cur_output), dim=-2)
+                
+        return attn_output, None, past_key_value

ktransformers/operators/base_operator.py

+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+from typing import Any
+from torch import nn, Tensor
+from ktransformers.util.custom_gguf import GGUFLoader
+from transformers.configuration_utils import PretrainedConfig
+import ktransformers.util.utils as utils
+class BaseInjectedModule(nn.Module):
+    
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 device: str = "cuda",
+                 **kwargs):
+        nn.Module.__init__(self)
+        nn.Module.__setattr__(self, "orig_module", orig_module)
+        object.__setattr__(self, "key", key)
+        object.__setattr__(self, "gguf_loader", gguf_loader)
+        object.__setattr__(self, "config", config)
+        object.__setattr__(self, "device", device)
+        
+    def __getattr__(self, name: str) -> Any:
+        # __getattr__ in nn.Module doesn't call super().__getattribute__ when name is not in nn.Module.__dict__,
+        # but __setattr__ in nn.Module call super().__setattr__ in that case, there may be some attribute set 
+        # but can't get using __getattr__, typically these attr is build in attr of the class, so class.attr does not
+        # call __getattr__.
+        # Example:
+        # ...import torch
+        # ...l=torch.nn.Linear(100,200)
+        # ...l.out_features # 200
+        # ...l.__getattr__("out_features") # AttributeError: 'Linear' object has no attribute 'out_features'
+        try:
+            return object.__getattribute__(self, name) # if this attr belongs to BaseInjectedModule
+        except:
+            if name == "orig_module":
+                return nn.Module.__getattr__(self, "orig_module")
+            try:
+                return nn.Module.__getattr__(self, "orig_module").__getattr__(name) # if this attr belongs to orig_module
+            except:
+                return super(nn.Module, nn.Module.__getattr__(self, "orig_module")).__getattribute__(name) # if this attr belongs to orig_module but not in nn.Module.__dict__
+
+    def __setattr__(self, name: str, value: Tensor | nn.Module) -> None:
+        if name == "orig_module":
+            return nn.Module.__setattr__(self, "orig_module", value)
+        elif hasattr(self, name):
+            return object.__setattr__(self, name, value)
+        return nn.Module.__getattr__(self, "orig_module").__setattr__(name, value)
+    
+    def forward(self, *args, **kwargs):
+        return self.orig_module.forward(*args, **kwargs)
+    
+    def load(self):
+        for name, child in self._modules.items():
+            utils.load_weights(child, self.gguf_loader, self.key+".")

