[LLM] Add quantize kv_cache for Baichuan2-13B (#10203)

* add quantize kv_cache for baichuan2-13b

* style fix

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