Clarifiy se_atten_v2 compression doc (deepmodeling#3727)

deepmodeling#3643  - **Documentation** - Simplified the description for the number of attention layers in the code documentation. - Added a notice about model compression compatibility for `se_atten_v2` descriptor in the documentation.  --------- Signed-off-by: Jinzhe Zeng <[email protected]> Co-authored-by: Jinzhe Zeng <[email protected]> (cherry picked from commit 62832e8) Signed-off-by: Jinzhe Zeng <[email protected]>
njzjz · Jul 2, 2024 · 776a0be · 776a0be
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diff --git a/deepmd_utils/utils/argcheck.py b/deepmd_utils/utils/argcheck.py
@@ -365,7 +365,7 @@ def descrpt_se_atten_common_args():
     doc_seed = "Random seed for parameter initialization"
     doc_exclude_types = "The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1."
     doc_attn = "The length of hidden vectors in attention layers"
-    doc_attn_layer = "The number of attention layers. Note that model compression of `se_atten` is only enabled when attn_layer==0 and stripped_type_embedding is True"
+    doc_attn_layer = "The number of attention layers."
     doc_attn_dotr = "Whether to do dot product with the normalized relative coordinates"
     doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix"
 

diff --git a/doc/freeze/compress.md b/doc/freeze/compress.md
@@ -148,6 +148,8 @@ The model compression interface requires the version of DeePMD-kit used in the o
 
 Descriptors with `se_e2_a`, `se_e3`, `se_e2_r` and `se_atten_v2` types are supported by the model compression feature. `Hybrid` mixed with the above descriptors is also supported.
 
+Notice: Model compression for the `se_atten_v2` descriptor is exclusively designed for models with the training parameter {ref}`attn_layer <model/descriptor[se_atten_v2]/attn_layer>` set to 0.
+
 **Available activation functions for descriptor:**
 
 - tanh

diff --git a/doc/model/train-se-atten.md b/doc/model/train-se-atten.md
@@ -120,6 +120,8 @@ We highly recommend using the version 2.0 of the attention-based descriptor `"se
 
 Practical evidence demonstrates that `"se_atten_v2"` offers better and more stable performance compared to `"se_atten"`.
 
+Notice: Model compression for the `se_atten_v2` descriptor is exclusively designed for models with the training parameter {ref}`attn_layer <model/descriptor[se_atten_v2]/attn_layer>` set to 0.
+
 ### Fitting `"ener"`
 
 DPA-1 only supports `"ener"` fitting type, and you can refer [here](train-energy.md) for detailed information.
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     Descriptors with `se_e2_a`, `se_e3`, `se_e2_r` and `se_atten_v2` types are supported by the model compression feature. `Hybrid` mixed with the above descriptors is also supported.
+    Notice: Model compression for the `se_atten_v2` descriptor is exclusively designed for models with the training parameter {ref}`attn_layer <model/descriptor[se_atten_v2]/attn_layer>` set to 0.
     **Available activation functions for descriptor:**
     - tanh
@@ Expand Down @@