diff --git "a/modeling_minicpm_sala.py" "b/modeling_minicpm_sala.py" new file mode 100644--- /dev/null +++ "b/modeling_minicpm_sala.py" @@ -0,0 +1,3277 @@ +# coding=utf-8 +# Copyright 2025 The OpenBMB Team. All rights reserved. +# +# 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 MiniCPMSALA model.""" +import math +import re +import warnings +from typing import Any, Dict, List, Optional, Tuple, Union +from einops import rearrange, repeat +import torch +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, DynamicLayer +from transformers.modeling_attn_mask_utils import ( + _prepare_4d_causal_attention_mask, + _prepare_4d_causal_attention_mask_for_sdpa, +) +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_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available +from fla.ops.simple_gla import chunk_simple_gla +from fla.ops.simple_gla.fused_recurrent import fused_recurrent_simple_gla +from fla.ops.utils.index import prepare_cu_seqlens_from_mask, prepare_lens_from_mask +from fla.utils import tensor_cache + + +from .configuration_minicpm_sala import MiniCPMSALAConfig + +try: + 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 + from infllm_v2 import ( + infllmv2_attn_stage1, + infllmv2_attn_varlen_func, + infllmv2_attn_with_kvcache, + max_pooling_1d, + max_pooling_1d_varlen, + ) +except ImportError: + pass + +from functools import lru_cache + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "MiniCPMSALAConfig" + + +def compressed_attention( + q: torch.Tensor, + k: torch.Tensor, + k2: torch.Tensor, + kernel_size: int, + kernel_stride: int, + block_size: int, + topk: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + cu_seqlens_k2: torch.Tensor, + max_seqlen_q: int, + max_seqlen_k: int, + sm_scale: float = None, + init_blocks: int = 1, + local_blocks: int = 2, + cache_lens=None, +) -> Tuple[torch.Tensor, torch.Tensor]: + with torch.no_grad(): + batch_size = cu_seqlens_q.shape[0] - 1 + + # Check if it's prefilling stage + is_prefilling = cache_lens is None or (cache_lens == 0).all().item() + + if is_prefilling: # prefilling stage + # Calculate q_idx for each query position in each batch + cache_lens = torch.zeros(batch_size, dtype=torch.int32, device=q.device) + q_idx = torch.cat( + [ + ( + torch.arange( + cu_seqlens_q[i + 1] - cu_seqlens_q[i], device=q.device + ) + + max_seqlen_q + - (cu_seqlens_q[i + 1] - cu_seqlens_q[i]) + ) + // block_size + for i in range(batch_size) + ], + dim=0, + ) # shape: [total_q_len] + else: # decoding stage + # Each batch has only one query (last position) + q_idx = ( + cache_lens // block_size + ) # shape: [batch_size] = [total_q_len] in decoding + + # Compute attention score + score = infllmv2_attn_stage1( + q.contiguous(), + k.contiguous(), + k2.contiguous(), + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + cu_seqlens_v=cu_seqlens_k2, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + causal=is_prefilling, + ) + score = score[:, : q_idx.shape[0], :] # [num_heads, total_q_len, num_blocks] + + block_score = max_pooling_1d_varlen( + score.contiguous(), + cu_seqlens_q, + cu_seqlens_k, + cache_lens, + max_seqlen_q, + max_seqlen_k, + local_blocks=local_blocks, + init_blocks=init_blocks, + block_size=block_size, + stride=kernel_stride, + ) # shape: [num_heads, total_q_len, num_blocks] + + # get topk + topk = min(topk, block_score.shape[-1]) + topk_idx = block_score.topk(topk, dim=-1).indices.sort(-1).values + topk_idx[topk_idx > q_idx[None, :, None]] = -1 + topk_idx = topk_idx.to(torch.int32) + + return topk_idx + + +@lru_cache(maxsize=16) +def calc_chunks_with_stride(cu_seqlen, chunk_size, kernel_stride): + """ + Compute the chunks that require Sparse attention, with stride support. + + Args: + cu_seqlen (torch.Tensor): Cumulative sequence lengths for each sample. + chunk_size (int): Chunk size used for Sparse attention. + kernel_stride (int): Stride size when sliding over the sequence. + + Returns: + filtered_indices (torch.Tensor): Indices used to directly index into the key/value tensors. + cu_seqlens_compressed (torch.Tensor): Cumulative sequence lengths after compression. + """ + # 1. Compute the length of each sequence + batch_sizes = cu_seqlen[1:] - cu_seqlen[:-1] + + # 2. Compute the start positions of chunks for each sequence (with stride) + max_seq_len = torch.max(batch_sizes) + max_num_chunks_per_seq = (max_seq_len - chunk_size) // kernel_stride + 1 + chunk_start_offsets = torch.arange( + 0, + max_num_chunks_per_seq * kernel_stride, + kernel_stride, + device=cu_seqlen.device, + ) + seq_starts = cu_seqlen[:-1] + chunk_start_in_seq = ( + seq_starts[:, None] + chunk_start_offsets[None, :] + ) # [batch_size, max_num_chunks_per_seq] + + # 3. Filter out chunks that exceed sequence length or are smaller than the full chunk size + chunk_end_in_seq = chunk_start_in_seq + chunk_size + valid_chunk_mask = chunk_end_in_seq <= (seq_starts[:, None] + batch_sizes[:, None]) + + # 4. Filter valid chunk start positions using the valid_chunk_mask + valid_chunk_starts = chunk_start_in_seq[valid_chunk_mask] # [num_valid_chunks] + del chunk_start_in_seq + # 5. Generate filtered_indices + chunk_indices = torch.arange(0, chunk_size, device=cu_seqlen.device)[ + None, : + ] # [1, chunk_size] + filtered_indices = ( + valid_chunk_starts[:, None] + chunk_indices + ) # [num_valid_chunks, chunk_size] + filtered_indices = filtered_indices.view(-1) # Flatten to 1D indices + + # 6. Compute compressed cumulative sequence lengths + num_filtered_chunks_per_batch = valid_chunk_mask.sum( + dim=1 + ) # Number of valid chunks per batch + cu_seqlens_compressed = torch.zeros( + len(cu_seqlen), dtype=torch.int32, device=cu_seqlen.device + ) + cu_seqlens_compressed[1:] = num_filtered_chunks_per_batch.cumsum(dim=0) + del ( + num_filtered_chunks_per_batch, + chunk_start_offsets, + seq_starts, + chunk_end_in_seq, + valid_chunk_mask, + chunk_indices, + ) + return filtered_indices, cu_seqlens_compressed + + +class CompressK(torch.nn.Module): + def __init__(self, head_num_k, head_dim, kernel_size, kernel_stride=16): + """ + Module for compressing key (K) representations. + + Args: + head_num_k (int): Number of key attention heads. + head_dim (int): Dimension of each attention head. + kernel_size (int): Size of each chunk used for compression. + kernel_stride (int, optional): Stride used when dividing input into chunks. Default is 16. + """ + super().