diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.cc b/paddle/fluid/framework/ir/graph_pattern_detector.cc index 7951514f8d3bb..0a9d599136ee9 100644 --- a/paddle/fluid/framework/ir/graph_pattern_detector.cc +++ b/paddle/fluid/framework/ir/graph_pattern_detector.cc @@ -28,10 +28,17 @@ using string::Style; size_t PDPattern::id_ = 0UL; +#ifdef PADDLE_WITH_TENSORRT +namespace patterns { +thread_local std::unordered_map KeyCounter::dic_; +} +#endif + PDNode *PDPattern::NewNode(const std::string &name) { if (!name.empty()) { PADDLE_ENFORCE_EQ( - node_map_.count(name), 0UL, + node_map_.count(name), + 0UL, platform::errors::PreconditionNotMet( "PDNode's name should be unique, get duplicate [%s]", name)); } @@ -45,7 +52,8 @@ PDNode *PDPattern::NewNode(const std::string &name) { PDNode *PDPattern::NewNode(PDNode::teller_t &&teller, const std::string &name) { if (!name.empty()) { PADDLE_ENFORCE_EQ( - node_map_.count(name), 0UL, + node_map_.count(name), + 0UL, platform::errors::PreconditionNotMet( "PDNode's name should be unique, get duplicate [%s]", name)); } @@ -70,8 +78,10 @@ void PDPattern::AddEdge(PDNode *a, PDNode *b) { a, platform::errors::NotFound("PDNode %s is not found.", a->name())); PADDLE_ENFORCE_NOT_NULL( b, platform::errors::NotFound("PDNode %s is not found.", b->name())); - PADDLE_ENFORCE_NE(a, b, platform::errors::PermissionDenied( - "Cannot connect the same node in the graph.")); + PADDLE_ENFORCE_NE(a, + b, + platform::errors::PermissionDenied( + "Cannot connect the same node in the graph.")); edges_.emplace_back(a, b); } @@ -128,7 +138,8 @@ void GraphPatternDetector::ValidateByNodeRole( subgraphs->erase( std::remove_if( - subgraphs->begin(), subgraphs->end(), + subgraphs->begin(), + subgraphs->end(), [](const GraphPatternDetector::subgraph_t &subgraph) -> bool { // Collect the inputs and outputs. std::set ios; @@ -310,7 +321,8 @@ void GraphPatternDetector::SortSubgraphs( } std::sort( - subgraphs->begin(), subgraphs->end(), + subgraphs->begin(), + subgraphs->end(), [](const GraphPatternDetector::subgraph_t &a, const GraphPatternDetector::subgraph_t &b) { for (auto &item : a) { @@ -438,7 +450,8 @@ PDNode *PDNode::assert_is_persistable_var() { } PDNode *PDNode::assert_is_op_nth_input(const std::string &op_type, - const std::string &argument, int nth) { + const std::string &argument, + int nth) { assert_is_var(); assert_is_op_input(op_type); asserts_.emplace_back([=](Node *x) { @@ -453,7 +466,8 @@ PDNode *PDNode::assert_is_op_nth_input(const std::string &op_type, } PDNode *PDNode::assert_is_op_nth_output(const std::string &op_type, - const std::string &argument, int nth) { + const std::string &argument, + int nth) { assert_is_var(); asserts_.emplace_back([=](Node *x) { for (auto *op : x->inputs) { @@ -580,7 +594,8 @@ PDNode *PDNode::assert_is_ops(const std::unordered_set &op_types) { PDNode *PDNode::assert_is_ops_nth_input( const std::unordered_set &op_types, - const std::string &argument, int nth) { + const std::string &argument, + int nth) { assert_is_var(); assert_is_ops_input(op_types); asserts_.emplace_back([=](Node *x) { @@ -596,7 +611,8 @@ PDNode *PDNode::assert_is_ops_nth_input( PDNode *PDNode::assert_is_ops_nth_output( const std::unordered_set &op_types, - const std::string &argument, int nth) { + const std::string &argument, + int nth) { assert_is_var(); asserts_.emplace_back([=](Node *x) { for (auto *op : x->inputs) { @@ -693,11 +709,13 @@ bool VarLinksToOp(Node *node, const std::string &op_type) { bool IsNthInput(Node *var, Node *op, const std::string &argument, size_t nth) { PADDLE_ENFORCE_EQ( - var->IsVar(), true, + var->IsVar(), + true, platform::errors::InvalidArgument( "First parameter of function IsNthInput must be Node::Var")); PADDLE_ENFORCE_EQ( - op->IsOp(), true, + op->IsOp(), + true, platform::errors::InvalidArgument( "Second parameter of function IsNthInput must be Node::Op")); if (!HasInput(op, argument) || op->Op()->Input(argument).size() <= nth) @@ -707,7 +725,8 @@ bool IsNthInput(Node *var, Node *op, const std::string &argument, size_t nth) { bool HasInput(Node *op, const std::string &argument) { PADDLE_ENFORCE_EQ( - op->IsOp(), true, + op->IsOp(), + true, platform::errors::InvalidArgument( "First parameter of function HasInput must be Node::Op")); auto const &names = op->Op()->InputNames(); @@ -718,7 +737,8 @@ bool HasInput(Node *op, const std::string &argument) { bool HasOutput(Node *op, const std::string &argument) { PADDLE_ENFORCE_EQ( - op->IsOp(), true, + op->IsOp(), + true, platform::errors::InvalidArgument( "First parameter of function HasOuput must be Node::Op")); auto const &names = op->Op()->OutputNames(); @@ -729,11 +749,13 @@ bool HasOutput(Node *op, const std::string &argument) { bool IsNthOutput(Node *var, Node *op, const std::string &argument, size_t nth) { PADDLE_ENFORCE_EQ( - var->IsVar(), true, + var->IsVar(), + true, platform::errors::InvalidArgument( "First parameter of function IsNthOutput must be Node::Var")); PADDLE_ENFORCE_EQ( - op->IsOp(), true, + op->IsOp(), + true, platform::errors::InvalidArgument( "Second parameter of function IsNthOutput must be Node::Op")); if (!HasOutput(op, argument) || op->Op()->Output(argument).size() <= nth) @@ -875,22 +897,35 @@ PDNode *patterns::ConvBN::operator()(paddle::framework::ir::PDNode *conv_input, eltwise_op->LinksFrom({conv_out_var, eltwise_y_in_var}) .LinksTo({eltwise_out_var}); batch_norm_op - ->LinksFrom({eltwise_out_var, bn_scale_var, bn_bias_var, bn_mean_var, + ->LinksFrom({eltwise_out_var, + bn_scale_var, + bn_bias_var, + bn_mean_var, bn_variance_var}) - .LinksTo({bn_out_var, bn_mean_out_var, bn_variance_out_var, - bn_saved_mean_var, bn_saved_variance_var}); + .LinksTo({bn_out_var, + bn_mean_out_var, + bn_variance_out_var, + bn_saved_mean_var, + bn_saved_variance_var}); } else { batch_norm_op - ->LinksFrom({conv_out_var, bn_scale_var, bn_bias_var, bn_mean_var, + ->LinksFrom({conv_out_var, + bn_scale_var, + bn_bias_var, + bn_mean_var, bn_variance_var}) - .LinksTo({bn_out_var, bn_mean_out_var, bn_variance_out_var, - bn_saved_mean_var, bn_saved_variance_var}); + .LinksTo({bn_out_var, + bn_mean_out_var, + bn_variance_out_var, + bn_saved_mean_var, + bn_saved_variance_var}); } return bn_out_var; } PDNode *patterns::ConvActivation::operator()( - paddle::framework::ir::PDNode *conv_input, std::string conv_type, + paddle::framework::ir::PDNode *conv_input, + std::string conv_type, std::string activation_type) { // Create Operators conv_input->assert_is_op_input(conv_type, "Input"); @@ -920,7 +955,8 @@ PDNode *patterns::ConvActivation::operator()( PDNode *patterns::ElementwiseActivation::operator()( paddle::framework::ir::PDNode *elementwise_a, - const std::string &elementwise_type, const std::string &activation_type) { + const std::string &elementwise_type, + const std::string &activation_type) { // Create Operators elementwise_a->assert_is_op_input(elementwise_type, "X"); auto *elementwise_op = @@ -995,7 +1031,8 @@ PDNode *patterns::SeqConvEltAddRelu::operator()( } PDNode *patterns::FC::operator()(paddle::framework::ir::PDNode *x, - bool with_bias, bool with_relu) { + bool with_bias, + bool with_relu) { // Create shared nodes. x->assert_is_op_input("mul", "X"); auto *mul = pattern->NewNode(mul_repr())->assert_is_op("mul"); @@ -1261,8 +1298,12 @@ PDNode *patterns::BatchNormAct::operator()( bn->LinksFrom( {bn_x_var, bn_scale_var, bn_bias_var, bn_variance_var, bn_mean_var}) - .LinksTo({bn_mean_out_var, bn_variance_out_var, bn_saved_variance_var, - bn_saved_mean_var, bn_reserve_space, bn_out_var}); + .LinksTo({bn_mean_out_var, + bn_variance_out_var, + bn_saved_variance_var, + bn_saved_mean_var, + bn_reserve_space, + bn_out_var}); act->LinksFrom({bn_out_var}).LinksTo({act_out_var}); return act_out_var; @@ -1319,8 +1360,13 @@ PDNode *patterns::BatchNormActGrad::operator()( .LinksTo({d_intermediate_var}); bn_grad - ->LinksFrom({bn_x_var, d_intermediate_var, bn_scale_var, bn_bias_var, - bn_saved_mean_var, bn_saved_variance_var, bn_reserve_space}) + ->LinksFrom({bn_x_var, + d_intermediate_var, + bn_scale_var, + bn_bias_var, + bn_saved_mean_var, + bn_saved_variance_var, + bn_reserve_space}) .LinksTo({d_bn_x_var, d_bn_scale_var, d_bn_bias_var}); return bn_grad; @@ -1404,8 +1450,12 @@ PDNode *patterns::BatchNormAddAct::operator()( pattern->NewNode(act_out_repr())->assert_is_ops_output(act_types, "Out"); bn->LinksFrom({bn_x_var, bn_scale_var, bn_bias_var}) - .LinksTo({bn_mean_out_var, bn_variance_out_var, bn_saved_variance_var, - bn_saved_mean_var, bn_reserve_space, bn_out_var}); + .LinksTo({bn_mean_out_var, + bn_variance_out_var, + bn_saved_variance_var, + bn_saved_mean_var, + bn_reserve_space, + bn_out_var}); elewise_add->LinksFrom({elewise_add_in_var, bn_out_var}) .LinksTo({elewise_add_out_var}); act->LinksFrom({elewise_add_out_var}).LinksTo({act_out_var}); @@ -1484,8 +1534,13 @@ PDNode *patterns::BatchNormAddActGrad::operator()( .LinksTo({d_elewise_add_in_var, d_bn_out_var}); bn_grad - ->LinksFrom({bn_x_var, d_bn_out_var, bn_scale_var, bn_bias_var, - bn_saved_mean_var, bn_saved_variance_var, bn_reserve_space}) + ->LinksFrom({bn_x_var, + d_bn_out_var, + bn_scale_var, + bn_bias_var, + bn_saved_mean_var, + bn_saved_variance_var, + bn_reserve_space}) .LinksTo({d_bn_x_var, d_bn_scale_var, d_bn_bias_var}); return bn_grad; @@ -1558,7 +1613,8 @@ PDNode *patterns::ElewiseAddAct::operator()( PDNode *patterns::LinearAct::operator()( paddle::framework::ir::PDNode *linear_x_var, - const std::unordered_set &act_types, bool with_grad_link, + const std::unordered_set &act_types, + bool with_grad_link, bool is_act_grad_x_from_act) { auto *matmul_w_var = pattern->NewNode(matmul_w_repr())->assert_is_op_input("matmul_v2", "Y"); @@ -1621,7 +1677,8 @@ PDNode *patterns::LinearAct::operator()( PDNode *patterns::ElewiseAddMatmulAct::operator()( paddle::framework::ir::PDNode *dout_var, const std::unordered_set &act_grad_types, - bool without_x_gradient, bool is_act_grad_x_from_act) { + bool without_x_gradient, + bool is_act_grad_x_from_act) { auto *ele_grad_bias_var = pattern->NewNode(ele_grad_bias_repr()) ->assert_is_op_input("elementwise_add_grad", "Y"); @@ -2052,7 +2109,8 @@ PDNode *patterns::Pool::operator()() { return output_var; } -PDNode *patterns::Elementwise::operator()(PDNode *x_var, PDNode *y_var, +PDNode *patterns::Elementwise::operator()(PDNode *x_var, + PDNode *y_var, const std::string elementwise_type) { auto elementwise_op = pattern->NewNode(elementwise_op_repr())->assert_is_op(elementwise_type); @@ -2084,7 +2142,9 @@ PDNode *patterns::ElementwiseOp::operator()( } PDNode *patterns::ResidualElementwise::operator()( - PDNode *op_var, PDNode *residual_var, const std::string elementwise_type, + PDNode *op_var, + PDNode *residual_var, + const std::string elementwise_type, bool as_x) { auto elementwise_op = pattern->NewNode(elementwise_op_repr())->assert_is_op(elementwise_type); @@ -3065,7 +3125,8 @@ void patterns::DeleteQuantDequantLinearOpPattern::operator()() { } PDNode *patterns::ReshapeTransposeMatmulPattern::operator()( - const std::string &op_name, bool with_reshape_xshape, + const std::string &op_name, + bool with_reshape_xshape, bool with_transpose_xshape) { auto reshape_op = pattern->NewNode(reshape_op_repr())->assert_is_op("reshape2"); @@ -3098,11 +3159,10 @@ PDNode *patterns::ReshapeTransposeMatmulPattern::operator()( transpose_out->assert_is_only_output_of_op("transpose2"); auto transpose_xshape = - with_transpose_xshape - ? pattern->NewNode(transpose_xshape_repr()) - ->AsIntermediate() - ->assert_is_op_output("transpose2", "XShape") - : nullptr; + with_transpose_xshape ? pattern->NewNode(transpose_xshape_repr()) + ->AsIntermediate() + ->assert_is_op_output("transpose2", "XShape") + : nullptr; auto matmul_out = pattern->NewNode(matmul_out_repr()) ->AsOutput() diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.h b/paddle/fluid/framework/ir/graph_pattern_detector.h index 45b191ea2299c..e492a3e93d88a 100644 --- a/paddle/fluid/framework/ir/graph_pattern_detector.h +++ b/paddle/fluid/framework/ir/graph_pattern_detector.h @@ -122,10 +122,12 @@ struct PDNode { PDNode* assert_is_op_input(const std::string& op_type, const std::string& argument); PDNode* assert_is_op_nth_input(const std::string& op_type, - const std::string& argument, int nth); + const std::string& argument, + int nth); PDNode* assert_is_not_op_input(const std::string& argument); PDNode* assert_is_op_nth_output(const std::string& op_type, - const std::string& argument, int nth); + const std::string& argument, + int nth); PDNode* assert_is_only_input_of_op(const std::string& op_type); PDNode* assert_is_only_output_of_op(const std::string& op_type); PDNode* assert_op_has_n_inputs(const std::string& op_type, size_t n); @@ -138,13 +140,15 @@ struct PDNode { const std::string& argument); PDNode* assert_is_ops_nth_input( const std::unordered_set& op_types, - const std::string& argument, int nth); + const std::string& argument, + int nth); PDNode* assert_is_ops_input(const std::unordered_set& op_types); PDNode* assert_is_ops_input(const std::unordered_set& op_types, const