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fix(tf): initialize NVNMD map outputs #5847

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46 changes: 42 additions & 4 deletions source/op/tf/map_flt_nvnmd.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,8 @@ y output
//

//- import the library of tensorflow
#include <algorithm>

#include "custom_op.h"
#include "env_mat_nvnmd.h"

Expand Down Expand Up @@ -54,24 +56,42 @@ class MapFltNvnmdOp : public OpKernel {
/// Compute the descriptor
/// param: context
void Compute(OpKernelContext* context) override {
DCHECK_EQ(3, context->num_inputs());
OP_REQUIRES(context, context->num_inputs() == 4,
deepmd::tf_compat::InvalidArgument(
"MapFltNvnmd expects four input tensors"));

const Tensor& t_x = context->input(0);
const Tensor& t_table = context->input(1);
const Tensor& t_table_grad = context->input(2);
const Tensor& t_table_info = context->input(3);

const TensorShape& shX = t_x.shape();
const TensorShape& shT = t_table.shape();
const TensorShape& shTG = t_table_grad.shape();
const TensorShape& shI = t_table_info.shape();

OP_REQUIRES(context, shX.dims() == 2,
deepmd::tf_compat::InvalidArgument("Dim of x should be 2"));
OP_REQUIRES(context, shT.dims() == 2,
deepmd::tf_compat::InvalidArgument("Dim of table should be 2"));
OP_REQUIRES(context, shTG == shT,
deepmd::tf_compat::InvalidArgument(
"table_grad shape should match table"));
OP_REQUIRES(
context, shI.dims() == 1,
deepmd::tf_compat::InvalidArgument("Dim of table_info should be 1"));
OP_REQUIRES(context, shT.dim_size(1) > 0 && shT.dim_size(1) % 4 == 0,
deepmd::tf_compat::InvalidArgument(
"table width should be a positive multiple of 4"));
OP_REQUIRES(context, shI.dim_size(0) > 0 && shI.dim_size(0) % 5 == 0,
deepmd::tf_compat::InvalidArgument(
"table_info length should be a positive multiple of 5"));

int N = shX.dim_size(0);
int D = shX.dim_size(1);
int M = shT.dim_size(1) / 4;
int S = shI.dim_size(0) / 5;

DCHECK_EQ(shX.dims(), 2);
DCHECK_EQ(shT.dims(), 2);

/*
* Calculate the output
* 1.create tensor
Expand All @@ -94,6 +114,24 @@ class MapFltNvnmdOp : public OpKernel {
auto info = t_table_info.flat<FPTYPE>().data();
auto y = t_y->flat<FPTYPE>().data();

// Values outside every configured interval intentionally map to zero.
// Initialize first so skipped entries never expose allocator contents.
if (t_y->NumElements() > 0) {
std::fill(y, y + t_y->NumElements(), FPTYPE(0));
}

for (int interval = 0; interval < S; ++interval) {
const FPTYPE x0 = info[interval * 5 + 0];
const FPTYPE x1 = info[interval * 5 + 1];
const FPTYPE dx = info[interval * 5 + 2];
const int n0 = int(info[interval * 5 + 3]);
const int n1 = int(info[interval * 5 + 4]);
OP_REQUIRES(
context,
x0 <= x1 && dx > 0 && n0 >= 0 && n1 > n0 && n1 <= shT.dim_size(0),
deepmd::tf_compat::InvalidArgument("invalid interval in table_info"));
}

int ss, ii, jj;
FPTYPE xi, x0, x1, dx;
FPTYPE xx, id;
Expand Down
65 changes: 65 additions & 0 deletions source/tests/tf/test_nvnmd_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -421,5 +421,70 @@ def test_op(self) -> None:
tf.reset_default_graph()


class TestOpMapFltNvnmd(tf.test.TestCase):
"""Verify the mapping op's four-input contract and range behavior."""

def test_out_of_range_values_map_to_zero(self) -> None:
sample_count = 4096
x = tf.placeholder(tf.float64, [sample_count, 1])
table = tf.constant(
[
[0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 11.0],
[0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 21.0],
],
dtype=tf.float64,
)
table_grad = tf.constant(
[
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0],
[0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 4.0],
],
dtype=tf.float64,
)
table_info = tf.constant([0.0, 2.0, 1.0, 0.0, 2.0], dtype=tf.float64)

mapped = op_module.map_flt_nvnmd(x, table, table_grad, table_info)
gradient = tf.gradients(tf.reduce_sum(mapped), x)[0]

warm_x = np.tile([[0.25], [1.25]], (sample_count // 2, 1))
test_x = np.full((sample_count, 1), -1.0)
test_x[:5, 0] = [-1.0, 0.25, 1.25, 2.0, 3.0]
with self.cached_session() as sess:
# Reuse a nonzero, same-sized allocation so the old skipped-write
# behavior cannot pass merely because fresh pages happen to be zero.
sess.run(mapped, feed_dict={x: warm_x})
actual, actual_gradient = sess.run(
[mapped, gradient], feed_dict={x: test_x}
)

expected = np.zeros((sample_count, 1, 2))
expected[:5, 0] = [
[0.0, 0.0],
[10.0, 11.0],
[20.0, 21.0],
[20.0, 21.0],
[0.0, 0.0],
]
expected_gradient = np.zeros((sample_count, 1))
expected_gradient[:5, 0] = [0.0, 3.0, 7.0, 7.0, 0.0]

np.testing.assert_array_equal(actual, expected)
np.testing.assert_array_equal(actual_gradient, expected_gradient)

def test_rejects_mismatched_table_gradient(self) -> None:
mapped = op_module.map_flt_nvnmd(
tf.zeros([1, 1], dtype=tf.float64),
tf.zeros([1, 4], dtype=tf.float64),
tf.zeros([2, 4], dtype=tf.float64),
tf.constant([0.0, 1.0, 1.0, 0.0, 1.0], dtype=tf.float64),
)
with self.cached_session() as sess:
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
"table_grad shape should match table",
):
sess.run(mapped)


if __name__ == "__main__":
unittest.main()
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