Optimize c_embedding op in deterministic mode (#53197)

* optimize embedding deterministic mode

* fix compile error

* change FLAGS_cudnn_deterministic to int64

* fix 700 error

* add ut

* fix ut

* fix ut

* fix win32 ci

* fix flags with PHI_DEFINE_EXPORTED_int64

paddle/fluid/operators/collective/c_embedding_op.cu

@@ -18,8 +18,9 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/float16.h"
 #include "paddle/phi/backends/gpu/gpu_primitives.h"
+#include "paddle/phi/kernels/funcs/embedding_grad.h"
 
-DECLARE_bool(cudnn_deterministic);
+DECLARE_int64(embedding_deterministic);
 
 namespace paddle {
 namespace operators {
@@ -154,7 +155,6 @@ class CEmbeddingGradCUDAKernel : public framework::OpKernel<T> {
     int D = d_table_t->dims()[1];
     int K = ids_t->numel();
 
-    const int64_t end_idx = start_idx + N;
     auto limit = K * D;
     int blocks = NumBlocks(limit);
     int threads = kNumCUDAThreads;
@@ -166,33 +166,64 @@ class CEmbeddingGradCUDAKernel : public framework::OpKernel<T> {
     t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(0));
 
     const auto &index_type = framework::TransToProtoVarType(ids_t->dtype());
-    if (FLAGS_cudnn_deterministic) {
-      VLOG(2) << "Run grad kernel of embedding with single thread.";
-      blocks = 1;
-    }
-    if (index_type == framework::proto::VarType::INT32) {
-      CEmbeddingGrad<T, int32_t>
-          <<<blocks, threads, 0, dev_ctx.stream()>>>(d_table,
-                                                     d_output,
-                                                     ids_t->data<int32_t>(),
-                                                     K,
-                                                     D,
-                                                     N,
-                                                     start_idx,
-                                                     end_idx,
-                                                     limit);
-    } else if (index_type == framework::proto::VarType::INT64) {
-      CEmbeddingGrad<T, int64_t>
-          <<<blocks, threads, 0, dev_ctx.stream()>>>(d_table,
-                                                     d_output,
-                                                     ids_t->data<int64_t>(),
-                                                     K,
-                                                     D,
-                                                     N,
-                                                     start_idx,
-                                                     end_idx,
-                                                     limit);
+    if (FLAGS_embedding_deterministic == 1) {
+      if (index_type == framework::proto::VarType::INT32) {
+        phi::funcs::LaunchEmbeddingGradDeterministicKernel<T, int32_t>(
+            dev_ctx,
+            ids_t->data<int32_t>(),
+            d_output,
+            d_table,
+            N,
+            D,
+            K,
+            start_idx);
+        return;
+      } else if (index_type == framework::proto::VarType::INT64) {
+        phi::funcs::LaunchEmbeddingGradDeterministicKernel<T, int64_t>(
+            dev_ctx,
+            ids_t->data<int64_t>(),
+            d_output,
+            d_table,
+            N,
+            D,
+            K,
+            start_idx);
+        return;
+      }
+    } else {
+      if (FLAGS_embedding_deterministic > 1) {
+        VLOG(2) << "Run grad kernel of embedding with single thread.";
+        blocks = 1;
+      }
+      const int64_t end_idx = start_idx + N;
+      if (index_type == framework::proto::VarType::INT32) {
+        CEmbeddingGrad<T, int32_t>
+            <<<blocks, threads, 0, dev_ctx.stream()>>>(d_table,
+                                                       d_output,
+                                                       ids_t->data<int32_t>(),
+                                                       K,
+                                                       D,
+                                                       N,
+                                                       start_idx,
+                                                       end_idx,
+                                                       limit);
+        return;
+      } else if (index_type == framework::proto::VarType::INT64) {
+        CEmbeddingGrad<T, int64_t>
+            <<<blocks, threads, 0, dev_ctx.stream()>>>(d_table,
+                                                       d_output,
+                                                       ids_t->data<int64_t>(),
+                                                       K,
+                                                       D,
+                                                       N,
+                                                       start_idx,
+                                                       end_idx,
+                                                       limit);
+        return;
+      }
     }
+    PADDLE_THROW(phi::errors::InvalidArgument(
+        "The data type of Input(Ids) must be int32 or int64."));
   }
 };
 

