# coding=utf-8 # Copyright 2025 The Moonshot AI Team, DeepSeek-AI, and HuggingFace Inc. team. All rights reserved. # # The code is based on llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py), but modified for KimiVL. # # Licensing Information: # - Code derived from llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py) is licensed under the Apache License, Version 2.0. # - Other parts of the code are licensed under the MIT License. # # Apache License, Version 2.0: # 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. # # MIT License: # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """PyTorch KimiVL model.""" import math import warnings from typing import List, Optional, Tuple, Union from copy import deepcopy from typing import Union, Tuple, Sequence, Optional, List import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint import torch.distributed as dist from torch.nn import CrossEntropyLoss from transformers.activations import GELUActivation, ACT2FN, PytorchGELUTanh from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_utils import PreTrainedModel from transformers.generation.utils import GenerationMixin from transformers.models.llava.modeling_llava import LlavaCausalLMOutputWithPast from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.pytorch_utils import 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_kimi_vl import MoonViTConfig, DeepseekV3Config, KimiVLConfig 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 # 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__) def multihead_attention( q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_cu_seqlens: Optional[torch.Tensor] = None, k_cu_seqlens: Optional[torch.Tensor] = None, ): """Multi-head attention using flash attention 2. Args: q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim), or (tot_seqlens, num_heads, head_dim) if packing. q_cu_seqlens (torch.Tensor): cumulative sequence lengths of q. The first element should be 0 and the last element should be q.shape[0]. k_cu_seqlens (torch.Tensor): cumulative sequence lengths of k. The first element should be 0 and the last element should be k.shape[0]. Returns: output: shape (batch_size, seqlen, dim) or (tot_seqlens, dim) if packing, where dim = num_heads * head_dim """ # Unified format legal check assert q.dim() == k.dim() == v.dim() == 3, "q, k, v must have 3 dims" assert q_cu_seqlens[-1] == q.shape[0], "q_cu_seqlens must sum to q.shape[0]" assert ( k_cu_seqlens[-1] == k.shape[0] == v.shape[0] ), "k_cu_seqlens must sum to k.shape[0]" assert q.dtype in [ torch.bfloat16, torch.float16, ], f"unsupported dtype {q.dtype} for multihead attn" max_seqlen_q = (q_cu_seqlens[1:] - q_cu_seqlens[:-1]).max().item() max_seqlen_k = (k_cu_seqlens[1:] - k_cu_seqlens[:-1]).max().item() attn_out = flash_attn_varlen_func( q, k, v, q_cu_seqlens, k_cu_seqlens, max_seqlen_q, max_seqlen_k, causal=False, ) attn_out = attn_out.flatten(start_dim=-2) return attn_out def sdpa_attention( q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_cu_seqlens: Optional[torch.Tensor] = None, k_cu_seqlens: Optional[torch.Tensor] = None, ) -> torch.Tensor: """SDPA attention. Args: q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim), or (tot_seqlens, num_heads, head_dim) if packing. """ seq_length = q.shape[0] attention_mask = torch.zeros( [1, seq_length, seq_length], device=q.device, dtype=torch.bool ) for i in range(1, len(q_cu_seqlens)): attention_mask[ ..., q_cu_seqlens[i - 1] : q_cu_seqlens[i], q_cu_seqlens[i - 1] : q_cu_seqlens[i], ] = True q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_output = F.scaled_dot_product_attention(q, k, v, attention_mask, dropout_p=0.0) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) return attn_output def eager_attention( q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_cu_seqlens: Optional[torch.Tensor] = None, k_cu_seqlens: Optional[torch.Tensor] = None, ) -> torch.Tensor: seq_length = q.shape[0] attention_mask = torch.zeros( [1, seq_length, seq_length], device=q.device, dtype=torch.bool ) for i in range(1, len(q_cu_seqlens)): attention_mask[ ..., q_cu_seqlens[i - 1] : q_cu_seqlens[i], q_cu_seqlens[i - 1] : q_cu_seqlens[i], ] = True q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weight = q @ k.transpose(-2, -1) / math.sqrt(q.shape[-1]) attn_weight += attention_mask attn_weight = torch.softmax(attn_weight, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = attn_weight @ v attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) return attn_output VL_VISION_ATTENTION_FUNCTIONS = { "flash_attention_2": multihead_attention, "sdpa": sdpa_attention, "eager": eager_attention, } def _apply_rope_input_validation(x, freqs_cis): assert x.ndim == freqs_cis.ndim + 1, (x.shape, freqs_cis.shape) assert