ktransformers/operators/experts.py

+#!/usr/bin/env python
+# coding=utf-8
+'''
+Description  :  
+Author       : Azure-Tang, Boxin Zhang, chenht2022
+Date         : 2024-07-25 11:25:24
+Version      : 0.1.0
+LastEditors  : Azure 
+LastEditTime : 2024-07-26 09:27:41
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+
+from typing import Any, Union
+import numpy as np
+import numpy.typing as npt
+from torch import Tensor, nn
+import torch.nn.functional as F
+import torch
+import sys, os
+from ktransformers.operators.base_operator import BaseInjectedModule
+
+sys.path.append(os.path.dirname(__file__) + "/../ktransformers_ext/build")
+import cpuinfer_ext
+from cpuinfer_ext.moe import MOEConfig, MOE
+import ctypes
+from ktransformers.util.custom_gguf import GGUFLoader
+from ktransformers.util.utils import InferenceState
+from ktransformers.server.config.config import Config
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from abc import ABC, abstractmethod
+from ktransformers.operators.linear import QuantizedLinearMarlin, QuantizedLinearTorch, KTransformerLinear
+import time
+
+
+# class Base(BaseInjectedModule, ABC):
+class MLPExpertsBase(ABC):
+    def __init__(self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module, device: str = "cuda", **kwargs):
+        # super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.key = key
+        self.gguf_loader = gguf_loader
+        self.config = config
+        self.device = device
+    
+    @abstractmethod
+    def forward(self, input_tensor, expert_ids, weights):
+        pass
+
+    @abstractmethod
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str = "cpu", warmup: bool = False):
+        pass
+    
+    @abstractmethod
+    def unload():
+        pass
+
+    def load_weights(self, override_key: str | None = None, device: str = "cpu"):
+        res = {}
+        if override_key is not None:
+            keys = override_key
+        else:
+            keys = [self.key]
+
+        gate = None
+        up = None
+        down = None
+        gate_type = None
+        up_type = None
+        down_type = None
+
+        for key in keys:
+            if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
+                targets = [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight" ]
+                tensors = self.load_multi(key, targets, device=device)
+                gate = tensors[".ffn_gate_exps.weight"]
+                up = tensors[".ffn_up_exps.weight"]
+                down = tensors[".ffn_down_exps.weight"]
+                gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"]
+                up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
+                down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
+            else:
+                raise ValueError(f"Experts {key} not found in gguf_loader")
+            res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
+        return res
+    
+    def load_multi(self, key: str, keys: list[str], device: str = "cpu"):
+        tensors = {}
+        for k in keys:
+            tensors[k] = self.gguf_loader.load_gguf_tensor(key + k, device=device)
+        return tensors
+
+class MLPCPUExperts(MLPExpertsBase):
+    input_tensor_cpu:Tensor = None
+    expert_ids_cpu:Tensor = None
+    weights_cpu:Tensor = None
+    output_cpu:Tensor = None
+    output_gpu:Tensor = None
+    CPU_INFER = cpuinfer_ext.CPUInfer(Config().cpu_infer)
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        n_routed_experts: int,
+        orig_module: nn.Module = None,
+        device: str = "cpu",
+        out_device: str = "cuda", # this device mean which device the output should on 
+        **kwargs
+    ):
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        assert device.lower() == "cpu", "MLPCPUExperts can only be loaded on CPU"
+        self.n_routed_experts = n_routed_experts
+        self.out_device = out_device
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device:str|None = None, warmup:bool = False):
+        if device:
+            assert device.lower() == "cpu", "MLPCPUExperts can only be loaded on CPU, Parameter \"device\" can be cpu or None."
+        if w is None: w = self.load_weights()[self.key]
+        self.gate = w["gate"]
+        self.up = w["up"]
+        self.down = w["down"]
+        self.gate_type = w["gate_type"]
+        self.up_type = w["up_type"]
+        self.down_type = w["down_type"]
+        gate_ptr = ctypes.addressof(
+            ctypes.cast(self.gate.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
+        )
+        up_ptr = ctypes.addressof(
+            ctypes.cast(self.up.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
+        )
+        down_ptr = ctypes.addressof(
+            ctypes.cast(self.down.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
+        )
+        # print(self.gate_qtype, self.up_qtype, self.down_qtype)
+        n_routed_experts = self.n_routed_experts
+        # n_routed_experts = len(self.orig_module)
+        moe_config = MOEConfig(
+            n_routed_experts,
+            self.config.num_experts_per_tok,
+            self.config.hidden_size,
+            self.config.moe_intermediate_size,
+            64,
+            10,
+            1024,
+            gate_ptr,
+            up_ptr,
+            down_ptr,
+            self.gate_type,
+            self.up_type,
+            self.down_type,
+            30, # TODO: get from model.dtype
+        )
+        # print(n_routed_experts, hidden_size, moe_intermediate_size)
+        num_experts_per_tok = self.config.num_experts_per_tok
+        self.moe = MOE(moe_config)
+        self.cpu_infer = MLPCPUExperts.CPU_INFER
+        if warmup:
+            self.cpu_infer.submit(self.moe.warm_up)
+            self.cpu_infer.sync()
+        if MLPCPUExperts.output_gpu == None:
+            MLPCPUExperts.input_tensor_cpu = torch.empty((self.config.hidden_size), device="cpu", pin_memory=True)
+            MLPCPUExperts.expert_ids_cpu = torch.empty((num_experts_per_tok), device="cpu", dtype=torch.long, pin_memory=True)
+            MLPCPUExperts.weights_cpu = torch.empty((num_experts_per_tok), device="cpu", dtype=torch.float32, pin_memory=True)
+            MLPCPUExperts.output_cpu = torch.empty((self.config.hidden_size), device="cpu", pin_memory=True)
+            MLPCPUExperts.output_gpu = torch.empty((self.config.hidden_size), device=self.out_device)
+
+    def submit_for_one_decode(self, input_tensor, expert_ids, weights):
+        MLPCPUExperts.input_tensor_cpu.copy_(input_tensor, non_blocking=True)
+        MLPCPUExperts.expert_ids_cpu.copy_(expert_ids, non_blocking=True)
+        MLPCPUExperts.weights_cpu.copy_(weights, non_blocking=True)
+        self.cpu_infer.submit_with_cuda_stream(torch.cuda.current_stream().cuda_stream, self.moe.forward, 1, expert_ids.size(0), MLPCPUExperts.expert_ids_cpu.data_ptr(), MLPCPUExperts.weights_cpu.data_ptr(), MLPCPUExperts.input_tensor_cpu.data_ptr(), MLPCPUExperts.output_cpu.data_ptr())
+    
+    def sync_for_one_decode(self):
+        self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream)
+        MLPCPUExperts.output_gpu.copy_(MLPCPUExperts.output_cpu, non_blocking=True)
+        #print("capturing experts finish")
+        return MLPCPUExperts.output_gpu
+
+    def forward(self, input_tensor, expert_ids, weights):
+        # generate, capture and run cuda graph
+        if input_tensor.size(0)==1:
+            #print("capturing experts")
+            MLPCPUExperts.input_tensor_cpu.copy_(input_tensor, non_blocking=True)
+            MLPCPUExperts.expert_ids_cpu.copy_(expert_ids, non_blocking=True)
+            MLPCPUExperts.weights_cpu.copy_(weights, non_blocking=True)
+            self.cpu_infer.submit_with_cuda_stream(torch.cuda.current_stream().cuda_stream, self.moe.forward, 1, expert_ids.size(1), MLPCPUExperts.expert_ids_cpu.data_ptr(), MLPCPUExperts.weights_cpu.data_ptr(), MLPCPUExperts.input_tensor_cpu.data_ptr(), MLPCPUExperts.output_cpu.data_ptr())
+            self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream)
+            MLPCPUExperts.output_gpu.copy_(MLPCPUExperts.output_cpu, non_blocking=True)
+            #print("capturing experts finish")
+            return MLPCPUExperts.output_gpu
+        else:
+            input_tensor = input_tensor.contiguous().cpu()
+            expert_ids = expert_ids.contiguous().cpu()
+            weights = weights.contiguous().to(torch.float32).cpu()
+            output = torch.empty_like(input_tensor).contiguous()
+            self.cpu_infer.submit(self.moe.forward, expert_ids.size(0), expert_ids.size(1), expert_ids.data_ptr(), weights.data_ptr(), input_tensor.data_ptr(), output.data_ptr())
+            self.cpu_infer.sync()
+            return output.to(device=object.__getattribute__(self, "device"))
+    
+    def unload(self):
+        return
+
