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1078 lines
40 KiB
1078 lines
40 KiB
/* 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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#include "paddle/fluid/framework/tensor_util.h"
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#include <algorithm>
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#include <limits>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/data_type.h"
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#include "paddle/fluid/platform/complex128.h"
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#include "paddle/fluid/platform/complex64.h"
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#include "paddle/fluid/platform/profiler.h"
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namespace paddle {
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namespace framework {
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void TensorCopy(const Tensor& src, const platform::Place& dst_place,
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const platform::DeviceContext& ctx, Tensor* dst) {
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if (&src == dst) {
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auto src_copy = src;
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TensorCopy(src_copy, dst_place, ctx, dst);
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return;
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}
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VLOG(3) << "TensorCopy " << src.dims() << " from " << src.place() << " to "
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<< dst_place;
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src.check_memory_size();
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dst->Resize(src.dims());
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dst->set_layout(src.layout());
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auto src_place = src.place();
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auto src_ptr = src.data<void>();
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#ifdef PADDLE_WITH_MKLDNN
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dst->set_format(src.format());
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// oneDNN tensors due to padding may be of bigger size
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// than numel()*size(type())
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auto dst_ptr =
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src.layout() == DataLayout::kMKLDNN
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? dst->mutable_data(dst_place, src.type(), src.memory_size())
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: dst->mutable_data(dst_place, src.type());
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#else
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auto dst_ptr = dst->mutable_data(dst_place, src.type());
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#endif
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if (src_ptr == dst_ptr && src_place == dst_place) {
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VLOG(3) << "Skip copy the same data async from " << src_place << " to "
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<< dst_place;
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return;
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}
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#ifdef PADDLE_WITH_MKLDNN
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auto size = src.layout() == DataLayout::kMKLDNN
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? src.memory_size()
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: src.numel() * SizeOfType(src.type());
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#else
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auto size = src.numel() * SizeOfType(src.type());
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#endif
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if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
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}
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#ifdef PADDLE_WITH_XPU
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else if (platform::is_xpu_place(src_place) && // NOLINT
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platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::XPUPlace, src_place), src_ptr, size);
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} else if (platform::is_cpu_place(src_place) &&
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platform::is_xpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::XPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
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} else if (platform::is_xpu_place(src_place) &&
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platform::is_xpu_place(dst_place)) {
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if (src_ptr == dst_ptr) {
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VLOG(3) << "Skip copy the same data async from " << src_place << " to "
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<< dst_place;
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return;
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}
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memory::Copy(BOOST_GET_CONST(platform::XPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::XPUPlace, src_place), src_ptr, size);
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} else {
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PADDLE_THROW(platform::errors::Unimplemented(
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"Copy from %s to %s is not supported.", src_place, dst_place));
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}
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#endif
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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else if (platform::is_cuda_pinned_place(src_place) && // NOLINT
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platform::is_cuda_pinned_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
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size);
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}
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else if (platform::is_cuda_pinned_place(src_place) && // NOLINT
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platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
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size);
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}
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else if (platform::is_cpu_place(src_place) && // NOLINT
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platform::is_cuda_pinned_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
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}
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else if (platform::is_gpu_place(src_place) && // NOLINT
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platform::is_cpu_place(dst_place)) {
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auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
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auto dst_cpu_place = BOOST_GET_CONST(platform::CPUPlace, dst_place);
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auto ctx_place = ctx.GetPlace();
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PADDLE_ENFORCE_EQ(
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platform::is_gpu_place(ctx_place), true,
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platform::errors::PreconditionNotMet(
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"Context place error, excepted GPUPlace, but actually %s.",
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ctx_place));
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auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
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PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place,
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platform::errors::Unavailable(
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"Source place and context place do not match, source "
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"place is %s, context place is %s.",
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src_gpu_place, ctx_gpu_place));
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size, stream);
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}
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else if (platform::is_cpu_place(src_place) && // NOLINT
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platform::is_gpu_place(dst_place)) {
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auto src_cpu_place = BOOST_GET_CONST(platform::CPUPlace, src_place);
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auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
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auto ctx_place = ctx.GetPlace();
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PADDLE_ENFORCE_EQ(
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platform::is_gpu_place(ctx_place), true,
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platform::errors::PreconditionNotMet(
