[LLM] First commit the llm deployment code

fastdeploy/model_executor/layers/backends/xpu/utils.py

@@ -0,0 +1,87 @@
+"""
+# 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.
+
+ !! This file will be deleted after the platform is fully functional
+"""
+
+import numpy as np
+import paddle
+
+
+def xpu_clip_and_round(x):
+    """
+    Clip and round the input array to the range [-127, 127] and convert to int8.
+
+    Args:
+        x (numpy.ndarray): The input array to be clipped and rounded.
+
+    Returns:
+        numpy.ndarray: The clipped and rounded array with dtype int8.
+    """
+    return np.clip(np.around(x), -127, 127).astype("int8")
+
+
+def xpu_quant_qkv_weight(weight_np):
+    """
+    Quantize the query, key, and value weights for the Transformer model.
+
+    Args:
+        weight_np (numpy.ndarray): The original weights of query, key, and value in numpy format.
+            It should be a 2D or higher dimensional tensor, where the last dimension represents the
+            embedding dimension.
+
+    Returns:
+        tuple: A tuple containing:
+            quanted_weight (paddle.Tensor): The quantized weights in paddle tensor format,
+                with the same shape as the input weight_np.
+            weight_scales (paddle.Tensor): The scaling factors for each element in the last dimension
+                of the input, used to recover the original value range from the quantized weights.
+    """
+    dim_embed = weight_np.shape[-1]
+    weight = np.reshape(weight_np, [-1, dim_embed])
+    max_value = np.max(np.abs(weight), axis=1).reshape(-1, 1)
+    quanted_weight = xpu_clip_and_round(weight / max_value * 127.0)
+    quanted_weight = np.reshape(quanted_weight, weight_np.shape)
+    quanted_weight = paddle.to_tensor(quanted_weight, place=paddle.CPUPlace())
+    weight_scales = (max_value / 127.0).astype(weight_np.dtype).reshape(-1)
+    weight_scales = paddle.to_tensor(weight_scales, place=paddle.CPUPlace())
+    weight_scales = paddle.cast(weight_scales, paddle.get_default_dtype())
+    return quanted_weight, weight_scales
+
+
+def xpu_quant_weight(weight_np):
+    """
+    Quantize the weight tensor for XPU devices.
+
+    Args:
+        weight_np (numpy.ndarray): The original weight tensor in numpy format,
+            expected to be a 2D array.
+
+    Returns:
+        tuple: A tuple containing two elements:
+            quanted_weight (paddle.Tensor): The quantized weight tensor,
+                converted to a Paddle Tensor on CPU.
+            weight_scales (paddle.Tensor): The corresponding scales for the quantized
+                weights, also converted to a Paddle Tensor on CPU and cast to the
+                default data type.
+    """
+    weight = np.transpose(weight_np, [1, 0])
+    max_value = np.max(np.abs(weight), axis=1).reshape(-1, 1)
+    quanted_weight = xpu_clip_and_round(weight / max_value * 127.0)
+    quanted_weight = paddle.to_tensor(quanted_weight, place=paddle.CPUPlace())
+    weight_scales = (max_value / 127.0).astype(weight_np.dtype).reshape(-1)
+    weight_scales = paddle.to_tensor(weight_scales, place=paddle.CPUPlace())
+    weight_scales = paddle.cast(weight_scales, paddle.get_default_dtype())
+    return quanted_weight, weight_scales