[NPU] Support npu op table_lookup_v2 and table_lookup_v2_grad

paddle/fluid/operators/CMakeLists.txt

@@ -155,6 +155,9 @@ cc_library(tensor_formatter SRCS tensor_formatter.cc DEPS ${OP_HEADER_DEPS})
 if (WITH_PYTHON)
   cc_library(py_func_op SRCS py_func_op.cc DEPS op_registry python pybind)
 endif()
+if (WITH_ASCEND_CL)
+    cc_test(lookup_table_v2_op_npu_test SRCS lookup_table_v2_op_npu_test.cc DEPS op_registry lookup_table_v2_op scope device_context enforce executor compare_op)
+endif()
 
 set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
 add_subdirectory(benchmark)

paddle/fluid/operators/lookup_table_v2_op_npu.cc

@@ -0,0 +1,80 @@
+/* Copyright (c) 2021 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 <iostream>
+#include <memory>
+#include <string>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class LookupTableV2NPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *ids_t = ctx.Input<framework::LoDTensor>("Ids");      // int tensor
+    auto *output_t = ctx.Output<framework::LoDTensor>("Out");  // float tensor
+    auto *table_t = ctx.Input<framework::LoDTensor>("W");
+    auto *table_var = ctx.InputVar("W");
+    PADDLE_ENFORCE_EQ(
+        table_var->IsType<framework::LoDTensor>(), true,
+        platform::errors::InvalidArgument("npu only accept LoDTensor"));
+    output_t->mutable_data<T>(ctx.GetPlace());
+    framework::NPUAttributeMap attr_input = {{"validate_indices", false}};
+
+    auto runner =
+        NpuOpRunner("Gather", {*table_t, *ids_t}, {*output_t}, attr_input);
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+
+template <typename T>
+class LookupTableV2GradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *ids_t = ctx.Input<framework::LoDTensor>("Ids");
+    auto *output_grad_t =
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+    auto *table_t = ctx.Input<framework::LoDTensor>("W");
+    auto *table_grad_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("W"));
+    framework::NPUAttributeMap attr_input = {{"use_locking", true}};
+
+    auto runner = NpuOpRunner("ScatterAdd", {*table_t, *ids_t, *output_grad_t},
+                              {*table_grad_t}, attr_input);
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    lookup_table_v2,
+    ops::LookupTableV2NPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::LookupTableV2NPUKernel<paddle::platform::NPUDeviceContext,
+                                paddle::platform::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    lookup_table_v2_grad, ops::LookupTableV2GradNPUKernel<float>,
+    ops::LookupTableV2GradNPUKernel<paddle::platform::float16>);

paddle/fluid/operators/lookup_table_v2_op_npu_test.cc

@@ -0,0 +1,142 @@
+/* Copyright (c) 2021 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. */
+
+#ifndef _WIN32
+#include <unistd.h>
+#endif
+
+#include <cmath>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <thread>  // NOLINT
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/operators/dropout_op.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/string/printf.h"
+
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+namespace m = paddle::operators::math;
+
+USE_OP(lookup_table_v2);
+USE_OP_DEVICE_KERNEL(lookup_table_v2, NPU);
+
+template <typename T>
+void Compare(f::Scope* scope, const p::DeviceContext& ctx) {
+  // init
+  auto ids = scope->Var("Ids");
+  auto out = scope->Var("Out");
+  auto w = scope->Var("W");
+
+  auto ids_t = ids->GetMutable<f::LoDTensor>();
+  auto out_t = out->GetMutable<f::LoDTensor>();
+  auto w_t = w->GetMutable<f::LoDTensor>();
+  int bsz = 10;
+  int dim = 32;
+  int seqlen = 8;
+  int vocab_size = 100;
+  TensorFromVector(std::vector<int64_t>(bsz * seqlen, 3), ctx, ids_t);
+  std::vector<T> val(vocab_size * dim, 10.);
+  TensorFromVector(val, ctx, w_t);
+  ids_t->Resize({bsz, seqlen});
+  w_t->Resize({vocab_size, dim});
+  out_t->Resize({bsz, seqlen, dim});
+  ctx.Wait();
+
+  auto place = ctx.GetPlace();
+  out_t->mutable_data<T>(place);
+  f::AttributeMap attrs = {{}};
+  auto op = f::OpRegistry::CreateOp("lookup_table_v2",
+                                    {{"W", {"W"}}, {"Ids", {"Ids"}}},
+                                    {{"Out", {"Out"}}}, attrs);
+  op->Run(*scope, place);
+  std::vector<T> out_v;
+  TensorToVector(*out_t, ctx, &out_v);
+  ctx.Wait();
+  EXPECT_EQ(out_t->numel(), bsz * seqlen * dim);
+  T res = std::accumulate(out_v.begin(), out_v.end(), 0.);
+  float eps = 1.e-6;
+  EXPECT_LT(fabs(res - bsz * seqlen * dim * 10.), eps);
+}
+
+template <typename T>
+void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx) {
+  // init
+  auto w = scope->Var("W");
+  auto ids = scope->Var("Ids");
+  auto out = scope->Var("DOut");
+  auto dw = scope->Var("DW");
+
+  auto w_t = w->GetMutable<f::LoDTensor>();
+  auto ids_t = ids->GetMutable<f::LoDTensor>();
+  auto out_t = out->GetMutable<f::LoDTensor>();
+  auto dw_t = dw->GetMutable<f::LoDTensor>();
+
+  int bsz = 2;
+  int dim = 2;
+  int seqlen = 2;
+  int vocab_size = 4;
+
+  std::vector<int64_t> val_int(bsz * seqlen, 3);
+  std::vector<T> val(vocab_size * dim, 0.);
+  std::vector<T> val_out(bsz * seqlen * dim, 1.);
+
+  TensorFromVector(val_int, ctx, ids_t);
+  TensorFromVector(val, ctx, w_t);
+  TensorFromVector(val, ctx, dw_t);
+  TensorFromVector(val_out, ctx, out_t);
+
+  w_t->Resize({vocab_size, dim});
+  ids_t->Resize({bsz, seqlen});
+  out_t->Resize({bsz, seqlen, dim});
+  dw_t->Resize({vocab_size, dim});
+
+  ctx.Wait();
+
+  auto place = ctx.GetPlace();
+  out_t->mutable_data<T>(place);
+  w_t->mutable_data<T>(place);
+  dw_t->mutable_data<T>(place);
+  f::AttributeMap attrs = {{}};
+  auto op = f::OpRegistry::CreateOp(
+      "lookup_table_v2_grad",
+      {{"Ids", {"Ids"}}, {"W", {"W"}}, {"Out@GRAD", {"DOut"}}},
+      {{"W@GRAD", {"DW"}}}, attrs);
+  op->Run(*scope, place);
+  ctx.Wait();
+  std::vector<T> w_v;
+  TensorToVector(*dw_t, ctx, &w_v);
+  ctx.Wait();
+  EXPECT_EQ(dw_t->numel(), vocab_size * dim);
+  T res = std::accumulate(w_v.begin(), w_v.end(), 0.);
+  float eps = 1.e-6;
+  EXPECT_LT(fabs(res - bsz * seqlen * dim), eps);
+}
+
+TEST(lookup_table_v2, NPU_fp32) {
+  f::Scope scope;
+  p::NPUDeviceContext ctx(p::NPUPlace(0));
+  Compare<float>(&scope, ctx);
+}
+
+TEST(lookup_table_v2_grad, NPU_fp32) {
+  f::Scope scope;
+  p::NPUDeviceContext ctx(p::NPUPlace(0));
+  CompareGrad<float>(&scope, ctx);
+}