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"Context place error, excepted GPUPlace, but actually %s.",
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ctx_place));
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auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
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PADDLE_ENFORCE_EQ(dst_gpu_place, ctx_gpu_place,
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platform::errors::Unavailable(
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"Destination place and context place do not match, "
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"destination place is %s, context place is %s.",
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dst_gpu_place, ctx_gpu_place));
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
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}
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else if (platform::is_gpu_place(src_place) && // NOLINT
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platform::is_cuda_pinned_place(dst_place)) {
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auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
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auto dst_cuda_pinned_place =
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BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place);
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auto ctx_place = ctx.GetPlace();
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PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx_place), true,
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platform::errors::PreconditionNotMet(
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"Device context place mismatch. When copying Tensor "
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"data from GPU memory to CUDA Pinned memory, current "
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"device context place should be GPU."));
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auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
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PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place,
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platform::errors::PreconditionNotMet(
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"The source GPU device and current device context do "
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"not match. The source GPU device number is %d, but "
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"device context GPU number is %d.",
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src_gpu_place.device, ctx_gpu_place.device));
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(dst_cuda_pinned_place, dst_ptr, src_gpu_place, src_ptr, size,
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stream);
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}
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else if (platform::is_cuda_pinned_place(src_place) && // NOLINT
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platform::is_gpu_place(dst_place)) {
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auto src_cuda_pinned_place =
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BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place);
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auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
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auto ctx_place = ctx.GetPlace();
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PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx_place), true,
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platform::errors::PreconditionNotMet(
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"Device context place mismatch. When copying Tensor "
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"data from CUDA Pinned memory to GPU memory, current "
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"device context place should be GPU."));
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auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
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PADDLE_ENFORCE_EQ(dst_gpu_place, ctx_gpu_place,
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platform::errors::PreconditionNotMet(
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"The target GPU device and current device context do "
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"not match. The target GPU device number is %d, but "
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"device context GPU number is %d.",
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dst_gpu_place.device, ctx_gpu_place.device));
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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memory::Copy(dst_gpu_place, dst_ptr, src_cuda_pinned_place, src_ptr, size,
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stream);
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}
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else if (platform::is_gpu_place(src_place) && // NOLINT
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platform::is_gpu_place(dst_place)) {
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auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
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auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
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auto ctx_place = ctx.GetPlace();
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PADDLE_ENFORCE_EQ(
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platform::is_gpu_place(ctx_place), true,
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platform::errors::PreconditionNotMet(
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"Context place error, excepted GPUPlace, but actually %s.",
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ctx_place));
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
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if (platform::is_same_place(src_place, dst_place)) {
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memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
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stream);
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} else {
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if (platform::is_same_place(ctx_place, src_place)) {
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memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
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stream);
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platform::DeviceContextPool::Instance().Get(src.place())->Wait();
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} else if (platform::is_same_place(ctx_place, dst_place)) {
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platform::DeviceContextPool::Instance().Get(src.place())->Wait();
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memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
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stream);
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} else {
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PADDLE_THROW(platform::errors::Unavailable(
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"Context place dose not match the source and destination place."));
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}
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}
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}
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else { // NOLINT
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PADDLE_THROW(platform::errors::Unimplemented(
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"Copying from %s to %s is not supported.", src_place, dst_place));
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}
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#endif
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}
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void TensorCopy(const Tensor& src, const platform::Place& dst_place,
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Tensor* dst) {
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platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
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const platform::DeviceContext* dev_ctx;
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if (platform::is_gpu_place(dst_place)) {
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dev_ctx = pool.Get(dst_place);
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} else {
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dev_ctx = pool.Get(src.place());
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}
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TensorCopy(src, dst_place, *dev_ctx, dst);
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}
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void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
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Tensor* dst) {
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if (&src == dst) {
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auto src_copy = src;
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TensorCopySync(src_copy, dst_place, dst);
