#include <initializer_list>
#include <ostream>
#include <set>
-#include <unordered_map>
#include <vector>
+#include <unordered_map>
#include "c10/util/Registry.h"
#include "caffe2/core/common.h"
#include "caffe2/core/logging.h"
-#include "caffe2/core/types.h"
#include "caffe2/proto/caffe2_pb.h"
#include "caffe2/utils/filler.h"
#include "caffe2/utils/proto_utils.h"
OpSchema&
Arg(const char* name, const char* description, bool required = false);
-#define DECLARE_STANDARD_ARG(name, str) \
- static const char* Arg_##name; \
+#define DECLARE_STANDARD_ARG(name, str) \
+ static const char* Arg_##name; \
OpSchema& Arg##name(const char* description);
DECLARE_STANDARD_ARG(IsTest, is_test)
return inplace_enforced_(x, y);
}
- TORCH_API friend std::ostream& operator<<(
- std::ostream& out,
- const OpSchema& schema);
+ TORCH_API friend std::ostream& operator<<(std::ostream& out, const OpSchema& schema);
const std::vector<Argument>& args() const {
return args_;
}
c.flops = nElemX * OpsPerPoint;
- auto const& X_element_size_byte =
- DataTypeToTypeMeta(X.data_type()).itemsize();
- c.bytes_read = nElemRead * X_element_size_byte;
- c.bytes_written = nElemX * X_element_size_byte;
+ c.bytes_read = nElemRead * sizeof(X.data_type());
+ c.bytes_written = nElemX * sizeof(X.data_type());
return c;
}
#include "caffe2/operators/batch_matmul_op.h"
#include "caffe2/core/operator_schema.h"
-#include "caffe2/core/types.h"
namespace caffe2 {
K = in[0].dims(ndims_A - 1);
}
- auto const& A_element_size_byte =
- DataTypeToTypeMeta(A.data_type()).itemsize();
- auto const& Y_element_size_byte =
- DataTypeToTypeMeta(Y.data_type()).itemsize();
c.flops = 2 * nElemY * K;
- c.bytes_read = (nElemA + nElemB) * A_element_size_byte;
- c.bytes_written = nElemY * Y_element_size_byte;
+ c.bytes_read = (nElemA + nElemB) * sizeof(A.data_type());
+ c.bytes_written = nElemY * sizeof(Y.data_type());
c.params_bytes = 0;
return c;
}
auto no_trans_arg = vector<Argument>();
auto trans_a_arg = vector<Argument>{MakeArgument<int>("trans_a", 1)};
auto trans_b_arg = vector<Argument>{MakeArgument<int>("trans_b", 1)};
- auto trans_both_arg = vector<Argument>{
- MakeArgument<int>("trans_a", 1), MakeArgument<int>("trans_b", 1)};
+ auto trans_both_arg = vector<Argument>{MakeArgument<int>("trans_a", 1),
+ MakeArgument<int>("trans_b", 1)};
if (trans_a) {
if (trans_b) {
// A'B':
// dA = B'G', dB = G'A'
- return vector<OperatorDef>{
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{I(1), GO(0)},
- vector<string>{GI(0)},
- trans_both_arg),
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{GO(0), I(0)},
- vector<string>{GI(1)},
- trans_both_arg)};
+ return vector<OperatorDef>{CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{I(1), GO(0)},
+ vector<string>{GI(0)},
+ trans_both_arg),
+ CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{GO(0), I(0)},
+ vector<string>{GI(1)},
+ trans_both_arg)};
} else {
// A'B:
// dA = BG', dB = AG
- return vector<OperatorDef>{
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{I(1), GO(0)},
- vector<string>{GI(0)},
- trans_b_arg),
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{I(0), GO(0)},
- vector<string>{GI(1)},
- no_trans_arg)};
+ return vector<OperatorDef>{CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{I(1), GO(0)},
+ vector<string>{GI(0)},
+ trans_b_arg),
+ CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{I(0), GO(0)},