+    def load_weights(self, override_key: str | None = None, device: str = "cpu"):
+        res = {}
+        if override_key is not None:
+            keys = override_key
+        else:
+            keys = [self.key]
+
+        gate = None
+        up = None
+        down = None
+        gate_type = None
+        up_type = None
+        down_type = None
+
+        for key in keys:
+            if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
+                gate = self.gguf_loader.get_mmap_tensor(key + ".ffn_gate_exps.weight")
+                up = self.gguf_loader.get_mmap_tensor(key + ".ffn_up_exps.weight")
+                down = self.gguf_loader.get_mmap_tensor(key + ".ffn_down_exps.weight")
+                gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"]
+                up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
+                down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
+            else:
+                raise ValueError(f"Experts {key} not found in gguf_loader")
+            res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
+        return res
+    
+class MLPExpertsMarlin(MLPExpertsBase):
+    expert_num: int
+    loaded_experts_idx: list[int]
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        n_routed_experts: int,
+        orig_module: nn.Module = None,
+        device: str = "cuda",
+        **kwargs
+    ):
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.expert_num = n_routed_experts
+        self.loaded_experts_idx = []
+        self.act_fn = ACT2FN[config.hidden_act]
+        assert device.lower() != "cpu", "Marlin experts can only be loaded on GPU"
+        self.device = device
+        # create empty marlin experts according to the number of experts per token
+        # up
+        self.up_projs = [QuantizedLinearMarlin(key+ "." + "ffn_up_exps", gguf_loader, config, device=device) for i in range(self.expert_num)]
+        # gate
+        self.gate_projs = [QuantizedLinearMarlin(key+ "." + "ffn_gate_exps", gguf_loader, config, device=device) for i in range(self.expert_num)]
+        # down
+        self.down_projs = [QuantizedLinearMarlin(key+ "." + "ffn_down_exps", gguf_loader, config, device=device) for i in range(self.expert_num)]
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str | None = None, warmup: bool = False):
+        if device is None: device = self.device
+        assert device.lower() != "cpu", "Marlin experts can only be loaded on GPU"
+        if w is None: w = self.load_weights()[self.key]
+
+        if isinstance(w, dict):
+            self.gate = nn.Parameter(torch.from_numpy(w["gate"]))
+            self.up = nn.Parameter(torch.from_numpy(w["up"]))
+            self.down = nn.Parameter(torch.from_numpy(w["down"]))
+            for i in range(self.expert_num):
+                self.up_projs[i].load(self.up[i,...], device=device)
+                self.gate_projs[i].load(self.gate[i,...], device=device)
+                self.down_projs[i].load(self.down[i,...], device=device)
+                self.loaded_experts_idx.append(i)
+        return 
+
+    def unload(self):
+        for i in self.loaded_experts_idx:
+            self.up_projs[i].unload()
+            self.gate_projs[i].unload()
+            self.down_projs[i].unload()
+        self.loaded_experts_idx = []
+
+    def load_weights(self, override_key: str | None = None):
+        res = {}
+        if override_key is not None:
+            keys = override_key
+        else:
+            keys = [self.key]
+
+        gate = None
+        up = None
+        down = None
+        gate_type = None
+        up_type = None
+        down_type = None
+
+        for key in keys:
+            if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
+                gate = self.gguf_loader.load_gguf_tensor(key + ".ffn_gate_exps.weight")
+                up = self.gguf_loader.load_gguf_tensor(key + ".ffn_up_exps.weight")
+                down = self.gguf_loader.load_gguf_tensor(key + ".ffn_down_exps.weight")
+                gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"]
+                up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
+                down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
+                # tensors = self.load_multi(key, [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight"])    
+            res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
+        return res
+
+    def forward(self, input_tensor:torch.Tensor, expert_ids, weights):
+        # forward
+        device = input_tensor.device
+        input_tensor = input_tensor.to("cuda")
+        outs = torch.zeros_like(input_tensor)
+        for expert_idx in range(expert_ids.size(0)):
+            down_proj = self.down_projs[expert_idx]
+            gate_proj = self.gate_projs[expert_idx]
+            up_proj = self.up_projs[expert_idx]
+
+            outs += down_proj(self.act_fn(gate_proj(input_tensor)) * up_proj(input_tensor)) * weights[expert_idx]
+        outs = outs.to(device)
+        return outs
+
+class MLPExpertsTorch(MLPExpertsBase):
+    expert_num: int
+    loaded_experts_idx: list[int]
+    gate: torch.Tensor
+    up: torch.Tensor
+    down: torch.Tensor
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        n_routed_experts: int,
+        orig_module: nn.Module = None,
+        device: str = "cpu",
+        **kwargs
+    ):
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.expert_num = n_routed_experts
+        # self.loaded_experts_idx = []
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.device = device
+        self.gate = None
+        self.up = None
+        self.donw = None
+        self.dtype = torch.get_default_dtype()
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str | None = None, warmup: bool = False):
+        if device is None: device = self.device
+        if w is None: w = self.load_weights(device=device)[self.key]
+
+        if isinstance(w, dict):
+            self.gate = w["gate"].to(device=device, dtype=self.dtype)
+            self.up = w["up"].to(device=device, dtype=self.dtype)
+            self.down = w["down"].to(device=device, dtype=self.dtype)
+
+    def unload(self):
+        if self.gate is not None:
+            self.gate = None
+            self.up = None
+            self.down = None
+
+    def forward(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor) -> torch.Tensor:
+        
+        batch_sequence_length, hidden_dim = hidden_states_cpu.size()
+
+        final_hidden_states = torch.zeros(
+            (batch_sequence_length, hidden_dim), dtype=self.gate.dtype, device=hidden_states_cpu.device
+        )
+        org_dtype = hidden_states_cpu.dtype
+        hidden_states_cpu = hidden_states_cpu.to(self.gate.dtype)
+        routing_weights_cpu = routing_weights_cpu.to(self.gate.dtype)
+        # 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_cpu, num_classes=self.expert_num).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.expert_num):
+            idx, top_x = torch.where(expert_mask[expert_idx])
+            # 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_cpu[None, top_x].reshape(-1, hidden_dim)
+            G = current_state @ self.gate[expert_idx,...].T
+            A = self.act_fn(G)
+            U = current_state @ self.up[expert_idx,...].T
+            H = A * U  # Element-wise multiplication
+            current_hidden_states = H @ self.down[expert_idx,...].T * routing_weights_cpu[top_x, idx, 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)
+
+        return final_hidden_states.to(org_dtype)
+
+EXPERTS_MAP = {
+    "MLPCPUExperts": MLPCPUExperts,
+    "MLPExpertsTorch": MLPExpertsTorch,
+    "MLPExpertsMarlin": MLPExpertsMarlin,
+}
+class KTransformersMLPExpert(BaseInjectedModule, MLPExpertsBase):
+    def __init__(self,
+                 key: str,
+                 gguf_loader: GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 device: str = "cuda",
+                 prefill_device:str = "cuda",
+                 prefill_mlp_type: str | None = "MLPExpertsTorch",
+                 generate_device: str = "cpu",
+                 generate_mlp_type: str | None = "MLPCPUExperts",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        MLPExpertsBase.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        if generate_mlp_type is not None:
+            self.generate_experts = EXPERTS_MAP[generate_mlp_type](key, gguf_loader, config, len(orig_module), device=generate_device, **kwargs)
+        else:
+            self.generate_experts = None
+        if prefill_mlp_type is not None:
+            self.prefill_experts = EXPERTS_MAP[prefill_mlp_type](key, gguf_loader, config, len(orig_module), device=prefill_device, **kwargs)
+        else:
+            self.prefill_experts = None
+        self.gpu_mlp_type = prefill_mlp_type
+        self.cpu_mlp_type = generate_mlp_type
+        self.mode = InferenceState.UNLOAD
