[XPU] Add embedding plugin (#56488)

paddle/fluid/framework/ir/CMakeLists.txt

@@ -239,6 +239,8 @@ if(WITH_XPU)
   pass_library(cast_mixed_precision_op_fuse_pass inference DIR xpu DEPS
                ${XPU_PASS_DEPS})
   pass_library(yolo_box_xpu_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
+  pass_library(cast_embedding_trans_ids_to_int32_pass inference DIR xpu DEPS
+               ${XPU_PASS_DEPS})
   pass_library(conv1d_xpu_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
   pass_library(conv2d_xpu_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
   pass_library(redundant_unsqueeze_squeeze_elimination_pass inference DIR xpu

paddle/fluid/framework/ir/xpu/cast_embedding_trans_ids_to_int32_pass.cc

+// Copyright (c) 2023 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
+//
+//     http://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 <string>
+
+#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/pass.h"
+#include "paddle/fluid/framework/ir/xpu/pass_utils.h"
+#include "paddle/fluid/framework/ir/xpu/quant_utils.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace phi {
+class DenseTensor;
+}  // namespace phi
+
+namespace paddle {
+namespace framework {
+class Scope;
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace framework {
+namespace ir {
+namespace patterns {
+
+struct CastEmbeddingTransIdsToInt32Pattern : public PatternBase {
+  CastEmbeddingTransIdsToInt32Pattern(PDPattern* pattern,
+                                      const std::string& name_scope);
+  // declare operator node's name
+  PATTERN_DECL_NODE(cast);
+  PATTERN_DECL_NODE(embedding);
+  // declare variable node's name
+  PATTERN_DECL_NODE(cast_x);
+  PATTERN_DECL_NODE(embedding_ids);
+  PATTERN_DECL_NODE(embedding_w);
+  PATTERN_DECL_NODE(embedding_out);
+};
+
+CastEmbeddingTransIdsToInt32Pattern::CastEmbeddingTransIdsToInt32Pattern(
+    PDPattern* pattern, const std::string& name_scope)
+    : PatternBase(pattern, name_scope, name_scope) {
+  auto cast = pattern->NewNode(cast_repr())->assert_is_op("cast");
+  auto cast_x = pattern->NewNode(cast_x_repr())
+                    ->assert_is_op_input("cast", "X")
+                    ->assert_var_not_persistable()
+                    ->AsInput();
+  auto embedding_ids = pattern->NewNode(embedding_ids_repr())
+                           ->assert_is_op_output("cast", "Out")
+                           ->assert_is_op_input("lookup_table_v2", "Ids")
+                           ->assert_has_n_outputs(1);
+  cast->LinksFrom({cast_x}).LinksTo({embedding_ids});
+  auto embedding_w = pattern->NewNode(embedding_w_repr())
+                         ->assert_is_op_input("lookup_table_v2", "W");
+  auto embedding =
+      pattern->NewNode(embedding_repr())->assert_is_op("lookup_table_v2");
+  auto embedding_out = pattern->NewNode(embedding_out_repr())
+                           ->assert_is_op_output("lookup_table_v2", "Out")
+                           ->AsOutput();
+  embedding->LinksFrom({embedding_ids, embedding_w}).LinksTo({embedding_out});
+}
+
+}  // namespace patterns
+
+class CastEmbeddingTransIdsToInt32Pass : public FusePassBase {
+ protected:
+  void ApplyImpl(ir::Graph* graph) const override;
+
+ private:
+  const std::string name_scope_{"cast_embedding_trans_ids_to_int32_pass"};
+};
+void CastEmbeddingTransIdsToInt32Pass::ApplyImpl(ir::Graph* graph) const {
+  PADDLE_ENFORCE_NOT_NULL(
+      graph, platform::errors::PreconditionNotMet("graph should not be null."));
+  Init(name_scope_, graph);
+
+  GraphPatternDetector gpd;
+  patterns::CastEmbeddingTransIdsToInt32Pattern pattern(gpd.mutable_pattern(),
+                                                        name_scope_);
+  int found_subgraph_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* graph) {
+    VLOG(4) << "handle CastEmbeddingTransIdsToInt32Pass";
+    GET_IR_NODE(cast);
+    GET_IR_NODE(embedding);
+    GET_IR_NODE(embedding_ids);
+    auto cast_node_attr_out_dtype =
+        cast->Op()->GetAttrIfExists<int>("out_dtype");
+    if (cast_node_attr_out_dtype !=
+        static_cast<int>(paddle::framework::proto::VarType::INT64)) {
+      return;
+    }
+    cast->Op()->SetAttr(
+        "out_dtype",
+        static_cast<int>(paddle::framework::proto::VarType::INT32));
+    embedding_ids->Var()->SetDataType(paddle::framework::proto::VarType::INT32);
+    embedding->Op()->Flush();
+    found_subgraph_count++;
+  };
+  gpd(graph, handler);
+  AddStatis(found_subgraph_count);
+  if (found_subgraph_count) {
+    VLOG(4) << "There is a risk of overflow when converting the data type of "
+               "embedded ids from int64 to int32."
+               "Please ensure that the numerical range of ids is within the "
+               "maximum value of int32."
+               "If it exceeds this range, it may result in incorrect results. "
+               "You can try removing this pass.";
+  }
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(cast_embedding_trans_ids_to_int32_pass,
+              paddle::framework::ir::CastEmbeddingTransIdsToInt32Pass);
+
+REGISTER_PASS_CAPABILITY(cast_embedding_trans_ids_to_int32_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination().LE(
+            "lookup_table_v2", 1));

