未验证 提交 4ce272ed 编写于 作者: P pangyoki 提交者: GitHub

add beam_search_decode npu op (#34967)

上级 7d86737c
...@@ -45,9 +45,15 @@ struct BeamSearchDecodeFunctor { ...@@ -45,9 +45,15 @@ struct BeamSearchDecodeFunctor {
id_tensor_(id_tensor), id_tensor_(id_tensor),
score_tensor_(score_tensor) { score_tensor_(score_tensor) {
tensor_on_gpu_ = false; tensor_on_gpu_ = false;
tensor_on_npu_ = false;
// First make a copy of GPU data on CPU // First make a copy of GPU data on CPU
if (platform::is_gpu_place(step_ids_origin_[0].place())) { if (platform::is_gpu_place(step_ids_origin_[0].place()) ||
tensor_on_gpu_ = true; platform::is_npu_place(step_ids_origin_[0].place())) {
if (platform::is_gpu_place(step_ids_origin_[0].place())) {
tensor_on_gpu_ = true;
} else {
tensor_on_npu_ = true;
}
platform::DeviceContextPool& pool = platform::DeviceContextPool& pool =
platform::DeviceContextPool::Instance(); platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(step_ids_origin_[0].place()); auto* dev_ctx = pool.Get(step_ids_origin_[0].place());
...@@ -55,7 +61,9 @@ struct BeamSearchDecodeFunctor { ...@@ -55,7 +61,9 @@ struct BeamSearchDecodeFunctor {
for (auto& step_id : step_ids_origin_) { for (auto& step_id : step_ids_origin_) {
framework::LoDTensor out; framework::LoDTensor out;
if (step_id.numel() > 0) { if (step_id.numel() > 0) {
dev_ctx->Wait(); if (tensor_on_gpu_) {
dev_ctx->Wait();
}
framework::TensorCopy(step_id, platform::CPUPlace(), *dev_ctx, &out); framework::TensorCopy(step_id, platform::CPUPlace(), *dev_ctx, &out);
dev_ctx->Wait(); dev_ctx->Wait();
} }
...@@ -64,8 +72,13 @@ struct BeamSearchDecodeFunctor { ...@@ -64,8 +72,13 @@ struct BeamSearchDecodeFunctor {
step_ids_.push_back(out); step_ids_.push_back(out);
} }
} }
if (platform::is_gpu_place(step_scores_origin_[0].place())) { if (platform::is_gpu_place(step_scores_origin_[0].place()) ||
tensor_on_gpu_ = true; platform::is_npu_place(step_scores_origin_[0].place())) {
if (platform::is_gpu_place(step_scores_origin_[0].place())) {
tensor_on_gpu_ = true;
} else {
tensor_on_npu_ = true;
}
platform::DeviceContextPool& pool = platform::DeviceContextPool& pool =
platform::DeviceContextPool::Instance(); platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(step_scores_origin_[0].place()); auto* dev_ctx = pool.Get(step_scores_origin_[0].place());
...@@ -73,7 +86,9 @@ struct BeamSearchDecodeFunctor { ...@@ -73,7 +86,9 @@ struct BeamSearchDecodeFunctor {
for (auto& step_score : step_scores_origin_) { for (auto& step_score : step_scores_origin_) {
framework::LoDTensor out; framework::LoDTensor out;
if (step_score.numel() > 0) { if (step_score.numel() > 0) {
dev_ctx->Wait(); if (tensor_on_gpu_) {
dev_ctx->Wait();
}
framework::TensorCopy(step_score, platform::CPUPlace(), *dev_ctx, framework::TensorCopy(step_score, platform::CPUPlace(), *dev_ctx,
&out); &out);
dev_ctx->Wait(); dev_ctx->Wait();
...@@ -89,6 +104,7 @@ struct BeamSearchDecodeFunctor { ...@@ -89,6 +104,7 @@ struct BeamSearchDecodeFunctor {
void apply() const; void apply() const;
bool tensor_on_gpu_; bool tensor_on_gpu_;
bool tensor_on_npu_;
size_t beam_size_; size_t beam_size_;
int end_id_; int end_id_;
// TODO(Superjomn) Here might result serious performance issue in the // TODO(Superjomn) Here might result serious performance issue in the
...@@ -105,8 +121,8 @@ struct BeamSearchDecodeFunctor { ...@@ -105,8 +121,8 @@ struct BeamSearchDecodeFunctor {
template <typename T> template <typename T>
void BeamSearchDecodeFunctor::apply() const { void BeamSearchDecodeFunctor::apply() const {
BeamSearchDecoder<T> beam_search_decoder(beam_size_, end_id_); BeamSearchDecoder<T> beam_search_decoder(beam_size_, end_id_);
// Check if the tensor is on GPU. If so, use the CPU copy instead // Check if the tensor is on GPU or NPU. If so, use the CPU copy instead
if (tensor_on_gpu_) { if (tensor_on_gpu_ || tensor_on_npu_) {
beam_search_decoder.Backtrace(step_ids_, step_scores_, id_tensor_, beam_search_decoder.Backtrace(step_ids_, step_scores_, id_tensor_,
score_tensor_); score_tensor_);
} else { } else {
......