+
+    def load(self, w: dict = None,  mode: InferenceState = None, warmup: bool = True):
+        # TODO support w as input
+        if not mode: mode = InferenceState.GENERATE
+        if mode == InferenceState.GENERATE:
+            self.prefill_experts.unload()
+            self.generate_experts.load(w, warmup=warmup)
+            self.device = self.generate_experts.device
+            self.mode = mode
+        elif mode == InferenceState.PREFILL:
+            self.generate_experts.unload()
+            self.prefill_experts.load(w, warmup=warmup)
+            self.device = self.prefill_experts.device
+            self.mode = mode
+        elif mode == InferenceState.UNLOAD:
+            self.unload()
+            self.mode = mode
+            self.device = self.generate_experts.device
+        else:
+            raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD")
+
+    def unload(self):
+        if self.generate_experts is not None:
+            self.generate_experts.unload()
+        if self.prefill_experts is not None:
+            self.prefill_experts.unload()
+        self.device = self.generate_experts.device
+
+    def forward(self, input_tensor, expert_ids, weights):
+        if self.mode == InferenceState.GENERATE:
+            assert self.generate_experts is not None, "generate_experts is None"
+            return self.generate_experts.forward(input_tensor, expert_ids, weights)
+        elif self.mode == InferenceState.PREFILL:
+            assert self.prefill_experts is not None, "prefill_experts is None"
+            return self.prefill_experts.forward(input_tensor, expert_ids, weights)
+        else:
+            raise ValueError("load or set_inference_mode before forward")
+
+    def set_inference_mode(self, mode: InferenceState):
+        if mode == InferenceState.GENERATE:
+            self.load(mode=InferenceState.GENERATE, warmup=False)
+        elif mode == InferenceState.PREFILL:
+            self.load(mode=InferenceState.PREFILL, warmup=False)
+        elif mode == InferenceState.UNLOAD:
+            self.unload()
+        else:
+            raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD")
+
+
+from ktransformers.models.modeling_deepseek import DeepseekV2MoE
+from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
+
+
+class Qwen2MoeSparseMoeBlockInjected(BaseInjectedModule, Qwen2MoeSparseMoeBlock):
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """ """
+        orig_shape = hidden_states.shape
+        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)
+        if self.norm_topk_prob:
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+        
+        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode"):
+            self.experts.generate_experts.submit_for_one_decode(hidden_states[0], selected_experts[0], routing_weights[0])
+            shared_expert_output = self.shared_expert(hidden_states)
+            shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
+            y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0)
+            y += shared_expert_output
+            y.resize_(*orig_shape)
+            return y, router_logits
+        
+        hidden_states_expert = hidden_states.to(self.experts.device)  if isinstance(self.experts, MLPExpertsBase) else hidden_states_expert.cpu()
+        selected_experts_expert = selected_experts.to(self.experts.device) if isinstance(self.experts, MLPExpertsBase) else selected_experts_expert.cpu()
+        routing_weights_expert = routing_weights.to(self.experts.device) if isinstance(self.experts, MLPExpertsBase) else routing_weights_expert.cpu()
+
+        shared_expert_output = self.shared_expert(hidden_states)
+        shared_expert_output = (
+            F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
+        )
+
+        if isinstance(self.experts, MLPExpertsBase):
+            y = (
+                self.moe_on_cpuinfer(
+                    hidden_states_expert, selected_experts_expert, routing_weights_expert
+                )
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            )
+        elif hidden_states_expert.size(0) > 10:
+            y = self.moe_infer(
+                hidden_states_expert, selected_experts_expert, routing_weights_expert, orig_shape
+            ).to(device=hidden_states.device)
+        else:
+            y = self.moe_infer_simple(
+                hidden_states_expert, selected_experts_expert, routing_weights_expert
+            ).to(device=hidden_states.device)
+        y += shared_expert_output
+        y.resize_(*orig_shape)
+        return y, router_logits
+    
+    @torch.no_grad()
+    def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor:
+        outs = torch.empty_like(x)
+        outs = self.experts(x, topk_ids, topk_weight)
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer_simple(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor) -> torch.Tensor:
+        '''
+        hidden_states_cpu: [num_tokens, hidden_size]
+        topk_ids, topk_weight: [num_tokens, num_selected_experts]
+        '''
+        outs = torch.zeros_like(hidden_states_cpu)
+        for token_idx in range(selected_experts_cpu.size(0)):
+            for expert_idx in range(selected_experts_cpu.size(1)):
+                expert = self.experts[selected_experts_cpu[token_idx, expert_idx]]
+                outs[token_idx] += expert.forward(hidden_states_cpu[token_idx]) * routing_weights_cpu[token_idx, expert_idx]
+        return outs
+    
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor, orig_shape: tuple) -> torch.Tensor:
+        
+        batch_size, sequence_length, hidden_dim = orig_shape
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states_cpu.dtype, device=hidden_states_cpu.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_cpu, 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])
+
+            # 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_cpu[None, top_x].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer.forward(current_state) * routing_weights_cpu[top_x, idx, 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_cpu.dtype))
+
+        return final_hidden_states
+
+
+class DeepseekV2MoEInjected(BaseInjectedModule, DeepseekV2MoE):
+    def forward(self, hidden_states):
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        sequence_length = orig_shape[1]
+        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        
+        if sequence_length == 1:
+            self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
+            if self.config.n_shared_experts is not None:
+                y_ = self.shared_experts(identity).squeeze(0)
+            y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0)
+            y += y_
+            y.resize_(*orig_shape)
+            return y
+
+        if self.config.n_shared_experts is not None:
+            y_ = self.shared_experts(identity).squeeze(0)
+            
+        if isinstance(self.experts, MLPExpertsBase):
+            y = self.moe_on_cpuinfer(hidden_states, topk_idx, topk_weight).view(*orig_shape).to(device=hidden_states.device)
+        elif hidden_states.size(0) > 10:
+            # TODO may bugs here
+            y = (
+                self.moe_infer(hidden_states, topk_idx, topk_weight)
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            )
+        else:
+            # TODO may bugs here
+            y = (
+                self.moe_infer_simple(hidden_states, topk_idx, topk_weight)
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            )
+        if self.config.n_shared_experts is not None:
+            y += y_
+        return y
+
+    @torch.no_grad()
+    def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor:
+        outs = torch.empty_like(x)
+        outs = self.experts(x, topk_ids, topk_weight)
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer_simple(
+        self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        x: [num_tokens, hidden_size]
+        topk_ids, topk_weight: [num_tokens, num_selected_experts]
+        """
+        outs = torch.zeros_like(x)
+        for token_idx in range(topk_ids.size(0)):
+            for expert_idx in range(topk_ids.size(1)):
+                expert = self.experts[topk_ids[token_idx, expert_idx]]
+                outs[token_idx] += (
+                    expert.forward(x[token_idx]) * topk_weight[token_idx, expert_idx]
+                )
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer(self, x, topk_ids, topk_weight):
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            expert_out = expert.forward(tokens_for_this_expert)
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            new_x.view(*topk_ids.shape, -1)
+            .type(topk_weight.dtype)
+            .mul_(topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+        return final_out