+ vector<string>{GI(1)},
+ no_trans_arg)};
}
} else {
if (trans_b) {
// AB':
// dA = GB, dB = G'A
- return vector<OperatorDef>{
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{GO(0), I(1)},
- vector<string>{GI(0)},
- no_trans_arg),
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{GO(0), I(0)},
- vector<string>{GI(1)},
- trans_a_arg)};
+ return vector<OperatorDef>{CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{GO(0), I(1)},
+ vector<string>{GI(0)},
+ no_trans_arg),
+ CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{GO(0), I(0)},
+ vector<string>{GI(1)},
+ trans_a_arg)};
} else {
// AB:
// dA = GB', dB = A'G
- return vector<OperatorDef>{
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{GO(0), I(1)},
- vector<string>{GI(0)},
- trans_b_arg),
- CreateOperatorDef(
- "BatchMatMul",
- "",
- vector<string>{I(0), GO(0)},
- vector<string>{GI(1)},
- trans_a_arg)};
+ return vector<OperatorDef>{CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{GO(0), I(1)},
+ vector<string>{GI(0)},
+ trans_b_arg),
+ CreateOperatorDef(
+ "BatchMatMul",
+ "",
+ vector<string>{I(0), GO(0)},
+ vector<string>{GI(1)},
+ trans_a_arg)};
}
}
}
CAFFE_ENFORCE_GT(in.size(), 0);
struct OpSchema::Cost cost;
cost.flops = 0;
- auto const& input_0_element_size_byte =
- DataTypeToTypeMeta(in[0].data_type()).itemsize();
- auto const& input_1_element_size_byte =
- (in.size() > 1) ? DataTypeToTypeMeta(in[1].data_type()).itemsize() : 0;
- auto input_bytes_count = nElemFromDim(in[0]) * input_0_element_size_byte;
- auto split_bytes_count = nElemFromDim(in[1]) * input_1_element_size_byte;
+ auto input_bytes_count = nElemFromDim(in[0]) * sizeof(in[0].data_type());
+ auto split_bytes_count =
+ (in.size() == 1) ? 0 : nElemFromDim(in[1]) * sizeof(in[1].data_type());
// There can be two input blobs:
// (1) actual tensor to be split
// (2) lengths of outputs along split axis
}
auto split_info_bytes_count = in.size() * sizeof(int);
- auto const& input_0_element_size_byte =
- DataTypeToTypeMeta(in[0].data_type()).itemsize();
struct OpSchema::Cost cost;
cost.flops = 0;
- cost.bytes_read = nElemRead * input_0_element_size_byte;
+ cost.bytes_read = nElemRead * sizeof(in[0].data_type());
cost.bytes_written =
- size * input_0_element_size_byte + split_info_bytes_count;
+ size * sizeof(in[0].data_type()) + split_info_bytes_count;
cost.params_bytes = 0;
return cost;
}
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
-#include "caffe2/core/types.h"
#include "caffe2/proto/caffe2_legacy.pb.h"
#include "caffe2/utils/math.h"
uint64_t nElemW = nElemFromDim(W);
uint64_t nElemBias = inputs.size() > 2 ? nElemFromDim(inputs[2]) : 0;
- auto const& X_elemenet_size_byte =
- DataTypeToTypeMeta(X.data_type()).itemsize();
- auto const& Y_element_size_byte =
- DataTypeToTypeMeta(Y.data_type()).itemsize();
- auto const& W_element_size_byte =
- DataTypeToTypeMeta(W.data_type()).itemsize();
-
// grouping is NOT properly handled yet
c.flops = N * Y_t * Y_h * Y_w * kernel_t * kernel_w * kernel_h *
in_channels * out_channels * 2;
- c.bytes_read = (nElemX + nElemW + nElemBias) * X_elemenet_size_byte;
- c.bytes_written = N * out_channels * Y_t * Y_h * Y_w * Y_element_size_byte;
+ c.bytes_read = (nElemX + nElemW + nElemBias) * sizeof(X.data_type());
+ c.bytes_written =
+ N * out_channels * Y_t * Y_h * Y_w * sizeof(Y.data_type());
c.params_bytes = out_channels * in_channels * kernel_t * kernel_h *
