initial trial

Signed-off-by: fishbell <[email protected]>

sink reshape for further optimization

Signed-off-by: fishbell <[email protected]>

clean up code

Signed-off-by: fishbell <[email protected]>

optimize code

Signed-off-by: fishbell <[email protected]>

relaxed pattern match to allow reorder not in place

Signed-off-by: fishbell <[email protected]>

use random values in test

Signed-off-by: fishbell <[email protected]>

src/plugins/intel_gpu/include/intel_gpu/graph/program.hpp

@@ -47,6 +47,7 @@ struct program {
     friend class post_optimize_weights;             // to be removed when possible
     friend class prepare_primitive_fusing_through;  // to be removed when possible
     friend class reorder_transfer;                  // to be removed when possible
+    friend class reshape_transfer;                  // to be removed when possible
     friend class fuse_constant_transposes;          // to be removed when possible
     friend class program_wrapper;                   // this class is intended to extend the interface of program for
                                                     // the usage within tests_core_internal project only

src/plugins/intel_gpu/src/graph/graph_optimizer/reshape_transfer.cpp

@@ -0,0 +1,141 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <algorithm>
+
+#include "pass_manager.h"
+#include "permute_inst.h"
+#include "program_helpers.h"
+#include "reorder_inst.h"
+#include "reshape_inst.h"
+
+using namespace cldnn;
+
+void reshape_transfer::run(program& p) {
+    // (reorder) + reshape + transpose
+    // sink reshape for further possible optimization
+    auto is_suitable_permute = [](cldnn::program_node* node) {
+        return node->get_users().size() == 1 && node->is_dynamic() == false &&
+               node->get_output_layout().get_rank() == 4;
+    };
+
+    auto is_suitable_reshape = [](cldnn::program_node* node) -> bool {
+        if (node->get_users().size() != 1 || node->is_dynamic())
+            return false;
+        auto& input_lay = node->get_input_layout(0);
+        auto& output_lay = node->get_output_layout();
+        if (input_lay.compatible(output_lay))
+            return true;
+        return false;
+    };
+
+    std::function<bool(cldnn::program_node*)> is_suitable_parent;
+    is_suitable_parent = [&is_suitable_parent](const cldnn::program_node* node) -> bool {
+        if (node->get_users().size() != 1 || node->is_dynamic())
+            return false;
+        for (size_t idx = 0; idx < node->get_dependencies().size(); idx++) {
+            auto& input = node->get_dependency(idx);
+            if (!input.is_in_data_flow() || input.is_constant())
+                continue;
+            if (node->is_type<convolution>() || input.is_type<convolution>()) {
+                return true;
+            } else if (input.is_type<eltwise>() && input.get_dependency(1).is_constant()) {
+                return is_suitable_parent(&input);
+            } else if (input.is_type<activation>()) {
+                return is_suitable_parent(&input);
+            }
+            return false;
+        }
+        return false;
+    };
+
+    auto update_order = [](std::vector<uint16_t> original_order, cldnn::program_node* reshape) {
+        if (!reshape)
+            return original_order;
+        // Example. For this sequence, there is Reshape node which merges 2 consecutive dims into one
+        // order must be updated like permute is done before reshape
+        // [1,3,4,6] -> Reshape[1,3,24,1]-> permute(0,2,1) -> [1,24,3,1]
+        // updated order must be (0,2,3,1):
+        // dim with index=2 is split into 2 parts: 2 and 3
+        const auto& reshape_in_shape = reshape->get_input_layout().get_dims();
+        const auto& reshape_out_shape = reshape->get_output_layout().get_dims();
+        auto transformed_order = original_order;
+        ov::Shape new_shape(transformed_order.size());
+        const uint16_t merge_dim_idx = [&]() {
+            for (uint16_t i = 0; i < reshape_in_shape.size(); ++i) {