std::string& argument); PDNode* assert_is_ops_nth_output( const std::unordered_set& op_types, - const std::string& argument, int nth); + const std::string& argument, + int nth); PDNode* assert_is_only_input_of_ops( const std::unordered_set& op_types); @@ -164,10 +168,13 @@ struct PDNode { } private: - PDNode(PDPattern* pattern, const std::string& name = "", + PDNode(PDPattern* pattern, + const std::string& name = "", Type type = Type::kVar) : pattern_(pattern), name_(name), type_(type) {} - PDNode(teller_t&& teller, PDPattern* pattern, const std::string& name = "", + PDNode(teller_t&& teller, + PDPattern* pattern, + const std::string& name = "", Type type = Type::kVar) : teller_(std::move(teller)), pattern_(pattern), @@ -398,16 +405,25 @@ struct KeyCounter { return x; } +#ifdef PADDLE_WITH_TENSORRT + static int IncCounter(const std::string& key) { return dic_[key]++; } + static void CleanCounter() { dic_.clear(); } + + private: + static thread_local std::unordered_map dic_; +#else int IncCounter(const std::string& key) { return dic_[key]++; } private: std::unordered_map dic_; +#endif }; // Generate a unique PDNode's name with name_scope and id. // The format is {name_scope}/{repr}/{id}/{name} static std::string PDNodeName(const std::string& name_scope, - const std::string& repr, size_t id, + const std::string& repr, + size_t id, const std::string& name) { return string::Sprintf("%s/%s/%d/%s", name_scope, repr, id, name); } @@ -415,15 +431,15 @@ static std::string PDNodeName(const std::string& name_scope, // The format is {name_scope}/{repr}/{id} static std::string PDNodeName(const std::string& name_scope, const std::string& repr) { - return string::Sprintf("%s/%s/%d", name_scope, repr, - KeyCounter::Instance().IncCounter(repr)); + return string::Sprintf( + "%s/%s/%d", name_scope, repr, KeyCounter::Instance().IncCounter(repr)); } // Generate a unique key. It can be used for a universally unique temporary // name. // The format is {repr}/{id} static std::string UniqueKey(const std::string& repr) { - return string::Sprintf("%s/%d", repr, - KeyCounter::Instance().IncCounter(repr)); + return string::Sprintf( + "%s/%d", repr, KeyCounter::Instance().IncCounter(repr)); } // Declare a PDNode in a pattern, will create two methods: @@ -440,17 +456,19 @@ static std::string UniqueKey(const std::string& repr) { // arg: the argument declared by PATTERN_DECL_NODE in a pattern definition. // pat: the pattern object. #define GET_IR_NODE_FROM_SUBGRAPH(var, arg, pat) \ - PADDLE_ENFORCE_NE(subgraph.count(pat.arg##_n()), 0UL, \ + PADDLE_ENFORCE_NE(subgraph.count(pat.arg##_n()), \ + 0UL, \ platform::errors::NotFound("Node not found for PDNode %s", \ pat.arg##_repr())); \ Node* var = subgraph.at(pat.arg##_n()); \ - PADDLE_ENFORCE_NOT_NULL( \ - var, platform::errors::NotFound("node %s not exists in the sub-graph", \ - #arg)); + PADDLE_ENFORCE_NOT_NULL(var, \ + platform::errors::NotFound( \ + "node %s not exists in the sub-graph", #arg)); // The base class of all the patterns. struct PatternBase { - PatternBase(PDPattern* pattern, const std::string& name_scope, + PatternBase(PDPattern* pattern, + const std::string& name_scope, const std::string& repr) : pattern(pattern), name_scope_(name_scope), @@ -476,7 +494,8 @@ struct ConvBN : public PatternBase { ConvBN(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_bn") {} - PDNode* operator()(PDNode* conv_input, const std::string& conv_type, + PDNode* operator()(PDNode* conv_input, + const std::string& conv_type, bool with_eltwise_add); // declare operator node's name @@ -514,7 +533,8 @@ struct ConvActivation : public PatternBase { ConvActivation(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_activation") {} - PDNode* operator()(PDNode* conv_input, std::string conv_type = "conv2d", + PDNode* operator()(PDNode* conv_input, + std::string conv_type = "conv2d", std::string activation_type = "relu"); // declare operator node's name @@ -536,7 +556,8 @@ struct ElementwiseActivation : public PatternBase { ElementwiseActivation(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "elementwise_add_activation") {} - PDNode* operator()(PDNode* elementwise_a, const std::string& elementwise_type, + PDNode* operator()(PDNode* elementwise_a, + const std::string& elementwise_type, const std::string& activation_type); // declare operator node's name @@ -936,7 +957,8 @@ struct LinearAct : public PatternBase { PDNode* operator()(PDNode* x, const std::unordered_set& act_types, - bool with_grad_link, bool is_act_grad_x_from_act); + bool with_grad_link, + bool is_act_grad_x_from_act); // declare operator node's name PATTERN_DECL_NODE(matmul); @@ -965,7 +987,8 @@ struct ElewiseAddMatmulAct : public PatternBase { PDNode* operator()(PDNode* x, const std::unordered_set& act_grad_types, - bool without_x_gradient, bool is_act_grad_x_from_act); + bool without_x_gradient, + bool is_act_grad_x_from_act); // declare operator node's name PATTERN_DECL_NODE(ele_add_grad); @@ -1062,7 +1085,8 @@ struct Elementwise : public PatternBase { Elementwise(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "elementwise") {} - PDNode* operator()(PDNode* x_var, PDNode* y_var, + PDNode* operator()(PDNode* x_var, + PDNode* y_var, const std::string elementwise_type); PATTERN_DECL_NODE(elementwise_op); @@ -1088,11 +1112,14 @@ struct ElementwiseOp : public PatternBase { // This pattern allows operator output to be X or Y // and residual data Y or X, based on as_x flag struct ResidualElementwise : public PatternBase { - ResidualElementwise(PDPattern* pattern, const std::string& name_scope, + ResidualElementwise(PDPattern* pattern, + const std::string& name_scope, bool as_x) : PatternBase(pattern, name_scope, "residual_elementwise") {} - PDNode* operator()(PDNode* op_var, PDNode* residual_var, - const std::string elementwise_type, bool as_x); + PDNode* operator()(PDNode* op_var, + PDNode* residual_var, + const std::string elementwise_type, + bool as_x); PATTERN_DECL_NODE(operator_output); PATTERN_DECL_NODE(residual_data); @@ -1467,8 +1494,8 @@ struct ConvElementwiseaddAct : public PatternBase { // Conv + ElementwiseAdd + ElementwiseAdd + Activation struct ConvElementwiseadd2Act : public PatternBase { ConvElementwiseadd2Act(PDPattern* pattern, const std::string& name_scope) - : PatternBase(pattern, name_scope, - "conv_elementwiseadd2_elementwiseadd_act") {} + : PatternBase( + pattern, name_scope, "conv_elementwiseadd2_elementwiseadd_act") {} PDNode* operator()(PDNode* conv_in); @@ -1702,7 +1729,8 @@ struct DequantOpFuse : public PatternBase { DequantOpFuse(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "dequant_fuse") {} - void operator()(PDNode* quant_op_input, const std::string& quantized_op_type, + void operator()(PDNode* quant_op_input, + const std::string& quantized_op_type, const std::string& dequant_type, const std::string& weight_name); @@ -1758,8 +1786,8 @@ struct DeleteQuantDequantOpPattern : public PatternBase { struct DeleteQuantDequantFilterOpPattern : public PatternBase { DeleteQuantDequantFilterOpPattern(PDPattern* pattern, const std::string& name_scope) - : PatternBase(pattern, name_scope, - "delete_quantdequant_filter_op_pattern") {} + : PatternBase( + pattern, name_scope, "delete_quantdequant_filter_op_pattern") {} void operator()(); @@ -1773,7 +1801,8 @@ struct DeleteQuantDequantFilterOpPattern : public PatternBase { struct DeleteWeightQuantDequantLinearOpPattern : public PatternBase { DeleteWeightQuantDequantLinearOpPattern(PDPattern* pattern, const std::string& name_scope) - : PatternBase(pattern, name_scope, + : PatternBase(pattern, + name_scope, "delete_weight_quant_dequant_linear_op_pattern") {} void operator()(); @@ -1788,8 +1817,8 @@ struct DeleteWeightQuantDequantLinearOpPattern : public PatternBase { struct DeleteQuantDequantLinearOpPattern : public PatternBase { DeleteQuantDequantLinearOpPattern(PDPattern* pattern, const std::string& name_scope) - : PatternBase(pattern, name_scope, - "delete_quant_dequant_linear_op_pattern") {} + : PatternBase( + pattern, name_scope, "delete_quant_dequant_linear_op_pattern") {} void operator()(); @@ -1814,7 +1843,8 @@ struct ReshapeTransposeMatmulPattern : public PatternBase { const std::string& name_scope) : PatternBase(pattern, name_scope, "reshape_transpose_matmul") {} - PDNode* operator()(const std::string& op_name, bool with_reshape_xshape, + PDNode* operator()(const std::string& op_name, + bool with_reshape_xshape, bool with_transpose_xshape); PATTERN_DECL_NODE(reshape_in); diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 3a69aabcee4bb..4358795d93919 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -82,9 +82,9 @@ namespace paddle { using inference::Singleton; #if