- kernel_w * W_element_size_byte;
+ kernel_w * sizeof(W.data_type());
return c;
}
#include "caffe2/operators/distance_op.h"
-#include "caffe2/core/types.h"
#include "caffe2/utils/eigen_utils.h"
#ifdef CAFFE2_USE_MKLDNN
#include <caffe2/ideep/operators/operator_fallback_ideep.h>
namespace caffe2 {
-template <>
+template<>
bool SquaredL2DistanceOp<float, CPUContext>::RunOnDevice() {
auto& X = Input(0);
auto& Y = Input(1);
CAFFE_ENFORCE_EQ(out[0].dims().size(), 1);
struct OpSchema::Cost c = PointwiseCostInference<2>(def, in);
- auto const& out_0_element_size_byte =
- DataTypeToTypeMeta(out[0].data_type()).itemsize();
- c.bytes_written = out[0].dims(0) * out_0_element_size_byte;
+ c.bytes_written = out[0].dims(0) * sizeof(out[0].data_type());
c.params_bytes = 0;
return c;
}
}
// L2
-REGISTER_CPU_OPERATOR(
- SquaredL2Distance,
- SquaredL2DistanceOp<float, CPUContext>);
-REGISTER_CPU_OPERATOR(
- SquaredL2DistanceGradient,
- SquaredL2DistanceGradientOp<float, CPUContext>);
+REGISTER_CPU_OPERATOR(SquaredL2Distance,
+ SquaredL2DistanceOp<float, CPUContext>);
+REGISTER_CPU_OPERATOR(SquaredL2DistanceGradient,
+ SquaredL2DistanceGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(SquaredL2Distance)
.NumInputs(2)
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
- "SquaredL2DistanceGradient",
- "",
+ "SquaredL2DistanceGradient", "",
vector<string>{I(0), I(1), GO(0)},
vector<string>{GI(0), GI(1)});
}
replicate = GetArgument(Def(), "replicate").i();
}
- const auto dot_arg = vector<Argument>{
- MakeArgument<float>("pad_value", pad_value),
- MakeArgument<bool>("replicate", replicate)};
+ const auto dot_arg =
+ vector<Argument>{MakeArgument<float>("pad_value", pad_value),
+ MakeArgument<bool>("replicate", replicate)};
return SingleGradientDef(
"DotProductWithPaddingGradient",
}
};
REGISTER_GRADIENT(DotProductWithPadding, GetDotProductWithPaddingGradient);
-} // namespace caffe2
+} // namespace caffe2
#include "caffe2/operators/fc_inference.h"
-#include "caffe2/core/types.h"
namespace caffe2 {
std::vector<TensorShape> FCShapeInference(
? size_from_dim_(canonical_axis_w, GetDimsVector(in[1]))
: size_to_dim_(canonical_axis_w, GetDimsVector(in[1]));
- auto const& X_element_size_byte =
- DataTypeToTypeMeta(in[0].data_type()).itemsize();
+ const auto& X = in[0];
c.flops = M * N * (2 * K + 1);
- c.bytes_read = (K * (M + N) + N) * X_element_size_byte;
- c.bytes_written = M * N * X_element_size_byte;
- c.params_bytes = (K * N + N) * X_element_size_byte;
+ c.bytes_read = (K * (M + N) + N) * sizeof(X.data_type());
+ c.bytes_written = M * N * sizeof(X.data_type());
+ c.params_bytes = (K * N + N) * sizeof(X.data_type());
return c;
}
CAFFE_ENFORCE_LT(0, out.size());
const TensorShape dW = out[0];
- auto const& dW_element_size_byte =
- DataTypeToTypeMeta(dW.data_type()).itemsize();
const TensorShape db = out[1];
- auto const& db_element_size_byte =
- DataTypeToTypeMeta(db.data_type()).itemsize();
auto axis = helper.GetSingleArgument<int32_t>("axis", 1);
const auto canonical_axis = canonical_axis_index_(axis, in[0].dims().size());
uint64_t size_db = nElemFromDim(db);
c.flops = M * N * (2 * K + 1);
- c.bytes_written =
- size_dW * dW_element_size_byte + size_db * db_element_size_byte;
+ c.bytes_written = (size_dW + size_db) * sizeof(float);
c.params_bytes = (K * N + N) * sizeof(float);
if (out.size() == 3) {
const TensorShape dX = out[2];
uint64_t size_dX = nElemFromDim(dX);
