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fix(tf): validate flattened multi-device op widths #5827

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fix(tf): validate flattened multi-device op widths #5827

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4cf48fd
fix(tf): validate flattened multi-device op widths
Jul 16, 2026
e736a90
test(tf): cover multi-device width guards
Jul 23, 2026
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43 changes: 43 additions & 0 deletions source/op/tf/custom_op.h
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
// SPDX-License-Identifier: LGPL-3.0-or-later
#pragma once
#include <iostream>
#include <limits>
#include <string>
#include <utility>
#include <vector>
Expand Down Expand Up @@ -51,6 +52,48 @@ inline Status InvalidArgument(Args&&... args) {
return tensorflow::errors::InvalidArgument(std::forward<Args>(args)...);
#endif
}

/**
* @brief Derive a dense tensor's per-atom width without truncating division.
*
* Several low-level TensorFlow ops flatten atom and feature dimensions into a
* single axis. Validate the flattened width before dividing by `nloc`; raw
* CPU/GPU kernels cannot safely consume a leftover partial atom row.
*
* @param per_atom_width Receives the validated feature width for one atom.
* @param shape Rank-two tensor shape whose second dimension is flattened.
* @param nloc Number of local atoms encoded in the flattened dimension.
* @param tensor_name Human-readable input name used in validation errors.
* @return An OK status, or InvalidArgument when the width is incompatible.
*/
inline Status GetPerAtomWidth(int* per_atom_width,
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const TensorShape& shape,
const int nloc,
const char* tensor_name) {
const int64_t flattened_width = shape.dim_size(1);
if (nloc < 0) {
return InvalidArgument("number of local atoms should be non-negative");
}
if (nloc == 0) {
if (flattened_width != 0) {
return InvalidArgument(tensor_name,
" width should be zero when nloc is zero");
}
*per_atom_width = 0;
return Status();
}
if (flattened_width % nloc != 0) {
return InvalidArgument(tensor_name, " width ", flattened_width,
" should be divisible by nloc ", nloc);
}
const int64_t width = flattened_width / nloc;
if (width > std::numeric_limits<int>::max()) {
return InvalidArgument(tensor_name,
" width per atom exceeds the supported int range");
}
*per_atom_width = static_cast<int>(width);
return Status();
}
} // namespace tf_compat
} // namespace deepmd

Expand Down
22 changes: 18 additions & 4 deletions source/op/tf/prod_force_grad_multi_device.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -73,8 +73,12 @@ class ProdForceSeAGradOp : public OpKernel {

int nframes = net_deriv_tensor.shape().dim_size(0);
int nloc = natoms(0);
int ndescrpt = nloc > 0 ? net_deriv_tensor.shape().dim_size(1) / nloc : 0;
int nnei = nloc > 0 ? nlist_tensor.shape().dim_size(1) / nloc : 0;
int ndescrpt;
int nnei;
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&ndescrpt, net_deriv_shape, nloc, "net deriv"));
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&nnei, nlist_shape, nloc, "nlist"));

// check the sizes
OP_REQUIRES(
Expand All @@ -97,6 +101,9 @@ class ProdForceSeAGradOp : public OpKernel {
OP_REQUIRES(
context, (nnei == n_a_sel + n_r_sel),
deepmd::tf_compat::InvalidArgument("number of neighbors should match"));
OP_REQUIRES(context, (static_cast<int64_t>(nnei) * 4 == ndescrpt),
deepmd::tf_compat::InvalidArgument(
"descriptor width should be four times neighbor width"));

// Create an output tensor
TensorShape grad_net_shape;
Expand Down Expand Up @@ -194,8 +201,12 @@ class ProdForceSeRGradOp : public OpKernel {

int nframes = net_deriv_tensor.shape().dim_size(0);
int nloc = natoms(0);
int ndescrpt = nloc > 0 ? net_deriv_tensor.shape().dim_size(1) / nloc : 0;
int nnei = nloc > 0 ? nlist_tensor.shape().dim_size(1) / nloc : 0;
int ndescrpt;
int nnei;
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&ndescrpt, net_deriv_shape, nloc, "net deriv"));
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&nnei, nlist_shape, nloc, "nlist"));

// check the sizes
OP_REQUIRES(
Expand All @@ -215,6 +226,9 @@ class ProdForceSeRGradOp : public OpKernel {
(int_64(nloc) * ndescrpt * 3 == in_deriv_shape.dim_size(1)),
deepmd::tf_compat::InvalidArgument(
"number of descriptors should match"));
OP_REQUIRES(context, (nnei == ndescrpt),
deepmd::tf_compat::InvalidArgument(
"descriptor width should equal neighbor width"));