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -516,6 +516,7 @@ XpuPassStrategy::XpuPassStrategy() : PassStrategy({}) {
       "reshape_unstack_concat_fuse_pass",
       "delete_op_device_pass",
       "constant_folding_pass",
+      "cast_embedding_trans_ids_to_int32_pass",
       "delete_elementwise_mul_op_pass",
       "generate_sequence_xpu_fuse_pass",
       "embedding_with_eltwise_add_xpu_fuse_pass",

paddle/phi/kernels/xpu/embedding_kernel.cc

@@ -44,18 +44,6 @@ void EmbeddingKernel(const Context &ctx,
   auto *table = table_t->data<T>();
   auto *output = dev_ctx.template Alloc<T>(output_t);
 
-  xpu::ctx_guard RAII_GUARD(ctx.x_context());
-  const int64_t *ids;
-  if (ids_t->dtype() == phi::DataType::INT64) {
-    ids = ids_t->data<int64_t>();
-  } else {
-    int64_t *ids_tt = RAII_GUARD.alloc_l3_or_gm<int64_t>(ids_t->numel());
-    int r = xpu::cast<int32_t, int64_t>(
-        ctx.x_context(), ids_t->data<int>(), ids_tt, ids_t->numel());
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
-    ids = reinterpret_cast<const int64_t *>(ids_tt);
-  }
-
   PADDLE_ENFORCE_EQ(
       ids_numel <= std::numeric_limits<int32_t>::max(),
       true,
@@ -68,7 +56,38 @@ void EmbeddingKernel(const Context &ctx,
   size_t xm = table_t->dims()[0];
   size_t n = table_t->dims()[1];
 
