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fix -Wsign-compare warnings for some files inside c2 (#18123)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18123

the motivation of this fix is to resolve things like:
for(auto i = 0; i < N; i++) where N is bigger than int32

These instances of comparison were found by enabling -Wsign-compare

There are way too many things to fix, so issuing this as a series of fixes

The plan is to fix all these issues and then enable this flag into Caffe2 to catch future instances

Reviewed By: ZolotukhinM

Differential Revision: D14497094

fbshipit-source-id: bca3927a2188bd33a508fa503ba221c220cdaefe
This commit is contained in:
Hector Yuen
2019-03-19 10:30:29 -07:00
committed by Facebook Github Bot
parent 1c76746f61
commit 7bb36ada1f
16 changed files with 33 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
caffe2/core/blob_serialization.h
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@ -162,7 +162,7 @@ inline void CopyToProtoAsIs(
"The source type and dest type cannot be copied as-is. Did "
"you mean CopyToProtoWithCast?");
field->Reserve(size);
for (int i = 0; i < size; ++i) {
for (size_t i = 0; i < size; ++i) {
field->Add(0);
}
context->template CopyToCPU<SrcType>(
@ -183,7 +183,7 @@ inline void CopyToProtoWithCast(
context->template CopyToCPU<SrcType>(size, src, buffer.get());
context->FinishDeviceComputation();
field->Reserve(size);
for (int i = 0; i < size; ++i) {
for (size_t i = 0; i < size; ++i) {
field->Add(static_cast<DstType>(buffer[i]));
}
}
@ -214,7 +214,7 @@ inline void CopyFromProtoWithCast(
// CPUContext. Remove it if it is performance critical.
unique_ptr<DstType[]> buffer(new DstType[size]);
const SrcType* src = field.data();
for (int i = 0; i < size; ++i) {
for (size_t i = 0; i < size; ++i) {
buffer[i] = static_cast<DstType>(src[i]);
}
context->template CopyFromCPU<DstType>(size, buffer.get(), dst);

2
caffe2/ideep/utils/ideep_context.h
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@ -83,7 +83,7 @@ class IDEEPContext final : public BaseContext {
static_cast<const void*>(src),
static_cast<void*>(dst));
} else {
for (int i = 0; i < n; ++i) {
for (size_t i = 0; i < n; ++i) {
dst[i] = src[i];
}
}

2
caffe2/operators/conv_op_impl.h
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@ -738,7 +738,7 @@ bool ConvGradientOp<T, Context>::RunOnDeviceWithOrderNHWC() {
CAFFE_ENFORCE_EQ(C, filter.dim32(filter.dim() - 1) * group_);
int kernel_dims_size = 1;
for (int i = 0; i < kernel_.size(); ++i) {
for (size_t i = 0; i < kernel_.size(); ++i) {
CAFFE_ENFORCE_EQ(filter.dim32(i + 1), kernel_[i]);
kernel_dims_size *= kernel_[i];
}

8
caffe2/operators/conv_pool_op_base.h
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@ -138,7 +138,7 @@ class ConvPoolOpBase : public Operator<Context> {
}
if (global_pooling_) {
for (int dim = 0; dim < kernel_.size(); ++dim) {
for (size_t dim = 0; dim < kernel_.size(); ++dim) {
CAFFE_ENFORCE(
pads_[2 * dim] == 0 && pads_[2 * dim + 1] == 0 &&
dilation_[dim] == 1 && stride_[dim] == 1,
@ -151,7 +151,7 @@ class ConvPoolOpBase : public Operator<Context> {
// need to clean this up.
if (operator_def.name().find("Conv") == 0 ||
operator_def.name().find("Pool") != std::string::npos) {
for (int dim = 0; dim < kernel_.size(); ++dim) {
for (size_t dim = 0; dim < kernel_.size(); ++dim) {
CAFFE_ENFORCE_GE(pads_[dim], 0);
CAFFE_ENFORCE_GE(pads_[kernel_.size() + dim], 0);
CAFFE_ENFORCE(
@ -161,7 +161,7 @@ class ConvPoolOpBase : public Operator<Context> {
}
}
for (int dim = 0; dim < kernel_.size(); ++dim) {
for (size_t dim = 0; dim < kernel_.size(); ++dim) {
CAFFE_ENFORCE_GE(kernel_[dim], 0);
CAFFE_ENFORCE_GE(dilation_[dim], 0);
CAFFE_ENFORCE_GE(stride_[dim], 0);
@ -408,7 +408,7 @@ class ConvPoolOpBase : public Operator<Context> {
bool RunOnDevice() override {
if (!global_pooling_) {
for (int dim = 0; dim < kernel_.size(); ++dim) {
for (size_t dim = 0; dim < kernel_.size(); ++dim) {
CAFFE_ENFORCE_GT(kernel_[dim], 0);
}
}