- auto const& dX_element_size_byte =
- DataTypeToTypeMeta(dX.data_type()).itemsize();
+
c.flops += 2 * M * N * K;
- c.bytes_written += size_dX * dX_element_size_byte;
+ c.bytes_written += size_dX * sizeof(float);
}
return c;
}
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
-#include "caffe2/core/types.h"
namespace caffe2 {
const auto& length = in[1];
const auto& values = in[2];
- auto const& data_element_size_byte =
- DataTypeToTypeMeta(data.data_type()).itemsize();
- auto const& length_element_size_byte =
- DataTypeToTypeMeta(length.data_type()).itemsize();
- auto const& values_element_size_byte =
- DataTypeToTypeMeta(values.data_type()).itemsize();
- auto const& output_element_size_byte =
- DataTypeToTypeMeta(output.data_type()).itemsize();
-
- uint64_t nBytesData = nElemFromDim(data) * data_element_size_byte;
- uint64_t nBytesLength = nElemFromDim(length) * length_element_size_byte;
- uint64_t nBytesValues = nElemFromDim(values) * values_element_size_byte;
+ uint64_t nBytesData = nElemFromDim(data) * sizeof(data.data_type());
+ uint64_t nBytesLength = nElemFromDim(length) * sizeof(length.data_type());
+ uint64_t nBytesValues = nElemFromDim(values) * sizeof(values.data_type());
c.flops = 0;
c.bytes_read = nBytesData + nBytesLength + nBytesValues;
- c.bytes_written = nElemFromDim(output) * output_element_size_byte;
+ c.bytes_written = nElemFromDim(output) * sizeof(output.data_type());
c.params_bytes = 0;
return c;
}
for (int64_t j = 0; j < D; j++) {
// here we assume the boundary values for each feature are sorted
int64_t lower_bucket_idx = std::lower_bound(
- boundaries_offset,
- boundaries_offset + lens_data[j],
- input_data[pos]) -
+ boundaries_offset,
+ boundaries_offset + lens_data[j],
+ input_data[pos]) -
boundaries_offset;
int64_t upper_bucket_idx = std::upper_bound(
- boundaries_offset,
- boundaries_offset + lens_data[j],
- input_data[pos]) -
+ boundaries_offset,
+ boundaries_offset + lens_data[j],
+ input_data[pos]) -
boundaries_offset;
int64_t bucket_idx = (lower_bucket_idx + upper_bucket_idx) / 2;
#include "caffe2/operators/utility_ops.h"
#include <cmath>
#include <iostream>
-#include "caffe2/core/types.h"
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
const auto& nElem = nElemFromDim(X0);
const auto& nInputs = in.size();
c.flops = (nInputs - 1) * nElem;
- auto const& X0_element_size_byte =
- DataTypeToTypeMeta(X0.data_type()).itemsize();
- c.bytes_read = (nInputs / 2) * (nElem + 1) * X0_element_size_byte;
- c.bytes_written = nElem * X0_element_size_byte;
- c.params_bytes = (nInputs / 2) * X0_element_size_byte;
+ c.bytes_read = (nInputs / 2) * (nElem + 1) * sizeof(X0.data_type());
+ c.bytes_written = nElem * sizeof(X0.data_type());
+ c.params_bytes = (nInputs / 2) * sizeof(X0.data_type());
return c;
}
REGISTER_CPU_OPERATOR(SumInt, SumOp<CPUContext>);
REGISTER_CPU_OPERATOR(WeightedSum, WeightedSumOp<CPUContext>);
REGISTER_CPU_OPERATOR(WeightedSumGradient, WeightedSumGradientOp<CPUContext>);
-REGISTER_CPU_OPERATOR(ScatterWeightedSum, ScatterWeightedSumOp<CPUContext>);
+REGISTER_CPU_OPERATOR(
+ ScatterWeightedSum,
+ ScatterWeightedSumOp<CPUContext>);
REGISTER_CPU_OPERATOR(ScatterAssign, ScatterAssignOp<CPUContext>);
REGISTER_CPU_OPERATOR(Scatter, ScatterOp<CPUContext>);
class TestConcatOpCost(TestCase):
def test_columnwise_concat(self):
- def _test_columnwise_concat_for_type(dtype):