x.shape[:-2] == freqs_cis.shape[:-1], (x.shape, freqs_cis.shape) assert x.shape[-1] == 2 * freqs_cis.shape[-1], (x.shape, freqs_cis.shape) assert freqs_cis.dtype == torch.complex64, freqs_cis.dtype def apply_rope( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor]: """ Args: (The leading dimensions of all inputs should be the same) xq: query, tensor of shape (..., num_heads, head_dim) xk: key, tensor of shape (..., num_heads, head_dim) freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid. Returns: xq_out, xk_out: tensors of shape (..., num_heads, head_dim) """ _apply_rope_input_validation(xq, freqs_cis) _apply_rope_input_validation(xk, freqs_cis) freqs_cis = freqs_cis.unsqueeze(-2) # ..., 1, head_dim/2 # ..., num_heads, head_dim/2 xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2)) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim return xq_out.type_as(xq), xk_out.type_as(xk) class Learnable2DInterpPosEmb(nn.Module): def __init__( self, height: int, width: int, dim: int, interpolation_mode: str = "bicubic" ) -> None: super().__init__() self.height = height self.width = width self.interpolation_mode = interpolation_mode self.weight = nn.Parameter(torch.empty(height, width, dim)) self.reset_parameters() def reset_parameters(self): nn.init.normal_(self.weight) def forward(self, x: torch.Tensor, grid_hws: torch.Tensor) -> torch.Tensor: pos_embs = [] for shape in grid_hws.tolist(): if shape == self.weight.shape[:-1]: pos_embs.append(self.weight.flatten(end_dim=1)) else: pos_embs.append( F.interpolate( self.weight.permute((2, 0, 1)).unsqueeze(0), size=shape, mode=self.interpolation_mode, ) .squeeze(0) .permute((1, 2, 0)) .flatten(end_dim=1) ) out = x + torch.cat(pos_embs) return out class MoonVisionPatchEmbed(nn.Module): def __init__( self, out_dim: int, in_dim: int = 3, patch_size: Union[int, Tuple[int, int]] = (14, 14), pos_emb_height: int = 14, pos_emb_width: int = 14, ): super().__init__() assert isinstance( patch_size, (int, Sequence) ), f"Invalid patch_size type: {type(patch_size)}" if isinstance(patch_size, int): patch_size = (patch_size, patch_size) assert ( len(patch_size) == 2 ), f"Expected patch_size to be a tuple of 2, got {patch_size}" self.patch_size = patch_size self.proj = nn.Conv2d( in_dim, out_dim, kernel_size=patch_size, stride=patch_size ) self.pos_emb = Learnable2DInterpPosEmb( height=pos_emb_height, width=pos_emb_width, dim=out_dim ) def forward(self, x: torch.Tensor, grid_hws: torch.Tensor) -> torch.Tensor: """ Args: x (L, Channels): input tensor grid_hws (N, 2): grid height and width Returns: (L, Cout) tensor """ x = self.proj(x).view(x.size(0), -1) # apply positional embedding x = self.pos_emb(x, grid_hws) return x class Rope2DPosEmb(nn.Module): """2D rotary position embedding with multi-resolution support. This class is intended to be used in the following way: 1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis. 2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration. 3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation. The rope is shared across all attention layers and all heads. Refs: - RoFormer: https://arxiv.org/abs/2104.09864 - VisionLLaMA: https://arxiv.org/abs/2403.00522 - https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py Args: dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed) max_height (int): the maximum height of the 2D grid max_width (int): the maximum width of the 2D grid theta_base (float): the base of the theta device (str): the device to store the precomputed cis """ def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000): super().__init__() self.dim = dim assert self.dim % 4 == 0, "dim must be divisible by 4" self.max_height = max_height self.max_width = max_width self.theta_base = theta_base self.freqs_cis = None def extra_repr(self): return f"dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}" def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor: """Calculate the cis(freqs) for each position in the 2D grid. Return: complex tensor of shape (max_height, max_width, dim//2) and value: height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim)) weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim)) with (i in [0, dim//4)) note: `cis` is a mathematical notation defined by cis x = cos x + i sin x, """ N = self.max_height * self.max_width flat_pos = torch.arange(0, N).float().to(device) x_pos = flat_pos % self.max_width y_pos = flat_pos // self.max_width dim_range = ( torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device) ) # C/4 freqs = 1.0 / (self.theta_base ** (dim_range / self.dim)) x_freqs = torch.outer(x_pos, freqs).float() # N, C/4 y_freqs = torch.outer(y_pos, freqs).float() # N, C/4 x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs) # N, C/4 