# # Copyright 2016 The BigDL Authors. # # 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. # # This file is adapted from # https://huggingface.co/THUDM/chatglm2-6b-32k/blob/main/configuration_chatglm.py # import os import torch from typing import Optional, Tuple, Union from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, update_past_key_value from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, \ use_sdp_causal, should_use_compresskv, is_enough_kv_cache_room_4_36, \ get_compresskv_attn_mask from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb from ipex_llm.transformers.models.chatglm2 import repeat_kv from ipex_llm.transformers.kv import DynamicCompressCache, DynamicCompressFp8Cache from transformers.modeling_outputs import BaseModelOutputWithPast import math KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256)) def chatglm4_model_forward( self, input_ids, position_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.BoolTensor] = None, full_attention_mask: Optional[torch.BoolTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, BaseModelOutputWithPast]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if use_cache: inputs = input_ids if input_ids is not None else inputs_embeds use_compress_kv = should_use_compresskv(inputs, inputs.shape[1]) use_quantize_kv = use_quantize_kv_cache(self.encoder.layers[0].mlp.dense_h_to_4h, inputs) if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache): if use_quantize_kv: past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values) else: past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values) if inputs_embeds is None: batch_size, seq_length = input_ids.shape inputs_embeds = self.embedding(input_ids) else: batch_size, seq_length, _ = inputs_embeds.shape input_ids = torch.empty((batch_size, seq_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device) if full_attention_mask is None: if (attention_mask is not None and not attention_mask.all()) or\ (past_key_values and seq_length != 1): full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) # ipex-llm changes begin # 1. replace `rotary_pos_emb` with `inv_freq` and `position_ids` # 2. generate `causal_mask` and replace `full_attention_mask` with it if position_ids is None: if past_key_values is None: position_ids = torch.arange(seq_length, dtype=torch.int64, device=inputs_embeds.device) else: if isinstance(past_key_values, DynamicCompressCache): kv_length = past_key_values.get_seq_length() else: kv_length = past_key_values[0][0].size(2) position_ids = torch.arange(kv_length, kv_length + seq_length, dtype=torch.int64, device=inputs_embeds.device) position_ids = position_ids.repeat(batch_size, 1) if not getattr(self.rotary_pos_emb, "cached", False): rot_dim = self.rotary_pos_emb.dim base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1) # We should generate float inv_freq to avoid overflow, as base is too large. inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2, dtype=torch.float, device=inputs_embeds.device) / rot_dim)) inv_freq = inv_freq.to(inputs_embeds.dtype) self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False) self.rotary_pos_emb.cached = True # `full_attention_mask` is not None only when # `past_key_values` is not None and `seq_length` > 1 if full_attention_mask is not None: causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)], dtype=inputs_embeds.dtype, device=inputs_embeds.device) mask_value = torch.finfo(inputs_embeds.dtype).min causal_mask.masked_fill_(full_attention_mask, mask_value) elif self.training or (inputs_embeds.device.type != "xpu" and past_key_values is None): full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)], dtype=inputs_embeds.dtype, device=inputs_embeds.device) mask_value = torch.finfo(inputs_embeds.dtype).min causal_mask.masked_fill_(full_attention_mask, mask_value) else: causal_mask = None hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( inputs_embeds, causal_mask, rotary_pos_emb=(self.rotary_pos_emb.inv_freq, position_ids), kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states ) # ipex-llm changes end if presents is not None and type(presents) is torch.Tensor: presents = presents.split(1, dim=0) presents = list(presents) presents = [list(x.squeeze(0).split(1, dim=0)) for x in presents] presents = [tuple([x.squeeze(0) for x in y]) for y in presents] presents = tuple(presents) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) def chatglm4_attention_forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True ): # hidden_states: [b, sq, h] bsz, q_len, _ = hidden_states.size() # [CompressKV] use_compresskv = isinstance(kv_cache, DynamicCompressCache) # past_key_value: [bsz, n_kv_head, seq_len, head_dim] if use_compresskv: past_key_value = kv_cache else: past_key_value = None if kv_cache is None else (kv_cache[0], kv_cache[1]) n_head = self.num_attention_heads_per_partition n_kv_head = self.num_multi_query_groups_per_partition if self.multi_query_attention else n_head head_dim = self.hidden_size_per_attention_head qkv = self.query_key_value(hidden_states) # [bs, q_len, np * 3 * hn] -> [bsz, n_head, seq_len, head_dim] qkv = qkv.view(bsz, q_len, n_head + 2 * n_kv_head, head_dim) qkv = qkv.transpose(1, 2) query_states, key_states, value_states = qkv.split([n_head, n_kv_head, n_kv_head], dim=1) kv_seq_len = key_states.shape[2] if past_key_value is not None: if