paddle/phi/core/flags.cc

@@ -236,17 +236,19 @@ PHI_DEFINE_EXPORTED_bool(
  * CUDA related FLAG
  * Name: FLAGS_embedding_deterministic
  * Since Version: 2.5
- * Value Range: bool, default=false
+ * Value Range: int64, default=0
  * Example:
  * Note: whether to use deterministic algorithm in embedding op.
- *       If true, it will use deterministic CUDA kernel in embedding op.
+ *       If it is 1, it will use the optimized deterministic CUDA kernel in
+ *       embedding op. If it is 2, it will use the legacy deterministic
+ *       CUDA kernel in embedding op.
  */
-PHI_DEFINE_EXPORTED_bool(
+PHI_DEFINE_EXPORTED_int64(
     embedding_deterministic,
-    false,
+    0,
     "Whether allow using an deterministic algorithm for embedding "
     "operator. The deterministic algorithm may be slower. If "
-    "true, the algorithm is deterministic.");
+    "it is larger than 0, the algorithm is deterministic.");
 
 /**
  * CUDNN related FLAG

paddle/phi/kernels/funcs/embedding_grad.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/amp_type_traits.h"
+
+namespace phi {
+namespace funcs {
+
+template <typename T, typename IdT, int WarpSize, int BlockDimY, bool UseLimit>
+__global__ void EmbeddingGradDeterministicKernel(T* table,
+                                                 const T* output,
+                                                 const IdT* ids,
+                                                 const int64_t K,
+                                                 const int64_t D,
+                                                 const int64_t start_idx,
+                                                 const int64_t end_idx) {
+  using MT = typename dtype::MPTypeTrait<T>::Type;
+  constexpr int64_t kInvalidId = -1;
+  extern __shared__ char buf[];
+  MT* smem = reinterpret_cast<MT*>(buf);
+  MT* my_s = smem + WarpSize * threadIdx.y;
+  IdT* indices_batch =
+      reinterpret_cast<IdT*>(buf + sizeof(MT) * WarpSize * BlockDimY);
+
+  const int stride = static_cast<int>(D);
+
+  const int feature = threadIdx.x + blockIdx.x * WarpSize;
+
+  // To ensure determinism. If any other warps pulled grad data targeting
+  // dst_row, we elect the first warp in each matching group as the leader.
+  // Each leader warp serializes the accumulates targeting dst_row in shared
+  // memory, then adding the accumulated buffer to dst_row in table.
+  for (int batch_start = 0; batch_start < K;
+       batch_start += WarpSize * BlockDimY) {
+    int tid = threadIdx.x + threadIdx.y * WarpSize;
+    if (batch_start + tid < K) {
+      int64_t cur_id = static_cast<int64_t>(ids[batch_start + tid]);
+      if (UseLimit) {
+        if (cur_id >= start_idx && cur_id < end_idx) {
+          cur_id -= start_idx;
+        } else {
+          cur_id = kInvalidId;
+        }
+      }
+      indices_batch[tid] = cur_id;
+    }
+
+    int batch_end =
+        min(static_cast<int64_t>(batch_start + WarpSize * BlockDimY), K);
+
+    // Loop over the batch of <= 1024 loaded indices in chunks of BLOCKDIMY
+    for (int chunk_start = batch_start; chunk_start < batch_end;
+         chunk_start += BlockDimY) {
+      // This sync makes sure that indices_batch is ready and match-group
+      // leaders are done with their accumulates before other warps start
+      // loading again.
+      __syncthreads();
+
+      int n_this_chunk = min(batch_end - chunk_start, BlockDimY);
+
+      int64_t src_row = static_cast<int64_t>(chunk_start + threadIdx.y);
+      int64_t dst_row = indices_batch[src_row - batch_start];
+      if (src_row < K && feature < stride) {
+        if (UseLimit && dst_row == kInvalidId) {
+          my_s[threadIdx.x] = static_cast<MT>(0);
+        } else {
+          my_s[threadIdx.x] = static_cast<MT>(output[src_row * D + feature]);
+        }
+      }
+
+      __syncthreads();
+
+      if (src_row < K) {
+        int match_found_this_thread = 0;
+        if (threadIdx.x < n_this_chunk &&