- workspace.ResetWorkspace()
- workspace.FeedBlob("input_1", np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype))
- workspace.FeedBlob("input_2", np.array([[7], [8]], dtype=dtype))
- concat_op = core.CreateOperator(
- "Concat",
- ["input_1", "input_2"],
- ["output", "split_info"],
- )
- workspace.RunOperatorOnce(concat_op)
-
- output = workspace.FetchBlob("output")
- self.assertTupleEqual(output.shape, (2, 4))
- np.testing.assert_array_equal(output, [[1, 2, 3, 7], [4, 5, 6, 8]])
+ workspace.ResetWorkspace()
+ workspace.FeedBlob("input_1", np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+ workspace.FeedBlob("input_2", np.array([[7], [8]], dtype=np.int32))
+ concat_op = core.CreateOperator(
+ "Concat",
+ ["input_1", "input_2"],
+ ["output", "split_info"],
+ )
+ workspace.RunOperatorOnce(concat_op)
- flops, bytes_written, bytes_read = workspace.GetOperatorCost(
- concat_op, concat_op.input
- )
+ output = workspace.FetchBlob("output")
+ self.assertTupleEqual(output.shape, (2, 4))
+ np.testing.assert_array_equal(output, [[1, 2, 3, 7], [4, 5, 6, 8]])
- self.assertEqual(flops, 0)
- self.assertEqual(
- bytes_read,
- sum(workspace.FetchBlob(b).nbytes for b in concat_op.input),
- )
- self.assertEqual(
- bytes_written,
- sum(workspace.FetchBlob(b).nbytes for b in concat_op.output),
- )
+ flops, bytes_written, bytes_read = workspace.GetOperatorCost(
+ concat_op, concat_op.input
+ )
- [
- _test_columnwise_concat_for_type(t)
- for t in [np.int64, np.float, np.half, np.int8]
- ]
+ self.assertEqual(flops, 0)
+ self.assertEqual(
+ bytes_read,
+ sum(workspace.FetchBlob(b).nbytes for b in concat_op.input),
+ )
+ self.assertEqual(
+ bytes_written,
+ sum(workspace.FetchBlob(b).nbytes for b in concat_op.output),
+ )
def test_split_then_concat(self):
workspace.ResetWorkspace()
self.assertTupleEqual(
op_cost,
namedtuple("Cost", ["flops", "bytes_written", "bytes_read"])(
- 1152, 256, 4168
+ 1152, 256, 2084
),
)
#include "adagrad_op.h"
-#include "caffe2/core/types.h"
namespace caffe2 {
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
c.flops = grad_size * 10;
- auto const& moment_element_size_byte =
- DataTypeToTypeMeta(moment.data_type()).itemsize();
- auto const& param_element_size_byte =
- DataTypeToTypeMeta(param.data_type()).itemsize();
- auto const& grad_element_size_byte =
- DataTypeToTypeMeta(grad.data_type()).itemsize();
- auto const& lr_element_size_byte =
- DataTypeToTypeMeta(lr.data_type()).itemsize();
uint64_t bytes_written =
- grad_size * param_element_size_byte + moment_element_size_byte;
+ grad_size * (sizeof(param.data_type()) + sizeof(moment.data_type()));
if (output_size == 3) {
// also need to output effective learning rate in this case
// assume it's the same data type as lr
- bytes_written += grad_size * lr_element_size_byte;
+ bytes_written += grad_size * sizeof(lr.data_type());
} else if (output_size == 4) {
// also need to output effective learning rate and updates in this case
// assume update is the same data type as param
bytes_written +=
- grad_size * (lr_element_size_byte + param_element_size_byte);
+ grad_size * (sizeof(lr.data_type()) + sizeof(param.data_type()));
}
c.bytes_written = bytes_written;
c.bytes_read = c.bytes_written +
- grad_size * (grad_element_size_byte + lr_element_size_byte);
+ grad_size * (sizeof(grad.data_type()) + sizeof(lr.data_type()));
return c;
}
// (optimistically count sqrt as one flop).
