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Mar 28, 2025
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Improve ET_CHECK_OR_RETURN_FALSE error messages in kernels/portable #9751

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8 changes: 6 additions & 2 deletions kernels/portable/cpu/op_convolution_backward.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,9 @@ bool check_convolution_backward_args(
ET_CHECK_OR_RETURN_FALSE(
transposed == false, "Transposed Convolution Backward not supported yet");
ET_CHECK_OR_RETURN_FALSE(
weight.dim() == 4, "Only 2D Convolution Backward supported for now");
weight.dim() == 4,
"Only 2D Convolution Backward supported for now; weight.dim() = %zd",
weight.dim());

ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(weight, input));
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(grad_output, input));
Expand Down Expand Up @@ -91,7 +93,9 @@ bool check_convolution_backward_args(

ET_CHECK_OR_RETURN_FALSE(
grad_output.dim() == input.dim(),
"grad_output should have same number of dimensions as input");
"grad_output should have same number of dimensions as input; grad_output.dim() = %zd, input.dim() = %zd",
grad_output.dim(),
input.dim());

ET_LOG_AND_RETURN_IF_FALSE(
tensor_has_expected_size(grad_output, {output_sizes, output_ndim}));
Expand Down
15 changes: 12 additions & 3 deletions kernels/portable/cpu/op_linear_scratch_example.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -41,13 +41,22 @@ bool check_linear_scratch_example_args(
Tensor& out,
Tensor& scratch) {
ET_CHECK_OR_RETURN_FALSE(
input.size(1) == weight.size(1), "Unexpected weight size 1");
input.size(1) == weight.size(1),
"Unexpected weight size 1; input.size(1) = %zd, weight.size(1) = %zd",
input.size(1),
weight.size(1));

ET_CHECK_OR_RETURN_FALSE(
scratch.size(0) == input.size(0), "Unexpected scratch size 0");
scratch.size(0) == input.size(0),
"Unexpected scratch size 0; scratch.size(0) = %zd, input.size(0) = %zd",
scratch.size(0),
input.size(0));

ET_CHECK_OR_RETURN_FALSE(
scratch.size(1) == weight.size(0), "Unexpected scratch size 1");
scratch.size(1) == weight.size(0),
"Unexpected scratch size 1; scratch.size(1) = %zd, weight.size(0) = %zd",
scratch.size(1),
weight.size(0));

return true;
}
Expand Down
4 changes: 3 additions & 1 deletion kernels/portable/cpu/op_max_pool2d_with_indices_backward.cpp
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Expand Up @@ -62,7 +62,9 @@ bool check_max_pool2d_backward_args(

ET_CHECK_OR_RETURN_FALSE(
grad_output.dim() == input.dim(),
"grad_output should have same number of dimensions as input");
"grad_output should have same number of dimensions as input; grad_output.dim() = %zd, input.dim() = %zd",
grad_output.dim(),
input.dim());

ET_LOG_AND_RETURN_IF_FALSE(
tensor_has_expected_size(grad_output, {output_sizes, output_ndim}));
Expand Down
23 changes: 18 additions & 5 deletions kernels/portable/cpu/op_repeat_interleave.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -22,24 +22,37 @@ bool check_repeat_interleave_args(
ET_CHECK_OR_RETURN_FALSE(
repeats.scalar_type() == ScalarType::Int ||
repeats.scalar_type() == ScalarType::Long,
"repeats must be int or long");
ET_CHECK_OR_RETURN_FALSE(repeats.dim() == 1, "repeats must be 1D");
"repeats must be int or long; repeats.scalar_type() = %d",
static_cast<int>(repeats.scalar_type()));
ET_CHECK_OR_RETURN_FALSE(
repeats.dim() == 1,
"repeats must be 1-D; repeats.dim() = %zd",
repeats.dim());
ET_CHECK_OR_RETURN_FALSE(
output_size_value == repeats_sum,
"output_size, if provided, must be equal to repeats.sum()");
"output_size, if provided, must be equal to repeats.sum(); output_size_value = %" PRId64
", repeats_sum = %" PRId64,
output_size_value,
repeats_sum);
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(repeats, out));

if (repeats.scalar_type() == ScalarType::Long) {
const int64_t* const repeats_data = repeats.const_data_ptr<int64_t>();
for (const auto i : c10::irange(repeats.numel())) {
ET_CHECK_OR_RETURN_FALSE(
repeats_data[i] >= 0, "repeats cannot be negative");
repeats_data[i] >= 0,
"repeats cannot be negative; repeats_data[%" PRId64 "] = %" PRId64,
static_cast<int64_t>(i),
repeats_data[i]);
}
} else {
const int32_t* const repeats_data = repeats.const_data_ptr<int32_t>();
for (const auto i : c10::irange(repeats.numel())) {
ET_CHECK_OR_RETURN_FALSE(
repeats_data[i] >= 0, "repeats cannot be negative");
repeats_data[i] >= 0,
"repeats cannot be negative; repeats_data[%" PRId64 "] = %d",
static_cast<int64_t>(i),
repeats_data[i]);
}
}