+            (!UseLimit || dst_row != kInvalidId)) {
+          match_found_this_thread =
+              (dst_row ==
+               indices_batch[chunk_start - batch_start + threadIdx.x]);
+        }
+#ifdef PADDLE_WITH_HIP
+        unsigned long long int matchmask =      // NOLINT
+            __ballot(match_found_this_thread);  // NOLINT
+        int first_remaining_peer = __ffsll(matchmask) - 1;
+#else
+        // If and only if match_found_this_thread of the Nth thread is non-zero,
+        // set the Nth bit of matchmask to 1.
+        unsigned int matchmask =
+            __ballot_sync(0xffffffff, match_found_this_thread);
+        // Find the position of the first bit set to 1 in matchmask.
+        int first_remaining_peer = __ffs(matchmask) - 1;
+#endif
+
+        // select lowest-indexed warp as the leader
+        if (threadIdx.y == first_remaining_peer) {
+          // Set the first bit 1 in matchmask to 0.
+          matchmask ^= (1 << first_remaining_peer);
+          while (matchmask) {
+#ifdef PADDLE_WITH_HIP
+            first_remaining_peer = __ffsll(matchmask) - 1;
+#else
+            first_remaining_peer = __ffs(matchmask) - 1;
+#endif
+            my_s[threadIdx.x] +=
+                smem[threadIdx.x + WarpSize * first_remaining_peer];
+            matchmask ^= (1 << first_remaining_peer);
+          }
+          if (feature < stride && (!UseLimit || dst_row != kInvalidId)) {
+            auto table_idx = dst_row * D + feature;
+            table[table_idx] = static_cast<T>(
+                static_cast<MT>(table[table_idx]) + my_s[threadIdx.x]);
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename IdT>
+void LaunchEmbeddingGradDeterministicKernel(const GPUContext& ctx,
+                                            const IdT* ids,
+                                            const T* d_out,
+                                            T* d_table,
+                                            int64_t N,
+                                            int64_t D,
+                                            int64_t K,
+                                            int64_t start_idx = -1) {
+#ifdef PADDLE_WITH_HIP
+  constexpr int kWarpSize = 64;
+  constexpr int kBlockDimY = 16;
+#else
+  constexpr int kWarpSize = 32;
+  constexpr int kBlockDimY = 32;
+#endif
+  dim3 threads(kWarpSize, kBlockDimY);
+  dim3 grids(static_cast<int>((D + kWarpSize - 1) / kWarpSize));
+  using MT = typename dtype::MPTypeTrait<T>::Type;
+  constexpr auto kSharedMemSize = sizeof(MT) * kWarpSize * kBlockDimY +
+                                  sizeof(IdT) * kWarpSize * kBlockDimY;
+  if (start_idx < 0) {
+    EmbeddingGradDeterministicKernel<T, IdT, kWarpSize, kBlockDimY, false>
+        <<<grids, threads, kSharedMemSize, ctx.stream()>>>(
+            d_table, d_out, ids, K, D, -1, -1);
+  } else {
+    int64_t end_idx = start_idx + N;
+    EmbeddingGradDeterministicKernel<T, IdT, kWarpSize, kBlockDimY, true>
+        <<<grids, threads, kSharedMemSize, ctx.stream()>>>(
+            d_table, d_out, ids, K, D, start_idx, end_idx);
+  }
+}
+
+}  // namespace funcs
+}  // namespace phi

paddle/phi/kernels/gpu/embedding_grad_kernel.cu

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/embedding_grad_kernel.h"
+#include "paddle/phi/kernels/funcs/embedding_grad.h"
 
 #include "gflags/gflags.h"
 #include "glog/logging.h"
@@ -26,20 +27,10 @@
 #include "paddle/phi/kernels/funcs/eigen/common.h"
 #include "paddle/phi/kernels/funcs/embedding_util.h"
 
-DECLARE_bool(embedding_deterministic);
+DECLARE_int64(embedding_deterministic);
 
 namespace phi {
 
-#ifdef PADDLE_WITH_HIP
-#define WARP_SIZE 64
-#define BLOCKDIMY 16
-#else
-#define WARP_SIZE 32
-#define BLOCKDIMY 32
-#endif
-
-#define MASK 0xffffffff
-
 template <typename InT, typename OutT>
 __global__ void InputTypeConvert(const InT* in_ids,
                                  const int64_t K,
@@ -74,91 +65,6 @@ __global__ void EmbeddingGrad(T* table,
   }
 }
 