__init__() + self.kernel_size = kernel_size + self.head_num_k = head_num_k + self.head_dim = head_dim + self.kernel_stride = kernel_stride + + def forward(self, k: torch.Tensor, cu_seqlens): + """ + Forward pass for compressing the key (K) tensor. + + Args: + k (torch.Tensor): Input key tensor of shape (total_seq_len, num_heads, head_dim). + cu_seqlens (torch.Tensor): Cumulative sequence lengths for each sample in the batch, typically used for handling variable-length sequences. + + Returns: + compress_k (torch.Tensor): Compressed key tensor. + cu_seqlens_compressed (torch.Tensor): Updated cumulative sequence lengths after compression. + + """ + # Compute chunk-related metadata, with stride support + filtered_k_indices, cu_seqlens_compressed = calc_chunks_with_stride( + cu_seqlens, self.kernel_size, self.kernel_stride + ) + + # Extract filtered key vectors + filtered_k = k.index_select(0, filtered_k_indices.view(-1)) + + # split + filtered_k = filtered_k.view( + filtered_k.shape[0] // self.kernel_size, + self.kernel_size, + self.head_num_k, + self.head_dim, + ) # [l, block_size,h,d] + + compressed_k = filtered_k.mean(dim=1) + return compressed_k, cu_seqlens_compressed + + +class InfLLMv2CacheLayer(DynamicLayer): + def __init__(self): + super().__init__() + # Initialize any additional attributes specific to InfLLMv2CacheLayer + self.no_rope_keys = torch.tensor([], dtype=torch.float32) + self.compress_k_cache = [] + self.no_compress_k_cache = [] + self.cached_compressed_cu_seqlens = torch.tensor([], dtype=torch.int32) + self.compress_k_cache_varlen = torch.tensor([], dtype=torch.float32) + # Add support for compress_k2 + self.compress_k2_cache = [] + self.cached_compressed_cu_seqlens2 = torch.tensor([], dtype=torch.int32) + self.compress_k2_cache_varlen = torch.tensor([], dtype=torch.float32) + self.no_compress_k2_cache = [] + + def update_no_rope_key(self, key_states): + if self.no_rope_keys.numel() == 0: + self.no_rope_keys = key_states + else: + self.no_rope_keys = torch.cat([self.no_rope_keys, key_states], dim=1) + return self.no_rope_keys + + def update_compress_k(self, key_states, cu_seqlens=None): + if len(self.compress_k_cache) == 0: + if cu_seqlens is not None: + self.cached_compressed_cu_seqlens = cu_seqlens.clone() + self.compress_k_cache_varlen = key_states + split_sizes = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() + self.compress_k_cache = list(torch.split(key_states, split_sizes)) + else: + for index, k in enumerate(key_states): + if k is not None: + self.compress_k_cache[index] = torch.cat( + [self.compress_k_cache[index], k], dim=0 + ) + new_seq_lens = torch.tensor( + [tensor.shape[0] for tensor in self.compress_k_cache], dtype=torch.int32 + ) + new_cumsum = torch.cumsum(new_seq_lens, dim=0, dtype=torch.int32) + + self.compress_k_cache_varlen = torch.cat(self.compress_k_cache, dim=0) + self.cached_compressed_cu_seqlens = torch.cat( + [torch.tensor([0], dtype=torch.int32), new_cumsum] + ).to(self.compress_k_cache_varlen.device) + return self.compress_k_cache_varlen, self.cached_compressed_cu_seqlens + + def update_no_compress_k(self, key_states, kernel_size=32, kernel_stride=16): + k_chunk_list = [] + for index, k in enumerate(key_states): + if len(self.no_compress_k_cache) <= index: + self.no_compress_k_cache.append(k) + else: + self.no_compress_k_cache[index] = torch.cat( + [self.no_compress_k_cache[index], k], dim=0 + ) + current_len = self.no_compress_k_cache[index].shape[0] + if current_len >= kernel_size: + k_chunk_list.append(self.no_compress_k_cache[index][:kernel_size]) + self.no_compress_k_cache[index] = self.no_compress_k_cache[index][ + kernel_stride: + ] + else: + k_chunk_list.append(None) + return k_chunk_list + + def update_compress_k2(self, key_states, cu_seqlens=None): + if len(self.compress_k2_cache) == 0: + if cu_seqlens is not None: + self.cached_compressed_cu_seqlens2 = cu_seqlens.clone() + self.compress_k2_cache_varlen = key_states + split_sizes = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() + self.compress_k2_cache = list(torch.split(key_states, split_sizes)) + else: + for index, k in enumerate(key_states): + if k is not None: + self.compress_k2_cache[index] = torch.cat( + [self.compress_k2_cache[index], k], dim=0 + ) + new_seq_lens = torch.tensor( + [tensor.shape[0] for tensor in self.compress_k2_cache], + dtype=torch.int32, + ) + new_cumsum = torch.cumsum(new_seq_lens, dim=0, dtype=torch.int32) + + self.compress_k2_cache_varlen = torch.cat(self.compress_k2_cache, dim=0) + self.cached_compressed_cu_seqlens2 = torch.cat( + [torch.tensor([0], dtype=torch.int32), new_cumsum] + ).to(self.compress_k2_cache_varlen.device) + return self.compress_k2_cache_varlen, self.cached_compressed_cu_seqlens2 + + def update_no_compress_k2(self, key_states, kernel_size=128, kernel_stride=64): + k_chunk_list = [] + for index, k in enumerate(key_states): + if len(self.no_compress_k2_cache) <= index: + self.no_compress_k2_cache.append(k) + else: + self.no_compress_k2_cache[index] = torch.cat( + [self.no_compress_k2_cache[index], k], dim=0 + ) + current_len = self.no_compress_k2_cache[index].shape[0] + if current_len >= kernel_size: + k_chunk_list.append(self.no_compress_k2_cache[index][:kernel_size]) + self.no_compress_k2_cache[index] = self.no_compress_k2_cache[index][ + kernel_stride: + ] + else: + k_chunk_list.append(None) + return k_chunk_list + + +class LightningCacheLayer(DynamicLayer): + def __init__(self): + super().__init__() + self.state = {} + + def update( + self, + recurrent_state: torch.Tensor = None, + attn_state: Tuple[torch.Tensor, torch.Tensor] = None, + conv_state: Tuple[torch.Tensor] = None, + ffn_state: torch.Tensor = None, + layer_idx: int = 0, + offset: Optional[int] = 1, + cache_kwargs: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + """ + Updates the cache with the new `recurrent_state`/`attn_state`/`conv_state` for the layer `layer_idx`. + + Args: + recurrent_state (`torch.Tensor`, `optional`): + The new recurrent state to cache. + attn_state (`Tuple[torch.Tensor, torch.Tensor]`, `optional`): + The new attention key/value states to cache. + conv_state (`Tuple[torch.Tensor]`, `optional`): + The new convolution state to cache. + layer_idx (`int`, defaults to 0): + The index of the layer to cache the states for. + offset (`int`, `optional`, defaults to 1): + The number of new tokens being processed. + cache_kwargs (`Dict[str, Any]`, `optional`): + Additional arguments for the cache subclass. + + Return: + Dictionary of the updated state. + """ + + # Update the number of seen tokens + + if recurrent_state is not None: + self.state["recurrent_state"] = recurrent_state + if conv_state is not None: + self.state["conv_state"] = conv_state + if ffn_state is not None: + self.state["ffn_state"] = ffn_state + + return self.state + + +class MiniCPMSALACache(DynamicCache): + def __init__(self, config, num_hidden_layers: Optional[int] = None) -> None: + super().