ktransformers/operators/layer_wise_prefill.py

+#!/usr/bin/env python
+# coding=utf-8
+'''
+Description  :  
+Author       : Azure-Tang
+Date         : 2024-07-25 11:25:24
+Version      : 1.0.0
+LastEditors  : Azure 
+LastEditTime : 2024-07-26 09:27:48
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+
+import inspect
+import math
+from typing import List, Optional, Tuple, Union
+import time
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+)
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock, Qwen2MoeMLP, Qwen2MoeDecoderLayer
+from ktransformers.models.modeling_deepseek import BaseModelOutputWithPast, DeepseekV2DecoderLayer, DeepseekV2MoE
+from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
+from ktransformers.operators.base_operator import BaseInjectedModule
+from ktransformers.util.utils import InferenceState
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "Qwen/Qwen1.5-MoE-A2.7B"
+_CONFIG_FOR_DOC = "Qwen2MoeConfig"
+
+QWEN2MOE_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`Qwen2MoeConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Qwen2MoE Model outputting raw hidden-states without any specific head on top.",
+    QWEN2MOE_START_DOCSTRING,
+)
+class Qwen2MoePreTrainedModel(PreTrainedModel):
+    config_class = Qwen2MoeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2MoeDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_static_cache = True
+
+    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_()
+
+
+QWEN2MOE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        output_router_logits (`bool`, *optional*):
+            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+            should not be returned during inference.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+from ktransformers.util.custom_gguf import GGUFLoader
+from transformers.configuration_utils import PretrainedConfig
+@add_start_docstrings(
+    "The bare Qwen2MoE Model outputting raw hidden-states without any specific head on top.",
+    QWEN2MOE_START_DOCSTRING,
+)
+class Qwen2MoeModelPerLayerPrefill(BaseInjectedModule):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2MoeDecoderLayer`]
+
+    Args:
+        config: Qwen2MoeConfig
+    """
+    def __init__(
+        self,
+        key: str,
+        gguf_loader : GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module,
+        device: str = "cuda",
+        per_layer_prefill_intput_threshold: int = 30000, # if None, no per-layer prefill
+        **kwargs,
+    ):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        self.per_layer_prefill_intput_threshold = per_layer_prefill_intput_threshold
+
+    @add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+    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,
+        cache_position: Optional[torch.LongTensor] = None,
+        per_layer_prefill_intput_threshold: int | None = None, # if None or 0, close per-layer prefill
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        # print(f'Total length of input_ids: {input_ids.size(1)}, {input_ids.size()}')
+
+        if per_layer_prefill_intput_threshold is None: per_layer_prefill_intput_threshold = self.per_layer_prefill_intput_threshold
+        per_layer_prefill_flag = False
+        seq_lenth = inputs_embeds.size(1) if inputs_embeds is not None else input_ids.size(1)
+        if per_layer_prefill_intput_threshold and per_layer_prefill_intput_threshold < seq_lenth:
+            per_layer_prefill_flag = True
+            for layer in self.layers:
+                self.load_layer_to(layer, InferenceState.UNLOAD)
+        else:
+            pass
+        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
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        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
+
+        use_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            use_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            logger.warning_once(
+                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
+            )
+
+        if inputs_embeds is None:
+            input_ids = input_ids.to("cpu")
+            inputs_embeds = self.embed_tokens(input_ids)
+            inputs_embeds = inputs_embeds.to("cuda")
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # 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 = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                if per_layer_prefill_flag:
+                    # print(f"to gpu")
+                    self.load_layer_to(decoder_layer, InferenceState.PREFILL)
+                    torch.cuda.empty_cache()
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_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,
+                    cache_position=cache_position,
+                )
+                if per_layer_prefill_flag:
+                    # print(f"to cpu")
+                    self.load_layer_to(decoder_layer, InferenceState.UNLOAD)
+                    torch.cuda.empty_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 and layer_outputs[-1] is not None:
+                all_router_logits += (layer_outputs[-1],)
+
+        hidden_states = self.norm(hidden_states)
+
+
+        if per_layer_prefill_flag:
+            per_layer_prefill_flag = False
+            for layer in self.layers:
+                self.load_layer_to(layer, InferenceState.GENERATE)
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        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,
+        )
+
+    def load_layer_to(self,  layer:Qwen2MoeDecoderLayer, target: InferenceState):
+        assert isinstance(layer, Qwen2MoeDecoderLayer), "module should be nn.ModuleList of decoder layers"
+
+        # TODO Support restore to original device, not only cuda
+        device = "cpu" if target == InferenceState.UNLOAD else "cuda" 
+
+        # attn
+        layer.self_attn.q_proj.set_inference_mode(target)
+        layer.self_attn.k_proj.set_inference_mode(target)
+        layer.self_attn.v_proj.set_inference_mode(target)
+        layer.self_attn.o_proj.set_inference_mode(target)
+        layer.self_attn.rotary_emb = layer.self_attn.rotary_emb.to(device)
+
+        # mlp
+        if isinstance(layer.mlp, Qwen2MoeSparseMoeBlock):
+            layer.mlp.gate.set_inference_mode(target)
+            layer.mlp.experts.set_inference_mode(target)
+            layer.mlp.shared_expert.gate_proj.set_inference_mode(target)
+            layer.mlp.shared_expert.up_proj.set_inference_mode(target)
+            layer.mlp.shared_expert.down_proj.set_inference_mode(target)
+            layer.mlp.shared_expert.act_fn.to(device)
+            layer.mlp.shared_expert_gate.to(device)
+        else:
+            layer.mlp.gate_proj.set_inference_mode(target)
+            layer.mlp.up_proj.set_inference_mode(target)
+            layer.mlp.down_proj.set_inference_mode(target)
+            layer.mlp.act_fn.to(device)
+        # layer norm
+        layer.input_layernorm.to(device)
+        layer.post_attention_layernorm.to(device)
+
+
+DeepseekV2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class DeepseekV2ModelPerLayerPrefill(BaseInjectedModule):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
+
+    Args:
+        config: DeepseekV2Config
+    """
+    def __init__(
+        self,
+        key: str,
+        gguf_loader : GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module,
+        device: str = "cuda",
+        per_layer_prefill_intput_threshold: int = 30000, # if None, no per-layer prefill
+        **kwargs,
+    ):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        self.per_layer_prefill_intput_threshold = per_layer_prefill_intput_threshold
+
+    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+    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,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        per_layer_prefill_intput_threshold: int | None = None, # if None, no per-layer prefill
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        if per_layer_prefill_intput_threshold is None: per_layer_prefill_intput_threshold = self.per_layer_prefill_intput_threshold
+        per_layer_prefill_flag = False
+        seq_lenth = inputs_embeds.size(1) if inputs_embeds is not None else input_ids.size(1)
+        if per_layer_prefill_intput_threshold and per_layer_prefill_intput_threshold < seq_lenth:
+            per_layer_prefill_flag = True
+            for layer in self.layers:
+                self.load_layer_to(layer,  InferenceState.UNLOAD)
+            torch.cuda.empty_cache()
+        else:
+            pass
+        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")
+
+        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`transformers."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        if inputs_embeds is None:
+            org_device = input_ids.device
+            input_ids = input_ids.to("cpu")
+            inputs_embeds = self.embed_tokens(input_ids)
+            input_ids = input_ids.to(org_device)
+
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        # embed positions
+        hidden_states = inputs_embeds
+        if per_layer_prefill_flag:
+            print(f'Total length of input_ids: {hidden_states.size(1)}')
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        t_gpu = 0
+        t_cpu = 0
+        t_f = 0
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                t3 = time.time()
+                if per_layer_prefill_flag:
+                    # print(f"to gpu")
+                    self.load_layer_to(decoder_layer, InferenceState.PREFILL)
+                    torch.cuda.empty_cache()
+                t4 = time.time()
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+                t5 = time.time()
+                if per_layer_prefill_flag:
+                    # print(f"to cpu")
+                    self.load_layer_to(decoder_layer,  InferenceState.UNLOAD)
+                    torch.cuda.empty_cache()
+                t6 = time.time()
+            t_gpu += t4-t3
+            t_cpu += t6-t5
+            t_f += t5-t4
+
+            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],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if per_layer_prefill_flag:
+            t6 = time.time()
+            # print(f"restore")
+            per_layer_prefill_flag = False
+            for layer in self.layers:
+                self.load_layer_to(layer, InferenceState.GENERATE)
+            torch.cuda.empty_cache()
+            t7 = time.time()
+
+            print(f"total time: {t7-t3}, \n layer num{len(self.layers)}, gpu time: {t_gpu}, cpu time: {t_cpu}, forward time: {t_f}, restore time: {t7-t6}")
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = (
+                next_decoder_cache.to_legacy_cache()
+                if use_legacy_cache
+                else next_decoder_cache
+            )
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def load_layer_to(self,  layer: DeepseekV2DecoderLayer, target: InferenceState):
+        assert isinstance(layer, DeepseekV2DecoderLayer), "module should be nn.ModuleList of decoder layers"
+
+        # TODO Support restore to original device, not only cuda
+        device = "cpu" if target == InferenceState.UNLOAD else "cuda" 
+
+        # TODO Support DFS to auto use {to, set_inference_mode} according to the module type
+
+        # attn
+        layer.self_attn.to(device) #
+
+        # mlp
+        if isinstance(layer.mlp, DeepseekV2MoE):
+            layer.mlp.gate.to(device)
+            layer.mlp.experts.set_inference_mode(target)
+            layer.mlp.shared_experts.gate_proj.set_inference_mode(target)
+            layer.mlp.shared_experts.up_proj.set_inference_mode(target)
+            layer.mlp.shared_experts.down_proj.set_inference_mode(target)
+            layer.mlp.shared_experts.act_fn.to(device)
+            # layer.mlp.shared_expert_gate.to(device)
+        else:
+            layer.mlp.gate_proj.set_inference_mode(target)
+            layer.mlp.up_proj.set_inference_mode(target)
+            layer.mlp.down_proj.set_inference_mode(target)
+            layer.mlp.act_fn.to(device)
+        # layer norm
+        layer.input_layernorm.to(device)
+        layer.post_attention_layernorm.to(device)

