[LLM] Add quantize kv_cache for Baichuan2-13B (#10203)
* add quantize kv_cache for baichuan2-13b * style fix
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1 changed files with 128 additions and 0 deletions
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@ -24,6 +24,8 @@ import torch
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import torch.utils.checkpoint
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from torch.nn import functional as F
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from bigdl.llm.ggml.quantize import ggml_tensor_qtype
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from bigdl.llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
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restore_fp8_kv_cache, use_quantize_kv_cache
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from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \
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append_kv_cache, is_enough_kv_cache_room_4_31
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb
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@ -197,6 +199,132 @@ def baichuan_attention_forward_13b(
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if use_quantize_kv_cache(self.W_pack, hidden_states):
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forward_function = baichuan_attention_forward_13b_quantized
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else:
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forward_function = baichuan_attention_forward_13b_origin
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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def baichuan_attention_forward_13b_quantized(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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proj = self.W_pack(hidden_states)
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proj = (
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proj.unflatten(-1, (3, self.hidden_size))
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.unsqueeze(0)
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.transpose(0, -2)
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.squeeze(-2)
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)
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query_states = (
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proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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key_states = (
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proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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value_states = (
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proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is None:
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# should use origin attn here
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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if q_len == 1: # inference with cache
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if len(attention_mask.size()) == 4:
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attention_mask = attention_mask[:, :, -1:, :]
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else:
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attention_mask = attention_mask[:, -1:, :]
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(
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attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
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)
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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attn_output = torch.matmul(attn_weights, value_states)
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if use_cache:
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k_cache, v_cache = init_fp8_kv_cache(
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bsz, self.num_heads, kv_seq_len, self.head_dim,
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device=device
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)
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states)
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past_key_value = (key_states, value_states)
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else:
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k_cache, v_cache = past_key_value
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states)
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past_key_value = (key_states, value_states)
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if query_states.size(2) != 1 or device.type != 'xpu':
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
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else:
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import linear_q4_0
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attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states)
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attn_weights = attn_weights / math.sqrt(self.head_dim)
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if attention_mask is not None:
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if q_len == 1: # inference with cache
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if len(attention_mask.size()) == 4:
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attention_mask = attention_mask[:, :, -1:, :]
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else:
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attention_mask = attention_mask[:, -1:, :]
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(attn_weights,
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torch.tensor(torch.finfo(attn_weights.dtype).min))
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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if query_states.size(2) != 1 or device.type != 'xpu':
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attn_output = torch.matmul(attn_weights, value_states)
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else:
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import linear_q4_0
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attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights,
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value_states.transpose(-1, -2))
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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def baichuan_attention_forward_13b_origin(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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