2
caffe2/operators/ctc_greedy_decoder_op.cc
View File

@ -57,7 +57,7 @@ bool CTCGreedyDecoderOp<CPUContext>::RunOnDevice() {
auto* values =
Output(VALUES, vector<int64_t>{values_cach_size}, at::dtype<int>());
int* values_data = values->mutable_data<int>();
for (int i = 0; i < values_cach.size(); ++i) {
for (size_t i = 0; i < values_cach.size(); ++i) {
values_data[i] = values_cach.at(i);
}
values_cach.clear();

10
caffe2/operators/dataset_ops.cc
View File

@ -30,7 +30,7 @@ TreeIterator::TreeIterator(const std::vector<std::string>& fields) {
// populate field vector and split field names
fields_.resize(fields.size());
std::vector<std::vector<std::string>> nameParts(fields_.size());
for (int i = 0; i < fields.size(); ++i) {
for (size_t i = 0; i < fields.size(); ++i) {
auto& field = fields_.at(i);
field.name = fields[i];
field.id = i;
@ -49,7 +49,7 @@ TreeIterator::TreeIterator(const std::vector<std::string>& fields) {
// find length-field with maximum prefix matching for each field
for (auto& field : fields_) {
// by default, we are matching against the root domain
int maxMatchLevel = 1;
size_t maxMatchLevel = 1;
int maxMatchLengthFieldId = -1;
for (int j = 0; j < numLengthFields(); ++j) {
const auto& lenField = lengthField(j);
@ -260,12 +260,12 @@ class CheckDatasetConsistencyOp : public Operator<CPUContext> {
sizes.resize(iterator_.numOffsetFields());
// gather length data
lengths.resize(iterator_.numLengthFields());
for (int i = 0; i < lengths.size(); ++i) {
for (size_t i = 0; i < lengths.size(); ++i) {
lengths[i] = Input(iterator_.lengthField(i).id).data<TLength>();
}
// gather size limits
limits.assign(sizes.size(), std::numeric_limits<TOffset>::max());
for (int i = 0; i < iterator_.fields().size(); ++i) {
for (size_t i = 0; i < iterator_.fields().size(); ++i) {
int lengthIdx = iterator_.fields()[i].lengthFieldId + 1;
CAFFE_ENFORCE_GT(Input(i).dim(), 0);
TOffset size = (TOffset)Input(i).sizes()[0];
@ -290,7 +290,7 @@ class CheckDatasetConsistencyOp : public Operator<CPUContext> {
// advance to the end
offsets.assign(sizes.size(), 0);
iterator_.advance(lengths, offsets, sizes, limits, limits[0]);
for (int i = 0; i < limits.size(); ++i) {
for (size_t i = 0; i < limits.size(); ++i) {
CAFFE_ENFORCE(limits[i] == offsets[i]);
}
return true;

4
caffe2/operators/expand_squeeze_dims_op.h
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@ -89,9 +89,9 @@ class SqueezeOp : public Operator<Context> {
static std::vector<int> ComputeDims(
at::IntArrayRef inputDims,
std::vector<int> dims) {
int j = 0;
size_t j = 0;
std::vector<int> newDims;
for (int i = 0; i < inputDims.size(); ++i) {
for (size_t i = 0; i < inputDims.size(); ++i) {
if (j < dims.size() && dims[j] == i) {
CAFFE_ENFORCE_EQ(
inputDims[i],

8
caffe2/operators/experimental/c10/cpu/concat_cpu.cc
View File

@ -27,7 +27,7 @@ void concat_op_cpu_impl(
int adj_size = Tensor(inputs[0]).dim() + (add_axis ? 1 : 0);
int canonical_axis = caffe2::canonical_axis_index_(axis, adj_size);
CAFFE_ENFORCE_LT(canonical_axis, adj_size, "Axis not in input ndim range.");
for (int i = 1; i < inputs.size(); ++i) {
for (size_t i = 1; i < inputs.size(); ++i) {
CAFFE_ENFORCE(
Tensor(inputs[i]).dtype() == Tensor(inputs[0]).dtype(),
"All inputs must have the same type, expected: ",
@ -51,7 +51,7 @@ void concat_op_cpu_impl(
after *= dim;
}
// check the input dims are compatible.
for (int j = 1; j < inputs.size(); ++j) {
for (size_t j = 1; j < inputs.size(); ++j) {
int dim_j = Tensor(inputs[j]).dim32(i);
CAFFE_ENFORCE(
dim == dim_j,
@ -75,7 +75,7 @@ void concat_op_cpu_impl(
}
int output_channels = 0;
for (int i = 0; i < inputs.size(); ++i) {
for (size_t i = 0; i < inputs.size(); ++i) {
axis_data[i] = add_axis ? 1 : Tensor(inputs[i]).dim32(canonical_axis);
output_channels += axis_data[i];
}
@ -86,7 +86,7 @@ void concat_op_cpu_impl(
}
output.Resize(output_dims);
size_t output_offset = 0;
for (int i = 0; i < inputs.size(); ++i) {
for (size_t i = 0; i < inputs.size(); ++i) {
Tensor input(inputs[i]);
auto axis_dim = add_axis ? 1 : input.dim32(canonical_axis);
caffe2::math::CopyMatrix<Context>(