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return;
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}
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VLOG(3) << "TensorCopySync " << src.dims() << " from " << src.place()
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<< " to " << dst_place;
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src.check_memory_size();
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dst->Resize(src.dims());
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dst->set_layout(src.layout());
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#ifdef PADDLE_WITH_MKLDNN
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dst->set_format(src.format());
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#endif
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auto src_place = src.place();
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auto src_ptr = src.data<void>();
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auto dst_ptr = dst->mutable_data(dst_place, src.type());
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if (src_ptr == dst_ptr && src_place == dst_place) {
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VLOG(3) << "Skip copy the same data from " << src_place << " to "
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<< dst_place;
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return;
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}
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auto size = src.numel() * SizeOfType(src.type());
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if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
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}
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#ifdef PADDLE_WITH_XPU
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else if (platform::is_xpu_place(src_place) && // NOLINT
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platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::XPUPlace, src_place), src_ptr, size);
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} else if (platform::is_cpu_place(src_place) && // NOLINT
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platform::is_xpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::XPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
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} else if (platform::is_xpu_place(src_place) && // NOLINT
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platform::is_xpu_place(dst_place)) {
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if (src_ptr == dst_ptr) {
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VLOG(3) << "Skip copy the same data async from " << src_place << " to "
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<< dst_place;
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return;
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}
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memory::Copy(BOOST_GET_CONST(platform::XPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::XPUPlace, src_place), src_ptr, size);
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} else { // NOLINT
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PADDLE_THROW(platform::errors::Unimplemented(
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"Copy from %s to %s is not supported.", src_place, dst_place));
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}
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#endif
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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else if (platform::is_cuda_pinned_place(src_place) && // NOLINT
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platform::is_cuda_pinned_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
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size);
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}
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else if (platform::is_cuda_pinned_place(src_place) && // NOLINT
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platform::is_cpu_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
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size);
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}
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else if (platform::is_cpu_place(src_place) && // NOLINT
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platform::is_cuda_pinned_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
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}
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else if (platform::is_gpu_place(src_place) && // NOLINT
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platform::is_cuda_pinned_place(dst_place)) {
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memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
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BOOST_GET_CONST(platform::CUDAPlace, src_place), src_ptr, size,
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nullptr);
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}
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else if (platform::is_gpu_place(src_place) && // NOLINT
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platform::is_cpu_place(dst_place)) {
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auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
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auto dst_cpu_place = BOOST_GET_CONST(platform::CPUPlace, dst_place);
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memory::Copy(dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
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}
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else if (platform::is_cpu_place(src_place) && // NOLINT
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platform::is_gpu_place(dst_place)) {
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auto src_cpu_place = BOOST_GET_CONST(platform::CPUPlace, src_place);
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auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
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memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, nullptr);
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}
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else if (platform::is_gpu_place(src_place) && // NOLINT
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platform::is_gpu_place(dst_place)) {
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auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
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auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
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memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
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}
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else if (platform::is_cuda_pinned_place(src_place) && // NOLINT
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platform::is_gpu_place(dst_place)) {
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auto src_pinned_place =
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BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place);
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auto dst_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, dst_place);
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memory::Copy(dst_gpu_place, dst_ptr, src_pinned_place, src_ptr, size,
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nullptr);
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}
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else { // NOLINT
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PADDLE_THROW(platform::errors::Unimplemented(
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"Copy from %s to %s is not supported.", src_place, dst_place));
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}
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#endif
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}
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template <typename Predicate, typename DevCtx>
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struct AnyDTypeVisitor {
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Predicate predicate_;
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const Tensor& tensor_;
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const DevCtx& ctx_;
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Tensor* out_;
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AnyDTypeVisitor(Predicate predicate, const Tensor& tensor, const DevCtx& ctx,
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Tensor* out)
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: predicate_(predicate), tensor_(tensor), ctx_(ctx), out_(out) {}
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template <typename T>
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void apply() const {
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auto t = EigenVector<T>::Flatten(tensor_);
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auto o = EigenScalar<bool>::From(*out_);
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// return any of predicate_(t) is true.