-template <typename T, typename IdT>
-__global__ void EmbeddingGradDeterministic(
-    T* table, const T* output, const IdT* ids, const IdT K, const IdT D) {
-  using MT = typename dtype::MPTypeTrait<T>::Type;
-  extern __shared__ char buf[];
-  MT* smem = reinterpret_cast<MT*>(buf);
-  MT* my_s = smem + WARP_SIZE * threadIdx.y;
-  IdT* indices_batch =
-      reinterpret_cast<IdT*>(buf + sizeof(MT) * WARP_SIZE * BLOCKDIMY);
-
-  const int stride = static_cast<int>(D);
-
-  const int feature = threadIdx.x + blockIdx.x * WARP_SIZE;
-
-  // To ensure determinism. If any other warps pulled grad data targeting
-  // dst_row, we elect the first warp in each matching group as the leader.
-  // Each leader warp serializes the accumulates targeting dst_row in shared
-  // memory, then adding the accumulated buffer to dst_row in table.
-  for (int batch_start = 0; batch_start < K;
-       batch_start += WARP_SIZE * BLOCKDIMY) {
-    int tid = threadIdx.x + threadIdx.y * WARP_SIZE;
-    if (batch_start + tid < K)
-      indices_batch[tid] = static_cast<IdT>(ids[batch_start + tid]);
-
-    int batch_end =
-        min(static_cast<IdT>(batch_start + WARP_SIZE * BLOCKDIMY), K);
-
-    // Loop over the batch of <= 1024 loaded indices in chunks of BLOCKDIMY
-    for (int chunk_start = batch_start; chunk_start < batch_end;
-         chunk_start += BLOCKDIMY) {
-      // This sync makes sure that indices_batch is ready and match-group
-      // leaders are done with their accumulates before other warps start
-      // loading again.
-      __syncthreads();
-
-      int n_this_chunk = min(batch_end - chunk_start, BLOCKDIMY);
-
-      IdT src_row = static_cast<IdT>(chunk_start + threadIdx.y);
-      IdT dst_row = indices_batch[src_row - batch_start];
-      if (src_row < K && feature < stride)
-        my_s[threadIdx.x] = static_cast<MT>(output[src_row * D + feature]);
-
-      __syncthreads();
-
-      if (src_row < K) {
-        int match_found_this_thread = 0;
-        if (threadIdx.x < n_this_chunk) {
-          match_found_this_thread =
-              (dst_row ==
-               indices_batch[chunk_start - batch_start + threadIdx.x]);
-        }
-#ifdef PADDLE_WITH_HIP
-        unsigned long long int matchmask =      // NOLINT
-            __ballot(match_found_this_thread);  // NOLINT
-        int first_remaining_peer = __ffsll(matchmask) - 1;
-#else
-        // If and only if match_found_this_thread of the Nth thread is non-zero,
-        // set the Nth bit of matchmask to 1.
-        unsigned int matchmask = __ballot_sync(MASK, match_found_this_thread);
-        // Find the position of the first bit set to 1 in matchmask.
-        int first_remaining_peer = __ffs(matchmask) - 1;
-#endif
-
-        // select lowest-indexed warp as the leader
-        if (threadIdx.y == first_remaining_peer) {
-          // Set the first bit 1 in matchmask to 0.
-          matchmask ^= (1 << first_remaining_peer);
-          while (matchmask) {
-#ifdef PADDLE_WITH_HIP
-            first_remaining_peer = __ffsll(matchmask) - 1;
-#else
-            first_remaining_peer = __ffs(matchmask) - 1;
-#endif
-            my_s[threadIdx.x] +=
-                smem[threadIdx.x + WARP_SIZE * first_remaining_peer];
-            matchmask ^= (1 << first_remaining_peer);
-          }
-          if (feature < stride)
-            table[dst_row * D + feature] += static_cast<T>(my_s[threadIdx.x]);
-        }
-      }
-    }
-  }
-}
-
 template <typename T, typename Context>
 struct EmbeddingGradCUDAFunctor {
   EmbeddingGradCUDAFunctor(const Context& dev_ctx,
@@ -198,20 +104,18 @@ struct EmbeddingGradCUDAFunctor {
           cudaMemsetAsync(d_table, 0, N * D * sizeof(T), dev_ctx_.stream()));
 #endif
 