-  int r = xpu::embedding<XPUType>(dev_ctx.x_context(),
+  int r;
+  xpu::ctx_guard RAII_GUARD(ctx.x_context());
+  if (ids_t->dtype() == phi::DataType::INT64) {
+#ifndef PADDLE_WITH_XPU_PLUGIN
+    r = xpu::embedding<XPUType, int64_t>(
+        dev_ctx.x_context(),
+        reinterpret_cast<const XPUType *>(table),
+        ids_t->data<int64_t>(),
+        reinterpret_cast<XPUType *>(output),
+        xm,
+        n,
+        ym,
+        padding_idx);
+#else
+    r = xpu::plugin::fast_embedding<XPUType, int64_t>(
+        dev_ctx.x_context(),
+        reinterpret_cast<const XPUType *>(table),
+        ids_t->data<int64_t>(),
+        reinterpret_cast<XPUType *>(output),
+        xm,
+        n,
+        ym,
+        padding_idx);
+#endif
+  } else {
+#ifndef PADDLE_WITH_XPU_PLUGIN
+    int64_t *ids_tt = RAII_GUARD.alloc_l3_or_gm<int64_t>(ids_t->numel());
+    r = xpu::cast<int32_t, int64_t>(
+        ctx.x_context(), ids_t->data<int>(), ids_tt, ids_t->numel());
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
+    const int64_t *ids = reinterpret_cast<const int64_t *>(ids_tt);
+    r = xpu::embedding<XPUType>(dev_ctx.x_context(),
                                 reinterpret_cast<const XPUType *>(table),
                                 ids,
                                 reinterpret_cast<XPUType *>(output),
@@ -76,7 +95,18 @@ void EmbeddingKernel(const Context &ctx,
                                 n,
                                 ym,
                                 padding_idx);
-
+#else
+    r = xpu::plugin::fast_embedding<XPUType, int>(
+        dev_ctx.x_context(),
+        reinterpret_cast<const XPUType *>(table),
+        ids_t->data<int>(),
+        reinterpret_cast<XPUType *>(output),
+        xm,
+        n,
+        ym,
+        padding_idx);
+#endif
+  }
   PADDLE_ENFORCE_XDNN_SUCCESS(r, "embedding");
 }
 

paddle/phi/kernels/xpu/plugin/include/xpu/plugin.h

@@ -104,6 +104,17 @@ DLL_EXPORT int fast_reduce_min(Context* ctx,
                                const std::vector<int>& xshape,
                                const std::vector<int>& rdims);
 
+template <typename T, typename TID>
+DLL_EXPORT int fast_embedding(Context* ctx,
+                              const T* x,
+                              const TID* indices,
+                              T* y,
+                              int64_t xm,
+                              int64_t n,
+                              int64_t ym,
+                              int64_t padding_idx,
+                              TID start_index = 0);
+
 }  // namespace plugin
 }  // namespace api
 }  // namespace xpu