__init__(config=config) + self.mixer_type = config.mixer_types + if self.mixer_type[0] != "minicpm4": + raise ValueError("The first layer must be 'minicpm4' to track seen tokens.") + self.layers = ( + [ + ( + InfLLMv2CacheLayer() + if self.mixer_type[index] == "minicpm4" + else LightningCacheLayer() + ) + for index in range(num_hidden_layers) + ] + if num_hidden_layers + else [] + ) + self._seen_tokens = 0 + + def update(self, key_states, value_states, layer_idx, cache_kwargs=None): + if layer_idx == 0: + self._seen_tokens += key_states.shape[-2] + return self.layers[layer_idx].update(key_states, value_states, cache_kwargs) + + def update_no_rope_key(self, key_states, layer_idx, cache_kwargs=None): + return self.layers[layer_idx].update_no_rope_key(key_states) + + def update_compress_k( + self, key_states, layer_idx, cu_seqlens=None, cache_kwargs=None + ): + return self.layers[layer_idx].update_compress_k(key_states, cu_seqlens) + + def update_no_compress_k( + self, key_states, layer_idx, kernel_size=32, kernel_stride=16, cache_kwargs=None + ): + return self.layers[layer_idx].update_no_compress_k( + key_states, kernel_size, kernel_stride + ) + + def update_compress_k2( + self, key_states, layer_idx, cu_seqlens=None, cache_kwargs=None + ): + return self.layers[layer_idx].update_compress_k2(key_states, cu_seqlens) + + def update_no_compress_k2( + self, + key_states, + layer_idx, + kernel_size=128, + kernel_stride=64, + cache_kwargs=None, + ): + return self.layers[layer_idx].update_no_compress_k2( + key_states, kernel_size, kernel_stride + ) + + def crop(self, max_length): + for layer in self.layers: + layer.crop(max_length) + + def batch_repeat_interleave(self, repeats): + for layer in self.layers: + layer.batch_repeat_interleave(repeats) + + def batch_select_indices(self, indices): + for layer in self.layers: + layer.batch_select_indices(indices) + + +# 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) + + +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, + ) + + +def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float): + old_dtype = hidden.dtype + variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True) + hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype) + return hidden * weight + + +class MiniCPMRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + MiniCPMRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + return rms_layernorm(hidden_states, self.weight, self.variance_epsilon) + + +ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm) + + +class MiniCPMRotaryEmbedding(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.float32, + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange( + self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype + ) + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +class MiniCPMLongRoPE(MiniCPMRotaryEmbedding): + """MiniCPMRotaryEmbedding 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, + short_factor=None, + long_factor=None, + original_max_position_embeddings=None, + ): + self.short_factor = short_factor + self.long_factor = long_factor + self.original_max_position_embeddings = original_max_position_embeddings + scale = max_position_embeddings / self.original_max_position_embeddings + self.scaling_factor = math.sqrt( + 1 + math.log(scale) / math.log(self.original_max_position_embeddings) + ) + 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 + ) + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor( + self.long_factor, dtype=torch.float32, device=device + ) + else: + ext_factors = torch.tensor( + self.short_factor, dtype=torch.float32, device=device + ) + + freqs = torch.mul( + torch.outer(t, 1.0 / ext_factors).to(device=device), + self.inv_freq.to(device=device).to(dtype), + ) + # 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) * self.scaling_factor, persistent=False + ) + self.register_buffer( + "sin_cached", emb.sin().to(dtype) * self.scaling_factor, persistent=False + ) + + +class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding): + """MiniCPMRotaryEmbedding 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) + + +class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding): + """MiniCPMRotaryEmbedding 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) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def 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. + """ + orig_dtype = k.dtype + cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim] + q_fp32 = q.to(dtype=torch.float32, device=q.device) + k_fp32 = k.to(dtype=torch.float32, device=k.device) + q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin) + k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin) + return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype) + + +class MiniCPMMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.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): + if self.config.pretraining_tp > 1: + slice = self.intermediate_size // self.config.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat( + [ + F.linear(x, gate_proj_slices[i]) + for i in range(self.config.pretraining_tp) + ], + dim=-1, + ) + up_proj = torch.cat( + [ + F.linear(x, up_proj_slices[i]) + for i in range(self.config.pretraining_tp) + ], + dim=-1, + ) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [ + F.linear(intermediate_states[i], down_proj_slices[i]) + for i in range(self.config.pretraining_tp) + ] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def _unpad_one_tensor(hidden_states, attention_mask): + # Unpad the hidden states using the indices + indices, cu_seqlens, max_seqlen_in_batch = _get_unpad_data(attention_mask) + batch_size, seq_len = hidden_states.shape[:2] + + # Get the remaining dimensions + remaining_dims = hidden_states.shape[2:] + + # Reshape to (batch_size * seq_len, *remaining_dims) + reshaped_states = hidden_states.reshape(batch_size * seq_len, *remaining_dims) + + # Apply unpadding using indices + unpadded_states = index_first_axis(reshaped_states, indices) + + return unpadded_states, indices, cu_seqlens, max_seqlen_in_batch + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand( + batch, num_key_value_heads, n_rep, slen, head_dim + ) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class MiniCPMAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: MiniCPMSALAConfig, 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.