# 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 MiniCPM model.""" import math import re import warnings from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache, CacheLayerMixin, DynamicLayer from transformers.modeling_attn_mask_utils import ( AttentionMaskConverter, _prepare_4d_attention_mask, _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 .configuration_minicpm import MiniCPMConfig #!一定要改 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: pass from functools import lru_cache 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 # 计算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 InfLLMv2Cache(DynamicCache): def __init__(self, config,num_hidden_layers: Optional[int] = None) -> None: super().__init__(config=config) self.layers = [InfLLMv2CacheLayer() for _ 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) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = 'MiniCPMConfig' def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) # @torch.jit.script # type: ignore 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.get_default_dtype() 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. """ # cos = cos[position_ids].unsqueeze(unsqueeze_dim) # sin = sin[position_ids].unsqueeze(unsqueeze_dim) # q_embed = (q * cos) + (rotate_half(q) * sin) # k_embed = (k * cos) + (rotate_half(k) * sin) 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: MiniCPMConfig, 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() 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 = 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) 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 = 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() 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) # !save no rope 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 = 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() 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] # assert batch_size == 1, 'Only batch_size=1 is supported at the moment.' if past_key_value!=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 != 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==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): 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, # block_window_size=self.window_size // self.block_size, topk_idx=topk_idx ) 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), ) 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( 'MiniCPMModel 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 = 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) 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 MiniCPMDecoderLayer(nn.Module): def __init__(self, config: MiniCPMConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size 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) 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 ([`MiniCPMConfig`]): 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 MiniCPMPreTrainedModel(PreTrainedModel): config_class = MiniCPMConfig base_model_prefix = 'model' supports_gradient_checkpointing = True _no_split_modules = ['MiniCPMDecoderLayer'] _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 MiniCPMModel(MiniCPMPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`] Args: config: MiniCPMConfig """ def __init__(self, config: MiniCPMConfig): 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( [MiniCPMDecoderLayer(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: use_legacy_cache = not isinstance(past_key_values, Cache) if use_legacy_cache: raise ValueError( 'You must use the new past_key_values format, such as the Cache class, instead of the old tuple format.' ) # Calculate the usable length of past key values past_key_values_length = past_key_values.get_seq_length() if isinstance(past_key_values, InfLLMv2Cache) else 0 # Initialize InfLLMv2Cache if needed if self.config.sparse_config is not None and torch.cuda.is_available() and past_key_values_length == 0: past_key_values = InfLLMv2Cache(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 # attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None if attention_mask is None: raise ValueError( f'need attention_mask for flash attention, but got {attention_mask}.' ) 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.to_legacy_cache() if use_legacy_cache else next_decoder_cache if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) class MiniCPMForCausalLM(MiniCPMPreTrainedModel): _tied_weights_keys = ['lm_head.weight'] def __init__(self, config): super().__init__(config) self.model = MiniCPMModel(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, MiniCPMForCausalLM >>> model = MiniCPMForCausalLM.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: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs ): if past_key_values is not None: if isinstance(past_key_values, Cache): # Use the new Cache class methods cache_length = past_key_values.get_seq_length() if self.config.sparse_config is not None and torch.cuda.is_available() and cache_length == 0: past_key_values = InfLLMv2Cache(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). [`MiniCPMForSequenceClassification`] 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 MiniCPMForSequenceClassification(MiniCPMPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = MiniCPMModel(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, )