ktransformers/operators/linear.py

+#!/usr/bin/env python
+# coding=utf-8
+'''
+Description  :  
+Author       : Azure-Tang, Boxin Zhang
+Date         : 2024-07-25 11:25:24
+Version      : 0.1.0
+LastEditors  : Azure 
+LastEditTime : 2024-07-26 09:27:53
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+
+
+import torch
+from torch import nn
+import KTransformersOps 
+from ktransformers.util.custom_gguf import GGUFLoader
+from ktransformers.util.utils import InferenceState
+from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marlin_utils import (
+    MarlinWorkspace,
+    marlin_quantize,
+    GPTQ_MARLIN_MIN_THREAD_N,
+    GPTQ_MARLIN_MAX_PARALLEL,
+)
+from ktransformers.operators.base_operator import BaseInjectedModule
+from transformers.configuration_utils import PretrainedConfig
+from abc import ABC, abstractmethod
+
+
+#class QuantizedLinearBase(BaseInjectedModule, ABC):
+class QuantizedLinearBase(ABC):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module = None,
+        device: str = "cuda",
+        **kwargs,
+    ):
+        # super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        super().__init__()
+        self.key = key
+        self.gguf_loader = gguf_loader
+        self.device = device
+        self.config = config
+
+        self.has_bias = False
+        self.dtype = torch.get_default_dtype()
+        if orig_module is not None:
+            self.in_features = orig_module.in_features
+            self.out_features = orig_module.out_features
+        else:
+            shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"]
+            if len(shape) == 1:
+                print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF")
+            self.in_features  = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
+            self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
+
+    @abstractmethod
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pass
+
+    def load_weight(self, override_key: str | None = None, device: str | None = None):
+        if override_key is not None:
+            keys = override_key
+        else:
+            keys = [self.key]
+
+        for key in keys:
+            if key + ".weight" in self.gguf_loader.tensor_file_map:
+                if key + ".bias" in self.gguf_loader.tensor_file_map:
+                    tensors = self.load_multi(key, ["weight", "bias"], device=device)
+                    tensor = tensors["weight"]
+                    bias = tensors["bias"]
+                    # self.qtype = GGML_TYPE_QTYPE_MAP[tensorinfo[key + ".weight"]["ggml_type"]]
+                    # print(torch.isinf(tensor).any(), torch.isinf(bias).any())
+                    return nn.Parameter(tensor), nn.Parameter(bias)
+                else:
+                    tensors = self.load_multi(key, ["weight"], device=device)
+                    tensor = tensors["weight"]
+                    # self.qtype = GGML_TYPE_QTYPE_MAP[tensorinfo[key + ".weight"]["ggml_type"]]
+                    return nn.Parameter(tensor)
+            else:
+                raise FileNotFoundError(f"Weight file not found for key {key}")
+
+    def load_multi(self, key: str, keys: list[str], device: str = "cpu"):
+        tensors = {}
+        for k in keys:
+            tensors[k] = self.gguf_loader.load_gguf_tensor(key + "." + k, device=device)
+        return tensors
+
+    @abstractmethod
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = "cuda"):
+        pass
+
+    @abstractmethod
+    def unload(self):
+        pass
+
+
+class QuantizedLinearTorch(QuantizedLinearBase):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module = None,
+        device: str = "cuda",
+        **kwargs,
+    ):
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.has_bias = False
+        self.dtype = torch.get_default_dtype()
+        self.w = None
+        self.has_bias = False
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        dtype = x.dtype
+        out_device = x.device
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = x @ self.w
+        if self.has_bias:
+            x = x + self.bias
+        x = x.to(dtype=dtype, device=out_device)
+        return x
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
+        if device is None: device = self.device
+        if w is None: w = self.load_weight(device=device)
+
+        if isinstance(w, nn.Parameter):
+            self.w = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T
+            self.has_bias = False
+        elif isinstance(w, tuple):
+            self.w = w[0].to(dtype=self.dtype).view(self.out_features, self.in_features).T
+            self.bias = w[1].to(dtype=self.dtype)
+            self.has_bias = True
+        else:
+            raise ValueError("Invalid weight type")
+        # self.linear = self.linear.to(device)
+        self.w = self.w.to(device)
+        if self.has_bias:
+            self.bias = self.bias.to(device)
+
+    def unload(self):
+        if self.w is not None:
+            self.w = None
+        if self.has_bias:
+            self.bias = None
+
+
+class QuantizedLinearMarlin(QuantizedLinearBase):
+    marlin_q_w: torch.Tensor
+    marlin_s: torch.Tensor
+    g_idx: torch.Tensor
+    sort_indices: torch.Tensor
+    has_bias: bool
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module = None,
+        device: str = "cuda",
+        num_bits: int = 4,  # 4-bit/8-bit is supported
+        group_size: int = 64,  # -1, 32, 64, 128
+        act_order: bool = False,
+        is_k_full=True,
+        **kwargs,
+    ):
+        assert device.lower() != "cpu", "Marlin quantized linear only supports GPU device"
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.num_bits = num_bits
+        self.group_size = group_size
+        self.act_order = act_order
+        self.is_k_full = is_k_full
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = "cuda"):
+        if device is None: device = self.device
+        assert device.lower() != "cpu", "Marlin quantized linear only supports GPU device"
+        if w is None: w = self.load_weight(device=device)
+
+        if isinstance(w, nn.Parameter):
+            # pad weight
+            weight = w.view(self.out_features, self.in_features).T
+            self.has_bias = False
+        elif isinstance(w, tuple):
+            w = list(w)
+            weight = w[0].view(self.out_features, self.in_features).T
+            self.bias = w[1]
+            self.has_bias = True
+        else:
+            raise ValueError("Invalid weight type")
+        weight = weight.to(device)
+        if self.has_bias:
+            self.bias = self.bias.to(device)
+        # Pack Marlin linear
+        w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, _ = marlin_quantize(
+            weight, self.num_bits, self.group_size, self.act_order
+        )
+        self.workspace = MarlinWorkspace(
+            self.out_features, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
+        )
+        self.marlin_q_w = marlin_q_w
+        self.marlin_s = marlin_s
+        self.g_idx = g_idx
+        self.sort_indices = sort_indices
+        self.k = weight.shape[0]
+        self.n = weight.shape[1]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Only support input x as BF16 and FP16
+        x = x.to(self.device)
+        orig_shape = list(x.shape)
+        orig_dtype = x.dtype
+        x = x.reshape(-1, x.shape[-1])
+        marlin_s = self.marlin_s.to(x.dtype)
+        x = KTransformersOps.gptq_marlin_gemm(
+            x,
+            self.marlin_q_w,
+            marlin_s,
+            self.g_idx,
+            self.sort_indices,
+            self.workspace.scratch,
+            self.num_bits,
+            x.shape[0],
+            self.n,
+            x.shape[-1],
+            self.is_k_full,
+        )
+        if self.has_bias:
+            x = x + self.bias
+        orig_shape[-1] = self.n
+        return x.reshape(orig_shape).to(orig_dtype)
+
+    def unload(self):
+
+        if self.has_bias:
+            self.bias = None
+        self.marlin_q_w = None
+        self.marlin_s = None
+        self.g_idx = None
+        self.sort_indices = None
+        self.workspace = None
+    
+LINEAR_MAP = {
+    "QuantizedLinearMarlin": QuantizedLinearMarlin,
+    "QuantizedLinearTorch": QuantizedLinearTorch,
+    "QuantizedLinearTorch": QuantizedLinearTorch,
+}
+
+class KTransformerLinear(BaseInjectedModule, QuantizedLinearBase):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module,
+        device: str = "cuda",
+        generate_device: str = "cuda",
+        generate_op: str| None = "QuantizedLinearMarlin",
+        prefill_device: str = "cuda",
+        prefill_op: str| None = "QuantizedLinearTorch",
+        **kwargs,
+    ):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        QuantizedLinearBase.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
+        # build all the linear operators
+        if prefill_op is not None:
+            assert prefill_op in LINEAR_MAP, f"linear_type {prefill_op} not supported"
+            if prefill_op == "QuantizedLinearMarlin" and (orig_module.in_features%GPTQ_MARLIN_MIN_THREAD_N!=0 or orig_module.out_features%GPTQ_MARLIN_MIN_THREAD_N!=0):
+                print(f"This linear module's in_features or out_features is not divisible by GPTQ_MARLIN_MIN_THREAD_N({GPTQ_MARLIN_MIN_THREAD_N}), using QuantizedLinearTorch instead.")
+                print(f"module info: key:{key} orig_module:{orig_module}")
+                self.prefill_linear = QuantizedLinearTorch(key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+            else:
+                self.prefill_linear = LINEAR_MAP[prefill_op](key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        else:
+            self.prefill_linear = None
+
+        if generate_op is not None:
+            assert generate_op in LINEAR_MAP, f"linear_type {generate_op} not supported"
+            if generate_op == "QuantizedLinearMarlin" and (orig_module.in_features%GPTQ_MARLIN_MIN_THREAD_N!=0 or orig_module.out_features%GPTQ_MARLIN_MIN_THREAD_N!=0):
+                print(f"This linear module's in_features or out_features is not divisible by GPTQ_MARLIN_MIN_THREAD_N({GPTQ_MARLIN_MIN_THREAD_N}), using QuantizedLinearTorch instead.")
+                print(f"module info: key:{key} orig_module:{orig_module}")
+                self.generate_op = "QuantizedLinearTorch"
+                self.generate_linear = QuantizedLinearTorch(key, gguf_loader, config, orig_module, generate_device, **kwargs)
+            else:
+                self.generate_linear = LINEAR_MAP[generate_op](key, gguf_loader, config, orig_module, generate_device, **kwargs)
+        else:
+            self.generate_linear = None
+        self.device = device
+        self.mode = InferenceState.UNLOAD
+
+    def forward(self, x):
+        if self.mode == InferenceState.PREFILL:
+            assert self.prefill_linear is not None, "cpu linear is not initialized"
+            return self.prefill_linear.forward(x)
+        else:
+            assert self.generate_linear is not None, "gpu linear is not initialized"
+            return self.generate_linear.forward(x)
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, mode: InferenceState = InferenceState.GENERATE):
+        if not mode:
+            mode = InferenceState.GENERATE
+        # load to device
+        if mode == InferenceState.PREFILL:
+            self.generate_linear.unload()
+            self.prefill_linear.load(w=w)
+            self.device = self.prefill_linear.device 
+        elif mode == InferenceState.GENERATE:
+            self.prefill_linear.unload()
+            self.generate_linear.load(w=w)
+            self.device = self.generate_linear.device
+        elif mode == InferenceState.UNLOAD:
+            self.prefill_linear.unload()
+            self.generate_linear.unload()
+            self.device = "cpu"
+        else:
+            raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD")
+        self.mode = mode
+
+    def unload(self):
+        if self.prefill_linear is not None:
+            self.prefill_linear.unload()
+        if self.generate_linear is not None:
+            self.generate_linear.unload()
+        self.device = self.generate_linear.device
+
+    def set_inference_mode(self, mode: InferenceState):
+        if not mode: 
+            mode = InferenceState.GENERATE
+        if mode == InferenceState.GENERATE:
+            self.load(mode=InferenceState.GENERATE)
+        elif mode == InferenceState.PREFILL:
+            self.load(mode=InferenceState.PREFILL)
+        elif mode == InferenceState.UNLOAD:
+            self.unload()
+        else:
+            raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD")