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o.device(*ctx_.eigen_device()) = predicate_(t).any();
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}
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};
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template <typename Predicate, typename DevCtx>
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inline void AnyImpl(Predicate predicate, const framework::Tensor& tensor,
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const DevCtx& ctx, framework::Tensor* out) {
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VisitDataType(tensor.type(), AnyDTypeVisitor<Predicate, DevCtx>(
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predicate, tensor, ctx, out));
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}
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|
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template <typename Predicate>
|
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class AnyVisitor : public boost::static_visitor<bool> {
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private:
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const framework::Tensor& tensor_;
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Predicate predicate_;
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|
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bool GetResultHelper(const framework::Tensor& out,
|
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const platform::Place& place) const {
|
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platform::CPUPlace cpu;
|
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framework::Tensor tmp;
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tmp.Resize({1});
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tmp.mutable_data<bool>(cpu);
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auto ctx = platform::DeviceContextPool::Instance().Get(place);
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ctx->Wait();
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TensorCopy(out, cpu, *ctx, &tmp);
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ctx->Wait();
|
|
return GetResult(tmp, cpu);
|
|
}
|
|
|
|
public:
|
|
AnyVisitor(const framework::Tensor& tensor, Predicate predicate)
|
|
: tensor_(tensor), predicate_(std::move(predicate)) {}
|
|
|
|
template <typename Place>
|
|
bool operator()(const Place& place) const {
|
|
framework::Tensor out;
|
|
out.Resize({1});
|
|
out.mutable_data<bool>(place);
|
|
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
|
|
AnyImpl(predicate_, tensor_, *ctx, &out);
|
|
return this->GetResult(out, place);
|
|
}
|
|
|
|
bool GetResult(const framework::Tensor& out,
|
|
const platform::XPUPlace& xpu) const {
|
|
return GetResultHelper(out, xpu);
|
|
}
|
|
|
|
bool GetResult(const framework::Tensor& out,
|
|
const platform::CUDAPlace& gpu) const {
|
|
return GetResultHelper(out, gpu);
|
|
}
|
|
|
|
bool GetResult(const framework::Tensor& out,
|
|
const platform::CPUPlace& cpu) const {
|
|
return *out.data<bool>();
|
|
}
|
|
|
|
bool GetResult(const framework::Tensor& out,
|
|
const platform::CUDAPinnedPlace& cpu) const {
|
|
return *out.data<bool>();
|
|
}
|
|
};
|
|
|
|
template <typename Predicate>
|
|
class AnyOutVisitor : public boost::static_visitor<> {
|
|
private:
|
|
const framework::Tensor& tensor_;
|
|
mutable framework::Tensor* out_;
|
|
Predicate predicate_;
|
|
|
|
public:
|
|
AnyOutVisitor(const framework::Tensor& tensor, Predicate predicate,
|
|
framework::Tensor* out)
|
|
: tensor_(tensor), out_(out), predicate_(std::move(predicate)) {}
|
|
|
|
template <typename Place>
|
|
void operator()(const Place& place) const {
|
|
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
|
|
out_->Resize({1});
|
|
out_->mutable_data<bool>(place);
|
|
AnyImpl(predicate_, tensor_, *ctx, out_);
|
|
}
|
|
};
|
|
|
|