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 + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + self.q_proj = nn.Linear( + self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + self.hidden_size, + self.num_key_value_heads * self.head_dim, + bias=config.attention_bias, + ) + self.v_proj = nn.Linear( + self.hidden_size, + self.num_key_value_heads * self.head_dim, + bias=config.attention_bias, + ) + self.o_proj = nn.Linear( + self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias + ) + self._init_rope() + + # gated attn + self.use_output_gate = config.attn_use_output_gate + + if self.use_output_gate: + self.o_gate = nn.Linear( + self.hidden_size, + self.num_heads * self.head_dim, + bias=config.attention_bias, + ) + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = MiniCPMRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["rope_type"] + scaling_factor = self.config.rope_scaling.get("factor", None) + if scaling_type == "linear": + self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "longrope": + self.rotary_emb = MiniCPMLongRoPE( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + short_factor=self.config.rope_scaling["short_factor"], + long_factor=self.config.rope_scaling["long_factor"], + base=self.rope_theta, + original_max_position_embeddings=self.config.rope_scaling[ + "original_max_position_embeddings" + ], + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return ( + tensor.view(bsz, seq_len, self.num_heads, self.head_dim) + .transpose(1, 2) + .contiguous() + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[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.config.pretraining_tp > 1: + key_value_slicing = ( + self.num_key_value_heads * self.head_dim + ) // self.config.pretraining_tp + query_slices = self.q_proj.weight.split( + (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0 + ) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [ + F.linear(hidden_states, query_slices[i]) + for i in range(self.config.pretraining_tp) + ] + query_states = torch.cat(query_states, dim=-1) + + key_states = [ + F.linear(hidden_states, key_slices[i]) + for i in range(self.config.pretraining_tp) + ] + key_states = torch.cat(key_states, dim=-1) + + value_states = [ + F.linear(hidden_states, value_slices[i]) + for i in range(self.config.pretraining_tp) + ] + value_states = torch.cat(value_states, dim=-1) + + else: + 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 = position_ids.max().item() + 1 + cos, sin = None, None + if self.config.attn_use_rope: + cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb( + query_states, key_states, cos, sin, position_ids + ) + + if past_key_value is not None: + 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 + ) + + 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: + 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.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) + # gated attn + if self.use_output_gate: + o_gate = self.o_gate(hidden_states) + attn_output = attn_output * F.sigmoid(o_gate) + # gated attn + + if self.config.pretraining_tp > 1: + attn_output = attn_output.split( + self.hidden_size // self.config.pretraining_tp, dim=2 + ) + o_proj_slices = self.o_proj.weight.split( + self.hidden_size // self.config.pretraining_tp, dim=1 + ) + attn_output = sum( + [ + F.linear(attn_output[i], o_proj_slices[i]) + for i in range(self.config.pretraining_tp) + ] + ) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class MiniCPMFlashAttention2(MiniCPMAttention): + """ + MiniCPM flash attention module. This module inherits from `MiniCPMAttention` 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 alignment, 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]]]: + # MiniCPMFlashAttention2 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() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # 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 + 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 = position_ids.max().item() + 1 + cos, sin = None, None + if self.config.attn_use_rope: + cos, sin = self.rotary_emb( + value_states.to(torch.float32), seq_len=kv_seq_len + ) + query_states, key_states = apply_rotary_pos_emb( + query_states, key_states, cos, sin, position_ids + ) + + if past_key_value is not None: + 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. (MiniCPMRMSNorm 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 + 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) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + if self.use_output_gate: + o_gate = self.o_gate(hidden_states) + attn_output = attn_output * F.sigmoid(o_gate) + 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 MiniCPMFlashAttention2 __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), + ) + + +class MiniCPMInfLLMv2Attention(MiniCPMAttention): + """ + MiniCPM flash attention module. This module inherits from `MiniCPMAttention` 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) + assert ( + self.config._attn_implementation == "flash_attention_2" + ), "Only flash_attention_2 is supported for sparse attention" + # 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 alignment, 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() + + # -------sparse------- + self.kernel_size = self.config.sparse_config.get("kernel_size", 32) + self.kernel_stride = self.config.sparse_config.get("kernel_stride", 16) + self.init_blocks = self.config.sparse_config.get("init_blocks", 1) + self.block_size = self.config.sparse_config.get("block_size", 64) + self.window_size = self.config.sparse_config.get("window_size", 2048) + self.dense_len = self.config.sparse_config.get("dense_len", 8192) + + self.local_blocks = self.window_size // self.block_size # local_blocks + self.topk = self.config.sparse_config.get("topk", 64) + ( + self.window_size // self.block_size + ) + self.use_nope = self.config.sparse_config.get("use_nope", False) + + self.compress_k = CompressK( + self.num_key_value_heads, + self.head_dim, + kernel_size=self.kernel_size, + kernel_stride=self.kernel_stride, + ) + self.compress_k2 = CompressK( + self.num_key_value_heads, + self.head_dim, + kernel_size=self.kernel_size * 4, + kernel_stride=self.kernel_stride * 4, + ) + + 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]]]: + # MiniCPMFlashAttention2 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() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + if self.use_nope: + query_states_no_rope = query_states.view( + bsz, q_len, self.num_heads, self.head_dim + ) + key_states_no_rope = key_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ) + + # 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 + 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 = position_ids.max().item() + 1 + cos, sin = None, None + if self.config.attn_use_rope: + cos, sin = self.rotary_emb( + value_states.to(torch.float32), seq_len=kv_seq_len + ) + query_states, key_states = apply_rotary_pos_emb( + query_states, key_states, cos, sin, position_ids + ) + + if past_key_value is not None: + 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) + if self.use_nope: + key_states_no_rope = past_key_value.update_no_rope_key( + key_states_no_rope, self.layer_idx + ) + no_rope_param = { + "key_states_no_rope": key_states_no_rope, + "query_states_no_rope": query_states_no_rope, + } + + else: + no_rope_param = None + + 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. (MiniCPMRMSNorm 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 + 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) + if kv_seq_len < self.dense_len: + attn_output = self._flash_attention_forward_dense( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + ) + else: + attn_output = self._sparse_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + no_rope_param=no_rope_param, # if past_key_value is not None else None, + past_key_value=past_key_value, + ) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + if self.use_output_gate: + o_gate = self.o_gate(hidden_states) + attn_output = attn_output * F.sigmoid(o_gate) + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _sparse_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + no_rope_param=None, + past_key_value=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 MiniCPMFlashAttention2 __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] + if past_key_value is not None: + ( + compressed_k, + compressed_cu_seqlens, + compressed_k2, + compressed_cu_seqlens2, + ) = self.get_compress_k( + key_states=( + key_states + if self.use_nope == False + else no_rope_param["key_states_no_rope"] + ), # This can be optimized a bit; + attention_mask=attention_mask, + past_key_value=past_key_value, + ) + + ( + 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 no_rope_param is not None: + if max_seqlen_in_batch_q == 1: + no_rope_param["query_states_no_rope"] = no_rope_param[ + "query_states_no_rope" + ].squeeze(1) + else: + no_rope_param["query_states_no_rope"], _, _, _ = _unpad_one_tensor( + no_rope_param["query_states_no_rope"], + attention_mask=attention_mask, + ) + if past_key_value is None: + # compress_k use varlen form + compressed_k, compressed_cu_seqlens = self.compress_k( + key_states, cu_seqlens_k + ) + compressed_k2, compressed_cu_seqlens2 = self.compress_k2( + key_states, cu_seqlens_k + ) + else: + # compressed_k and compressed_k2 already retrieved from get_compress_k above + pass + + attn_output_unpad = self.sparse_forward( + query_states, + key_states, + value_states, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_in_batch_q, + max_seqlen_in_batch_k, + no_rope_param=no_rope_param, + compressed_k=compressed_k, + compressed_cu_seqlens=compressed_cu_seqlens, + compressed_k2=compressed_k2, + compressed_cu_seqlens2=compressed_cu_seqlens2, + ) + + attn_output = pad_input( + attn_output_unpad, indices_q, batch_size, query_length + ) + + else: + raise ValueError("Need attention mask") + + return attn_output + + def get_compress_k(self, key_states, attention_mask, past_key_value): + """ + Get compressed key states and corresponding cumulative sequence lengths. + + Args: + key_states: Key states tensor + cu_seqlens_k: Cumulative sequence lengths for keys + past_key_value: Past key-value cache + no_rope_param: Optional parameter containing key states without rope + + Returns: + Tuple of (compressed_k, compressed_cu_seqlens, compressed_k2, compressed_cu_seqlens2) + """ + + # Check if this is prefilling or initial compression condition + + is_prefilling = key_states.shape[1] >= self.dense_len and ( + not past_key_value.layers[self.layer_idx].compress_k_cache + ) + + if is_prefilling: + unpadded_key_states, indices, cu_seqlens, max_seqlen_in_batch = ( + _unpad_one_tensor(key_states, attention_mask=attention_mask) + ) + # Compress the keys + compressed_k, compressed_cu_seqlens = self.compress_k( + unpadded_key_states, cu_seqlens + ) + compressed_k2, compressed_cu_seqlens2 = self.compress_k2( + unpadded_key_states, cu_seqlens + ) + + past_key_value.update_compress_k( + compressed_k, self.layer_idx, compressed_cu_seqlens + ) + past_key_value.update_compress_k2( + compressed_k2, self.layer_idx, compressed_cu_seqlens2 + ) + + no_compress_k_list = [] + # Compute and update no_compress_k + for i in range(len(compressed_cu_seqlens) - 1): + no_compress_k_start = ( + compressed_cu_seqlens[i + 1] - compressed_cu_seqlens[i] + ) * self.kernel_stride + + no_compress_k_list.append( + unpadded_key_states[ + cu_seqlens[i] + no_compress_k_start : cu_seqlens[i + 1] + ].clone() + ) + + past_key_value.update_no_compress_k( + no_compress_k_list, + self.layer_idx, + kernel_stride=self.kernel_stride, + kernel_size=self.kernel_size, + ) + + # Also update no_compress_k2 + no_compress_k2_list = [] + for i in range(len(compressed_cu_seqlens2) - 1): + no_compress_k2_start = ( + (compressed_cu_seqlens2[i + 1] - compressed_cu_seqlens2[i]) + * self.kernel_stride + * 4 + ) + + no_compress_k2_list.append( + unpadded_key_states[ + cu_seqlens[i] + no_compress_k2_start : cu_seqlens[i + 1] + ].clone() + ) + + past_key_value.update_no_compress_k2( + no_compress_k2_list, + self.layer_idx, + kernel_stride=self.kernel_stride * 4, + kernel_size=self.kernel_size * 4, + ) + + else: + # Decode case: incremental update + batch_size = key_states.shape[ + 0 + ] # key_states.shape = [batch_size, seq, k_head_num, head_dim] + key_states_split = list( + torch.split( + key_states[:, -1:].squeeze( + 1 + ), # [batch_size, seq, k_head_num, head_dim]->[batch_size, 1, k_head_num, head_dim]-> [batch_size, k_head_num, head_dim] + [1] * batch_size, + dim=0, + ) + ) + # Try to update no_compress_k buffer + no_compress_k_list = past_key_value.update_no_compress_k( + key_states_split, + self.layer_idx, + kernel_stride=self.kernel_stride, + kernel_size=self.kernel_size, + ) + new_compressed_k_list = [] + for no_compress_k in no_compress_k_list: + + if no_compress_k is not None: + # We have enough tokens to compress + new_compressed_k = no_compress_k.mean( + dim=0, keepdim=True + ) # [1, n_heads_k, head_dim] + + new_compressed_k_list.append(new_compressed_k) + else: + new_compressed_k_list.append(None) + compressed_k, compressed_cu_seqlens = past_key_value.update_compress_k( + new_compressed_k_list, + self.layer_idx, + ) + + # For compress_k2, update no_compress_k2 buffer and compress when ready + no_compress_k2_list = past_key_value.update_no_compress_k2( + key_states_split, + self.layer_idx, + kernel_stride=self.kernel_stride * 4, + kernel_size=self.kernel_size * 4, + ) + new_compressed_k2_list = [] + for no_compress_k2 in no_compress_k2_list: + if no_compress_k2 is not None: + # We have enough tokens to compress for k2 + new_compressed_k2 = no_compress_k2.mean( + dim=0, keepdim=True + ) # [1, n_heads_k, head_dim] + new_compressed_k2_list.append(new_compressed_k2) + else: + new_compressed_k2_list.append(None) + compressed_k2, compressed_cu_seqlens2 = past_key_value.update_compress_k2( + new_compressed_k2_list, + self.layer_idx, + ) + + return ( + compressed_k, + compressed_cu_seqlens, + compressed_k2, + compressed_cu_seqlens2, + ) + + def sparse_forward( + self, + query_layer, + key_layer, + value_layer, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_in_batch_q, + max_seqlen_in_batch_k, + no_rope_param=None, + compressed_k=None, + compressed_cu_seqlens=None, + compressed_k2=None, + compressed_cu_seqlens2=None, + ): + # Handle q_head/k_head ratio for infllmv2 (requires 16:1 ratio) + num_q_heads = query_layer.shape[-2] + num_k_heads = key_layer.shape[-2] + current_ratio = num_q_heads // num_k_heads + required_ratio = 16 + + if current_ratio < required_ratio: + repeat_times = required_ratio // current_ratio + query_layer = query_layer.repeat_interleave(repeat_times, dim=-2) + else: + repeat_times = 1 + compressed_seqlens = compressed_cu_seqlens[1:] - compressed_cu_seqlens[:-1] + cache_lens = None + if max_seqlen_in_batch_q == 1 and max_seqlen_in_batch_k > 1: # decoding + seq_lens_k = cu_seqlens_k[1:] - cu_seqlens_k[:-1] + cache_lens = seq_lens_k - 1 + + topk_idx = compressed_attention( + ( + query_layer + if no_rope_param is None + else no_rope_param["query_states_no_rope"] + ), + compressed_k, + compressed_k2, + self.kernel_size, + self.kernel_stride, + self.block_size, + self.topk, + cu_seqlens_q, + compressed_cu_seqlens, + compressed_cu_seqlens2, + max_seqlen_in_batch_q, + compressed_seqlens.max().item(), + None, + init_blocks=self.init_blocks, + local_blocks=self.local_blocks, + cache_lens=cache_lens, + ) + topk_attn_output = infllmv2_attn_varlen_func( + query_layer, + key_layer, + value_layer, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_in_batch_q, + max_seqlen_in_batch_k, + dropout_p=0.0, + deterministic=False, + softmax_scale=None, + causal=max_seqlen_in_batch_q != 1, + return_attn_probs=False, + topk_idx=topk_idx, + ) + if repeat_times > 1: + topk_attn_output = topk_attn_output.view( + topk_attn_output.shape[0], + topk_attn_output.shape[-2] // repeat_times, + repeat_times, + -1, + ).mean(dim=-2) + return topk_attn_output + + def _flash_attention_forward_dense( + 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 MiniCPMFlashAttention2 __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), + ) + + +def index_first_axis(x, indices): + other_shape = x.shape[1:] + second_dim = other_shape.numel() + return torch.gather( + rearrange(x, "b ... -> b (...)"), + 0, + repeat(indices, "z -> z d", d=second_dim), + ).reshape(-1, *other_shape) + + +def index_put_first_axis(x, indices, first_axis_dim): + y = torch.zeros(first_axis_dim, *x.shape[1:], device=x.device, dtype=x.dtype) + # TODO [2022-03-04] For some reason torch.scatter is a bit faster than indexing. + y[indices] = x + # y.scatter_(0, repeat(indices, 'z -> z d', d=x.shape[1]), x) + return y + + +@tensor_cache +def get_unpad_data( + attention_mask: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, int]: + lens = prepare_lens_from_mask(attention_mask) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = lens.max().item() + cu_seqlens = prepare_cu_seqlens_from_mask(attention_mask) + return indices, cu_seqlens, max_seqlen_in_batch + + +def unpad_input( + q: torch.Tensor, + states: tuple[torch.Tensor], + attention_mask: torch.Tensor, + q_len: int, + keepdim: bool = False, +): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = get_unpad_data(attention_mask) + batch_size, seq_len, *_ = states[0].shape + + state = tuple( + index_first_axis(rearrange(s, "b s ... -> (b s) ..."), indices_k) + for s in states + ) + + if q_len == seq_len: + q = index_first_axis(rearrange(q, "b s ... -> (b s) ..."), indices_k) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif q_len == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange(batch_size + 1, dtype=torch.int32, device=q.device) + indices_q = cu_seqlens_q[:-1] + q = q.squeeze(1) + else: + raise NotImplementedError( + "We only support either q_len == k_len (prefilling) or q_len == 1 (decoding)" + ) + + if keepdim: + q = q.unsqueeze(0) + state = tuple(s.unsqueeze(0) for s in state) + + return ( + q, + state, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +def pad_input( + hidden_states: torch.Tensor, + indices: torch.LongTensor, + batch_size: int, + seq_len: int, +) -> torch.Tensor: + output = index_put_first_axis(hidden_states, indices, batch_size * seq_len) + return rearrange(output, "(b s) ... -> b s ...", b=batch_size) + + +def _build_slope_tensor(nheads: int): + def get_slopes(n): + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio**i for i in range(n)] + + if math.log2(n).is_integer(): + return get_slopes_power_of_2( + n + ) # In the paper, we only train models that have 2^a heads for some a. This function has + else: # some good properties that only occur when the input is a power of 2. To maintain that even + closest_power_of_2 = 2 ** math.floor( + math.log2(n) + ) # when the number of heads is not a power of 2, we use this workaround. + return ( + get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2] + ) + + slopes = torch.tensor(get_slopes(nheads)) # (nheads,) + return slopes + + +class LightningAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + config: MiniCPMSALAConfig, + layer_idx: int, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + head_dim: int, + attention_dropout: float = 0.0, + use_output_gate: bool = False, + attention_bias: bool = False, + rms_norm_eps: float = 1e-6, + use_rope: bool = False, + use_output_norm: bool = False, + qk_norm: bool = True, + rope_head_dim: Optional[int] = None, + scale: str = "1/sqrt(d)", + ): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.num_key_value_groups = num_attention_heads // num_key_value_heads + self.head_dim = head_dim + if scale == "1/sqrt(d)": + self.scale = self.head_dim ** (-0.5) + elif scale == "1/d": + self.scale = self.head_dim ** (-1.0) + else: + self.scale = 1.0 + self.attention_dropout = attention_dropout + self.is_causal = True + self.use_output_gate = use_output_gate + self.attention_bias = attention_bias + self.rms_norm_eps = rms_norm_eps + self.use_rope = use_rope + self.qk_norm = qk_norm + self.use_output_norm = use_output_norm + self.rope_head_dim = rope_head_dim if rope_head_dim is not None else head_dim + assert self.rope_head_dim <= self.head_dim + + self.q_proj = nn.Linear( + self.hidden_size, + self.num_attention_heads * self.head_dim, + bias=self.attention_bias, + ) + self.k_proj = nn.Linear( + self.hidden_size, + self.num_key_value_heads * self.head_dim, + bias=self.attention_bias, + ) + self.v_proj = nn.Linear( + self.hidden_size, + self.num_key_value_heads * self.head_dim, + bias=self.attention_bias, + ) + self.o_proj = nn.Linear( + self.num_attention_heads * self.head_dim, + self.hidden_size, + bias=self.attention_bias, + ) + if self.use_output_norm: + self.o_norm = MiniCPMRMSNorm( + self.num_attention_heads * self.head_dim, eps=self.rms_norm_eps + ) + + if self.use_output_gate: + self.z_proj = nn.Linear( + self.hidden_size, + self.num_attention_heads * self.head_dim, + bias=self.attention_bias, + ) + + if self.qk_norm: + self.q_norm = MiniCPMRMSNorm(self.head_dim, eps=self.rms_norm_eps) + self.k_norm = MiniCPMRMSNorm(self.head_dim, eps=self.rms_norm_eps) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = MiniCPMRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.config.max_position_embeddings, + base=self.config.