ktransformers/optimize/optimize.py

+'''
+Description  :  
+Author       : Boxin Zhang
+Version      : 0.1.0
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+from typing import Mapping, List
+import torch
+import yaml
+import re
+from torch import nn
+from transformers import AutoConfig
+from transformers.configuration_utils import PretrainedConfig
+# from operators import BaseInjectedModule
+from ktransformers.util.custom_gguf import GGUFLoader, translate_name_to_gguf
+from ktransformers.util.utils import set_module, load_weights
+import itertools
+
+def inject(module, local_optimization_dict, model_config:AutoConfig ,gguf_loader:GGUFLoader, prefix=''):
+    for name, child in module._modules.items():
+        if child is not None:
+            child_prefix = prefix + name
+            if child_prefix in local_optimization_dict:
+                inject_module_meta=local_optimization_dict[child_prefix]
+                if isinstance(inject_module_meta, Mapping):
+                    import_path = inject_module_meta["class"].split(".")
+                    import_module_name = ".".join(import_path[:-1])
+                    import_class_name = import_path[-1]
+                    module_cls=getattr(__import__(import_module_name, fromlist=[""]), import_class_name)
+                    print(f"Injecting {child_prefix} as", import_module_name, ".", import_class_name)
+                    inject_module=module_cls(key = inject_module_meta["key"], gguf_loader = gguf_loader, config = model_config, orig_module=child, device = inject_module_meta["device"], **inject_module_meta["kwargs"])
+                    set_module(module, name, inject_module)
+                elif isinstance(inject_module_meta, str):
+                    assert inject_module_meta=="default", "for str inject_module_meta, only support \"default\"."
+                else:
+                    raise Exception("inject_module_meta must be a dict or str")
+                child_prefix += "."
+                child_optimization_dict = {k: v for k, v in local_optimization_dict.items() if k.startswith(child_prefix)}
+                inject(child, child_optimization_dict, model_config, gguf_loader, child_prefix)
+
+def del_meta(module:nn.Module):
+    #print("default loading weights", prefix)
+    persistent_buffers = {k: v for k, v in module._buffers.items() if k not in module._non_persistent_buffers_set}
+    local_name_params = itertools.chain(module._parameters.items(), persistent_buffers.items())
+    local_state = {k: v for k, v in local_name_params if v is not None}
+    for name, param in local_state.items():
+        if param.device == "meta" or param.device == torch.device("meta"):
+            module.__delattr__(name)
+    for name, child in module._modules.items():
+        del_meta(child)
+
+def gen_optimize_config(module: nn.Module, out_data: Mapping, rule_list: List, prefix: str="", default_device: str = "cuda:0"):
+    module_name = prefix[:-1]
+    translated_name = translate_name_to_gguf(prefix)[:-1]
+    #print("gen_optimize_config", prefix, module_name, translated_name)
+    recursive = True
+    for rule in rule_list:
+        #print(rule)
+        match_meta = rule["match"]
+        if "class" in match_meta:
+            import_path = match_meta["class"].split(".")
+            import_module_name = ".".join(import_path[:-1])
+            import_class_name = import_path[-1]
+            module_cls=getattr(__import__(import_module_name, fromlist=[""]), import_class_name)
+            if not isinstance(module, module_cls):
+                continue
+        if "name" in match_meta:
+            if re.search(match_meta["name"], module_name) is None:
+                continue
+        replace_meta = rule["replace"]
+        out_data[module_name]={"key": translated_name,
+                               "class": replace_meta["class"],
+                               "device": replace_meta["device"] if "device" in replace_meta else default_device,
+                               "kwargs": replace_meta["kwargs"] if "kwargs" in replace_meta else dict()}
+        if "recursive" in rule:
+            recursive = bool(rule["recursive"])
+            
+    if module_name not in out_data:
+        out_data[module_name]="default"
+
+    #print(out_data[module_name])
+    #input()
+
+    if recursive:
+        for name, child in module._modules.items():
+            if child is not None:
+                child_prefix = prefix + name + "."
+                gen_optimize_config(child, out_data, rule_list, child_prefix)
+    
+
+def optimize_and_load_gguf(module: nn.Module, rule_file: str, gguf_path: str, model_config: PretrainedConfig, default_device: str = "cuda:0"):
+    with open(rule_file, 'r', encoding='utf-8') as f:
+        rule_list = yaml.load(f.read(), Loader=yaml.FullLoader)
+    
+    optimize_config = dict()
+    gen_optimize_config(module, optimize_config, rule_list, default_device = default_device)
+    
+    gguf_loader=GGUFLoader(gguf_path)
+    with torch.device("meta"):
+        inject(module, optimize_config, model_config, gguf_loader)
+    load_weights(module, gguf_loader)
+    del_meta(module)

ktransformers/optimize/optimize_rules/DeepSeek-V2-Chat.yaml

+- match:
+    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
+- match:
+    name: "^model\\.layers\\.(?!.*self_attn).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformerLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "QuantizedLinearMarlin"
+      prefill_op: "QuantizedLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
+  replace:
+    class: ktransformers.operators.experts.DeepseekV2MoEInjected     # mlp module with custom forward function
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
+    device: "cpu"   # which devices to load this module when initializing
+    kwargs:
+      prefill_device: "cuda"
+      prefill_mlp_type: "MLPExpertsTorch"
+      generate_device: "cpu"
+      generate_mlp_type:  "MLPCPUExperts"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.DeepseekV2AttentionInjected # optimized MLA implementation
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.layer_wise_prefill.DeepseekV2ModelPerLayerPrefill"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill

ktransformers/optimize/optimize_rules/Qwen2-57B-A14B-Instruct.yaml

+- match:
+    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.RotaryEmbedding
+- match:
+    name: "^model\\.layers\\..*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformerLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "QuantizedLinearMarlin"
+      prefill_op: "QuantizedLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeSparseMoeBlock
+  replace:
+    class: ktransformers.operators.experts.Qwen2MoeSparseMoeBlockInjected     # mlp module with custom forward function
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
+    device: "cpu"   # which devices to load this module when initializing
+    kwargs:
+      prefill_device: "cuda"
+      prefill_mlp_type: "MLPExpertsTorch"
+      generate_device: "cpu"
+      generate_mlp_type:  "MLPCPUExperts"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.layer_wise_prefill.Qwen2MoeModelPerLayerPrefill"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill

ktransformers/server/api/__init__.py

+from fastapi import APIRouter
+
+from .ollama import router as ollama_router
+from .openai import router as openai_router,post_db_creation_operations
+from .web import router as web_router
+
+router = APIRouter()
+router.include_router(ollama_router)
+router.include_router(openai_router)
+router.include_router(web_router)

ktransformers/server/api/ollama/__init__.py

+from fastapi import APIRouter
+
+from .completions import router as completions_router
+
+router = APIRouter()
+router.include_router(completions_router)