paddle/phi/kernels/xpu/plugin/src/kernel/kunlun2cpp/embedding_fwd_tiny_dict.xpu

+// Copyright (c) 2023 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
+//
+//     http://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.
+/*
+ * copyright (C) 2022 KUNLUNXIN, Inc
+ */
+
+#include "xpu/kernel/cluster.h"
+#include "xpu/kernel/cluster_partition.h"
+#include "xpu/kernel/cluster_primitive.h"
+#include "xpu/kernel/xtdk_io.h"
+
+namespace xpu2 {
+namespace plugin {
+
+/*
+Kernel usage conditions: Dict is tiny, Local memory can be loaded in at once.
+Optimizer ideas:
+  - Reduce frequent memory handling, allocate fixed size buffers, accumulate
+data to buffer size and move it out together.
+
+ ********** Local Memory Addr **********
+        Part 1: dict(size = dict_idx_len * emb_dim)
+ -----------------------------------
+        Part 2: index(size = idx_len * sizeof(emb_idx_type))
+ -----------------------------------
+        Part 3: result
+ -----------------------------------
+*/
+
+template <typename emb_idx_type>
+static inline __device__ void embedding_fwd_kl2_tiny_dict_align64(
+    _global_ptr_ const emb_idx_type* idx,
+    _global_ptr_ const char* dict,
+    _global_ptr_ char* featvec,
+    int64_t emb_dim,
+    int64_t dict_idx_len,
+    int64_t idx_len,
+    int64_t padding_idx,
+    emb_idx_type start_index) {
+  int cid = core_id();
+  int ncores = core_num();
+  int tid = cid * cluster_num() + cluster_id();
+  int nthreads = ncores * cluster_num();
+  int64_t row_start = -1;
+  int64_t row_end = -1;
+  partition(tid, nthreads, idx_len, 1, &row_start, &row_end);
+
+  // 1. Pre allocation total Local Memory size = 6 KB
+  const int TOTAL_LM_SIZE = 6144;  // 6 KB
+  __simd__ char lm[TOTAL_LM_SIZE];
+
+  // 2. Load dict from Global Memory to Local memory only once.
+  int total_emb_dict_size = dict_idx_len * emb_dim;
+  GM2LM(dict, lm, total_emb_dict_size);
+
+  // residual_lm_space = index + result
+  int residual_lm_space = TOTAL_LM_SIZE - total_emb_dict_size -
+                          64;  // 64 to preventing memory overflow, because the
+                               // total index memory need to align to 64.
+
+  // The maximum count that can be processed in one iteration.
+  int idx_cnt = residual_lm_space / (sizeof(emb_idx_type) + emb_dim);
+  int index_lm_offset = total_emb_dict_size;
+  int result_lm_offset =
+      total_emb_dict_size +
+      (idx_cnt * sizeof(emb_idx_type) + 64) / 64 * 64;  // Align to 64 bytes
+
+  // 3. Loop Calc
+  for (int64_t i = row_start; i < row_end; i += idx_cnt) {
+    int curr_idx_len = idx_cnt;
+    if (i + idx_cnt >= row_end) {
+      curr_idx_len = row_end - i;
+    }
+    // 3.1 Load idx to Local Memory
+    GM2LM(idx + i, lm + index_lm_offset, curr_idx_len * sizeof(emb_idx_type));
+
+    // 3.2 Save result into result memory buffer.
+    for (int j = 0; j < curr_idx_len; j++) {
+      emb_idx_type real_index =
+          *((emb_idx_type*)(lm + index_lm_offset + j * sizeof(emb_idx_type))) -
+          start_index;
+      if (real_index == padding_idx) {
+        for (int koffset = 0; koffset < emb_dim; koffset += 64) {
+          float32x16_t v_src = vload_lm_float32x16_mz((void*)lm, 0);
+          vstore_lm_float32x16(
+              (void*)(lm + result_lm_offset + j * emb_dim + koffset), v_src);
+        }
+      } else {
+        if (real_index >= 0 && real_index < dict_idx_len) {
+          for (int koffset = 0; koffset < emb_dim; koffset += 64) {
+            float32x16_t v_src = vload_lm_float32x16(
+                (void*)(lm + real_index * emb_dim + koffset));
+            vstore_lm_float32x16(
+                (void*)(lm + result_lm_offset + j * emb_dim + koffset), v_src);
+          }
+        } else {
+          for (int koffset = 0; koffset < emb_dim; koffset += 64) {
+            float32x16_t v_src = vload_lm_float32x16_mz((void*)lm, 0);
+            vstore_lm_float32x16(
+                (void*)(lm + result_lm_offset + j * emb_dim + koffset), v_src);
+          }
+        }
+      }
+      mfence_lm();
+    }
+    // 3.3 Save result into global memory buffer.
+    LM2GM(lm + result_lm_offset,
+          (_global_ptr_ char*)(featvec + i * emb_dim),
+          curr_idx_len * emb_dim);
+  }
+}
+
+template <typename emb_idx_type>
+static inline __device__ void embedding_fwd_kl2_tiny_dict_not_align64(
+    _global_ptr_ const emb_idx_type* idx,
+    _global_ptr_ const char* dict,
+    _global_ptr_ char* featvec,
+    int64_t emb_dim,
+    int64_t dict_idx_len,
+    int64_t idx_len,
+    int64_t padding_idx,
+    emb_idx_type start_index) {
+  int cid = core_id();
+  int ncores = core_num();