-      if (FLAGS_embedding_deterministic) {
-        dim3 threads(WARP_SIZE, BLOCKDIMY);
-        dim3 grids(static_cast<int>((D + WARP_SIZE - 1) / WARP_SIZE));
-        using MT = typename dtype::MPTypeTrait<T>::Type;
-        EmbeddingGradDeterministic<T, IdT>
-            <<<grids,
-               threads,
-               sizeof(MT) * WARP_SIZE * BLOCKDIMY +
-                   sizeof(IdT) * WARP_SIZE * BLOCKDIMY,
-               dev_ctx_.stream()>>>(d_table, d_output, ids, K, D);
+      if (FLAGS_embedding_deterministic == 1) {
+        phi::funcs::LaunchEmbeddingGradDeterministicKernel<T, IdT>(
+            dev_ctx_, ids, d_output, d_table, N, D, K);
       } else {
         const int gridx = 2 * dev_ctx_.GetSMCount();
         dim3 threads(128, 8);
         dim3 grids(gridx, 1);
+        if (FLAGS_embedding_deterministic > 1) {
+          VLOG(2) << "Run grad kernel of embedding with single thread.";
+          grids.x = 1;
+          threads.y = 1;
+        }
         EmbeddingGrad<T, IdT><<<grids, threads, 0, dev_ctx_.stream()>>>(
             d_table, d_output, ids, N, K, D);
       }

