add kunlun kernel: slice, slice_grad, top_k, cast. *test=kunlun (#28542)
* 1.add xpu slice op 2. add xpu top_k op 3.modify xpu cast to new api * 1.add xpu slice op 2. add xpu top_k op 3.modify xpu cast to new apimusl/fix_failed_unittests_in_musl
taixiurong
4 years ago
committed by
GitHub
parent
9362d85e0e
commit
d3d1a6b6e0
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <memory>
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#include "paddle/fluid/operators/top_k_op.h"
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#include "xpu/refactor/math.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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class TopkXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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// Get the top k elements of each row of input tensor
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auto* input = ctx.Input<Tensor>("X");
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auto* output = ctx.Output<Tensor>("Out");
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auto* indices = ctx.Output<Tensor>("Indices");
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size_t k = static_cast<int>(ctx.Attr<int>("k"));
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auto* k_t = ctx.Input<Tensor>("K");
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if (k_t) {
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k = k_t->data<int>()[0];
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framework::DDim output_dims = output->dims();
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output_dims[output_dims.size() - 1] = k;
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output->Resize(output_dims);
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indices->Resize(output_dims);
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}
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T* output_data = output->mutable_data<T>(ctx.GetPlace());
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int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
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Tensor indices_32_data_tensor;
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int32_t* indices_int_data = indices_32_data_tensor.mutable_data<int32_t>(
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ctx.GetPlace(), indices->numel());
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// reshape input to a flattern matrix(like flat_inner_dims)
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framework::DDim inputdims = input->dims();
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const size_t row = framework::product(
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framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
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const size_t col = inputdims[inputdims.size() - 1];
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auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
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int ret = xpu::sorted_topk<T>(dev_ctx.x_context(), input->data<T>(),
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output_data, indices_int_data, row, col, k);
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PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
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platform::errors::External(
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"XPU API return wrong value[%d] in call kernel name "
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"[%s], please check "
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"where Baidu Kunlun Card is properly installed.",
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ret, "sorted_topk"));
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ret = xpu::cast_v2<int32_t, int64_t>(dev_ctx.x_context(),
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(const int32_t*)indices_int_data,
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indices_data, indices->numel());
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PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
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platform::errors::External(
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"XPU API return wrong value[%d] in call kernel name "
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"[%s], please check "
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"where Baidu Kunlun Card is properly installed.",
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ret, "cast_v2"));
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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(top_k, ops::TopkXPUKernel<float>);
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#endif
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@ -0,0 +1,77 @@
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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 sys
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sys.path.append("..")
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from paddle.fluid.op import Operator
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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import paddle
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from op_test import OpTest
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paddle.enable_static()
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@unittest.skipIf(not paddle.is_compiled_with_xpu(),
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"core is not compiled with XPU")
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class TestTopkOp(OpTest):
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def setUp(self):
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self.variable_k = False
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self.use_xpu = True
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self.set_args()
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self.op_type = "top_k"
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self.dtype = np.float32
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self.init_dtype()
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k = self.top_k
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input = np.random.random((self.row, k)).astype(self.dtype)
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output = np.ndarray((self.row, k))
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indices = np.ndarray((self.row, k)).astype("int64")
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self.inputs = {'X': input}
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if self.variable_k:
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self.inputs['K'] = np.array([k]).astype("int32")
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else:
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self.attrs = {'k': k}
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for rowid in range(self.row):
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row = input[rowid]
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output[rowid] = np.sort(row)[::-1][:k]
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indices[rowid] = row.argsort()[::-1][:k]
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self.outputs = {'Out': output, 'Indices': indices}
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def init_dtype(self):
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self.dtype = np.float32
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def set_args(self):
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self.row = 100
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self.top_k = 1
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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def test_check_grad(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(place, ['X'], 'Out')
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if __name__ == "__main__":
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unittest.main()
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