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
c.flops = grad_size * 7;
- auto const& param_element_size_byte =
- DataTypeToTypeMeta(param.data_type()).itemsize();
- auto const& moment_element_size_byte =
- DataTypeToTypeMeta(moment.data_type()).itemsize();
c.bytes_written =
- grad_size * (param_element_size_byte + moment_element_size_byte);
- auto const& grad_element_size_byte =
- DataTypeToTypeMeta(grad.data_type()).itemsize();
- auto const& indices_element_size_byte =
- DataTypeToTypeMeta(indices.data_type()).itemsize();
- c.bytes_read = c.bytes_written + grad_size * grad_element_size_byte +
- n * indices_element_size_byte;
+ grad_size * (sizeof(param.data_type()) + sizeof(moment.data_type()));
+ c.bytes_read = c.bytes_written + grad_size * sizeof(grad.data_type()) +
+ n * sizeof(indices.data_type());
return c;
}
OpSchema::Cost c;
if (n > 0) {
- auto const& param_element_size_byte =
- DataTypeToTypeMeta(param.data_type()).itemsize();
- auto const& moment_element_size_byte =
- DataTypeToTypeMeta(moment.data_type()).itemsize();
- auto const& grad_element_size_byte =
- DataTypeToTypeMeta(grad.data_type()).itemsize();
- auto const& indices_element_size_byte =
- DataTypeToTypeMeta(indices.data_type()).itemsize();
- auto const& lr_element_size_byte =
- DataTypeToTypeMeta(lr.data_type()).itemsize();
auto block_size = grad_size / n;
if (block_size == 1) {
// +2: applying weight decay and add to grads
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
c.flops = n * 9;
c.bytes_written =
- n * (param_element_size_byte + moment_element_size_byte);
+ n * (sizeof(param.data_type()) + sizeof(moment.data_type()));
c.bytes_read = c.bytes_written +
n *
- (grad_element_size_byte + indices_element_size_byte +
- lr_element_size_byte);
+ (sizeof(grad.data_type()) + sizeof(indices.data_type()) +
+ sizeof(lr.data_type()));
} else {
// 5 per block (not counting index transforms)
// 8 for each value of a block
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
c.flops = n * (5 + (block_size * 8));
- c.bytes_written = n * moment_element_size_byte +
- n * block_size * param_element_size_byte;
+ c.bytes_written =
+ n * sizeof(moment.data_type()) + n * block_size * (param.data_type());
- c.bytes_read = c.bytes_written + n * lr_element_size_byte +
+ c.bytes_read = c.bytes_written + n * (sizeof(lr.data_type())) +
2 * n * block_size *
- (grad_element_size_byte + param_element_size_byte);
+ (sizeof(grad.data_type()) + sizeof(param.data_type()));
}
}
return c;
projects / platform / upstream / pytorch.git / commitdiff