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diff --git a/‎paddle/fluid/framework/ir/CMakeLists.txt
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+2Lines changed: 2 additions & 0 deletions b/‎paddle/fluid/framework/ir/CMakeLists.txt
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+2Lines changed: 2 additions & 0 deletions
diff --git a/‎paddle/fluid/framework/ir/xpu/weight_only_linear_xpu_pass.cc
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+239Lines changed: 239 additions & 0 deletions b/‎paddle/fluid/framework/ir/xpu/weight_only_linear_xpu_pass.cc
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+239Lines changed: 239 additions & 0 deletions
diff --git a/‎paddle/fluid/inference/api/analysis_predictor.cc
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+29-1Lines changed: 29 additions & 1 deletion b/‎paddle/fluid/inference/api/analysis_predictor.cc
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+29-1Lines changed: 29 additions & 1 deletion
diff --git a/‎paddle/fluid/inference/api/paddle_pass_builder.cc
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+1Lines changed: 1 addition & 0 deletions b/‎paddle/fluid/inference/api/paddle_pass_builder.cc
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+1Lines changed: 1 addition & 0 deletions
diff --git a/‎paddle/phi/backends/xpu/xpu2_op_list.cc
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+4Lines changed: 4 additions & 0 deletions b/‎paddle/phi/backends/xpu/xpu2_op_list.cc
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+4Lines changed: 4 additions & 0 deletions
@@ -332,6 +332,8 @@ if(WITH_XPU)
                ${XPU_PASS_DEPS})
   pass_library(spatial_transformer_resblock_xpu_fuse_pass inference DIR xpu
                DEPS ${XPU_PASS_DEPS})
+  pass_library(weight_only_linear_xpu_pass inference DIR xpu DEPS
+               ${XPU_PASS_DEPS})
 endif()
 
