diff options
author | Max Galkin <maxgalkin@google.com> | 2018-03-20 11:45:23 -0700 |
---|---|---|
committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-03-20 11:48:34 -0700 |
commit | 15d6e8310e1f2ffaa901110903ce7403717b4d2b (patch) | |
tree | f7653ce34fc0e4fa36a6554cf1ebe7b4c57cc122 /tensorflow/core/grappler/costs/op_level_cost_estimator.cc | |
parent | f57f7d09eeb7402f2455564fafbcebf7ac4b8fe3 (diff) |
Improved accuracy of op_level_cost_estimator (QuantizeV2, Dequantize, Gather).
PiperOrigin-RevId: 189779691
Diffstat (limited to 'tensorflow/core/grappler/costs/op_level_cost_estimator.cc')
-rw-r--r-- | tensorflow/core/grappler/costs/op_level_cost_estimator.cc | 81 |
1 files changed, 60 insertions, 21 deletions
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc index 29ef317e46..84ad8a3e84 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/attr_value_util.h" #include "tensorflow/core/framework/tensor_shape.pb.h" +#include "tensorflow/core/framework/types.h" #include "tensorflow/core/grappler/clusters/utils.h" namespace tensorflow { @@ -46,6 +47,7 @@ constexpr char kShape[] = "Shape"; constexpr char kSize[] = "Size"; constexpr char kStopGradient[] = "StopGradient"; constexpr char kPreventGradient[] = "PreventGradient"; +constexpr char kGather[] = "Gather"; static const Costs::Duration kMinComputeTime(1); @@ -167,6 +169,8 @@ OpLevelCostEstimator::OpLevelCostEstimator() { {kNoOp, wrap(&OpLevelCostEstimator::PredictNoOp)}, + {kGather, wrap(&OpLevelCostEstimator::PredictGather)}, + {kPlaceholder, wrap(&OpLevelCostEstimator::PredictIdentity)}, {kIdentity, wrap(&OpLevelCostEstimator::PredictIdentity)}, {kRefIdentity, wrap(&OpLevelCostEstimator::PredictIdentity)}, @@ -184,6 +188,17 @@ OpLevelCostEstimator::OpLevelCostEstimator() { {kShape, wrap(&OpLevelCostEstimator::PredictMetadata)}, {kSize, wrap(&OpLevelCostEstimator::PredictMetadata)}}; + // Quantize = apply min and max bounds, multiply by scale factor and round. + const int quantize_v2_cost = + Eigen::internal::functor_traits< + Eigen::internal::scalar_product_op<float>>::Cost + + Eigen::internal::functor_traits< + Eigen::internal::scalar_max_op<float>>::Cost + + Eigen::internal::functor_traits< + Eigen::internal::scalar_min_op<float>>::Cost + + Eigen::internal::functor_traits< + Eigen::internal::scalar_round_op<float>>::Cost; + elementwise_ops_ = { // Unary ops alphabetically sorted {"Acos", Eigen::internal::functor_traits< @@ -200,6 +215,8 @@ OpLevelCostEstimator::OpLevelCostEstimator() { Eigen::internal::scalar_ceil_op<float>>::Cost}, {"Cos", Eigen::internal::functor_traits< Eigen::internal::scalar_cos_op<float>>::Cost}, + {"Dequantize", Eigen::internal::functor_traits< + Eigen::internal::scalar_product_op<float>>::Cost}, {"Erf", 1}, {"Erfc", 1}, {"Exp", Eigen::internal::functor_traits< @@ -218,6 +235,7 @@ OpLevelCostEstimator::OpLevelCostEstimator() { Eigen::internal::scalar_log1p_op<float>>::Cost}, {"Neg", Eigen::internal::functor_traits< Eigen::internal::scalar_opposite_op<float>>::Cost}, + {"QuantizeV2", quantize_v2_cost}, {"Reciprocal", Eigen::internal::functor_traits< Eigen::internal::scalar_inverse_op<float>>::Cost}, {"Rint", 1}, @@ -411,28 +429,33 @@ Costs OpLevelCostEstimator::PredictCostOfAnUnknownOp( } Costs OpLevelCostEstimator::PredictOpCountBasedCost( - double operations, const OpInfo& op_features) const { - DeviceInfo device_perf = GetDeviceInfo(op_features.device()); - if (device_perf.gigaops <= 0 || device_perf.gb_per_sec <= 0) { - VLOG(1) << "BAD DEVICE. Op:" << op_features.op() - << " device