[LLM] First commit the llm deployment code

fastdeploy/model_executor/layers/attention/attention.py

@@ -0,0 +1,116 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from typing import Optional
+
+import paddle
+from paddle import nn
+
+from fastdeploy.worker.model_runner import ForwardMeta
+
+
+class Attention(nn.Layer):
+    """
+    The AttentionLayer.
+    """
+
+    def __init__(
+        self,
+        llm_config,
+        layer_id: int,
+        logit_cap: float = 0.0,
+        v_head_dim: int = -1,
+        rope_type: str = "",
+        qkv_bias: Optional[paddle.Tensor] = None,
+        qkv_scale: Optional[paddle.Tensor] = None,
+        prefix: str = "",
+        out_scale: float = -1.,
+        linear_shift=None,
+        linear_smooth=None,
+        use_neox_rotary_style=False,
+    ) -> None:
+        """
+        Initializes `LMLayer` with the given parameters.
+
+        Args:
+            llm_config (dict): The config of LM model.
+            layer_id (int): The id of current layer.
+            logit_cap (float, optional): The cap for logits. Defaults to 0.0.
+            v_head_dim (int, optional): The head dim of value. Defaults to -1.
+            rope_type (str, optional): The type of RoPE. Defaults to "".
+            qkv_bias (Optional[paddle.Tensor], optional): The bias of QKV. Defaults to None.
+            qkv_scale (Optional[paddle.Tensor], optional): The scale of QKV. Defaults to None.
+            prefix (str, optional): The name of current layer. Defaults to "".
+            linear_shift (Optional[paddle.Tensor], optional): The shift of linear. Defaults to None.
+            linear_smooth (Optional[paddle.Tensor], optional): The smooth of linear. Defaults to None.
+
+        Raises:
+            ValueError: If the `v_head_dim` is less than 0.
+        """
+        super().__init__()
+        self.num_heads = llm_config.model_config.num_attention_heads // llm_config.parallel_config.mp_size
+        self.head_dim = llm_config.model_config.hidden_size // llm_config.model_config.num_attention_heads
+        self.kv_num_heads = llm_config.model_config.num_key_value_heads // llm_config.parallel_config.mp_size
+        self.layer_id = layer_id
+        self.logit_cap = logit_cap
+        self.v_head_dim = v_head_dim if v_head_dim > 0 else self.head_dim
+        self.rope_type = rope_type
+        self.qk_head_dim = self.head_dim
+        # not use
+        self.tp_q_head_num = self.num_heads
+        self.tp_k_head_num = self.num_heads
+        self.tp_v_head_num = self.num_heads
+        # not use
+        self.scaling = 1.0 / (self.head_dim**0.5)
+        self.linear_shift = linear_shift
+        self.linear_smooth = linear_smooth
+        self.qkv_bias = qkv_bias
+        self.qkv_scale = qkv_scale
+        self._dtype = self._helper.get_default_dtype()
+        self.out_scale = out_scale
+        self.use_neox_rotary_style = use_neox_rotary_style
+        if llm_config.kvcache_config is not None:
+            self.kvcache_quant_method = llm_config.kvcache_config.kvcache_quant_config.get_quant_method(
+                self)
+            self.kvcache_quant_method.create_weights(self)
+        if llm_config.quant_config is not None:
+            self.quant_max_bound = llm_config.quant_config.quant_max_bound
+            self.quant_min_bound = llm_config.quant_config.quant_min_bound
+
+    def forward(
+        self,
+        q: paddle.Tensor = None,
+        k: paddle.Tensor = None,
+        v: paddle.Tensor = None,
+        qkv: paddle.Tensor = None,
+        forward_meta: ForwardMeta = None,
+    ):
+        """
+        The forward function of attention layer.
+        args:
+            q: the query tensor
+            k: the key tensor
+            v: the value tensor
+            forward_meta: the forward meta data
+        """
+        return forward_meta.attn_backend.forward(
+            q,
+            k,
+            v,
+            qkv,
+            self,
+            forward_meta,
+        )