 cc_library(
 
@@ -0,0 +1,239 @@
+// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "glog/logging.h"
+
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/framework/ir/xpu/pass_utils.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+namespace patterns {
+
+struct PermuteINT8WeightOnlyPattern : public PatternBase {
+  PermuteINT8WeightOnlyPattern(PDPattern* pattern,
+                               const std::string& name_scope);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(weight_only_linear);
+
+  // declare variable node's name
+  PATTERN_DECL_NODE(input);
+  PATTERN_DECL_NODE(weight);
+  PATTERN_DECL_NODE(weight_scale);
+  PATTERN_DECL_NODE(out);
+};
+
+PermuteINT8WeightOnlyPattern::PermuteINT8WeightOnlyPattern(
+    PDPattern* pattern, const std::string& name_scope)
+    : PatternBase(pattern, name_scope, name_scope) {
+  auto* input = pattern->NewNode(input_repr())
+                    ->assert_is_op_input("weight_only_linear_xpu", "x")
+                    ->AsInput();
+  auto* weight = pattern->NewNode(weight_repr())
+                     ->assert_is_op_input("weight_only_linear_xpu", "weight")
+                     ->AsInput();
+  auto* weight_scale =
+      pattern->NewNode(weight_scale_repr())
+          ->assert_is_op_input("weight_only_linear_xpu", "weight_scale")
+          ->AsInput();
+  auto* out = pattern->NewNode(out_repr())
+                  ->assert_is_op_output("weight_only_linear_xpu", "out")
+                  ->AsOutput();
+  auto* weight_only_linear = pattern->NewNode(weight_only_linear_repr())
+                                 ->assert_is_op("weight_only_linear_xpu");
+
+  std::vector<PDNode*> input_vars{input, weight, weight_scale};
+  std::vector<PDNode*> output_vars{out};
+  weight_only_linear->LinksFrom(input_vars).LinksTo(output_vars);
+}
+
+}  // namespace patterns
+
+class PermuteINT8WeightOnlyPass : public FusePassBase {
+ protected:
+  void ApplyImpl(ir::Graph* graph) const override;
+
+ private:
+  void ApplyPermuteINT8WeightOnly(ir::Graph* graph) const;
+  const std::string name_scope_{"weight_only_linear_xpu_pass"};
+};
+
+void PermuteINT8WeightOnlyPass::ApplyPermuteINT8WeightOnly(
+    ir::Graph* graph) const {
+  GraphPatternDetector gpd;
+  patterns::PermuteINT8WeightOnlyPattern pattern(gpd.mutable_pattern(),
+                                                 name_scope_);
+  int found_subgraph_count = 0;
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* graph) {
+    VLOG(4) << "handle PermuteINT8WeightOnlyPass";
+
+    // declare operator node's name
+    GET_IR_NODE(weight_only_linear);
+
+    // declare variable node's name
+    GET_IR_NODE(input);
+    GET_IR_NODE(weight);
+    GET_IR_NODE(weight_scale);
+    GET_IR_NODE(out);
+
+    auto* block = weight_only_linear->Op()->Block();
+    auto* scope = param_scope();
+    auto* cpu_ctx = static_cast<phi::CPUContext*>(
+        platform::DeviceContextPool::Instance().Get(phi::CPUPlace()));
+
+    auto permute_weight = [&](const std::string& input_name,
+                              const std::string& scale_name,
+                              std::vector<Node*>* weight_node,
+                              std::vector<std::string>* weight_name,
+                              std::vector<Node*>* old_weight_node,
+                              std::vector<std::string>* old_weight_name) {
+      auto GetPrefixWithoutHash = [](const std::string& name) -> std::string {
+        std::size_t found = name.find("_#");
+        return found == std::string::npos ? name : name.substr(0, found);
+      };
+
+      auto input_names = weight_only_linear->Op()->Input(input_name);
+      auto scale_names = weight_only_linear->Op()->Input(scale_name);
+      int id = 0;
+      for (auto name : input_names) {
+        phi::DenseTensor* scale_tensor =
+            scope->Var(scale_names[id])->GetMutable<phi::DenseTensor>();
+        PADDLE_ENFORCE_NOT_NULL(
+            scale_tensor,
+            platform::errors::Fatal(
+                "weight_scale tensor node should not be nullptr"));
+
+        size_t dst_hash = HashTensor<phi::dtype::float16>(*scale_tensor);
+        std::string pre_name = GetPrefixWithoutHash(name);
+        std::string dst_name = pre_name + "_#" + std::to_string(dst_hash);
+        auto* dst_node = FindNodeWithName(graph, dst_name);
+        if (dst_node == nullptr) {
+          phi::DenseTensor* curr_tensor =
+              scope->Var(name)->GetMutable<phi::DenseTensor>();
+          PADDLE_ENFORCE_NOT_NULL(
+              curr_tensor,
+              platform::errors::Fatal("tensor node should not be nullptr"));
+          // Create dst node
+          // Update dst var_desc in block
+          VarDesc dst_desc(dst_name);
+          dst_desc.SetPersistable(true);
+          dst_desc.SetShape(vectorize(curr_tensor->dims()));
+          dst_desc.SetDataType(
+              framework::TransToProtoVarType(curr_tensor->dtype()));
+          Node* dst = graph->CreateVarNode(&dst_desc);
+          auto* block_dst_desc = block->Var(dst_name);
+          block_dst_desc->SetPersistable(dst_desc.Persistable());
+          block_dst_desc->SetShape(dst_desc.GetShape());
+          block_dst_desc->SetDataType(dst_desc.GetDataType());
+          weight_node->push_back(dst);
+          weight_name->push_back(dst_name);
+
+          auto* src_node = FindNodeWithName(graph, name);
+          old_weight_node->push_back(src_node);
+          old_weight_name->push_back(name);
+          auto* dst_var = scope->FindVar(dst_name);
+          if (dst_var == nullptr) {
+            phi::DenseTensor tmp_tensor;
+            tmp_tensor.set_type(phi::DataType::INT8);
+            tmp_tensor.Resize(curr_tensor->dims());
+            cpu_ctx->Alloc<int8_t>(&tmp_tensor);
+
+            int k = curr_tensor->dims()[1];
+            for (int i = 0; i < curr_tensor->numel(); i += 8 * 16) {
+              int read_j_len = ((curr_tensor->numel() - i + 15) / 16) > 8
+                                   ? 8
+                                   : ((curr_tensor->numel() - i + 15) / 16);
+              for (int j = 0; j < read_j_len; ++j) {
+                int permuted_w_offset = i / (2 * k) * 2 * k +
+                                        ((j > 3) ? k : 0) + (i % (2 * k)) / 2 +
+                                        16 * (j % 4);
+                int w_offset = i + j * 16;
+                int read_l_len = (curr_tensor->numel() - i - j * 16) > 16
+                                     ? 16
+                                     : (curr_tensor->numel() - i - j * 16);
+                for (int l = 0; l < read_l_len; l++) {
+                  // {0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15}
+                  int offset_l = (l / 8) % 2 ? (l - 7 + (l % 8)) : l + (l % 8);
+                  tmp_tensor.data<int8_t>()[permuted_w_offset + l] =
+                      static_cast<int8_t>(
+                          static_cast<int32_t>(reinterpret_cast<uint8_t*>(
+                              curr_tensor
+                                  ->data<int8_t>())[w_offset + offset_l]) -
+                          128);
+                }
+              }
+            }
+            Assign(tmp_tensor,
+                   scope->Var(dst_name)->GetMutable<phi::DenseTensor>());
+          }
+        }
+        id++;
+      }
+    };
+    std::vector<Node*> weight_node;
+    std::vector<std::string> weight_name;
+    std::vector<Node*> old_weight_node;
+    std::vector<std::string> old_weight_name;
+    permute_weight("weight",
+                   "weight_scale",
+                   &weight_node,
+                   &weight_name,
+                   &old_weight_node,
+                   &old_weight_name);
+
+    framework::OpDesc* weight_only_linear_desc = weight_only_linear->Op();
+    weight_only_linear_desc->SetInput("weight", weight_name);
+
+    for (auto node : old_weight_node) {
+      IR_NODE_UNLINK(node, weight_only_linear);
+    }
+
+    for (auto node : weight_node) {
+      IR_NODE_LINK_TO(node, weight_only_linear);
+    }
+
+    found_subgraph_count++;
+  };
+
+  gpd(graph, handler);
+  AddStatis(found_subgraph_count);
+}
+
+void PermuteINT8WeightOnlyPass::ApplyImpl(ir::Graph* graph) const {
+  PADDLE_ENFORCE_NOT_NULL(
+      graph, platform::errors::PreconditionNotMet("graph should not be null."));
+  Init(name_scope_, graph);
+
+  ApplyPermuteINT8WeightOnly(graph);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(weight_only_linear_xpu_pass,
+              paddle::framework::ir::PermuteINT8WeightOnlyPass);
+
+REGISTER_PASS_CAPABILITY(weight_only_linear_xpu_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination().EQ(
+            "weight_only_linear_xpu", 0));
@@ -1527,6 +1527,30 @@ bool AnalysisPredictor::Run(const std::vector<paddle::Tensor> &inputs,
   if (config_.shape_range_info_collected()) {
     HookCollectShapeRangeInfo();
   }
+#ifdef PADDLE_WITH_XPU
+  InferXPUContext *infer_xpu_ctx = nullptr;
+  if (config_.use_xpu_) {
+    PADDLE_ENFORCE(
+        private_context_,
+        paddle::platform::errors::Fatal(
+            "Must use private context if run predictor on xpu place."));
+    auto *dev_ctxs = reinterpret_cast<const std::map<
+        phi::Place,
+        std::shared_future<std::unique_ptr<phi::DeviceContext>>> *>(
+        this->GetDeviceContexts());
+    infer_xpu_ctx =
+        static_cast<InferXPUContext *>(dev_ctxs->at(place_).get().get());
+    auto *x_context = static_cast<xpu::Context *>(config_.xpu_config_.context);
+    if (x_context != nullptr) {
+      infer_xpu_ctx->SetXContext(x_context);
+    }
+    infer_xpu_ctx->SetStream(predictor_stream_);
+    infer_xpu_ctx->SetL3Info(config_.xpu_config_.l3_size,
+                             config_.xpu_config_.l3_ptr,
+                             config_.xpu_config_.l3_autotune_size,
+                             place_);
+  }
+#endif
 