2
caffe2/operators/find_duplicate_elements_op.h
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@ -43,7 +43,7 @@ class FindDuplicateElementsOp final : public Operator<Context> {
auto* output = Output(0, {static_cast<int64_t>(dupSize)}, at::dtype<int64_t>());
auto* out_ptr = output->template mutable_data<int64_t>();
for (int64_t i = 0; i < dupSize; ++i) {
for (size_t i = 0; i < dupSize; ++i) {
out_ptr[i] = dupIndices[i];
}

2
caffe2/operators/onnxifi_op.h
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@ -56,7 +56,7 @@ class OnnxifiOp final : public Operator<Context> {
if (!output_shape_hint.empty()) {
TensorInfo info;
info.onnxifi_type = output_shape_hint.front();
for (int i = 1; i < output_shape_hint.size(); ++i) {
for (size_t i = 1; i < output_shape_hint.size(); ++i) {
info.dims.push_back(output_shape_hint[i]);
}
output_shape_hints_.emplace(output_idx, std::move(info));

2
caffe2/operators/reshape_op.h
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@ -59,7 +59,7 @@ class ReshapeOp : public Operator<Context> {
}
// Copy over the dimensions for those that are specified zero.
for (int i = 0; i < actual_new_shape.size() && i < input.dim(); ++i) {
for (size_t i = 0; i < actual_new_shape.size() && i < input.dim(); ++i) {
if (actual_new_shape[i] == 0) {
actual_new_shape[i] = input.size(i);
}

4
caffe2/operators/slice_op.h
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@ -129,7 +129,7 @@ bool SliceImpl(
char* src_offset_bytes = src_bytes + itemsize * src_offset;
char* dst_offset_bytes = dst_bytes;
for (int i = 0; i < num_blocks; ++i) {
for (size_t i = 0; i < num_blocks; ++i) {
char* local_src_offset_bytes =
src_offset_bytes + i * src_block_size_bytes;
char* local_dst_offset_bytes =
@ -175,7 +175,7 @@ bool SliceImpl(
return true;
}
for (int i = 0; i < num_blocks; ++i) {
for (size_t i = 0; i < num_blocks; ++i) {
char* local_src_offset_bytes =
src_offset_bytes + i * src_block_size_bytes;
char* local_dst_offset_bytes =

2
caffe2/operators/utility_ops.h
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@ -1107,7 +1107,7 @@ class GatherRangesOp : public Operator<Context> {
template <typename Index>
size_t accumulate(Index* ranges, size_t start, size_t end) {
size_t result = 0;
for (int i = start + 1; i < end; i += 2) {
for (size_t i = start + 1; i < end; i += 2) {
result += ranges[i];
}
return result;

4
caffe2/queue/blobs_queue.cc
View File

@ -36,10 +36,10 @@ BlobsQueue::BlobsQueue(
stats_.queue_dequeued_bytes.setDetails(fieldNames);
}
queue_.reserve(capacity);
for (auto i = 0; i < capacity; ++i) {
for (size_t i = 0; i < capacity; ++i) {
std::vector<Blob*> blobs;
blobs.reserve(numBlobs);
for (auto j = 0; j < numBlobs; ++j) {
for (size_t j = 0; j < numBlobs; ++j) {
const auto blobName = queueName + "_" + to_string(i) + "_" + to_string(j);
if (enforceUniqueName) {
CAFFE_ENFORCE(

6
caffe2/queue/rebatching_queue.cc
View File

@ -19,7 +19,7 @@ void concat(
// Precompute the output sizes to avoid resizing
std::vector<std::vector<int64_t>> outputDims(numTensors);
for (int i = 0; i < numTensors; ++i) {
for (size_t i = 0; i < numTensors; ++i) {
SmartTensorPrinter::PrintTensor(inputZero.at(i));
outputDims[i] = inputZero.at(i).sizes().vec();
outputDims[i].insert(outputDims[i].begin(), numRows);
@ -27,12 +27,12 @@ void concat(
// Resize to the final output size
std::vector<void*> destinations(numTensors);
for (int i = 0; i < numTensors; ++i) {
for (size_t i = 0; i < numTensors; ++i) {
outputs[i]->Resize(outputDims[i]);
destinations[i] = outputs[i]->raw_mutable_data(inputZero[i].meta());
}
for (int i = 0; i < numRows; ++i) {
for (size_t i = 0; i < numRows; ++i) {
CAFFE_ENFORCE_EQ(inputs[i].size(), numTensors);
for (int j = 0; j < numTensors; ++j) {

2
caffe2/transforms/pattern_net_transform.h
View File

@ -28,7 +28,7 @@ class CAFFE2_API PatternNetTransform : public Transform {
"External outputs do not match!");
ordered_ops_ = GetPatternTraversalOrder(p_);
inverse_ops_.resize(ordered_ops_.size());
for (int i = 0; i < ordered_ops_.size(); i++) {
for (size_t i = 0; i < ordered_ops_.size(); i++) {
inverse_ops_[ordered_ops_[i]] = i;
}
}