# 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 unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid.framework import Program, program_guard
class TestBeamSearchDecodeNPUOp(unittest.TestCase):
"""unittest of beam_search_decode npu op"""
def setUp(self):
self.scope = core.Scope()
self.place = paddle.NPUPlace(0)
def append_lod_tensor(self, tensor_array, lod, data):
lod_tensor = core.LoDTensor()
lod_tensor.set_lod(lod)
lod_tensor.set(data, self.place)
tensor_array.append(lod_tensor)
def test_get_set(self):
ids = self.scope.var("ids").get_lod_tensor_array()
scores = self.scope.var("scores").get_lod_tensor_array()
# Construct sample data with 5 steps and 2 source sentences
# beam_size = 2, end_id = 1
# start with start_id
[
self.append_lod_tensor(
array, [[0, 1, 2], [0, 1, 2]], np.array(
[0, 0], dtype=dtype))
for array, dtype in ((ids, "int64"), (scores, "float32"))
]
[
self.append_lod_tensor(
array, [[0, 1, 2], [0, 2, 4]],
np.array(
[2, 3, 4, 5], dtype=dtype))
for array, dtype in ((ids, "int64"), (scores, "float32"))
]
[
self.append_lod_tensor(
array, [[0, 2, 4], [0, 2, 2, 4, 4]],
np.array(
[3, 1, 5, 4], dtype=dtype))
for array, dtype in ((ids, "int64"), (scores, "float32"))
]
[
self.append_lod_tensor(
array, [[0, 2, 4], [0, 1, 2, 3, 4]],
np.array(
[1, 1, 3, 5], dtype=dtype))
for array, dtype in ((ids, "int64"), (scores, "float32"))
]
[
self.append_lod_tensor(
array, [[0, 2, 4], [0, 0, 0, 2, 2]],
np.array(
[5, 1], dtype=dtype))
for array, dtype in ((ids, "int64"), (scores, "float32"))
]
sentence_ids = self.scope.var("sentence_ids").get_tensor()
sentence_scores = self.scope.var("sentence_scores").get_tensor()
beam_search_decode_op = Operator(
"beam_search_decode",
# inputs
Ids="ids",
Scores="scores",
# outputs
SentenceIds="sentence_ids",
SentenceScores="sentence_scores",
beam_size=2,
end_id=1, )
beam_search_decode_op.run(self.scope, self.place)
expected_lod = [[0, 2, 4], [0, 4, 7, 12, 17]]
self.assertEqual(sentence_ids.lod(), expected_lod)
self.assertEqual(sentence_scores.lod(), expected_lod)
expected_data = np.array(
[0, 2, 3, 1, 0, 2, 1, 0, 4, 5, 3, 5, 0, 4, 5, 3, 1], "int64")
self.assertTrue(np.array_equal(np.array(sentence_ids), expected_data))
self.assertTrue(
np.array_equal(np.array(sentence_scores), expected_data))
if __name__ == '__main__':
unittest.main()
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