use_compresskv: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_number - 1) else: kv_seq_len += past_key_value[0].shape[2] # IPEX-LLM OPT: fuse rope inv_freq, position_ids = rotary_pos_emb rot_dim = inv_freq.size(-1) * 2 if should_use_fuse_rope(hidden_states, rotary_pos_emb[1], self.training): import xe_addons xe_addons.rotary_two_inplaced(inv_freq, position_ids, query_states[..., :rot_dim], key_states[..., :rot_dim]) else: idx_theta = torch.outer(position_ids[0].float(), inv_freq.float()).to(hidden_states.dtype) idx_theta = idx_theta.unsqueeze(0).unsqueeze(0) cos = torch.cos(idx_theta).repeat_interleave(2, -1) sin = torch.sin(idx_theta).repeat_interleave(2, -1) q_rot, k_rot = apply_rotary_pos_emb(query_states[..., :rot_dim], key_states[..., :rot_dim], cos, sin, position_ids, "chatglm") query_states[..., :rot_dim] = q_rot[...] key_states[..., :rot_dim] = k_rot[...] # IPEX-LLM OPT: kv cache and quantize kv use_quantize_kv = use_quantize_kv_cache(self.query_key_value, query_states) # [CompressKV] if use_compresskv: from transformers.configuration_utils import PretrainedConfig self.config = self.config if hasattr(self, "config") else PretrainedConfig() enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_number - 1, q_len) key_states, value_states = past_key_value.update( key_states, value_states, self.layer_number - 1, query_states, attention_mask, n_head // n_kv_head, self.config, enough_kv_room, KV_CACHE_ALLOC_BLOCK_LENGTH ) else: key_states, value_states = update_past_key_value( past_key_value, key_states, value_states, kv_seq_len, use_quantize_kv, hidden_states.device ) if use_cache: if past_key_value is None: past_key_value = torch.cat((key_states.unsqueeze(0).unsqueeze(0), value_states.unsqueeze(0).unsqueeze(0)), dim=1) else: past_key_value = (key_states, value_states) else: past_key_value = None # IPEX-LLM OPT: sdp attn_weights = None if use_sdp(q_len, kv_seq_len, head_dim, query_states): import xe_addons if use_compresskv: attention_mask = get_compresskv_attn_mask(key_states, attention_mask) if use_quantize_kv: attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask) else: attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask) elif use_sdp_causal(q_len, kv_seq_len, head_dim, query_states, self.training): import xe_addons if use_quantize_kv: attn_output = xe_addons.sdp_fp8_causal(query_states, key_states, value_states, attention_mask) else: attn_output = xe_addons.sdp_causal(query_states, key_states, value_states, attention_mask) elif query_states.device.type == "cpu": # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, n_head // n_kv_head) value_states = repeat_kv(value_states, n_head // n_kv_head) if q_len == kv_seq_len: attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, is_causal=True ) else: attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attention_mask ) else: if use_quantize_kv: key_states, value_states = restore_fp8_kv_cache(key_states, value_states, query_states.dtype) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, n_head // n_kv_head) value_states = repeat_kv(value_states, n_head // n_kv_head) attn_weights = torch.matmul(query_states / math.sqrt(head_dim), key_states.transpose(2, 3)) if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) attn_output = torch.matmul(attn_weights, value_states) # context_layer's shape: [bsz, n_head, seq_len, head_dim] -> [seq_len, bsz, n_head * head_dim] attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, n_head * head_dim) output = self.dense(attn_output) return output, past_key_value def chatglm4_encoder_forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, use_cache: Optional[bool] = True, output_hidden_states: Optional[bool] = False, ): # [CompressKV] use_compress_kv = isinstance(kv_caches, DynamicCompressCache) if not kv_caches and not use_compress_kv: kv_caches = [None for _ in range(self.num_layers)] presents = () if use_cache else None if self.gradient_checkpointing and self.training: if use_cache: use_cache = False all_self_attentions = None all_hidden_states = () if output_hidden_states else None for index in range(self.num_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer = self._get_layer(index) if self.gradient_checkpointing and self.training: layer_ret = torch.utils.checkpoint.checkpoint( layer, hidden_states, attention_mask, rotary_pos_emb, kv_caches[index], use_cache, use_reentrant=False ) else: # if kv_caches[index] is not None: layer_ret = layer( hidden_states, attention_mask, rotary_pos_emb, kv_cache=kv_caches if use_compress_kv else kv_caches[index], use_cache=use_cache ) hidden_states, kv_cache = layer_ret if use_cache: if use_compress_kv: presents = kv_caches else: # token by token decoding, use tuple format if kv_caches[0] is not None: presents = presents + (kv_cache,) # prefilling in decoding, use tensor format to save cuda memory else: if len(presents) == 0: presents = kv_cache else: # bigdl-llm change starts # to fix first token's kv cache error of tensor format in pipeline parallel if isinstance(kv_cache, tuple): kv_cache = torch.tensor( kv_cache, dtype=hidden_states.dtype).to(hidden_states.device) # bigdl-llm change ends presents = torch.cat((presents, kv_cache.to(presents.device)), dim=0) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # Final layer norm. if self.post_layer_norm: hidden_states = self.final_layernorm(hidden_states) return hidden_states, presents, all_hidden_states, all_self_attentions