template <typename Predicate>
|
|
inline bool Any(const framework::Tensor& tensor, Predicate predicate) {
|
|
AnyVisitor<Predicate> visitor(tensor, predicate);
|
|
auto place = tensor.place();
|
|
return platform::VisitPlace(place, visitor);
|
|
}
|
|
|
|
template <typename Predicate>
|
|
inline void Any(const framework::Tensor& tensor, Predicate predicate,
|
|
framework::Tensor* out) {
|
|
AnyOutVisitor<Predicate> visitor(tensor, predicate, out);
|
|
auto place = tensor.place();
|
|
platform::VisitPlace(place, visitor);
|
|
}
|
|
|
|
template <typename Predicate, typename DevCtx>
|
|
struct AllDTypeVisitor {
|
|
Predicate predicate_;
|
|
const Tensor& tensor_;
|
|
const DevCtx& ctx_;
|
|
Tensor* out_;
|
|
|
|
AllDTypeVisitor(Predicate predicate, const Tensor& tensor, const DevCtx& ctx,
|
|
Tensor* out)
|
|
: predicate_(predicate), tensor_(tensor), ctx_(ctx), out_(out) {}
|
|
|
|
template <typename T>
|
|
void apply() const {
|
|
auto t = EigenVector<T>::Flatten(tensor_);
|
|
auto o = EigenVector<bool>::Flatten(*out_);
|
|
o.device(*ctx_.eigen_device()) = predicate_(t);
|
|
}
|
|
};
|
|
|
|
template <typename Predicate, typename DevCtx>
|
|
inline void AllImpl(Predicate predicate, const framework::Tensor& tensor,
|
|
const DevCtx& ctx, framework::Tensor* out) {
|
|
VisitDataType(tensor.type(), AllDTypeVisitor<Predicate, DevCtx>(
|
|
predicate, tensor, ctx, out));
|
|
}
|
|
|
|
template <typename Predicate>
|
|
class AllOutVisitor : public boost::static_visitor<> {
|
|
private:
|
|
const framework::Tensor& tensor_;
|
|
mutable framework::Tensor* out_;
|
|
Predicate predicate_;
|
|
|
|
public:
|
|
AllOutVisitor(const framework::Tensor& tensor, Predicate predicate,
|
|
framework::Tensor* out)
|
|
: tensor_(tensor), out_(out), predicate_(predicate) {}
|
|
|
|
template <typename Place>
|
|
void operator()(const Place& place) const {
|
|
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
|
|
out_->Resize(tensor_.dims());
|
|
out_->mutable_data<bool>(place);
|
|
AllImpl(predicate_, tensor_, *ctx, out_);
|
|
}
|
|
};
|
|
|
|
template <typename Predicate>
|
|
inline void All(const framework::Tensor& tensor, Predicate predicate,
|
|
framework::Tensor* out) {
|
|
AllOutVisitor<Predicate> visitor(tensor, predicate, out);
|
|
auto place = tensor.place();
|
|
platform::VisitPlace(place, visitor);
|
|
}
|
|
|
|
struct ContainsNANPredicate {
|
|
template <typename T>
|
|
auto operator()(const T& eigen_vec) const
|
|
-> decltype(std::declval<T>().isnan()) {
|
|
// Cast eigen_vector to vector of bool. true if is inf.
|
|
return eigen_vec.isnan();
|
|
}
|
|
};
|
|
|
|
bool TensorContainsNAN(const framework::Tensor& tensor) {
|
|
ContainsNANPredicate predicate;
|
|
return Any(tensor, predicate);
|
|
}
|
|
|
|
void TensorContainsNAN(const framework::Tensor& tensor,
|
|
framework::Tensor* out) {
|
|
ContainsNANPredicate predicate;
|
|
Any(tensor, predicate, out);
|
|
}
|
|
|
|
void TensorContainsNANV2(const framework::Tensor& tensor,
|
|
framework::Tensor* out) {
|
|
ContainsNANPredicate predicate;
|
|
All(tensor, predicate, out);
|
|
}
|
|
|
|
struct ContainsInfPredicate {
|
|
template <typename T>
|
|
auto operator()(const T& eigen_vec) const
|
|
-> decltype(std::declval<T>().isinf()) {
|
|
// Cast eigen_vector to vector of bool. true if is inf.
|
|
return eigen_vec.isinf();
|
|
}
|
|
};
|
|
|
|
bool TensorContainsInf(const framework::Tensor& tensor) {
|
|
ContainsInfPredicate predicate;
|
|
return Any(tensor, predicate);
|
|
}
|
|
|
|
void TensorContainsInf(const framework::Tensor& tensor,
|
|
framework::Tensor* out) {
|
|
ContainsInfPredicate predicate;
|
|
Any(tensor, predicate, out);
|
|
}
|
|
|
|
void TensorContainsInfV2(const framework::Tensor& tensor,
|
|
framework::Tensor* out) {
|
|
ContainsInfPredicate predicate;
|
|
All(tensor, predicate, out);
|
|
}
|
|
|
|
// NOTE(dzhwinter):
|
|
// Isfinite need a AllVisitor to loop through all the elements.
