From ca1166a0e50922805027c6e439e65a0fd3d44c9c Mon Sep 17 00:00:00 2001 From: SONG Ge <38711238+sgwhat@users.noreply.github.com> Date: Thu, 22 Feb 2024 13:43:35 +0800 Subject: [PATCH] [LLM] Add quantize kv_cache for Baichuan2-13B (#10203) * add quantize kv_cache for baichuan2-13b * style fix --- .../llm/transformers/models/baichuan2.py | 128 ++++++++++++++++++ 1 file changed, 128 insertions(+) diff --git a/python/llm/src/bigdl/llm/transformers/models/baichuan2.py b/python/llm/src/bigdl/llm/transformers/models/baichuan2.py index 0bbf0038..063cb20a 100644 --- a/python/llm/src/bigdl/llm/transformers/models/baichuan2.py +++ b/python/llm/src/bigdl/llm/transformers/models/baichuan2.py @@ -24,6 +24,8 @@ import torch import torch.utils.checkpoint from torch.nn import functional as F from bigdl.llm.ggml.quantize import ggml_tensor_qtype +from bigdl.llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \ + restore_fp8_kv_cache, use_quantize_kv_cache from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \ append_kv_cache, is_enough_kv_cache_room_4_31 from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb @@ -197,6 +199,132 @@ def baichuan_attention_forward_13b( past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if use_quantize_kv_cache(self.W_pack, hidden_states): + forward_function = baichuan_attention_forward_13b_quantized + else: + forward_function = baichuan_attention_forward_13b_origin + return forward_function( + self=self, + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + +def baichuan_attention_forward_13b_quantized( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + device = hidden_states.device + + proj = self.W_pack(hidden_states) + proj = ( + proj.unflatten(-1, (3, self.hidden_size)) + .unsqueeze(0) + .transpose(0, -2) + .squeeze(-2) + ) + query_states = ( + proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + ) + key_states = ( + proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + ) + value_states = ( + proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + ) + + kv_seq_len = key_states.shape[-2] + + if past_key_value is None: + # should use origin attn here + attn_weights = torch.matmul(query_states, + key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: + if q_len == 1: # inference with cache + if len(attention_mask.size()) == 4: + attention_mask = attention_mask[:, :, -1:, :] + else: + attention_mask = attention_mask[:, -1:, :] + attn_weights = attn_weights + attention_mask + attn_weights = torch.max( + attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min) + ) + + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1) + attn_output = torch.matmul(attn_weights, value_states) + + if use_cache: + k_cache, v_cache = init_fp8_kv_cache( + bsz, self.num_heads, kv_seq_len, self.head_dim, + device=device + ) + key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, + key_states, value_states) + past_key_value = (key_states, value_states) + + else: + k_cache, v_cache = past_key_value + key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, + key_states, value_states) + past_key_value = (key_states, value_states) + + if query_states.size(2) != 1 or device.type != 'xpu': + key_states, value_states = restore_fp8_kv_cache(key_states, value_states, + query_states.dtype) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) + else: + import linear_q4_0 + attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states) + + attn_weights = attn_weights / math.sqrt(self.head_dim) + + if attention_mask is not None: + if q_len == 1: # inference with cache + if len(attention_mask.size()) == 4: + attention_mask = attention_mask[:, :, -1:, :] + else: + attention_mask = attention_mask[:, -1:, :] + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, + torch.tensor(torch.finfo(attn_weights.dtype).min)) + + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1) + + if query_states.size(2) != 1 or device.type != 'xpu': + attn_output = torch.matmul(attn_weights, value_states) + else: + import linear_q4_0 + attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, + value_states.transpose(-1, -2)) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +def baichuan_attention_forward_13b_origin( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() device = hidden_states.device