// Create an output tensor
TensorShape grad_net_shape;
Expand Down
24 changes: 20 additions & 4 deletions source/op/tf/prod_force_multi_device.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -92,8 +92,13 @@ class ProdForceSeAOp : public OpKernel {
int nloc = natoms[0];
int nall = natoms[1];
int nframes = net_deriv_tensor.shape().dim_size(0);
int ndescrpt = nloc > 0 ? net_deriv_tensor.shape().dim_size(1) / nloc : 0;
int nnei = nloc > 0 ? nlist_tensor.shape().dim_size(1) / nloc : 0;
int ndescrpt;
int nnei;
OP_REQUIRES_OK(context,
deepmd::tf_compat::GetPerAtomWidth(
&ndescrpt, net_deriv_tensor.shape(), nloc, "net deriv"));
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&nnei, nlist_tensor.shape(), nloc, "nlist"));
// check the sizes
OP_REQUIRES(
context, (nframes == in_deriv_tensor.shape().dim_size(0)),
Expand All @@ -106,6 +111,9 @@ class ProdForceSeAOp : public OpKernel {
(int_64(nloc) * ndescrpt * 3 == in_deriv_tensor.shape().dim_size(1)),
deepmd::tf_compat::InvalidArgument(
"number of descriptors should match"));
OP_REQUIRES(context, (static_cast<int64_t>(nnei) * 4 == ndescrpt),
deepmd::tf_compat::InvalidArgument(
"descriptor width should be four times neighbor width"));
// Create an output tensor
TensorShape force_shape;
force_shape.AddDim(nframes);
Expand Down Expand Up @@ -199,8 +207,13 @@ class ProdForceSeROp : public OpKernel {
int nloc = natoms[0];
int nall = natoms[1];
int nframes = net_deriv_tensor.shape().dim_size(0);
int ndescrpt = nloc > 0 ? net_deriv_tensor.shape().dim_size(1) / nloc : 0;
int nnei = nloc > 0 ? nlist_tensor.shape().dim_size(1) / nloc : 0;
int ndescrpt;
int nnei;
OP_REQUIRES_OK(context,
deepmd::tf_compat::GetPerAtomWidth(
&ndescrpt, net_deriv_tensor.shape(), nloc, "net deriv"));
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&nnei, nlist_tensor.shape(), nloc, "nlist"));
// check the sizes
OP_REQUIRES(
context, (nframes == in_deriv_tensor.shape().dim_size(0)),
Expand All @@ -213,6 +226,9 @@ class ProdForceSeROp : public OpKernel {
in_deriv_tensor.shape().dim_size(1)),
deepmd::tf_compat::InvalidArgument(
"number of descriptors should match"));
OP_REQUIRES(context, (nnei == ndescrpt),
deepmd::tf_compat::InvalidArgument(
"descriptor width should equal neighbor width"));
// Create an output tensor
TensorShape force_shape;
force_shape.AddDim(nframes);
Expand Down
22 changes: 18 additions & 4 deletions source/op/tf/prod_virial_grad_multi_device.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -79,8 +79,12 @@ class ProdVirialSeAGradOp : public OpKernel {

int nframes = net_deriv_tensor.shape().dim_size(0);
int nloc = natoms(0);
int ndescrpt = nloc > 0 ? net_deriv_tensor.shape().dim_size(1) / nloc : 0;
int nnei = nloc > 0 ? nlist_tensor.shape().dim_size(1) / nloc : 0;
int ndescrpt;
int nnei;
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&ndescrpt, net_deriv_shape, nloc, "net deriv"));
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&nnei, nlist_shape, nloc, "nlist"));

// check the sizes
OP_REQUIRES(
Expand Down Expand Up @@ -109,6 +113,9 @@ class ProdVirialSeAGradOp : public OpKernel {
OP_REQUIRES(
context, (nnei == n_a_sel + n_r_sel),
deepmd::tf_compat::InvalidArgument("number of neighbors should match"));
OP_REQUIRES(context, (static_cast<int64_t>(nnei) * 4 == ndescrpt),
deepmd::tf_compat::InvalidArgument(
"descriptor width should be four times neighbor width"));

// Create an output tensor
TensorShape grad_net_shape;
Expand Down Expand Up @@ -223,8 +230,12 @@ class ProdVirialSeRGradOp : public OpKernel {

int nframes = net_deriv_tensor.shape().dim_size(0);
int nloc = natoms(0);
int ndescrpt = nloc > 0 ? net_deriv_tensor.shape().dim_size(1) / nloc : 0;
int nnei = nloc > 0 ? nlist_tensor.shape().dim_size(1) / nloc : 0;
int ndescrpt;
int nnei;
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&ndescrpt, net_deriv_shape, nloc, "net deriv"));
OP_REQUIRES_OK(context, deepmd::tf_compat::GetPerAtomWidth(
&nnei, nlist_shape, nloc, "nlist"));

// check the sizes
OP_REQUIRES(
Expand All @@ -250,6 +261,9 @@ class ProdVirialSeRGradOp : public OpKernel {
OP_REQUIRES(
context, (int_64(nloc) * nnei * 3 == rij_shape.dim_size(1)),
deepmd::tf_compat::InvalidArgument("dim of rij should be nnei * 3"));
OP_REQUIRES(context, (nnei == ndescrpt),
deepmd::tf_compat::InvalidArgument(
"descriptor width should equal neighbor width"));

// Create an output tensor
TensorShape grad_net_shape;
Expand Down
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