|
|
// We choose two cuda call instead of one allvisitor. The AllVisitor
|
|
// should be implemented if the performance hurts.
|
|
bool TensorIsfinite(const framework::Tensor& tensor) {
|
|
ContainsInfPredicate pred_inf;
|
|
ContainsNANPredicate pred_nan;
|
|
return !Any(tensor, pred_inf) && !Any(tensor, pred_nan);
|
|
}
|
|
|
|
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
|
|
template <typename T>
|
|
static inline void __global__ BothFalse(const T* cmp, T* out, int element_num) {
|
|
CUDA_KERNEL_LOOP(i, element_num) { out[i] = (!cmp[i]) && (!out[i]); }
|
|
}
|
|
#endif
|
|
|
|
struct BothFalseVisitor : public boost::static_visitor<> {
|
|
const framework::Tensor& in_;
|
|
mutable framework::Tensor* out_;
|
|
BothFalseVisitor(const framework::Tensor& in, framework::Tensor* out)
|
|
: in_(in), out_(out) {}
|
|
|
|
template <typename Place>
|
|
void operator()(const Place& place) const {
|
|
VisitorImpl(place);
|
|
}
|
|
|
|
void VisitorImpl(const platform::XPUPlace& xpu) const {
|
|
PADDLE_THROW(platform::errors::Unimplemented("XPUPlace is not supported"));
|
|
}
|
|
|
|
void VisitorImpl(const platform::CUDAPlace& gpu) const {
|
|
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
|
|
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(gpu);
|
|
constexpr int MAX_BLOCK_DIM = 512;
|
|
const int MAX_GRID_DIM = ctx->GetMaxPhysicalThreadCount() / MAX_BLOCK_DIM;
|
|
int element_num = in_.numel();
|
|
int block_size = (element_num >= MAX_BLOCK_DIM)
|
|
? MAX_BLOCK_DIM
|
|
: (1 << static_cast<int>(std::log2(element_num)));
|
|
int grid_size = element_num / block_size;
|
|
grid_size = (grid_size >= MAX_GRID_DIM) ? MAX_GRID_DIM : grid_size;
|
|
BothFalse<bool><<<grid_size, block_size, 0, ctx->stream()>>>(
|
|
in_.data<bool>(), out_->mutable_data<bool>(gpu), element_num);
|
|
#endif
|
|
}
|
|
|
|
void VisitorImpl(const platform::CPUPlace& cpu) const {
|
|
int num = in_.numel();
|
|
const bool* in_ptr = in_.data<bool>();
|
|
bool* out_ptr = out_->data<bool>();
|
|
for (int i = 0; i < num; ++i) {
|
|
bool lhs = !in_ptr[i];
|
|
bool rhs = !out_ptr[i];
|
|
out_ptr[i] = lhs && rhs;
|
|
}
|
|
}
|
|
|
|
void VisitorImpl(
|
|
const platform::CUDAPinnedPlace& cpu /* equals to cpu*/) const {
|
|
int num = in_.numel();
|
|
const bool* in_ptr = in_.data<bool>();
|
|
bool* out_ptr = out_->data<bool>();
|
|
for (int i = 0; i < num; ++i) {
|
|
bool lhs = !in_ptr[i];
|
|
bool rhs = !out_ptr[i];
|
|
out_ptr[i] = lhs && rhs;
|
|
}
|
|
}
|
|
};
|
|
|
|
void TensorIsfinite(const framework::Tensor& tensor, framework::Tensor* out) {
|
|
framework::Tensor tmp;
|
|
TensorContainsInf(tensor, &tmp);
|
|
TensorContainsNAN(tensor, out);
|
|
BothFalseVisitor visitor(tmp, out);
|
|
auto place = tensor.place();
|
|
platform::VisitPlace(place, visitor);
|
|
}
|
|
|
|
void TensorIsfiniteV2(const framework::Tensor& tensor, framework::Tensor* out) {
|
|
framework::Tensor tmp;
|
|
TensorContainsInfV2(tensor, &tmp);
|
|
TensorContainsNANV2(tensor, out);
|
|