y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs) # N, C/4 # N, C/4, 2 freqs_cis = torch.cat( [x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1 ) # max_height, max_width, C/2 freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1) return freqs_cis def get_freqs_cis(self, grid_hws: torch.Tensor) -> torch.Tensor: """ Args: grid_hws (torch.Tensor): grid height and width Returns: freqs_cis: tensor of shape (sum(t * height * width), dim//2) """ if self.freqs_cis is None: self.freqs_cis = self._precompute_freqs_cis(grid_hws.device) shapes = grid_hws.tolist() assert all( 1 <= h <= self.max_height and 1 <= w <= self.max_width for h, w in shapes ), ( shapes, self.max_height, self.max_width, ) freqs_cis = torch.cat( [self.freqs_cis[:h, :w].reshape(-1, self.dim // 2) for h, w in shapes], dim=0, ) return freqs_cis class MLP2(nn.Module): """ Args: dims: [in_dim, hidden_dim, out_dim] bias: whether to use bias in linear layer. """ def __init__(self, dims: list[int], activation, bias=True): super().__init__() assert len(dims) == 3 self.fc0 = nn.Linear(dims[0], dims[1], bias=bias) self.fc1 = nn.Linear(dims[1], dims[2], bias=bias) self.activation = activation for m in [self.fc0, self.fc1]: nn.init.trunc_normal_(m.weight, std=math.sqrt(2 / m.in_features)) if m.bias is not None: nn.init.zeros_(m.bias) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.fc0(x) x = self.activation(x) return self.fc1(x) class MoonVitEncoderLayer(nn.Module): def __init__( self, num_heads: int, hidden_dim: int, mlp_dim: int, *, attn_implementation: str = "eager", activation=F.gelu, attn_bias: bool = False, ): super().__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads self.attn_implementation = attn_implementation self.norm0 = nn.LayerNorm(hidden_dim) self.norm1 = nn.LayerNorm(hidden_dim) self.mlp = MLP2([hidden_dim, mlp_dim, hidden_dim], activation) self.wqkv = nn.Linear(hidden_dim, hidden_dim * 3, bias=attn_bias) self.wo = nn.Linear(hidden_dim, hidden_dim, bias=attn_bias) def attention_qkvpacked( self, x: torch.Tensor, cu_seqlens: torch.Tensor, rope_freqs_cis: Optional[torch.Tensor] = None, ): """ Args: x (torch.Tensor): (batch_size, seqlen, hidden_dim) cu_seqlens (torch.Tensor): """ xqkv = self.wqkv(x) qkv_shape = xqkv.size()[:-1] + ( 3, self.num_heads, self.hidden_size_per_attention_head, ) # xqkv: (batch_size, seqlen, 3, nheads, headdim) xqkv = xqkv.view(*qkv_shape) xq, xk, xv = torch.unbind(xqkv, dim=-3) xq, xk = apply_rope(xq, xk, rope_freqs_cis) attn_func = VL_VISION_ATTENTION_FUNCTIONS[self.attn_implementation] attn_out = attn_func( xq, xk, xv, q_cu_seqlens=cu_seqlens, k_cu_seqlens=cu_seqlens ) attn_out = self.wo(attn_out) return attn_out def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rope_freqs_cis: Union[torch.Tensor, None] = None, ) -> torch.Tensor: """ Args: hidden_states: non-packed (B, N, D) or packed (L, D). if non-packed, seqlens should be None, if packed, seqlens should be set Returns: output: same shape of input, non-packed (B, N, D) for non-packed input, (L, D) for packed input """ residual = hidden_states hidden_states = self.norm0(hidden_states) attn_out = self.attention_qkvpacked( hidden_states, cu_seqlens, rope_freqs_cis=rope_freqs_cis ) hidden_states = residual + attn_out residual = hidden_states hidden_states = self.mlp(self.norm1(hidden_states)) hidden_states = residual + hidden_states return hidden_states class MoonVitEncoder(nn.Module): def __init__( self, hidden_dim: int, num_layers: int, block_cfg: dict, ) -> None: super().__init__() self.rope_2d = Rope2DPosEmb( block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512 ) self.blocks = nn.ModuleList( [MoonVitEncoderLayer(**block_cfg) for _ in range(num_layers)] ) self.final_layernorm = nn.LayerNorm(hidden_dim) def forward( self, hidden_states: torch.Tensor, grid_hws: torch.Tensor ) -> torch.Tensor: rope_freqs_cis = self.rope_2d.get_freqs_cis(grid_hws=grid_hws) lengths = torch.cat( ( torch.zeros(1, device=hidden_states.device, dtype=grid_hws.dtype), grid_hws[:, 0] * grid_hws[:, 1], ) ) cu_seqlens = lengths.cumsum(dim=0, dtype=torch.int32) for _, block in enumerate(self.blocks): hidden_states = block( hidden_states, cu_seqlens, rope_freqs_cis=rope_freqs_cis ) hidden_states = self.final_layernorm(hidden_states) return hidden_states def patch_merger( x: torch.Tensor, grid_hws: torch.Tensor, merge_kernel_size: list[int, int] = (2, 2), ) -> List[torch.Tensor]: d_model = x.size(-1) outputs = [] pre_sum = 0 for x_shape in grid_hws.tolist(): height, width = x_shape[0], x_shape[1] # Get the current sequence seq = x[pre_sum : pre_sum + height * width] # Reshape along self.merge_kernel_size and concat to the last dimension kernel_height, kernel_width = merge_kernel_size new_height, new_width = height // kernel_height, width // kernel_width reshaped_seq = seq.view( new_height, kernel_height, new_width, kernel_width, d_model ) reshaped_seq = reshaped_seq.permute(0, 2, 1, 3, 4).contiguous() padded_seq = reshaped_seq.view( new_height * new_width, kernel_height * kernel_width, -1 ) outputs.append(padded_seq) pre_sum += height * width return outputs 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 DeepseekV3RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ DeepseekV3RMSNorm 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) class DeepseekV3RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / ( self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) ) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(), ) self.max_seq_len_cached = None def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange( self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype ) freqs = torch.outer(t, self.inv_freq.to(t.device)) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV3 class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding): """DeepseekV3RotaryEmbedding 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, ): 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->DeepseekV3 class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding): """DeepseekV3RotaryEmbedding 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, ): 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 DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding): 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, ): 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 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 dim = self.dim 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) t = torch.arange(seq_len, device=device, dtype=torch.float32) freqs = torch.outer(t, inv_freq) _mscale = float( yarn_get_mscale(self.scaling_factor, self.mscale) / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim) ) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer( "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False ) self.register_buffer( "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False ) # Copied from transformers.models.llama.modeling_llama.rotate_half def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) 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 DeepseekV3MLP(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): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) 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.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)) ) if self.topk_method == "noaux_tc": self.e_score_correction_bias = nn.Parameter( torch.empty((self.n_routed_experts)) ) 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 == "sigmoid": scores = logits.sigmoid() else: raise NotImplementedError( f"insupportable scoring function for MoE gating: {self.scoring_func}" ) # select top-k experts if self.topk_method == "noaux_tc": assert not self.training scores_for_choice = scores.view( bsz * seq_len, -1 ) + self.e_score_correction_bias.unsqueeze(0) group_scores = ( scores_for_choice.view(bsz * seq_len, self.n_group, -1) .topk(2, dim=-1)[0] .sum(dim=-1) ) # [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_for_choice.masked_fill( ~score_mask.bool(), 0.0 ) # [n, e] _, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False) topk_weight = scores.gather(1, topk_idx) else: raise NotImplementedError( f"insupportable TopK function for MoE gating: {self.topk_method}" ) # 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 topk_weight = ( topk_weight * self.routed_scaling_factor ) # must multiply the scaling factor return topk_idx, topk_weight class DeepseekV3MoE(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( [ ( DeepseekV3MLP( 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( [ DeepseekV3MLP( 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 = DeepseekV3MLP( config=config, intermediate_size=intermediate_size ) def forward(self, hidden_states): identity = hidden_states orig_shape = hidden_states.shape topk_idx, topk_weight = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) if not self.training: 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->DeepseekV3 class DeepseekV3Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: DeepseekV3Config, 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 = DeepseekV3RMSNorm(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 = DeepseekV3RMSNorm(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 = DeepseekV3RotaryEmbedding( 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 = DeepseekV3LinearScalingRotaryEmbedding( 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 = DeepseekV3DynamicNTKScalingRotaryEmbedding( 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 = DeepseekV3YarnRotaryEmbedding( 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, **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(value_states, seq_len=kv_seq_len) q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) 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} # 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 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()}" ) 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->DeepseekV3 class DeepseekV3FlashAttention2(DeepseekV3Attention): """ DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` 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, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # DeepseekV3FlashAttention2 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() 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 ) # 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(value_states, seq_len=kv_seq_len) q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) 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} # 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. (DeepseekV3RMSNorm 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_proj.weight.dtype if self.q_lora_rank is None else 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, 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, 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 DeepseekV3FlashAttention2 __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: 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": DeepseekV3Attention, "flash_attention_2": DeepseekV3FlashAttention2, } class DeepseekV3DecoderLayer(nn.Module): def __init__(self, config: DeepseekV3Config, 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 = ( DeepseekV3MoE(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 DeepseekV3MLP(config) ) self.input_layernorm = DeepseekV3RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.post_attention_layernorm = DeepseekV3RMSNorm( 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, **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, **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 DeepseekV3_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 ([`DeepseekV3Config`]): 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 DeepseekV3 Model outputting raw hidden-states without any specific head on top.", DeepseekV3_START_DOCSTRING, ) class DeepseekV3PreTrainedModel(PreTrainedModel): config_class = DeepseekV3Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["DeepseekV3DecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = 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_() DeepseekV3_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 DeepseekV3 Model outputting raw hidden-states without any specific head on top.", DeepseekV3_START_DOCSTRING, ) class DeepseekV3Model(DeepseekV3PreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`] Args: config: DeepseekV3Config """ def __init__(self, config: DeepseekV3Config): 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( [ DeepseekV3DecoderLayer(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 = DeepseekV3RMSNorm(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(DeepseekV3_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, ) -> 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") 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 position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device, ) position_ids = position_ids.unsqueeze(0) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = ( attention_mask if (attention_mask is not None and 0 in attention_mask) else None ) else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, ) # 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,) layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) 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, ) class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = DeepseekV3Model(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(DeepseekV3_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class="DeepseekV3Config" ) 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, 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, DeepseekV3ForCausalLM >>> model = DeepseekV3ForCausalLM.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, ) 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) 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, **kwargs, ): if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens max_cache_length = past_key_values.get_seq_length() 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 exclusivelly 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_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs @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 class MoonVitVLProjector(nn.Module): def __init__( self, in_channels: int, merge_kernel_size: list[int, int], hidden_act: str = "gelu", ln_eps: float = 1e-5, out_dim: int = 4096, ): super().