support transformer v2.0 (#30381)
taixiurong
5 years ago
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#ifdef PADDLE_WITH_XPU
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/framework.pb.h"
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#include "paddle/fluid/operators/one_hot_op.h"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class OneHotV2XPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<LoDTensor>("X");
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auto* out = context.Output<LoDTensor>("Out");
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int depth = context.Attr<int>("depth");
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if (context.HasInput("depth_tensor")) {
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auto* depth_tensor = context.Input<Tensor>("depth_tensor");
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auto* depth_data = depth_tensor->data<int32_t>();
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if (platform::is_xpu_place(depth_tensor->place())) {
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xpu_memcpy(static_cast<void*>(&depth),
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static_cast<const void*>(depth_data), sizeof(int32_t),
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XPU_DEVICE_TO_HOST);
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} else {
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depth = depth_data[0];
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}
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auto out_dims = out->dims();
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out_dims[out_dims.size() - 1] = depth;
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out->Resize(out_dims);
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}
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int len = in->numel();
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int ret = xpu::one_hot<T>(dev_ctx.x_context(), in->data<T>(),
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out->mutable_data<float>(context.GetPlace()), len,
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depth, 1.0, 0.0);
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PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
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platform::errors::External(
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"XPU one_hot kernel return wrong value[%d %s]", ret,
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XPUAPIErrorMsg[ret]));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_XPU_KERNEL(
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one_hot_v2, ops::OneHotV2XPUKernel<paddle::platform::XPUDeviceContext, int>,
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ops::OneHotV2XPUKernel<paddle::platform::XPUDeviceContext, int64_t>);
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#endif
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid.core as core
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import sys
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sys.path.append("..")
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from op_test_xpu import XPUOpTest
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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import time
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paddle.enable_static()
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class TestOneHotOp(XPUOpTest):
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def setUp(self):
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self.use_xpu = True
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self.op_type = 'one_hot_v2'
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depth = 10
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depth_np = np.array(10).astype('int32')
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# dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
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out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')
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for i in range(np.product(x.shape)):
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out[i, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
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self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place, check_dygraph=False)
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class TestOneHotOp_attr(XPUOpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
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out = np.zeros(shape=(np.product(x.shape[:-1]), 1,
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depth)).astype('float32')
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for i in range(np.product(x.shape)):
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out[i, 0, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place, check_dygraph=False)
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class TestOneHotOp_default_dtype(XPUOpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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depth_np = np.array(10).astype('int32')
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
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out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')
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for i in range(np.product(x.shape)):
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out[i, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
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self.attrs = {}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place, check_dygraph=False)
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class TestOneHotOp_default_dtype_attr(XPUOpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
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out = np.zeros(shape=(np.product(x.shape[:-1]), 1,
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depth)).astype('float32')
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for i in range(np.product(x.shape)):
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out[i, 0, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'depth': depth}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place, check_dygraph=False)
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class TestOneHotOp_out_of_range(XPUOpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.choice([-1, depth]) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
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out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'depth': depth, 'allow_out_of_range': True}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place, check_dygraph=False)
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class TestOneHotOpApi(unittest.TestCase):
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def test_api(self):
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depth = 10
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self._run(depth)
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def test_api_with_depthTensor(self):
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depth = fluid.layers.assign(input=np.array([10], dtype=np.int32))
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self._run(depth)
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def test_api_with_dygraph(self):
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depth = 10
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label = np.array([np.random.randint(0, depth - 1)
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for i in range(6)]).reshape([6, 1])
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with fluid.dygraph.guard():
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one_hot_label = fluid.one_hot(
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input=fluid.dygraph.to_variable(label), depth=depth)
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def _run(self, depth):
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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one_hot_label = fluid.one_hot(input=label, depth=depth)
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place = fluid.XPUPlace(0)
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label_data = np.array([np.random.randint(0, 10 - 1)
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for i in range(6)]).reshape([6, 1])
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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ret = exe.run(feed={'label': label_data, },
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fetch_list=[one_hot_label],
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return_numpy=False)
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class BadInputTestOnehotV2(unittest.TestCase):
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def test_error(self):
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with fluid.program_guard(fluid.Program()):
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def test_bad_x():
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label = fluid.layers.data(
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name="label",
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shape=[4],
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append_batch_size=False,
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dtype="float32")
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one_hot_label = fluid.one_hot(input=label, depth=4)
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self.assertRaises(TypeError, test_bad_x)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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