init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

csrc/common/include/op_graph/op_transformer_proto_extend.h

@@ -0,0 +1,100 @@
+/**
+ * Copyright (c) 2025 Huawei Technologies Co., Ltd.
+ * This program is free software, you can redistribute it and/or modify it under the terms and conditions of
+ * CANN Open Software License Agreement Version 2.0 (the "License").
+ * Please refer to the License for details. You may not use this file except in compliance with the License.
+ * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
+ * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
+ * See LICENSE in the root of the software repository for the full text of the License.
+ */
+
+/*!
+ * \file op_transformer_proto_extend.h
+ * \brief
+ */
+#ifndef OPS_OP_MATH_PROTO_EXTEND_H_
+#define OPS_OP_MATH_PROTO_EXTEND_H_
+
+#include "graph/operator_reg.h"
+
+namespace ge {
+/**
+* @brief swin_transformer model specific structure.Operator only supports swin_transformer.
+
+* @par Inputs:
+* Three inputs, including:
+* @li x: An ND Tensor. Must be one of the following types: float16, float, bfloat16,
+         the shape should be (B*W, N, S1, S2) or (B, W, N, S1, S2).
+* @li atten_mask: An ND Tensor. Must be one of the following types: float16, float, bfloat16,
+                  the shape should be (W, S1, S2) or (W, 1, S1, S2) or (1, W, 1, S1, S2)
+* @li relative_pos_bias: An ND Tensor. Must be one of the following types: float16, float, bfloat16.
+                         the shape sholud be (N, S1, S2) or (1, N, S1, S2) or (1, 1, N, S1, S2)
+
+* @par Attributes:
+* @li scale_value: A optional attribute, the type is float. Defaults to 1.0.
+* @li inner_precision_mode: A optional attribute, the type is int. Defaults to 0, reserved field.
+
+* @par Outputs:
+* One output, including:
+* @li y: An ND Tensor. Must be one of the following types: float16, float, bfloat16,
+         the shape should be same with x.
+*/
+REG_OP(MaskedSoftmaxWithRelPosBias)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_BFLOAT16, DT_FLOAT}))
+    .OPTIONAL_INPUT(atten_mask, TensorType({DT_FLOAT16, DT_BFLOAT16, DT_FLOAT}))
+    .INPUT(relative_pos_bias, TensorType({DT_FLOAT16, DT_BFLOAT16, DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_BFLOAT16, DT_FLOAT}))
+    .ATTR(scale_value, Float, 1.0)
+    .ATTR(inner_precision_mode, Int, 0)
+    .OP_END_FACTORY_REG(MaskedSoftmaxWithRelPosBias)
+
+/**
+* @brief AttentionScore's forward calculation.
+
+* @par Inputs:
+* six inputs, including:
+* @li query: A matrix Tensor. The type only support float16. Enter a 4D Tensor.
+* @li key: A matrix Tensor. The type only support float16. Enter a 4D Tensor.
+* @li value: A matrix Tensor. The type only support float16. Enter a 4D Tensor.
+* @li padding_mask: A matrix Tensor. The type only support float16. Enter a 4D Tensor.
+* @li scale: A scalar. The type only support float16. Enter a 4D Tensor.
+* @li drop_mask: A matrix Tensor. An optional input parameter. The type only support uint8. Enter a 4D Tensor.
+
+* @par Attributes:
+* @li keep_prob: A float. The keep probability of dropout. Default: 1.0.
+* @li query_transpose: A bool. If True, changes the shape of "query" from [B, N, S, D] to [B, N, D, S].
+* Default: false.
+* @li key_transpose: A bool. If True, changes the shape of "key" from [B, N, S, D] to [B, N, D, S].
+* Default: false.
+* @li bmm_score_transpose_a: A bool. If True, changes the shape of "mid_data" from [B, N, S, D] to [B, N, D, S].
+* Default: false.
+* @li bmm_score_transpose_b: A bool. If True, changes the shape of "value" from [B, N, S, D] to [B, N, D, S].
+* Default: false.
+* @li softmax_axes: A list of int. The dimension softmax would be performed on. Defaults to "[-1]".
+
+* @par Outputs:
+* attention_score: The result matrix Tensor. The type only support float16. The output shape is the same as query.
+* softmax_output: The result matrix Tensor. The type only support float16. The output shape is the same as query.
+
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+*/
+REG_OP(AttentionScore)
+    .INPUT(query, TensorType({DT_FLOAT16}))
+    .INPUT(key, TensorType({DT_FLOAT16}))
+    .INPUT(value, TensorType({DT_FLOAT16}))
+    .INPUT(padding_mask, TensorType({DT_FLOAT16}))
+    .INPUT(scale, TensorType({DT_FLOAT16}))
+    .OPTIONAL_INPUT(drop_mask, TensorType({DT_INT8}))
+    .OUTPUT(attention_score, TensorType({DT_FLOAT16}))
+    .OUTPUT(softmax_output, TensorType({DT_FLOAT16}))
+    .ATTR(keep_prob, Float, 1.0)
+    .ATTR(query_transpose, Bool, false)
+    .ATTR(key_transpose, Bool, false)
+    .ATTR(bmm_score_transpose_a, Bool, false)
+    .ATTR(bmm_score_transpose_b, Bool, false)
+    .ATTR(softmax_axes, ListInt, {-1})
+    .OP_END_FACTORY_REG(AttentionScore)
+}
+
+#endif