PADDLE_WITH_TENSORRT -using inference::tensorrt::TRTInt8Calibrator; using inference::tensorrt::TRTCalibratorEngine; using inference::tensorrt::TRTCalibratorEngineManager; +using inference::tensorrt::TRTInt8Calibrator; #endif int AnalysisPredictor::clone_num_ = 1; @@ -101,7 +101,8 @@ bool IsPersistable(const framework::VarDesc *var) { } } // namespace -bool PaddleTensorToLoDTensor(const PaddleTensor &pt, framework::LoDTensor *t, +bool PaddleTensorToLoDTensor(const PaddleTensor &pt, + framework::LoDTensor *t, const platform::Place &place) { framework::DDim ddim = phi::make_ddim(pt.shape); void *input_ptr; @@ -129,18 +130,19 @@ bool PaddleTensorToLoDTensor(const PaddleTensor &pt, framework::LoDTensor *t, if (platform::is_cpu_place(place)) { // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy. - std::memcpy(static_cast(input_ptr), pt.data.data(), - pt.data.length()); + std::memcpy( + static_cast(input_ptr), pt.data.data(), pt.data.length()); } else if (platform::is_ipu_place(place)) { #ifdef PADDLE_WITH_IPU - std::memcpy(static_cast(input_ptr), pt.data.data(), - pt.data.length()); + std::memcpy( + static_cast(input_ptr), pt.data.data(), pt.data.length()); #else PADDLE_THROW(paddle::platform::errors::Fatal( "Not compile with WITH_IPU, should not reach here.")); #endif } else if (platform::is_gpu_place(place)) { - PADDLE_ENFORCE_EQ(platform::is_xpu_place(place), false, + PADDLE_ENFORCE_EQ(platform::is_xpu_place(place), + false, platform::errors::InvalidArgument( "Only one choice can be made between CPU and XPU.")); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) @@ -148,8 +150,11 @@ bool PaddleTensorToLoDTensor(const PaddleTensor &pt, framework::LoDTensor *t, auto *dev_ctx = static_cast(pool.Get(place)); auto dst_gpu_place = place; - memory::Copy(dst_gpu_place, static_cast(input_ptr), - platform::CPUPlace(), pt.data.data(), pt.data.length(), + memory::Copy(dst_gpu_place, + static_cast(input_ptr), + platform::CPUPlace(), + pt.data.data(), + pt.data.length(), dev_ctx->stream()); #else PADDLE_THROW(paddle::platform::errors::Fatal( @@ -158,8 +163,11 @@ bool PaddleTensorToLoDTensor(const PaddleTensor &pt, framework::LoDTensor *t, } else if (platform::is_xpu_place(place)) { #ifdef PADDLE_WITH_XPU auto dst_xpu_place = place; - memory::Copy(dst_xpu_place, static_cast(input_ptr), - platform::CPUPlace(), pt.data.data(), pt.data.length()); + memory::Copy(dst_xpu_place, + static_cast(input_ptr), + platform::CPUPlace(), + pt.data.data(), + pt.data.length()); #else PADDLE_THROW(paddle::platform::errors::Fatal( "Not compile with XPU, should not reach here.")); @@ -263,7 +271,8 @@ bool AnalysisPredictor::PrepareProgram( } bool AnalysisPredictor::CreateExecutor() { if (config_.use_gpu()) { - PADDLE_ENFORCE_EQ(config_.use_xpu(), false, + PADDLE_ENFORCE_EQ(config_.use_xpu(), + false, platform::errors::InvalidArgument( "Only one choice can be made between CPU and XPU.")); place_ = paddle::platform::CUDAPlace(config_.gpu_device_id()); @@ -357,7 +366,8 @@ static bool IsPrepareDataOptTargetOp(framework::OpDesc *op) { } static void DisablePrepareDataOpt( - std::shared_ptr inference_program, int block, + std::shared_ptr inference_program, + int block, bool pre_disable_opt) { bool disable_opt = false; auto &infer_block = inference_program->Block(block); @@ -367,8 +377,8 @@ static void DisablePrepareDataOpt( } if (op->HasAttr("sub_block")) { int blockID = op->GetBlockAttrId("sub_block"); - DisablePrepareDataOpt(inference_program, blockID, - disable_opt || pre_disable_opt); + DisablePrepareDataOpt( + inference_program, blockID, disable_opt || pre_disable_opt); } // disable prepare data if unfriendly op is found if (!disable_opt) { @@ -386,8 +396,8 @@ bool AnalysisPredictor::PrepareExecutor() { #endif DisablePrepareDataOpt(inference_program_, 0, false); - executor_->Prepare(sub_scope_, *inference_program_, 0, - config_.use_feed_fetch_ops_); + executor_->Prepare( + sub_scope_, *inference_program_, 0, config_.use_feed_fetch_ops_); PADDLE_ENFORCE_NOT_NULL(sub_scope_, platform::errors::PreconditionNotMet( @@ -433,8 +443,13 @@ bool AnalysisPredictor::PrepareFleetExecutor() { feed_fetch_vars.emplace_back(pair.second); } fleet_exe_->Init(config_.dist_config().carrier_id(), - *(inference_program_.get()), scope_.get(), place_, 1, - {task_node_.get()}, id_to_rank, feed_fetch_vars); + *(inference_program_.get()), + scope_.get(), + place_, + 