__init__() self.hidden_size = in_channels * merge_kernel_size[0] * merge_kernel_size[1] self.pre_norm = nn.nn.LayerNorm(in_channels, eps=ln_eps) self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=True) self.act = ACT2FN[hidden_act] self.linear_2 = nn.Linear(self.hidden_size, out_dim, bias=True) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.pre_norm(hidden_states).view(-1, self.hidden_size) hidden_states = self.linear_1(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states class MoonVitPretrainedModel(PreTrainedModel): config_class = MoonViTConfig model_type = "moonvit" _no_split_modules = ["PackingTransformer"] _supports_flash_attn_2 = True _supports_sdpa = True def __init__(self, config: MoonViTConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) config = deepcopy(config) self.merge_kernel_size = config.merge_kernel_size self.patch_size = config.patch_size self.patch_embed = MoonVisionPatchEmbed( out_dim=config.hidden_size, patch_size=config.patch_size, pos_emb_height=config.init_pos_emb_height, pos_emb_width=config.init_pos_emb_width, ) self.encoder = MoonVitEncoder( hidden_dim=config.hidden_size, num_layers=config.num_hidden_layers, block_cfg={ "num_heads": config.num_attention_heads, "hidden_dim": config.hidden_size, "mlp_dim": config.intermediate_size, "activation": PytorchGELUTanh(), "attn_bias": True, "attn_implementation": config._attn_implementation, }, ) def forward( self, pixel_values: torch.Tensor, grid_hws: torch.Tensor ) -> torch.Tensor: """ Args: pixel_values (torch.Tensor): The input pixel values. grid_hws (torch.Tensor): The grid height and width. Returns: torch.Tensor: The output tokens. """ hidden_states = self.patch_embed(pixel_values, grid_hws) hidden_states = self.encoder(hidden_states, grid_hws) hidden_states = patch_merger( hidden_states, grid_hws, merge_kernel_size=self.merge_kernel_size ) return hidden_states class KimiVLMultiModalProjector(nn.Module): def __init__(self, config: KimiVLConfig): super().__init__() self.hidden_size = ( config.vision_config.hidden_size * config.vision_config.merge_kernel_size[0] * config.vision_config.merge_kernel_size[1] ) self.pre_norm = torch.nn.LayerNorm(config.vision_config.hidden_size, eps=1e-05) self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=True) self.act = GELUActivation() self.linear_2 = nn.Linear( self.hidden_size, config.text_config.hidden_size, bias=True ) def forward(self, image_features: list[torch.Tensor]) -> torch.Tensor: image_features = torch.cat(image_features, dim=0) hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size) hidden_states = self.linear_1(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states class KimiVLPreTrainedModel(PreTrainedModel, GenerationMixin): config_class = KimiVLConfig base_model_prefix = "model" _no_split_modules = ["MoonVitEncoderLayer", "DeepseekV3DecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True def _init_weights(self, module): # important: this ported version of Llava isn't meant for training from scratch - only # inference and fine-tuning - so the proper init weights code has been removed - the original codebase # https://github.com/haotian-liu/LLaVA/tree/main/llava should serve for that purpose std = ( self.config.initializer_range if hasattr(self.config, "initializer_range") else self.config.text_config.initializer_range ) if hasattr(module, "class_embedding"): module.class_embedding.data.normal_(mean=0.0, std=std) if isinstance(module, (nn.Linear, nn.Conv2d)): 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_() @property def _supports_sdpa(self): """ Retrieve language_model's attribute to check whether the model supports SDPA or not. """ return self.language_model._supports_sdpa class KimiVLForConditionalGeneration(KimiVLPreTrainedModel, GenerationMixin): def __init__(self, config: KimiVLConfig): super().__init__(config) vision_config: MoonViTConfig = config.vision_config self.vision_tower = MoonVitPretrainedModel(vision_config) self.multi_modal_projector = KimiVLMultiModalProjector(config) self.language_model = DeepseekV3ForCausalLM(config.text_config) self.post_init() def get_input_embeddings(self): return self.language_model.get_input_embeddings() def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value) def get_output_embeddings(self): return self.language_model.get_output_embeddings() def set_output_embeddings(self, new_embeddings): self.language_model.set_output_embeddings(new_embeddings) def set_decoder(self, decoder): self.language_model.set_decoder(decoder) def get_decoder(self): return