+                if (reshape_in_shape[i] != reshape_out_shape[i])
+                    return i;
+            }
+            OPENVINO_THROW("same input/output for reshape node");
+        }();
+        auto insertIt = transformed_order.end();
+        for (auto it = transformed_order.begin(); it != transformed_order.end(); ++it) {
+            auto& elem = *it;
+            if (elem > merge_dim_idx) {
+                elem++;
+            } else if (elem == merge_dim_idx) {
+                insertIt = it + 1;
+            }
+        }
+        transformed_order.insert(insertIt, merge_dim_idx + 1);
+        // remove invalid orders
+        if (transformed_order.size() > reshape_out_shape.size()) {
+            transformed_order.erase(
+                std::remove_if(transformed_order.begin(), transformed_order.end(), [&](uint16_t& order) {
+                    return order >= reshape_out_shape.size();
+                }));
+        }
+        return transformed_order;
+    };
+
+    auto itr = p.get_processing_order().begin();
+    while (itr != p.get_processing_order().end()) {
+        auto& node = *itr++;
+        if (!node->is_type<permute>())
+            continue;
+        auto& transpose_node = node->as<permute>();
+        if (!is_suitable_permute(&transpose_node))
+            continue;
+        auto& child_node = transpose_node;
+        auto parent_node = child_node.get_dependency_with_port(0).first;
+        cldnn::program_node* inter_node;
+        if (parent_node->is_type<reshape>()) {
+            inter_node = parent_node;
+            if (!is_suitable_reshape(inter_node)) {
+                continue;
+            }
+            parent_node = inter_node->get_dependency_with_port(0).first;
+        } else {
+            continue;
+        }
+
+        if (!is_suitable_parent(parent_node)) {
+            continue;
+        }
+        reshape_node* reshape_node = nullptr;
+        if (inter_node && inter_node->is_type<reshape>())
+            reshape_node = &(inter_node->as<reshape>());
+
+        auto transpose_order = update_order(transpose_node.get_permute_order(), reshape_node);
+        auto new_permute =
+            std::make_shared<permute>(transpose_node.id() + "_reordered", parent_node->id(), transpose_order);
+        auto& new_permute_node = p.get_or_create(new_permute);
+        if (new_permute_node.as<permute>().is_rotating_except_batch()) {
+            auto next_node = transpose_node.get_users().front();
+            auto new_reshape_tensor = transpose_node.get_output_layout().get_tensor();
+            p.move_node(*reshape_node, *node, *next_node);
+            // replace the permute node and reshape node
+            auto new_reshape =
+                std::make_shared<reshape>(reshape_node->id() + "_sinked", new_permute_node.id(), new_reshape_tensor);
+            auto& new_reshape_node = p.get_or_create(new_reshape);
+
+            p.replace(transpose_node, new_permute_node);
+            p.replace(*reshape_node, new_reshape_node);
+            new_permute_node.recalc_output_layout(false);
+            new_reshape_node.recalc_output_layout(false);
+        } else {
+            p.remove_if_dangling(new_permute_node);
+        }
+    }
+}

src/plugins/intel_gpu/src/graph/include/pass_manager.h

@@ -366,6 +366,14 @@ class reorder_transfer : public base_pass {
     void run(program& p) override;
 };
 
+class reshape_transfer : public base_pass {
+public:
+    reshape_transfer() : base_pass("reshape_transfer") {}
+
+private:
+    void run(program& p) override;
+};
+
 class mark_runtime_skippable_nodes : public base_pass {
 public:
     mark_runtime_skippable_nodes() : base_pass("mark_runtime_skippable_nodes") {}

src/plugins/intel_gpu/src/graph/program.cpp

@@ -569,6 +569,7 @@ void program::pre_optimize_graph(bool is_internal) {
 
         apply_opt_pass<reorder_transfer>();
 
+        apply_opt_pass<reshape_transfer>();
 #ifdef GPU_DEBUG_CONFIG
         GPU_DEBUG_IF(!debug_config->disable_primitive_fusing) {
 #else