LLM: add quantize kv cache support for baichuan 7b and 13b. (#10330)
* add quantize kv cache for baichuan 7b and 13b. * fix typo. * fix. * fix style. * fix style.
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Cengguang Zhang
GitHub
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1 changed files with 269 additions and 0 deletions
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@ -28,6 +28,8 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from bigdl.llm.utils.common import invalidInputError
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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 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 rotate_half, apply_rotary_pos_emb
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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@ -42,6 +44,160 @@ def baichuan_attention_forward_7b(
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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_7b_quantized
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else:
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forward_function = baichuan_attention_forward_7b_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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position_ids=position_ids,
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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_7b_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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position_ids: Optional[torch.LongTensor] = 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 = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
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# batch_size x source_len x hidden_size
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query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# batch_size x target_len x head_size
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key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# batch_size x source_len x hidden_size
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value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"baichuan")
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "baichuan")
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# [bsz, nh, t, hd]
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if past_key_value is None:
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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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention weights should be of size "
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f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
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f", but is {attn_weights.size()}"
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)
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if attention_mask is not None:
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invalidInputError(
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attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
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f"but is {attention_mask.size()}"
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)
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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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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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kv_seq_len = key_states.shape[-2]
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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_kv_cache(k_cache, v_cache, 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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kv_seq_len = key_states.shape[-2]
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past_key_value = (key_states, value_states)
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if query_states.size(2) != 1 or query_states.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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention weights should be of size "
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f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
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f", but is {attn_weights.size()}"
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)
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if attention_mask is not None:
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invalidInputError(
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attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
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f"but is {attention_mask.size()}"
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)
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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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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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if query_states.size(2) != 1 or query_states.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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invalidInputError(
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attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
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f"`attn_output` should be of size "
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f"{(bsz, self.num_heads, q_len, self.head_dim)},"
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f"but is {attn_output.size()}"
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)
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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_7b_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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position_ids: Optional[torch.LongTensor] = 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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@ -155,6 +311,119 @@ def baichuan_attention_forward_13b(
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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 = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
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query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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if past_key_value is None:
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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(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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attn_output = torch.matmul(attn_weights, value_states)
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kv_seq_len = key_states.shape[-2]
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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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kv_seq_len = key_states.shape[-2]
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past_key_value = (key_states, value_states)
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if query_states.size(2) != 1 or query_states.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 query_states.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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