Expand Down
8 changes: 7 additions & 1 deletion kernels/portable/cpu/op_topk.cpp
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Expand Up @@ -30,7 +30,13 @@ bool check_topk_args(
dim += nonzero_dim(in);
}
ET_CHECK_OR_RETURN_FALSE(
k >= 0 && k <= nonempty_size(in, dim), "selected index k out of range");
k >= 0 && k <= nonempty_size(in, dim),
"selected index k out of range; k = %" PRId64 ", dim = %" PRId64
", in.dim() = %zd, nonempty_size(in, dim) = %zd",
k,
dim,
in.dim(),
nonempty_size(in, dim));
return true;
}

Expand Down
5 changes: 4 additions & 1 deletion kernels/portable/cpu/util/activation_ops_util.cpp
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Expand Up @@ -43,7 +43,10 @@ bool check_glu_args(const Tensor& in, int64_t dim, Tensor& out) {
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_rank(in, out));
ET_CHECK_OR_RETURN_FALSE(
out.size(non_negative_dim) == dim_size / 2,
"output tensor must have half the size of the input tensor along the specified dimension.");
"output tensor must have half the size of the input tensor along the specified dimension; out.size(%zu) = %zd, dim_size = %zd",
non_negative_dim,
out.size(non_negative_dim),
dim_size);

for (const auto i : c10::irange(in.dim())) {
if (static_cast<size_t>(i) != non_negative_dim) {
Expand Down
14 changes: 11 additions & 3 deletions kernels/portable/cpu/util/advanced_index_util.cpp
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Expand Up @@ -28,7 +28,8 @@ bool check_indices_dtypes(TensorOptList indices) {
ET_CHECK_OR_RETURN_FALSE(
ix_type == ScalarType::Long || ix_type == ScalarType::Int ||
ix_type == ScalarType::Byte || ix_type == ScalarType::Bool,
"Index tensors should be Long, Int, Byte or Bool");
"Index tensors should be Long, Int, Byte or Bool; got %d",
static_cast<int>(ix_type));
}
}
return true;
Expand Down Expand Up @@ -295,11 +296,18 @@ bool get_index_out_target_size(

ET_CHECK_OR_RETURN_FALSE(
static_cast<ssize_t>(num_null_indices + num_indexed_dims) <= in.dim(),
"Indexing too many dimensions");
"Indexing too many dimensions; num_null_indices = %zu, num_indexed_dims = %zu, in.dim() = %zd",
num_null_indices,
num_indexed_dims,
in.dim());

ET_CHECK_OR_RETURN_FALSE(
in.dim() + broadcast_ndim - num_indexed_dims <= kTensorDimensionLimit,
"Out tensor would exceed number of allowed dimensions");
"Out tensor would exceed number of allowed dimensions; in.dim() = %zd, broadcast_ndim = %zu, num_indexed_dims = %zu, kTensorDimensionLimit = %zu",
in.dim(),
broadcast_ndim,
num_indexed_dims,
kTensorDimensionLimit);

(*out_ndim) = in.dim() + broadcast_ndim - num_indexed_dims;

Expand Down
23 changes: 18 additions & 5 deletions kernels/portable/cpu/util/copy_ops_util.cpp
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Expand Up @@ -46,7 +46,10 @@ bool check_as_strided_copy_args(
Tensor& out) {
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(in, out));
ET_CHECK_OR_RETURN_FALSE(
size.size() == stride.size(), "mismatch in length of strides and shape");
size.size() == stride.size(),
"mismatch in length of strides and shape; size.size() = %zu, stride.size() = %zu",
size.size(),
stride.size());
for (const auto& val : stride) {
ET_CHECK_OR_RETURN_FALSE(
val >= 0,
Expand Down Expand Up @@ -242,7 +245,9 @@ bool check_permute_copy_args(const Tensor& in, IntArrayRef dims, Tensor& out) {

// Check that the dimension hasn't been seen previously.
ET_CHECK_OR_RETURN_FALSE(
dim_exist[dim] == false, "duplicate dims are not allowed.");
dim_exist[dim] == false,
"duplicate dims are not allowed; dim = %zu",
dim);

dim_exist[dim] = true;
}
Expand Down Expand Up @@ -424,19 +429,27 @@ bool check_split_with_sizes_copy_args(