type:" << op_features.device().type() - << " device model:" << op_features.device().model(); - } + double operations, const OpInfo& op_info) const { + bool unknown_shapes = false; + const double input_size = CalculateInputSize(op_info, &unknown_shapes); + const double output_size = CalculateOutputSize(op_info, &unknown_shapes); + const double total_io_bytes = input_size + output_size; + Costs costs = PredictOpCountBasedCost(operations, total_io_bytes, op_info); + costs.inaccurate = unknown_shapes; + costs.max_memory = output_size; + return costs; +} - Costs::NanoSeconds compute_cost(std::ceil(operations / device_perf.gigaops)); - VLOG(1) << "Op:" << op_features.op() << " GOps:" << operations / 1e9 - << " Execution Time (ns):" << compute_cost.count(); +Costs OpLevelCostEstimator::PredictOpCountBasedCost( + double operations, double total_io_bytes, const OpInfo& op_info) const { + const DeviceInfo device_info = GetDeviceInfo(op_info.device()); + if (device_info.gigaops <= 0 || device_info.gb_per_sec <= 0) { + VLOG(1) << "BAD DEVICE. Op:" << op_info.op() + << " device type:" << op_info.device().type() + << " device model:" << op_info.device().model(); + } - bool found_unknown_shapes = false; - const double total_input_size = - CalculateInputSize(op_features, &found_unknown_shapes); - const double total_output_size = - CalculateOutputSize(op_features, &found_unknown_shapes); - const double total_io_size = total_input_size + total_output_size; + Costs::NanoSeconds compute_cost(std::ceil(operations / device_info.gigaops)); + VLOG(1) << "Op:" << op_info.op() << " GOps:" << operations / 1e9 + << " Compute Time (ns):" << compute_cost.count(); Costs::NanoSeconds memory_cost( - std::ceil(total_io_size / device_perf.gb_per_sec)); - VLOG(1) << "Op:" << op_features.op() << " Size (KB):" << (total_io_size) / 1e3 + std::ceil(total_io_bytes / device_info.gb_per_sec)); + VLOG(1) << "Op:" << op_info.op() << " Size (KB):" << (total_io_bytes) / 1e3 << " Memory Time (ns):" << memory_cost.count(); Costs costs; @@ -443,8 +466,6 @@ Costs OpLevelCostEstimator::PredictOpCountBasedCost( } else { costs.execution_time = compute_cost + memory_cost; } - costs.inaccurate = found_unknown_shapes; - costs.max_memory = total_output_size; return costs; } @@ -867,7 +888,7 @@ int64 OpLevelCostEstimator::CountConv2DBackpropFilterOperations( int64 OpLevelCostEstimator::CalculateTensorElementCount( const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) const { - VLOG(2) << " with " << tensor.dtype() << " tensor of shape " + VLOG(2) << " with " << DataTypeString(tensor.dtype()) << " tensor of shape " << tensor.shape().DebugString(); int64 tensor_size = 1; int num_dims = std::max(1, tensor.shape().dim_size()); @@ -1028,5 +1049,23 @@ Costs OpLevelCostEstimator::PredictMetadata(const OpContext& op_context) const { return costs; } +Costs OpLevelCostEstimator::PredictGather(const OpContext& op_context) const { + // Gather op can have a very large input, but only the size of the output + // matters, because indices may select only a very small subset of input. + + const auto& op_info = op_context.op_info; + + bool unknown_shapes = false; + const int64 op_count = + CalculateTensorElementCount(op_info.outputs(0), &unknown_shapes); + const double output_size = CalculateOutputSize(op_info, &unknown_shapes); + const double total_io = 2 * output_size; + Costs costs = PredictOpCountBasedCost(op_count, total_io, op_info); + costs.inaccurate = unknown_shapes; + costs.max_memory = output_size; + + return costs; +} + } // end namespace grappler } // end namespace tensorflow |