python/paddle/fluid/tests/unittests/npu/test_lookup_table_v2_op_npu.py

@@ -0,0 +1,143 @@
+#  Copyright (c) 2021 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.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestLookupTableV2(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "lookup_table_v2"
+        self.place = paddle.NPUPlace(0)
+
+        self.init_dtype()
+        np.random.seed(SEED)
+        bsz=2
+        seqlen=2
+        vocab=3
+        dim=2
+        w = np.ones([vocab, dim]).astype(self.dtype)
+        x = np.random.randint(0, vocab, size=(bsz, seqlen)).astype(np.int64)
+        out = np.ones([bsz, seqlen, dim]).astype(self.dtype)
+
+        self.inputs = {'W': OpTest.np_dtype_to_fluid_dtype(w), 'Ids': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.attrs = {
+            'is_sparse': False,
+            'is_distributed': False,
+            'remote_prefetch':False,
+            'padding_idx': -1
+        }
+        self.outputs = {'Out': out}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+    # TODO(ascendrc): Add grad test
+    # def test_check_grad(self):
+    #     if self.dtype == np.float16:
+    #         return
+    #     self.check_grad(['X'], 'Out')
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestLookupTableV2FP16(TestLookupTableV2):
+    no_need_check_grad = True
+    def init_dtype(self):
+        self.dtype = np.float16
+
+#@unittest.skipIf(not paddle.is_compiled_with_npu(),
+#                 "core is not compiled with NPU")
+#class TestLookupTableV2Int8(TestLookupTableV2):
+#    def init_dtype(self):
+#        self.dtype = np.int8
+#
+#@unittest.skipIf(not paddle.is_compiled_with_npu(),
+#                 "core is not compiled with NPU")
+#class TestLookupTableV2UInt8(TestLookupTableV2):
+#    def init_dtype(self):
+#        self.dtype = np.uint8
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestLookupTableV2Net(unittest.TestCase):
+    def _test(self, run_npu=True):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+        np.random.seed(SEED)
+
+        bsz=3
+        seqlen=2
+        vocab=3
+        dim=2
+
+        ids_np = np.random.randint(0, vocab, size=(bsz, seqlen)).astype('int64')
+
+        with paddle.static.program_guard(main_prog, startup_prog):
+            emb = paddle.nn.Embedding(vocab, dim)
+            ids = paddle.static.data(name="ids", shape=[bsz, seqlen], dtype='int64')
+            #res = paddle.static.nn.embedding(ids, (vocab, dim), param_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(1.)))
+            res = emb(ids)
+            loss = res.sum()
+
+        if run_npu:
+            place = paddle.NPUPlace(0)
+        else:
+            place = paddle.CPUPlace()
+
+        exe = paddle.static.Executor(place)
+        exe.run(startup_prog)
+
+        for epoch in range(1):
+            loss_res, w = exe.run(
+                main_prog,
+                feed={"ids": ids_np},
+                fetch_list=[loss, emb.weight])
+            if epoch % 10 == 0:
+                print(w)
+                print("Epoch {} | Loss: {}".format(epoch, loss))
+
+        return loss_res
+
+    def test_npu(self):
+        cpu_loss = self._test(False)
+        npu_loss = self._test(True)
+        self.assertTrue(np.allclose(npu_loss, cpu_loss))
+
+
+
+if __name__ == '__main__':
+    unittest.main()
+