ET_CHECK_OR_RETURN_FALSE(
split_sizes.size() == out.size(),
"Number of split sizes must match the number of output tensors");
"Number of split sizes must match the number of output tensors; split_sizes.size() = %zu, out.size() = %zu",
split_sizes.size(),
out.size());

int64_t sum = 0;
for (const auto i : c10::irange(split_sizes.size())) {
ET_CHECK_OR_RETURN_FALSE(
split_sizes[i] >= 0, "All split sizes must be non negative.");
split_sizes[i] >= 0,
"All split sizes must be non negative; split_sizes[%zu] = %" PRId64,
i,
split_sizes[i]);
sum += split_sizes[i];
}

const ssize_t dim_size = in.size(dim);
ET_CHECK_OR_RETURN_FALSE(
sum == dim_size,
"Sum of split sizes does not match input size at given dim");
"Sum of split sizes does not match input size at given dim; sum = %" PRId64
", dim_size = %zd",
sum,
dim_size);

return true;
}
Expand Down
6 changes: 4 additions & 2 deletions kernels/portable/cpu/util/distance_util.cpp
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Expand Up @@ -14,7 +14,8 @@ namespace executor {
bool check_pdist_args(const Tensor& in, double p, const Tensor& out) {
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(in, out));
ET_LOG_AND_RETURN_IF_FALSE(tensor_is_rank(in, 2));
ET_CHECK_OR_RETURN_FALSE(p >= 0, "pdist only supports non-negative p values");
ET_CHECK_OR_RETURN_FALSE(
p >= 0, "pdist only supports non-negative p values; p = %.6f", p);
return true;
}

Expand All @@ -39,7 +40,8 @@ bool check_cdist_args(
ET_LOG_AND_RETURN_IF_FALSE(tensor_has_rank_greater_or_equal_to(x2, 2));
ET_LOG_AND_RETURN_IF_FALSE(
tensors_have_same_size_at_dims(x1, x1.dim() - 1, x2, x2.dim() - 1));
ET_CHECK_OR_RETURN_FALSE(p >= 0, "cdist only supports non-negative p values");
ET_CHECK_OR_RETURN_FALSE(
p >= 0, "cdist only supports non-negative p values; p = %.6f", p);
if (compute_mode.has_value()) {
int64_t mode = compute_mode.value();
ET_CHECK_OR_RETURN_FALSE(
Expand Down
21 changes: 15 additions & 6 deletions kernels/portable/cpu/util/index_util.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -23,12 +23,15 @@ bool check_gather_args(
ET_LOG_AND_RETURN_IF_FALSE(tensor_has_dim(in, dim));
ET_CHECK_OR_RETURN_FALSE(
index.scalar_type() == ScalarType::Long,
"Expected dypte int64 for index");
"Expected dypte int64 for index; index.scalar_type() = %d",
static_cast<int>(index.scalar_type()));
if (index.numel() != 0) {
ET_CHECK_OR_RETURN_FALSE(
nonzero_dim(in) == nonzero_dim(index),
"self and index should have the same dimensionality when index is not empty "
"except for the case when one has dimension 0 and the other has dimension 1");
"except for the case when one has dimension 0 and the other has dimension 1; nonzero_dim(in) = %zd, nonzero_dim(index) = %zd",
nonzero_dim(in),
nonzero_dim(index));
}

// Normalize dim to non-negative value
Expand Down Expand Up @@ -67,7 +70,8 @@ bool check_index_select_args(
dim = dim < 0 ? dim + nonzero_dim(in) : dim;
ET_CHECK_OR_RETURN_FALSE(
nonempty_size(in, dim) > 0,
"index_select: Indexing axis dim should be positive");
"index_select: Indexing axis dim should be positive; nonempty_size(in, dim) = %zd",
nonempty_size(in, dim));

ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(in, out));
ET_CHECK_OR_RETURN_FALSE(
Expand All @@ -80,7 +84,8 @@ bool check_index_select_args(
if (index.dim() > 0 && in.dim() == 0) {
ET_CHECK_OR_RETURN_FALSE(
index.numel() == 1,
"index_select: Index to scalar must have exactly 1 value");
"index_select: Index to scalar must have exactly 1 value; index.numel() = %zd",
index.numel());
}

if (index.scalar_type() == ScalarType::Long) {
Expand Down Expand Up @@ -150,7 +155,8 @@ bool check_scatter_add_args(
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(self, src));
ET_CHECK_OR_RETURN_FALSE(
index.scalar_type() == ScalarType::Long,
"Expected dypte int64 for index");
"Expected dypte int64 for index; index.scalar_type() = %d",
static_cast<int>(index.scalar_type()));
ET_LOG_AND_RETURN_IF_FALSE(tensor_has_dim(self, dim));

if (index.numel() == 0) {
Expand All @@ -160,7 +166,10 @@ bool check_scatter_add_args(
ET_CHECK_OR_RETURN_FALSE(
nonzero_dim(self) == nonzero_dim(src) &&
nonzero_dim(self) == nonzero_dim(index),
"self, index and src should have same number of dimensions.");
"self, index and src should have same number of dimensions; nonzero_dim(self) = %zd, nonzero_dim(src) = %zd, nonzero_dim(index) = %zd",
nonzero_dim(self),
nonzero_dim(src),
nonzero_dim(index));

// Normalize dim to non-negative value
if (dim < 0) {
Expand Down
28 changes: 20 additions & 8 deletions kernels/portable/cpu/util/kernel_ops_util.cpp
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Expand Up @@ -254,7 +254,10 @@ bool check_arange_args(double start, double end, double step, Tensor& out) {

ET_CHECK_OR_RETURN_FALSE(
(step > 0 && (end >= start)) || (step < 0 && (end <= start)),
"upper bound and larger bound inconsistent with step sign");
"upper bound and larger bound inconsistent with step sign; step = %.6f, start = %.6f, end = %.6f",
step,
start,
end);

return true;
}
Expand All @@ -276,7 +279,8 @@ bool check_avg_pool2d_args(
ET_CHECK_OR_RETURN_FALSE(
(in.dim() == 3 && in.size(0) > 0 && in.size(1) > 0 && in.size(2) > 0) ||
(in.dim() == 4 && in.size(1) > 0 && in.size(2) > 0 && in.size(3) > 0),
"Expected 3D or 4D (batch mode) tensor with optional 0 dim batch size for input");
"Expected 3D or 4D (batch mode) tensor with optional 0 dim batch size for input; in.dim() = %zd",
in.dim());

ET_LOG_AND_RETURN_IF_FALSE(
kernel_size_is_valid(kernel_size, /*kernel_ndim=*/2));
Expand Down Expand Up @@ -347,8 +351,9 @@ bool check_convolution_args(
ET_CHECK_OR_RETURN_FALSE(
bias.value().size(0) == transposed ? groups * weight.size(1)
: weight.size(0),
"bias length must equal number of output channels, but got %zd",
bias.value().size(0));
"bias length must equal number of output channels, but got %zd; expected %" PRId64,
bias.value().size(0),
transposed ? groups * weight.size(1) : weight.size(0));
}

int64_t kernel_size[2];
Expand Down Expand Up @@ -398,7 +403,9 @@ bool check_convolution_args(
} else {
ET_CHECK_OR_RETURN_FALSE(
in.size(1) == weight.size(0),
"input channels must match weight.size(0) in transposed convolution");
"input channels must match weight.size(0) in transposed convolution; in.size(1) = %zd, weight.size(0) = %zd",
in.size(1),
weight.size(0));
}

return true;
Expand Down Expand Up @@ -484,7 +491,8 @@ bool check_max_pool2d_with_indices_args(
ET_CHECK_OR_RETURN_FALSE(
(in.dim() == 3 && in.size(0) > 0 && in.size(1) > 0 && in.size(2) > 0) ||
(in.dim() == 4 && in.size(1) > 0 && in.size(2) > 0 && in.size(3) > 0),
"Expected 3D or 4D (batch mode) tensor with optional 0 dim batch size for input");
"Expected 3D or 4D (batch mode) tensor with optional 0 dim batch size for input; in.dim() = %zd",
in.dim());

ET_LOG_AND_RETURN_IF_FALSE(
kernel_size_is_valid(kernel_size, /*kernel_ndim=*/2));
Expand Down Expand Up @@ -545,11 +553,15 @@ bool check_constant_pad_args(
ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_rank(in, out));

ET_CHECK_OR_RETURN_FALSE(
pad.size() % 2 == 0, "Padding array must be a multiple of 2");
pad.size() % 2 == 0,
"Padding array must be a multiple of 2; pad.size() = %zu",
pad.size());

ET_CHECK_OR_RETURN_FALSE(
static_cast<ssize_t>(pad.size() / 2) <= in.dim(),
"Padding array contains too many elements");
"Padding array contains too many elements; pad.size()/2 = %zu, in.dim() = %zd",
pad.size() / 2,
in.dim());

return true;
}
Expand Down
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