ktransformers/server/api/ollama/completions.py

+from datetime import datetime
+from http.client import NOT_IMPLEMENTED
+import json
+from time import time
+from uuid import uuid4
+from typing import List, Optional
+
+from fastapi import APIRouter, Request
+from pydantic import BaseModel, Field
+
+from ktransformers.server.config.config import Config
+from ktransformers.server.utils.create_interface import get_interface
+from ktransformers.server.schemas.assistants.streaming import check_link_response
+from ktransformers.server.backend.base import BackendInterfaceBase
+router = APIRouter(prefix='/api')
+
+
+# https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-completion
+class OllamaGenerateCompletionRequest(BaseModel):
+    model: str = Field(..., description="The model name, which is required.")
+    prompt: Optional[str] = Field(
+        None, description="The prompt to generate a response for.")
+    images: Optional[List[str]] = Field(
+        None, description="A list of base64-encoded images for multimodal models such as llava.")
+    # Advanced parameters
+    format: Optional[str] = Field(
+        None, description="The format to return a response in, accepted value is json.")
+    options: Optional[dict] = Field(
+        None, description="Additional model parameters as listed in the documentation.")
+    system: Optional[str] = Field(
+        None, description="System message to override what is defined in the Modelfile.")
+    template: Optional[str] = Field(
+        None, description="The prompt template to use, overriding what is defined in the Modelfile.")
+    context: Optional[str] = Field(
+        None, description="The context parameter from a previous request to keep a short conversational memory.")
+    stream: Optional[bool] = Field(
+        None, description="If false, the response will be returned as a single response object.")
+    raw: Optional[bool] = Field(
+        None, description="If true, no formatting will be applied to the prompt.")
+    keep_alive: Optional[str] = Field(
+        "5m", description="Controls how long the model will stay loaded into memory following the request.")
+
+
+class OllamaGenerationStreamResponse(BaseModel):
+    model: str
+    created_at: str
+    response: str
+    done: bool = Field(...)
+
+
+class OllamaGenerationResponse(BaseModel):
+    pass
+
+
+@router.post("/generate", tags=['ollama'])
+async def generate(request: Request, input: OllamaGenerateCompletionRequest):
+    id = str(uuid4())
+
+    interface: BackendInterfaceBase = get_interface()
+    print(f'COMPLETION INPUT:----\n{input.prompt}\n----')
+
+    config = Config()
+
+    if input.stream:
+        async def inner():
+            async for token in interface.inference(input.prompt,id): 
+                d = OllamaGenerationStreamResponse(model=config.model_name,created_at=str(datetime.now()),response=token,done=False)
+                yield d.model_dump_json()+'\n' 
+                # d = {'model':config.model_name,'created_at':"", 'response':token,'done':False}
+                # yield f"{json.dumps(d)}\n"
+            # d = {'model':config.model_name,'created_at':"", 'response':'','done':True}
+            # yield f"{json.dumps(d)}\n"
+            d = OllamaGenerationStreamResponse(model=config.model_name,created_at=str(datetime.now()),response='',done=True)   
+            yield d.model_dump_json()+'\n'
+        return check_link_response(request,inner())
+    else:
+        raise NotImplementedError
+
+# https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-chat-completion
+
+
+class OllamaChatCompletionRequest(BaseModel):
+    pass
+
+
+class OllamaChatCompletionStreamResponse(BaseModel):
+    pass
+
+
+class OllamaChatCompletionResponse(BaseModel):
+    pass
+
+
+@router.post("/chat", tags=['ollama'])
+async def chat(request: Request, input: OllamaChatCompletionRequest):
+    raise NotImplementedError
+
+
+# https://github.com/ollama/ollama/blob/main/docs/api.md#list-local-models
+class OllamaModel(BaseModel):
+    name: str
+    modified_at: str
+    size: int
+    # TODO: fill the rest correctly
+
+
+# mock ollama
+@router.get("/tags",tags=['ollama'])
+async def tags():
+    config = Config()
+    # TODO: fill this correctly, although it does not effect Tabby
+    return {"models": [OllamaModel(name=config.model_name, modified_at="123", size=123)]}
+
+class OllamaModelInfo(BaseModel):
+    # TODO: fill this correctly
+    pass
+
+class OllamaShowRequest(BaseModel):
+    name: str = Field(..., description="Name of the model to show")
+    verbose: Optional[bool] = Field(
+        None, description="If set to true, returns full data for verbose response fields")
+
+class OllamaShowDetial(BaseModel):
+    parent_model: str
+    format: str
+    family: str
+    families: List[str]
+    parameter_size: str
+    quantization_level: str
+
+class OllamaShowResponse(BaseModel):
+    modelfile: str
+    parameters: str
+    template: str
+    details: OllamaShowDetial
+    model_info: OllamaModelInfo
+
+
+
+
+@router.post("/show", tags=['ollama'])
+async def show(request: Request, input: OllamaShowRequest):
+    config = Config()
+    # TODO: Add more info in config to return, although it does not effect Tabby
+    return OllamaShowResponse(
+        modelfile = "# Modelfile generated by ...",
+        parameters = " ",
+        template = " ",
+        details = OllamaShowDetial(
+            parent_model = " ",
+            format = "gguf",
+            family = " ",
+            families = [
+                " " 
+            ],
+            parameter_size = " ",
+            quantization_level = " "
+        ),
+        model_info = OllamaModelInfo()
+    )
\ No newline at end of file

ktransformers/server/api/openai/__init__.py

+from fastapi import APIRouter
+
+from .assistants import router as assistants_router,create_default_assistant
+from .endpoints.chat import router as chat_router
+from .legacy import router as legacy_router
+
+router = APIRouter(prefix='/v1')
+
+
+router.include_router(assistants_router)
+router.include_router(chat_router)
+router.include_router(legacy_router)
+
+def post_db_creation_operations():
+    create_default_assistant()

ktransformers/server/api/openai/assistants/__init__.py

+from fastapi import APIRouter
+
+from .assistants import router as assistants_router, create_default_assistant
+from .messages import router as messages_router
+from .runs import router as runs_router
+from .threads import router as threads_router
+
+router = APIRouter()
+
+threads_router.include_router(runs_router)
+threads_router.include_router(messages_router)
+
+router.include_router(assistants_router)
+router.include_router(threads_router)

ktransformers/server/api/openai/assistants/assistants.py

+from typing import Optional
+
+from fastapi import APIRouter
+from fastapi.testclient import TestClient
+
+from ktransformers.server.crud.assistants.assistants import AssistantDatabaseManager
+from ktransformers.server.crud.assistants.runs import RunsDatabaseManager
+from ktransformers.server.schemas.assistants.assistants import AssistantCreate, AssistantModify, ObjectID, AssistantBuildStatus, AssistantObject
+from ktransformers.server.schemas.base import DeleteResponse, Order
+from ktransformers.server.config.log import logger
+
+
+router = APIRouter(prefix="/assistants")
+assistant_manager = AssistantDatabaseManager()
+runs_manager = RunsDatabaseManager()
+
+
+@router.post("/", tags=['openai'])
+async def create_assistant(
+    assistant: AssistantCreate,
+):
+    return assistant_manager.db_create_assistant(assistant).as_api_response()
+
+
+@router.get("/", tags=['openai'])
+async def list_assistants(
+    limit: Optional[int] = 20,
+    order: Order = Order.DESC,
+    after: Optional[str] = None,
+    before: Optional[str] = None,
+):
+    return [assistant.as_api_response() for assistant in assistant_manager.db_list_assistants(limit, order)]
+
+# list assistant with status
+
+
+@router.get("/status", tags=['openai-ext'])
+async def list_assistants_with_status(
+    limit: Optional[int] = 20,
+    order: Order = Order.DESC,
+    after: Optional[str] = None,
+    before: Optional[str] = None,
+):
+    return assistant_manager.db_list_assistants(limit, order)
+
+
+@router.get("/{assistant_id}", tags=['openai'])
+async def retrieve_assistant(
+    assistant_id: str,
+):
+    return assistant_manager.db_get_assistant_by_id(assistant_id).as_api_response()
+
+
+@router.post("/{assistant_id}", tags=['openai'])
+async def modify_assistant(
+    assistant_id: str,
+    assistant: AssistantModify,
+):
+    return assistant_manager.db_update_assistant_by_id(assistant_id, assistant).as_api_response()
+
+
+@router.delete("/{assistant_id}", tags=['openai'], response_model=DeleteResponse)
+async def delete_assistant(assistant_id: str):
+    assistant_manager.db_delete_assistant_by_id(assistant_id)
+    return DeleteResponse(id=assistant_id, object="assistant.deleted")
+
+
+@router.get("/{assistant_id}/related_thread", tags=['openai'])
+async def get_related_thread(assistant_id: ObjectID):
+    assistant = assistant_manager.db_get_assistant_by_id(assistant_id)
+    return assistant.get_related_threads_ids()
+
+
+def create_default_assistant():
+    logger.info('Creating default assistant')
+    if assistant_manager.db_count_assistants() == 0:
+        default_assistant = assistant_manager.db_create_assistant(AssistantCreate(name="KT Assistant",
+                                                                                  model="default model",
+                                                                                  instructions="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  """ +
+                                                                                  """Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. """ +
+                                                                                  """Please ensure that your responses are socially unbiased and positive in nature."""))
+        default_assistant.build_status.status = AssistantBuildStatus.Status.completed
+        default_assistant.sync_db()
+
+
+# unit test
+client = TestClient(router)
+
+
+def test_create_assistant():
+    ass_create = AssistantCreate(model="awesome model", instructions="hello")
+
+    res = client.post("/", json=ass_create.model_dump(mode="json"))
+
+    assert res.status_code == 200
+    assistant = AssistantObject.model_validate(res.json())
+
+    assert assistant.model == ass_create.model
+    assert assistant.instructions == ass_create.instructions
+
+    res = client.get(f"/{assistant.id}")
+    ass1 = AssistantObject.model_validate(res.json())
+    assert assistant == ass1