   if (config_.new_executor_enabled()) {  // NOLINT
     executor_->RunInterpreterCore();
@@ -1537,7 +1561,11 @@ bool AnalysisPredictor::Run(const std::vector<paddle::Tensor> &inputs,
   }
 
   inference::DisplayMemoryInfo(place_, "after run");
-
+#ifdef PADDLE_WITH_XPU
+  if (config_.use_xpu_ && infer_xpu_ctx != nullptr) {
+    infer_xpu_ctx->L3CacheAutotune();
+  }
+#endif
   // get fetch variable
   if (!GetFetch(outputs, scope)) {
     LOG(ERROR) << "fail to get fetches";
 
@@ -541,6 +541,7 @@ XpuPassStrategy::XpuPassStrategy() : PassStrategy({}) {
       "multi_encoder_xpu_fuse_pass",
       "multi_encoder_xpu_adaptive_seqlen_fuse_pass",
       "multi_encoder_xpu_slice_fuse_pass",
+      "weight_only_linear_xpu_pass",
       "fused_multi_transformer_cachekv_layout_trans_pass",
       "fused_multi_transformer_int8_cachekv_layout_trans_pass",
       "cross_attention_xpu_fuse_pass",
 
@@ -1152,6 +1152,8 @@ XPUOpMap& get_kl2_ops() {
                      phi::DataType::FLOAT32})},
       {"warpctc_grad", XPUKernelSet({phi::DataType::FLOAT32})},
       {"warpctc", XPUKernelSet({phi::DataType::FLOAT32})},
+      {"weight_only_linear_xpu",
+       XPUKernelSet({phi::DataType::FLOAT16, phi::DataType::BFLOAT16})},
       {"where_index",
        XPUKernelSet({phi::DataType::INT32,
                      phi::DataType::BOOL,
@@ -1177,6 +1179,8 @@ XPUOpMap& get_kl2_ops() {
                      phi::DataType::INT32,
                      phi::DataType::INT64})},
       {"randint", XPUKernelSet({phi::DataType::INT32, phi::DataType::INT64})},
+      {"rms_norm",
+       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"group_norm", XPUKernelSet({phi::DataType::FLOAT32})},
       {"group_norm_grad", XPUKernelSet({phi::DataType::FLOAT32})},
       {"meshgrid",