LLM: add quantize kv support for llama transformer 4.36 (#10298)

* add quantize kv support for llama transformer 4.36

* fix style.

* fix style.

python/llm/src/bigdl/llm/transformers/convert.py

@@ -742,10 +742,15 @@ def _optimize_post(model, lightweight_bmm=False):
         if version.parse(trans_version) >= version.parse("4.36.0"):
             # transformers version >= 4.36.0
             from bigdl.llm.transformers.models.llama import llama_attention_forward_4_36
+            from bigdl.llm.transformers.models.llama import llama_model_forward_4_36
             convert_forward(
                 model,
                 transformers.models.llama.modeling_llama.LlamaAttention,
                 llama_attention_forward_4_36, )
+            convert_forward(
+                model,
+                transformers.models.llama.modeling_llama.LlamaModel,
+                llama_model_forward_4_36)
         else:
             # transformers version between 4.31.0 - 4.35.2
             convert_forward(

python/llm/src/bigdl/llm/transformers/models/llama.py

@@ -49,6 +49,7 @@
 from bigdl.llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
 from bigdl.llm.transformers.models.utils import mlp_fusion_check, fp16_fusion_check
 from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.llama.modeling_llama import LlamaModel
 from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5, IQ2_XXS
 from bigdl.llm.ggml.quantize import ggml_tensor_qtype
 from bigdl.llm.utils.common import invalidInputError
@@ -84,6 +85,37 @@ def get_ipex_version():
     return _ipex_version
 
 
+def llama_model_forward_4_36(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    from bigdl.llm.transformers.kv import DynamicFp8Cache
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+    if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
+        if not isinstance(past_key_values, DynamicFp8Cache):
+            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
+    return LlamaModel.forward(
+        self=self,
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+
 def llama_rms_norm_forward(self, hidden_states):
     if hidden_states.device.type == "xpu" and not (self.training and hidden_states.requires_grad):
         import linear_q4_0
@@ -906,6 +938,212 @@ def llama_attention_forward_4_36(
     output_attentions: bool = False,
     use_cache: bool = False,
     **kwargs
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    if use_quantize_kv_cache(self.q_proj, hidden_states):
+        forward_function = llama_attention_forward_4_36_quantized
+    else:
+        forward_function = llama_attention_forward_4_36_original
+    return forward_function(
+        self=self,
+        hidden_states=hidden_states,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_value=past_key_value,
+        output_attentions=output_attentions,
+        use_cache=use_cache,
+        kwargs=kwargs
+    )
+
+
+def llama_attention_forward_4_36_quantized(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    **kwargs
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    if "padding_mask" in kwargs:
+        warnings.warn(
+            "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+            "Please make sure use `attention_mask` instead.`"
+        )
+
+    bsz, q_len, _ = hidden_states.size()
+    device = hidden_states.device
+    use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
+    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx, seq_len=q_len)
+    qtype = getattr(self.q_proj, "qtype", None)
+    qtype_check = qtype in [SYM_INT4, FP8E5]
+    no_tp = not self.config.pretraining_tp > 1
+    decoding_fast_path = (no_tp and qtype_check and use_fuse_rope
+                          and enough_kv_room and bsz * q_len == 1)
+    if decoding_fast_path:
+        hidden_states = hidden_states.view(1, -1)
+        tmp_cache_k, tmp_cache_v = init_kv_cache(
+            bsz,
+            self.num_key_value_heads,
+            self.head_dim,
+            0,
+            1,
+            dtype=hidden_states.dtype,
+            device=device
+        )
+        import linear_q4_0
+        query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
+                                                                         self.q_proj.weight,
+                                                                         self.k_proj.weight,
+                                                                         self.v_proj.weight,
+                                                                         position_ids,
+                                                                         tmp_cache_k, tmp_cache_v,
+                                                                         self.q_proj.weight.qtype,
+                                                                         0,
+                                                                         self.head_dim)
+    else:
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len,
+                                         self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len,
+                                     self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len,
+                                         self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                invalidInputError(
+                    False,
+                    f"The cache structure has changed since version v4.36."
+                    f" If you are using {self.__class__.__name__} "
+                    f"for auto-regressive decoding with k/v caching,"
+                    f" please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        if use_fuse_rope:
+            query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
+                                                                         key_states,
+                                                                         position_ids,
+                                                                         "llama")
+        else:
+            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
+                                                            cos, sin, position_ids, "llama")
+    kv_seq_len = key_states.shape[-2]
+
+    if len(past_key_value.key_cache) <= self.layer_idx:
+        attn_weights = torch.matmul(query_states,
+                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            invalidInputError(
+                False,
+                f"Attention weights should be of size "
+                f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                invalidInputError(
+                    False,
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
+                    f" but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1,
+                                             dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+        if use_cache:
+            cache_kwargs = None
+            key_states, value_states = past_key_value.update(key_states, value_states,
+                                                             self.layer_idx, cache_kwargs)
+    else:
+        cache_kwargs = None  # Specific to RoPE models
+        key_states, value_states = past_key_value.update(key_states, value_states,
+                                                         self.layer_idx, cache_kwargs)
+        kv_seq_len = key_states.shape[-2]
+        if query_states.size(2) != 1 or query_states.device.type != 'xpu':
+            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
+                                                            query_states.dtype)
+            key_states = repeat_kv(key_states, self.num_key_value_groups)\
+                .to(device, dtype=query_states.dtype)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)\
+                .to(device, dtype=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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            invalidInputError(
+                False,
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
+                f" but is {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                invalidInputError(
+                    False,
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
+                    f" but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights,
+                                             dim=-1, dtype=torch.float32).to(query_states.dtype)
+
+        if query_states.size(2) != 1 or query_states.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))
+
+    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+        invalidInputError(
+            False,
+            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
+            f" but is {attn_output.size()}"
+        )
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    if self.config.pretraining_tp > 1:
+        attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+        o_proj_slices = self.o_proj.weight.split(self.hidden_size
+                                                 // self.config.pretraining_tp, dim=1)
+        attn_output = sum([F.linear(attn_output[i],
+                                    o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+    else:
+        attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+
+def llama_attention_forward_4_36_original(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    **kwargs
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     if "padding_mask" in kwargs:
         warnings.warn(