+  int tid = cid * cluster_num() + cluster_id();
+  int nthreads = ncores * cluster_num();
+  int64_t row_start = -1;
+  int64_t row_end = -1;
+  partition(tid, nthreads, idx_len, 1, &row_start, &row_end);
+
+  // 1. Pre allocation total Local Memory size = 6 KB
+  const int TOTAL_LM_SIZE = 6144;  // 6 KB
+  __local__ char lm[TOTAL_LM_SIZE];
+
+  // 2. Load dict from Global Memory to Local memory only once.
+  GM2LM(dict, lm, dict_idx_len * emb_dim);
+
+  // residual_lm_space = index + result
+  int residual_lm_space = TOTAL_LM_SIZE - dict_idx_len * emb_dim;
+
+  // The maximum count that can be processed in one iteration.
+  int idx_cnt = residual_lm_space / (sizeof(emb_idx_type) + emb_dim);
+  int index_lm_offset = dict_idx_len * emb_dim;
+  int result_lm_offset = index_lm_offset + idx_cnt * sizeof(emb_idx_type);
+
+  // 3. Loop Calc
+  for (int64_t i = row_start; i < row_end; i += idx_cnt) {
+    int curr_idx_len = idx_cnt;
+    if (i + idx_cnt >= row_end) {
+      curr_idx_len = row_end - i;
+    }
+    // 3.1 Load idx to Local Memory
+    GM2LM(idx + i, lm + index_lm_offset, curr_idx_len * sizeof(emb_idx_type));
+
+    // 3.2 Save result into result memory buffer.
+    for (int j = 0; j < curr_idx_len; j++) {
+      emb_idx_type real_index =
+          *((emb_idx_type*)(lm + index_lm_offset + j * sizeof(emb_idx_type))) -
+          start_index;
+      if (real_index == padding_idx) {
+        for (int k = 0; k < emb_dim; k++) {
+          lm[result_lm_offset + j * emb_dim + k] = 0;
+        }
+      } else {
+        if (real_index >= 0 && real_index < dict_idx_len) {
+          for (int k = 0; k < emb_dim; k++) {
+            lm[result_lm_offset + j * emb_dim + k] =
+                lm[real_index * emb_dim + k];
+          }
+        } else {
+          for (int k = 0; k < emb_dim; k++) {
+            lm[result_lm_offset + j * emb_dim + k] = 0;
+          }
+        }
+      }
+      mfence_lm();
+    }
+    // 3.3 Save result into global memory buffer.
+    LM2GM(lm + result_lm_offset,
+          (_global_ptr_ char*)(featvec + i * emb_dim),
+          curr_idx_len * emb_dim);
+  }
+}
+
+template <typename emb_idx_type>
+__global__ void embedding_fwd_kl2_tiny_dict(const emb_idx_type* idx,
+                                            const char* dict,
+                                            char* featvec,
+                                            int64_t emb_dim,
+                                            int64_t dict_idx_len,
+                                            int64_t idx_len,
+                                            int64_t padding_idx,
+                                            emb_idx_type start_index) {
+  if (emb_dim % 64 == 0) {
+    embedding_fwd_kl2_tiny_dict_align64<emb_idx_type>(idx,
+                                                      dict,
+                                                      featvec,
+                                                      emb_dim,
+                                                      dict_idx_len,
+                                                      idx_len,
+                                                      padding_idx,
+                                                      start_index);
+  } else {
+    embedding_fwd_kl2_tiny_dict_not_align64<emb_idx_type>(idx,
+                                                          dict,
+                                                          featvec,
+                                                          emb_dim,
+                                                          dict_idx_len,
+                                                          idx_len,
+                                                          padding_idx,
+                                                          start_index);
+  }
+}
+
+#define _XPU_DEF__EMBEDDING_FWD_KL2_TINY_DICT_(EMB_IDX_TYPE)          \
+  template __global__ void embedding_fwd_kl2_tiny_dict<EMB_IDX_TYPE>( \
+      const EMB_IDX_TYPE* idx,                                        \
+      const char* dict,                                               \
+      char* featvec,                                                  \
+      int64_t emb_dim,                                                \
+      int64_t dict_idx_len,                                           \
+      int64_t idx_len,                                                \
+      int64_t padding_idx,                                            \
+      EMB_IDX_TYPE start_index);
+_XPU_DEF__EMBEDDING_FWD_KL2_TINY_DICT_(int);
+_XPU_DEF__EMBEDDING_FWD_KL2_TINY_DICT_(int64_t);
+
+}  // namespace plugin
+}  // namespace xpu2