self.language_model.get_decoder() def tie_weights(self): return self.language_model.tie_weights() def resize_token_embeddings( self, new_num_tokens: int | None = None, pad_to_multiple_of=None ) -> nn.Embedding: model_embeds = self.language_model.resize_token_embeddings( new_num_tokens, pad_to_multiple_of ) # update vocab size self.config.text_config.vocab_size = model_embeds.num_embeddings self.vocab_size = model_embeds.num_embeddings return model_embeds def _merge_with_image_features( self, inputs_embeds: torch.Tensor, input_ids: torch.Tensor, image_features: torch.Tensor, ): """ Args: inputs_embeds (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length, input_embed_dim)`): The input embeddings. input_ids (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`): The input ids. image_features (:obj:`torch.Tensor` of shape :obj:`(image_token_nums, image_feature_dim)`): The image features to merge with the input embeddings. """ image_token_index: int = self.config.media_placeholder_token_id batch_size, sequence_length, input_embed_dim = inputs_embeds.shape image_feature_nums, image_feature_dim = image_features.shape assert image_feature_dim == input_embed_dim image_token_nums = (input_ids == image_token_index).sum().item() assert image_feature_nums == image_token_nums # (batch_size, sequence_length, input_embed_dim) -> (batch_size * sequence_length, input_embed_dim) inputs_embeds = inputs_embeds.reshape(-1, input_embed_dim) # (batch_size, sequence_length) -> (batch_size * sequence_length) input_ids = input_ids.flatten() inputs_embeds[input_ids == image_token_index] = image_features inputs_embeds = inputs_embeds.reshape( (batch_size, sequence_length, input_embed_dim) ) return inputs_embeds def _extract_image_features( self, pixel_values: torch.FloatTensor, image_grid_hws: torch.LongTensor ): """ Args: pixel_values (:obj:`torch.FloatTensor` of shape :obj:`(image_token_nums, 3, patch_size, patch_size)`): The pixel values of the images processed by image processor. Returns: image_features (:obj:`torch.FloatTensor` of shape :obj:`(image_token_nums, image_feature_dim)`): The selected image features to use as input to the projector head. """ # [(image_token_nums_0, image_feature_dim), (image_token_nums_1, image_feature_dim), ...] image_features: list[torch.Tensor] = self.vision_tower( pixel_values, image_grid_hws ) # (image_token_nums_0 + image_token_nums_1 + ..., image_feature_dim) image_features: torch.Tensor = self.multi_modal_projector(image_features) return image_features def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: list[torch.FloatTensor] | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, pixel_values: torch.FloatTensor | list[torch.FloatTensor] | None = None, image_grid_hws: Optional[torch.LongTensor] = None, ) -> Union[tuple, LlavaCausalLMOutputWithPast]: 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]`. Example: ```python >>> from PIL import Image >>> # generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> # decode >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] ```""" 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 ) if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) if pixel_values is not None and pixel_values.size(0) > 0: pixel_values = pixel_values.to(self.vision_tower.dtype) image_features: torch.Tensor = self._extract_image_features( pixel_values, image_grid_hws ) inputs_embeds = inputs_embeds.to(image_features[0].dtype) inputs_embeds = self._merge_with_image_features( inputs_embeds, input_ids, image_features ) outputs = self.language_model( 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, ) logits = outputs[0] loss = None if labels is not None: # Shift so that tokens < n predict n if attention_mask is not None: shift_attention_mask = attention_mask[..., 1:] shift_logits = logits[..., :-1, :][ shift_attention_mask.to(logits.device) != 0 ].contiguous() shift_labels = labels[..., 1:][ shift_attention_mask.to(labels.device) != 0 ].contiguous() else: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() loss = loss_fct( shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device), ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return LlavaCausalLMOutputWithPast( 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, inputs_embeds=None, pixel_values=None, attention_mask=None, image_grid_hws=None, cache_position=None, **kwargs, ): model_inputs = self.language_model.prepare_inputs_for_generation( input_ids=input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, **kwargs, ) # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore # Otherwise we need pixel values to be passed to model if cache_position[0] == 0: model_inputs["pixel_values"] = pixel_values model_inputs["image_grid_hws"] = image_grid_hws return model_inputs