1, + {task_node_.get()}, + id_to_rank, + feed_fetch_vars); return true; } @@ -471,8 +486,12 @@ bool AnalysisPredictor::CommInit() { peer_endpoints.emplace_back( config_.dist_config().trainer_endpoints()[rank]); } - InsertCommOp(var_name_base + std::to_string(order), ranks_in_group, - rank_in_group, peer_endpoints, comm_init_block, ring_id); + InsertCommOp(var_name_base + std::to_string(order), + ranks_in_group, + rank_in_group, + peer_endpoints, + comm_init_block, + ring_id); order += 1; } framework::NaiveExecutor e(place_); @@ -484,8 +503,11 @@ bool AnalysisPredictor::CommInit() { } void AnalysisPredictor::InsertCommOp( - std::string tmp_var_name, int nranks, int rank, - const std::vector &peer_endpoints, framework::BlockDesc *block, + std::string tmp_var_name, + int nranks, + int rank, + const std::vector &peer_endpoints, + framework::BlockDesc *block, int ring_id) { /* * tmp_var_name: the var name for var comm_id @@ -542,7 +564,8 @@ bool AnalysisPredictor::LoadConverterConfig( << config_.dist_config().comm_init_config() << "\n"; std::ifstream fin(config_.dist_config().comm_init_config(), std::ios::in); PADDLE_ENFORCE_EQ( - static_cast(fin.is_open()), true, + static_cast(fin.is_open()), + true, platform::errors::NotFound( "Cannot open file %s, please confirm whether the file is normal.", config_.dist_config().comm_init_config())); @@ -686,8 +709,9 @@ bool AnalysisPredictor::Run(const std::vector &inputs, timer.tic(); // set feed variable framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get(); - PADDLE_ENFORCE_NOT_NULL(scope, platform::errors::PreconditionNotMet( - "The scope should not be nullptr.")); + PADDLE_ENFORCE_NOT_NULL( + scope, + platform::errors::PreconditionNotMet("The scope should not be nullptr.")); if (!SetFeed(inputs, scope)) { LOG(ERROR) << "fail to set feed"; return false; @@ -790,9 +814,11 @@ bool AnalysisPredictor::GetFetch(std::vector *outputs, for (size_t i = 0; i < fetches_.size(); ++i) { int idx = BOOST_GET_CONST(int, fetches_[i]->GetAttr("col")); PADDLE_ENFORCE_EQ( - static_cast(idx), i, + static_cast(idx), + i, platform::errors::InvalidArgument( - "Fetch op's col attr(%d) should be equal to the index(%d)", idx, + "Fetch op's col attr(%d) should be equal to the index(%d)", + idx, i)); framework::FetchType &fetch_var = framework::GetFetchVariable(*scope, "fetch", idx); @@ -833,7 +859,8 @@ void AnalysisPredictor::PrepareArgument() { if (!config_.model_dir().empty()) { argument_.SetModelDir(config_.model_dir()); } else { - PADDLE_ENFORCE_EQ(config_.prog_file().empty(), false, + PADDLE_ENFORCE_EQ(config_.prog_file().empty(), + false, platform::errors::PreconditionNotMet( "Either model_dir or prog_file should be set.")); std::string dir = inference::analysis::GetDirRoot(config_.prog_file()); @@ -969,7 +996,8 @@ void AnalysisPredictor::OptimizeInferenceProgram() { Analyzer().Run(&argument_); PADDLE_ENFORCE_EQ( - argument_.scope_valid(), true, + argument_.scope_valid(), + true, platform::errors::InvalidArgument("The argument scope should be valid.")); VLOG(5) << "to prepare executor"; ARGUMENT_CHECK_FIELD((&argument_), ir_analyzed_program); @@ -1008,8 +1036,9 @@ void AnalysisPredictor::OptimizeInferenceProgram() { } template <> -std::unique_ptr CreatePaddlePredictor< - AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) { +std::unique_ptr +CreatePaddlePredictor( + const AnalysisConfig &config) { // TODO(NHZlX): Should add the link to the doc of // paddle_infer::CreatePredictor if (config.glog_info_disabled()) { @@ -1018,7 +1047,8 @@ std::unique_ptr CreatePaddlePredictor< } VLOG(3) << "create AnalysisConfig"; PADDLE_ENFORCE_EQ( - config.is_valid(), true, + config.is_valid(), + true, platform::errors::InvalidArgument( "Note: Each config can only be used for one predictor.")); @@ -1035,11 +1065,13 @@ std::unique_ptr CreatePaddlePredictor< std::call_once(gflags_initialized, [&]() { std::vector gflags; PADDLE_ENFORCE_GE( - config.memory_pool_init_size_mb(), 0.f, + config.memory_pool_init_size_mb(), + 0.f, platform::errors::InvalidArgument( "The size of memory pool should be greater than 0.")); PADDLE_ENFORCE_GE( - config.gpu_device_id(), 0, + config.gpu_device_id(), + 0, platform::errors::InvalidArgument( "Invalid device id (%d). The device id should be greater than 0.", config.gpu_device_id())); @@ -1105,6 +1137,10 @@ std::unique_ptr CreatePaddlePredictor< config.SetInValid(); auto predictor_p = dynamic_cast(predictor.get()); +#ifdef PADDLE_WITH_TENSORRT + paddle::framework::ir::patterns::KeyCounter::Instance().CleanCounter(); +#endif + if (!predictor_p->Init(nullptr)) { return nullptr; } @@ -1154,8 +1190,9 @@ void AnalysisPredictor::PrepareFeedFetch() { } void AnalysisPredictor::CreateFeedFetchVar(framework::Scope *scope) { - PADDLE_ENFORCE_NOT_NULL(scope, platform::errors::InvalidArgument( - "The scope should not be nullptr.")); + PADDLE_ENFORCE_NOT_NULL( + scope, + platform::errors::InvalidArgument("The scope should not be nullptr.")); auto *var = scope->Var("feed"); var->GetMutable(); var = scope->Var("fetch"); @@ -1176,8 +1213,9 @@ AnalysisPredictor::GetInputTensorShape() { std::vector names = GetInputNames(); for (std::string name : names) { auto *var = inference_program_->Block(0).FindVar(name); - PADDLE_ENFORCE_NOT_NULL(var, platform::errors::PreconditionNotMet( - "Input %s does not exist.", name)); + PADDLE_ENFORCE_NOT_NULL( + var, + platform::errors::PreconditionNotMet("Input %s does not exist.", name)); input_shapes[name] = var->GetShape(); } return input_shapes; @@ -1398,7 +1436,8 @@ void AnalysisPredictor::StatisticShapeRangeInfo() { std::vector> counter; for (auto &it : m) counter.push_back(it); std::sort( - counter.begin(), counter.end(), + counter.begin(), + counter.end(), [](std::pair &a, std::pair &b) { return a.second > b.second; }); @@ -1420,8 +1459,8 @@ void AnalysisPredictor::StatisticShapeRangeInfo() { opt_shapes[name] = opt_shape; } - inference::SerializeShapeRangeInfo(config_.shape_range_info_path(), - min_shapes, max_shapes, opt_shapes); + inference::SerializeShapeRangeInfo( + config_.shape_range_info_path(), min_shapes, max_shapes, opt_shapes); } bool AnalysisPredictor::LoadProgramDesc() { @@ -1441,7 +1480,8 @@ bool AnalysisPredictor::LoadProgramDesc() { return false; } LOG(ERROR) << string::Sprintf( - "not valid model path '%s' or program path '%s'.", config_.model_dir(), + "not valid model path '%s' or program path '%s'.", + config_.model_dir(), config_.params_file()); return false; } @@ -1453,7 +1493,8 @@ bool AnalysisPredictor::LoadProgramDesc() { // Read binary std::ifstream fin(filename, std::ios::in | std::ios::binary); PADDLE_ENFORCE_EQ( - static_cast(fin.is_open()), true, + static_cast(fin.is_open()), + true, platform::errors::NotFound( "Cannot open file %s, please confirm whether the file is normal.", filename)); @@ -1555,7 +1596,8 @@ void AnalysisPredictor::ClearIntermediateTensor() { #if PADDLE_WITH_TENSORRT bool AnalysisPredictor::SaveTrtCalibToDisk() { - PADDLE_ENFORCE_EQ(config_.tensorrt_engine_enabled(), true, + PADDLE_ENFORCE_EQ(config_.tensorrt_engine_enabled(), + true, platform::errors::PreconditionNotMet( "This func can be invoked only in trt mode")); auto &block = inference_program_->Block(0); @@ -1782,8 +1824,10 @@ Predictor::Predictor(const Config &config) { << "Paddle2ONNX do't support convert the Model， fall back to using " "Paddle Inference."; } else { - predictor_ = paddle::CreatePaddlePredictor< - Config, paddle::PaddleEngineKind::kONNXRuntime>(config); + predictor_ = + paddle::CreatePaddlePredictor( + config); return; } #else @@ -1793,8 +1837,10 @@ Predictor::Predictor(const Config &config) { "fall back to using Paddle Inference."; #endif } - predictor_ = paddle::CreatePaddlePredictor< - Config, paddle::PaddleEngineKind::kAnalysis>(config); + predictor_ = + paddle::CreatePaddlePredictor( + config); } std::vector Predictor::GetInputNames() { @@ -1876,7 +1922,8 @@ std::shared_ptr CreatePredictor(const Config &config) { // NOLINT namespace services { PredictorPool::PredictorPool(const Config &config, size_t size) { PADDLE_ENFORCE_GE( - size, 1UL, + size, + 1UL, paddle::platform::errors::InvalidArgument( "The predictor pool size should be greater than 1, but it's (%d)", size)); @@ -1895,9 +1942,11 @@ PredictorPool::PredictorPool(const Config &config, size_t size) { Predictor *PredictorPool::Retrive(size_t idx) { PADDLE_ENFORCE_LT( - idx, preds_.size() + 1, + idx, + preds_.size() + 1, paddle::platform::errors::InvalidArgument( - "There are (%d) predictors in the pool, but the idx is (%d)", idx, + "There are (%d) predictors in the pool, but the idx is (%d)", + idx, preds_.size() + 1)); if (idx == 0) { return main_pred_.get();