paddle/phi/kernels/xpu/plugin/src/wrapper/fast_embedding.cpp

+// Copyright (c) 2023 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
+//
+//     http://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.
+/*
+ * copyright (C) 2022 KUNLUNXIN, Inc
+ */
+
+#include "xpu/plugin.h"
+#include "xpu/refactor/impl_public/wrapper_check.h"
+#include "xpu/refactor/util/vector_util.h"
+
+namespace xpu2 {
+namespace plugin {
+template <typename emb_idx_type>
+__attribute__((global)) void embedding_fwd_kl2_tiny_dict(
+    const emb_idx_type* idx,
+    const char* dict,
+    char* featvec,
+    int64_t emb_dim,
+    int64_t dict_idx_len,
+    int64_t idx_len,
+    int64_t padding_idx,
+    emb_idx_type start_index);
+}  // namespace plugin
+}  // namespace xpu2
+
+namespace baidu {
+namespace xpu {
+namespace api {
+namespace plugin {
+
+// CPU implementation
+template <typename T, typename TID>
+static int cpu_wrapper(Context* ctx,
+                       const T* x,
+                       const TID* indices,
+                       T* y,
+                       int64_t xm,
+                       int64_t n,
+                       int64_t ym,
+                       int64_t padding_idx,
+                       TID start_index) {
+  for (int64_t i = 0; i < ym; i++) {
+    TID real_index = indices[i] - start_index;  // -start_index BEFORE compare
+    if (real_index == padding_idx) {
+      ::memset(y + i * n, 0, sizeof(T) * n);
+    } else {
+      if (real_index >= 0 && real_index < xm) {
+        std::memcpy(y + i * n, x + real_index * n, sizeof(T) * n);
+      } else {
+        // set zeros
+        for (int64_t k = 0; k < n; ++k) {
+          y[i * n + k] = 0;
+        }
+      }
+    }
+  }
+  return api::SUCCESS;
+}
+
+template <typename T, typename TID>
+static int xpu2_wrapper(Context* ctx,
+                        const T* x,
+                        const TID* indices,
+                        T* y,
+                        int64_t xm,
+                        int64_t n,
+                        int64_t ym,
+                        int64_t padding_idx,
+                        TID start_index) {
+  const int TOTAL_LM_SIZE = 6144;  // 6 KB
+  int total_emb_dict_size = xm * n * sizeof(T);
+  // residual_lm_space = index + result
+  int residual_lm_space = TOTAL_LM_SIZE - total_emb_dict_size - 64;
+  // The maximum count that can be processed in one iteration.
+  int idx_cnt = residual_lm_space / (sizeof(TID) + n * sizeof(T));
+  bool plugin_entry_condition = idx_cnt >= 16;
+  // This plugin is suitable for scenarios with relatively small dictionary
+  // sizes, requiring process greater than 16 index count one iter, in order to
+  // load the dictionary into local memory at once, and to leave enough space
+  // for the local memory to store the results.
+  if (plugin_entry_condition) {
+    using XPU_TID = typename XPUIndexType<TID>::type;
+    const XPU_TID* casted_indices =
+        static_cast<const XPU_TID*>(static_cast<const void*>(indices));
+    XPU_TID casted_start_index = static_cast<XPU_TID>(start_index);
+    if (ctx->dev().type() == api::kXPU2) {
+      xpu2::plugin::embedding_fwd_kl2_tiny_dict<XPU_TID>
+          <<<ctx->ncluster(), 64, ctx->xpu_stream>>>(
+              casted_indices,
+              reinterpret_cast<const char*>(x),
+              reinterpret_cast<char*>(y),
+              n * sizeof(T),
+              xm,
+              ym,
+              padding_idx,
+              casted_start_index);
+    }
+  } else {
+    embedding<T, TID>(ctx, x, indices, y, xm, n, ym, padding_idx, start_index);
+  }
+
+  return api::SUCCESS;
+}
+
+template <typename T, typename TID>
+int fast_embedding(Context* ctx,
+                   const T* x,
+                   const TID* indices,
+                   T* y,
+                   int64_t xm,
+                   int64_t n,
+                   int64_t ym,
+                   int64_t padding_idx,
+                   TID start_index) {
+  WRAPPER_CHECK_CTX(ctx);
+  WRAPPER_DUMP_FUNCTION_T2(ctx, "fast_embedding", T, TID);
+  WRAPPER_DUMP_PARAM6(ctx, x, indices, y, xm, n, ym);
+  WRAPPER_DUMP_PARAM3(ctx, padding_idx, start_index, ctx->_l3_mgr.get_size());
+  WRAPPER_DUMP(ctx);
+  int64_t xlen = -1;
+  int64_t ylen = -1;
+  WRAPPER_CHECK_SHAPE(ctx, &xlen, {xm, n});
+  WRAPPER_CHECK_SHAPE(ctx, &ylen, {ym, n});
+  WRAPPER_CHECK_PTR(ctx, T, xlen, x);
+  WRAPPER_CHECK_PTR(ctx, T, ylen, y);
+  WRAPPER_CHECK_PTR(ctx, TID, ym, indices);
+  if (ctx->dev().type() == api::kCPU) {
+    return cpu_wrapper<T>(
+        ctx, x, indices, y, xm, n, ym, padding_idx, start_index);
+  }
+  if (ctx->dev().type() == api::kXPU2) {
+    return xpu2_wrapper<T, TID>(
+        ctx, x, indices, y, xm, n, ym, padding_idx, start_index);
+  }
+  WRAPPER_UNIMPLEMENTED(ctx);
+}
+
+template int fast_embedding(Context*,
+                            const float*,
+                            const int*,
+                            float*,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int);
+template int fast_embedding(Context*,
+                            const float*,
+                            const int64_t*,
+                            float*,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int64_t);
+template int fast_embedding(Context*,
+                            const float16*,
+                            const int*,
+                            float16*,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int);
+template int fast_embedding(Context*,
+                            const float16*,
+                            const int64_t*,
+                            float16*,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int64_t,
+                            int64_t);
+
+}  // namespace plugin
+}  // namespace api
+}  // namespace xpu
+}  // namespace baidu