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["rope_type"] + scaling_factor = self.config.rope_scaling.get("factor", None) + if scaling_type == "linear": + self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.config.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.config.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.config.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.config.rope_theta, + ) + elif scaling_type == "longrope": + self.rotary_emb = MiniCPMLongRoPE( + self.head_dim, + max_position_embeddings=self.config.max_position_embeddings, + short_factor=self.config.rope_scaling["short_factor"], + long_factor=self.config.rope_scaling["long_factor"], + base=self.config.rope_theta, + original_max_position_embeddings=self.config.rope_scaling[ + "original_max_position_embeddings" + ], + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def attn_fn( + self, + q: Tensor, # (b, t, h, d) + k: Tensor, # (b, t, h, d) + v: Tensor, # (b, t, h, d) + decay: Tensor, # (h,) + scale: float | None = None, # will use dk^(-1) if None. + initial_state: Tensor | None = None, # (b, h, dk, dv) + mode: str = "chunk", + attention_mask=None, + ) -> tuple[Tensor, Tensor]: + seqlen = q.shape[1] + q_len = q.shape[1] + mode = "fused_recurrent" if seqlen < 64 else "chunk" + batch_size = q.shape[0] + cu_seqlens = None + if attention_mask is not None: + indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) + q = index_first_axis( + rearrange(q, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + k = index_first_axis( + rearrange(k, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + v = index_first_axis( + rearrange(v, "b s ... -> (b s) ..."), indices + ).unsqueeze(0) + elif batch_size > 1: + raise ValueError("attention_mask must be provided when batch size > 1") + if mode == "chunk": + o, final_state = chunk_simple_gla( + q=q, + k=k, + v=v, + g_gamma=decay, # (h,) + initial_state=initial_state, + output_final_state=True, + scale=scale, + head_first=False, + cu_seqlens=cu_seqlens, + ) # (b, t, h, d) + elif mode == "fused_recurrent": + o, final_state = fused_recurrent_simple_gla( + q=q, + k=k, + v=v, + g_gamma=decay, + scale=scale, + initial_state=initial_state, + output_final_state=True, + cu_seqlens=cu_seqlens, + ) + else: + raise ValueError(f"Invalid mode: {mode}") + if attention_mask is not None: + o = pad_input(o.squeeze(0), indices, batch_size, q_len) + + return o, final_state + + def forward( + self, + hidden_states: torch.Tensor, + position_ids: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Cache] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + + bsz, seqlen, _ = hidden_states.shape + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + q = rearrange(q, "b t (h d) -> b h t d", d=self.head_dim) + k = rearrange(k, "b t (h d) -> b h t d", d=self.head_dim) + v = rearrange(v, "b t (h d) -> b h t d", d=self.head_dim) + + if self.qk_norm: + q = self.q_norm(q) + k = self.k_norm(k) + + if self.use_rope: + kv_seq_len = position_ids.max().item() + 1 + cos, sin = self.rotary_emb(v.to(torch.float32), seq_len=kv_seq_len) + q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids) + + k = repeat_kv(k, self.num_key_value_groups) + v = repeat_kv(v, self.num_key_value_groups) + + s = _build_slope_tensor(self.num_attention_heads).to( + k.device, dtype=torch.float32 + ) * ( + -1.0 + ) # (h) + + initial_state = None + if past_key_value is not None: + layer_state = past_key_value.layers[self.layer_idx].state + initial_state = layer_state.get("recurrent_state", None) + + q = rearrange(q, "b h t d -> b t h d").to(torch.float32) + k = rearrange(k, "b h t d -> b t h d").to(torch.float32) + v = rearrange(v, "b h t d -> b t h d").to(torch.float32) + s = s.to(torch.float32) + + o, final_state = self.attn_fn( + q=q, + k=k, + v=v, + decay=s, + initial_state=initial_state, + scale=self.scale, + attention_mask=attention_mask, + ) + + if past_key_value is not None: + past_key_value.layers[self.layer_idx].update( + recurrent_state=final_state, + layer_idx=self.layer_idx, + offset=seqlen, + ) + + o = ( + rearrange(o, "b t h d -> b t (h d)").contiguous().to(hidden_states.dtype) + ) # (b, t, d) + + if self.use_output_norm: + o = self.o_norm(o) # (b, t, d) + + if self.use_output_gate: + z = F.sigmoid(self.z_proj(hidden_states)) # (b, t, d) + o = o * z # (b, t, d) + + y = self.o_proj(o) + return y, None, past_key_value + + +class MiniCPMSdpaAttention(MiniCPMAttention): + """ + MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from MiniCPMAttention.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, + ) -> 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( + "MiniCPMSALAModel is using MiniCPMSdpaAttention, 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 = position_ids.max().item() + 1 + cos, sin = None, None + if self.config.attn_use_rope: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb( + query_states, key_states, cos, sin, position_ids + ) + + if past_key_value is not None: + 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 + ) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + 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()}" + ) + + # 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() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # 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=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + if self.use_output_gate: + o_gate = self.o_gate(hidden_states) + attn_output = attn_output * F.sigmoid(o_gate) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +MINICPM_ATTENTION_CLASSES = { + "eager": MiniCPMAttention, + "flash_attention_2": MiniCPMFlashAttention2, + "sdpa": MiniCPMSdpaAttention, +} + + +class MiniCPMSALADecoderLayer(nn.Module): + def __init__(self, config: MiniCPMSALAConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.mixer_type = config.mixer_types[layer_idx] + if self.mixer_type == "minicpm4": + if config.sparse_config is not None and torch.cuda.is_available(): + self.self_attn = MiniCPMInfLLMv2Attention( + config=config, layer_idx=layer_idx + ) + else: + self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation]( + config=config, layer_idx=layer_idx + ) + elif self.mixer_type in ["lightning", "lightning_attn", "lightning-attn"]: + assert ( + config.head_dim is not None + ), "head_dim must be provided for LightningAttention" + self.self_attn = LightningAttention( + config=config, + layer_idx=layer_idx, + hidden_size=config.hidden_size, + num_attention_heads=config.num_attention_heads, + num_key_value_heads=config.lightning_nkv, + head_dim=config.head_dim, + attention_dropout=config.attention_dropout, + use_output_gate=config.use_output_gate, + attention_bias=config.attention_bias, + rms_norm_eps=config.rms_norm_eps, + use_rope=config.lightning_use_rope, + qk_norm=config.qk_norm, + use_output_norm=config.use_output_norm, + scale=config.lightning_scale, + ) + else: + raise ValueError(f"Unsupported mixer type: {self.mixer_type}") + + self.mlp = MiniCPMMLP(config) + self.input_layernorm = MiniCPMRMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + self.post_attention_layernorm = MiniCPMRMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + + self.scale_depth = config.scale_depth + self.num_hidden_layers = config.num_hidden_layers + + 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 * ( + self.scale_depth / math.sqrt(self.num_hidden_layers) + ) + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states * ( + self.scale_depth / math.sqrt(self.num_hidden_layers) + ) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +MINICPM_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 ([`MiniCPMSALAConfig`]): + 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 MiniCPM Model outputting raw hidden-states without any specific head on top.", + MINICPM_START_DOCSTRING, +) +class MiniCPMSALAPreTrainedModel(PreTrainedModel): + config_class = MiniCPMSALAConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["MiniCPMSALADecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = 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_() + + +MINICPM_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 MiniCPM Model outputting raw hidden-states without any specific head on top.", + MINICPM_START_DOCSTRING, +) +class MiniCPMSALAModel(MiniCPMSALAPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMSALADecoderLayer`] + + Args: + config: MiniCPMSALAConfig + """ + + def __init__(self, config: MiniCPMSALAConfig): + 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( + [ + MiniCPMSALADecoderLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self._use_sdpa = config._attn_implementation == "sdpa" + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + + self.norm = MiniCPMRMSNorm(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(MINICPM_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") + + 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 + + past_key_values_length = 0 + + if use_cache: + # Calculate the usable length of past key values + past_key_values_length = ( + past_key_values.get_seq_length() + if isinstance(past_key_values, MiniCPMSALACache) + else 0 + ) + + # Initialize MiniCPMSALACache if needed + if ( + self.config.sparse_config is not None + and torch.cuda.is_available() + and past_key_values_length == 0 + ): + past_key_values = MiniCPMSALACache( + config=self.config, num_hidden_layers=self.config.num_hidden_layers + ) + + 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) * self.config.scale_emb + + if self._use_flash_attention_2: + # 2d mask is passed through the layers + pass + elif self._use_sdpa and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + 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,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + 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 + 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 MiniCPMSALAForCausalLM(MiniCPMSALAPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = MiniCPMSALAModel(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(MINICPM_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, + logits_to_keep: Union[int, torch.Tensor] = 0, + **kwargs, + ) -> 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, ..., + 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, MiniCPMSALAForCausalLM + + >>> model = MiniCPMSALAForCausalLM.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] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = ( + slice(-logits_to_keep, None) + if isinstance(logits_to_keep, int) + else logits_to_keep + ) + hidden_states = hidden_states[:, slice_indices, :].contiguous() + if self.config.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split( + self.vocab_size // self.config.pretraining_tp, dim=0 + ) + logits = [ + F.linear(hidden_states, lm_head_slices[i]) + for i in range(self.config.pretraining_tp) + ] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head( + hidden_states / (self.config.hidden_size / self.config.dim_model_base) + ) + logits = logits.float() + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **kwargs, + ) + + 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): + # Use the new Cache class methods + cache_length = past_key_values.get_seq_length() + + if torch.cuda.is_available() and cache_length == 0: + past_key_values = MiniCPMSALACache( + config=self.config, + num_hidden_layers=self.config.num_hidden_layers, + ) + past_length = cache_length + max_cache_length = None + else: + raise ValueError( + "You must use the new past_key_values format, such as the Cache class, instead of the old tuple format." + ) + + # 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_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, + } + ) + # Forward ALL kwargs that are uninitialized (e.g. `use_cache`). + for key, value in kwargs.items(): + if key not in model_inputs: + model_inputs[key] = value + 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 + + @torch.inference_mode() + def chat( + self, + tokenizer, + query: str, + history: List[Dict] = None, + role: str = "user", + max_length: int = 4096, + num_beams=1, + do_sample=True, + top_p=0.8, + temperature=0.3, + logits_processor=None, + **kwargs, + ): + if history is None: + history = [] + if logits_processor: + gen_kwargs = { + "max_length": max_length, + "num_beams": num_beams, + "do_sample": do_sample, + "top_p": top_p, + "temperature": temperature, + "logits_processor": logits_processor, + **kwargs, + } + else: + gen_kwargs = { + "max_length": max_length, + "num_beams": num_beams, + "do_sample": do_sample, + "top_p": top_p, + "temperature": temperature, + "logits_processor": logits_processor, + **kwargs, + } + + history.append({"role": role, "content": query}) + history_str = tokenizer.apply_chat_template( + history, tokenize=False, add_generation_prompt=False + ) + inputs = tokenizer(history_str, return_tensors="pt").to(self.device) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) : -1] + response = tokenizer.decode(outputs) + pattern = re.compile(r".*?(?=|<用户>)", re.DOTALL) + matches = pattern.findall(response) + if len(matches) > 0: + response = matches[0] + history.append({"role": "assistant", "content": response}) + return response, history + + +@add_start_docstrings( + """ + The MiniCPM Model transformer with a sequence classification head on top (linear layer). + + [`MiniCPMSALAForSequenceClassification`] 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). + """, + MINICPM_START_DOCSTRING, +) +class MiniCPMSALAForSequenceClassification(MiniCPMSALAPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = MiniCPMSALAModel(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(MINICPM_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, ..., + 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, + )