python/paddle/fluid/tests/unittests/test_embedding_deterministic.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 contextlib
+import random
+import sys
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.distributed.fleet.layers.mpu.mp_ops import _c_lookup_table
+
+
+@contextlib.contextmanager
+def deterministic_guard(value):
+    flag_name = 'FLAGS_embedding_deterministic'
+    old_value = paddle.get_flags(flag_name)[flag_name]
+    paddle.set_flags({flag_name: value})
+    assert paddle.get_flags(flag_name)[flag_name] == value
+    yield
+    paddle.set_flags({flag_name: old_value})
+    assert paddle.get_flags(flag_name)[flag_name] == old_value
+
+
+def to_numpy(tensor):
+    if tensor.dtype in [paddle.float16, paddle.bfloat16]:
+        tensor = tensor.astype(paddle.float32)
+    return tensor.numpy()
+
+
+def clone_weight(weight):
+    if weight.dtype == paddle.bfloat16:
+        weight = weight.astype(paddle.float32).numpy()
+        weight = paddle.to_tensor(weight, dtype=paddle.float32).astype(
+            paddle.bfloat16
+        )
+    else:
+        weight = paddle.to_tensor(weight.numpy())
+    weight.stop_gradient = False
+    return weight
+
+
+def embedding(ids, weight, out_grad, deterministic_level=0, rank=None):
+    weight = clone_weight(weight)
+    with deterministic_guard(deterministic_level):
+        if rank is not None:
+            vocab_size, _ = weight.shape
+            start_idx = vocab_size * rank
+            out = _c_lookup_table(weight, ids, start_index=start_idx)
+        else:
+            out = paddle.nn.functional.embedding(ids, weight)
+        out.backward(out_grad.clone())
+        return to_numpy(out), to_numpy(weight.grad)
+
+
+def embedding_ground_truth(ids, weight, out_grad, rank=None):
+    weight = clone_weight(weight.astype(paddle.float32))
+    out_grad = out_grad.astype(paddle.float32)
+    return embedding(ids, weight, out_grad, deterministic_level=2, rank=rank)
+
+
+def generate_input_data(
+    ids_shape,
+    vocab_size,
+    hidden_size,
+    weight_dtype,
+    ids_dtype,
+    allow_duplicate_id=True,
+    rank=None,
+    nranks=None,
+    allow_pure_random=False,
+):
+    max_id = vocab_size if rank is None else vocab_size * nranks
+    if allow_duplicate_id:
+        ids = np.random.randint(low=0, high=max_id, size=ids_shape)
+    else:
+        sequence = list(range(max_id))
+        numel = int(np.prod(ids_shape))
+        if len(sequence) < numel:
+            return None, None, None
+        ids = np.array(random.sample(sequence, numel)).reshape(ids_shape)
+
+    ids = paddle.to_tensor(ids).astype(ids_dtype)
+    ids.stop_gradient = True
+
+    weight = paddle.randn([vocab_size, hidden_size]).astype(weight_dtype)
+    weight.stop_gradient = False
+
+    out_grad_shape = list(ids_shape) + [hidden_size]
+    if allow_duplicate_id and not allow_pure_random:
+        out_grad = paddle.randint(low=-10, high=10, shape=out_grad_shape)
+    else:
+        out_grad = paddle.randn(out_grad_shape)
+    out_grad = out_grad.astype(weight.dtype)
+    return ids, weight, out_grad
+
+
+def get_all_dtypes():
+    if not paddle.is_compiled_with_cuda() or paddle.is_compiled_with_rocm():
+        return []
+
+    dtypes = [paddle.float32, paddle.float16]
+    if 'A100' in paddle.device.cuda.get_device_properties().name:
+        dtypes.append(paddle.bfloat16)
+    return dtypes
+
+
+class TestEmbeddingBase(unittest.TestCase):
+    def setUp(self):
+        self.ids_shape = [32, 3]
+        self.vocab_size = 128
+        self.hidden_size = 1024
+        self.nranks = 8
+
+    def check_main(
+        self,
+        weight_dtype,
+        ids_dtype,
+        deterministic_level=0,
+        rank=None,
+        allow_duplicate_id=True,
+        allow_pure_random=False,
+    ):
+        if sys.platform == 'win32' and rank is not None:
+            return
+
+        ids, weight, out_grad = generate_input_data(
+            ids_shape=self.ids_shape,
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            weight_dtype=weight_dtype,
+            ids_dtype=ids_dtype,
+            allow_duplicate_id=allow_duplicate_id,
+            rank=rank,
+            nranks=self.nranks,
+            allow_pure_random=allow_pure_random,
+        )
+        if ids is None:
+            return
+
+        if allow_pure_random:
+            out_1, weight_grad_1 = embedding_ground_truth(
+                ids, weight, out_grad, rank
+            )
+            out_2, weight_grad_2 = embedding_ground_truth(
+                ids, weight, out_grad, rank
+            )
+        else:
+            out_1, weight_grad_1 = embedding_ground_truth(
+                ids, weight, out_grad, rank
+            )
+            out_2, weight_grad_2 = embedding(
+                ids,
+                weight,
+                out_grad,
+                deterministic_level=deterministic_level,
+                rank=rank,
+            )
+        np.testing.assert_equal(out_1, out_2)
+        np.testing.assert_equal(weight_grad_1, weight_grad_2)
+
+    def test_main(self):
+        weight_dtypes = get_all_dtypes()
+        ids_dtypes = [paddle.int64, paddle.int32]
+        deterministic_levels = [0, 1]
+        ranks = [None, 0, 2, 4, 8]
+        allow_duplicate_ids = [False, True]
+        allow_pure_randoms = [False, True]
+        for weight_dtype in weight_dtypes:
+            for ids_dtype in ids_dtypes:
+                for deterministic_level in deterministic_levels:
+                    for rank in ranks:
+                        for allow_duplicate_id in allow_duplicate_ids:
+                            for allow_pure_random in allow_pure_randoms:
+                                self.check_main(
+                                    weight_dtype,
+                                    ids_dtype,
+                                    deterministic_level,
+                                    rank,
+                                    allow_duplicate_id,
+                                    allow_pure_random,
+                                )
+
+
+class TestEmbedding2(TestEmbeddingBase):
+    def setUp(self):
+        self.ids_shape = [32, 16]
+        self.vocab_size = 128
+        self.hidden_size = 1024
+        self.nranks = 8
+
+
+class TestEmbeddingDeterministic(unittest.TestCase):
+    def setUp(self):
+        self.ids_shape = [32, 16]
+        self.vocab_size = 128
+        self.hidden_size = 1024
+
+
+if __name__ == "__main__":
+    unittest.main()