test/ir/inference/test_xpu_cast_embedding_trans_ids_to_int32_pass.py

+# Copyright (c) 2023 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
+#
+#     http://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.
+
+import unittest
+from functools import partial
+
+import hypothesis.strategies as st
+import numpy as np
+from auto_scan_test import PassAutoScanTest
+from program_config import OpConfig, ProgramConfig, TensorConfig
+
+
+class TestXpuCastEmbeddingTransIdsToInt32Pass(PassAutoScanTest):
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_xpu=True)
+        yield config, ["cast", "lookup_table_v2"], (1e-5, 1e-5)
+
+    def sample_program_config(self, draw):
+        ids_shape = draw(st.integers(min_value=1, max_value=128))
+        w_shape = draw(
+            st.sampled_from([[20, 64], [32, 32], [23, 15], [24, 33]])
+        )
+        padding_idx = draw(st.sampled_from([-1]))
+
+        cast_op = OpConfig(
+            "cast",
+            inputs={
+                "X": ["cast_input"],
+            },
+            outputs={"Out": ["cast_out"]},
+            in_dtype=5,
+            out_dtype=3,
+        )
+        lookup_table_op = OpConfig(
+            "lookup_table_v2",
+            inputs={
+                "Ids": ["cast_out"],
+                "W": ["lookup_table_w"],
+            },
+            outputs={"Out": ["lookup_table_out"]},
+            padding_idx=padding_idx,
+        )
+
+        def gen_lookup_table_weights_data():
+            weights = {}
+            w_name = "lookup_table_w"
+            weights[w_name] = TensorConfig(shape=w_shape)
+            return weights
+
+        def generate_cast_input(*args, **kwargs):
+            return np.random.randint(0, w_shape[0], ids_shape).astype(
+                np.float32
+            )
+
+        def gen_input_data(*args, **kwargs):
+            inputs = {}
+            input_name = "cast_input"
+            inputs[input_name] = TensorConfig(
+                data_gen=partial(generate_cast_input)
+            )
+            return inputs
+
+        inputs = gen_input_data()
+        weights = gen_lookup_table_weights_data()
+
+        program_config = ProgramConfig(
+            ops=[cast_op, lookup_table_op],
+            weights=weights,
+            inputs=inputs,
+            outputs=["lookup_table_out"],
+        )
+        return program_config
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            max_examples=25,
+            passes=["cast_embedding_trans_ids_to_int32_pass"],
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()