BothFalseVisitor visitor(tmp, out);
|
|
auto place = tensor.place();
|
|
platform::VisitPlace(place, visitor);
|
|
}
|
|
|
|
void TensorToStream(std::ostream& os, const Tensor& tensor,
|
|
const platform::DeviceContext& dev_ctx) {
|
|
{ // the 1st field, uint32_t version
|
|
constexpr uint32_t version = 0;
|
|
os.write(reinterpret_cast<const char*>(&version), sizeof(version));
|
|
}
|
|
{ // the 2nd field, tensor description
|
|
// int32_t size
|
|
// void* protobuf message
|
|
proto::VarType::TensorDesc desc;
|
|
desc.set_data_type(tensor.type());
|
|
auto dims = framework::vectorize(tensor.dims());
|
|
auto* pb_dims = desc.mutable_dims();
|
|
pb_dims->Resize(static_cast<int>(dims.size()), 0);
|
|
std::copy(dims.begin(), dims.end(), pb_dims->begin());
|
|
int32_t size = desc.ByteSize();
|
|
os.write(reinterpret_cast<const char*>(&size), sizeof(size));
|
|
auto out = desc.SerializeAsString();
|
|
os.write(out.data(), size);
|
|
}
|
|
{ // the 3rd field, tensor data
|
|
uint64_t size = tensor.numel() * framework::SizeOfType(tensor.type());
|
|
|
|
auto* data_ptr = tensor.data<void>();
|
|
PADDLE_ENFORCE_LT(size, (std::numeric_limits<std::streamsize>::max)(),
|
|
platform::errors::ResourceExhausted(
|
|
"tensor size %d overflow when writing tensor", size));
|
|
if (platform::is_gpu_place(tensor.place())) {
|
|
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
|
|
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
|
|
std::unique_ptr<char[]> buf(new char[kBufSize]);
|
|
auto& gpu_dev_ctx =
|
|
static_cast<const platform::CUDADeviceContext&>(dev_ctx);
|
|
platform::CPUPlace cpu;
|
|
uintptr_t data = reinterpret_cast<uintptr_t>(data_ptr);
|
|
while (size != 0) {
|
|
size_t size_to_write = std::min(kBufSize, static_cast<size_t>(size));
|
|
memory::Copy(cpu, buf.get(),
|
|
BOOST_GET_CONST(platform::CUDAPlace, tensor.place()),
|
|
reinterpret_cast<const void*>(data), size_to_write,
|
|
gpu_dev_ctx.stream());
|
|
gpu_dev_ctx.Wait();
|
|
os.write(buf.get(), size_to_write);
|
|
data += size_to_write;
|
|
size -= size_to_write;
|
|
}
|
|
#else
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"CUDAPlace is not supported when not compiled with CUDA"));
|
|
#endif
|
|
} else if (platform::is_xpu_place(tensor.place())) {
|
|
#ifdef PADDLE_WITH_XPU
|
|
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
|
|
std::unique_ptr<char[]> buf(new char[kBufSize]);
|
|
auto& xpu_dev_ctx =
|
|
static_cast<const platform::XPUDeviceContext&>(dev_ctx);
|
|
platform::CPUPlace cpu;
|
|
uintptr_t data = reinterpret_cast<uintptr_t>(data_ptr);
|
|
while (size != 0) {
|
|
size_t size_to_write = std::min(kBufSize, static_cast<size_t>(size));
|
|
memory::Copy(cpu, buf.get(),
|
|
BOOST_GET_CONST(platform::XPUPlace, tensor.place()),
|
|
reinterpret_cast<const void*>(data), size_to_write);
|
|
xpu_dev_ctx.Wait();
|
|
os.write(buf.get(), size_to_write);
|
|
data += size_to_write;
|
|
size -= size_to_write;
|
|
}
|
|
#else
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"XPUPlace is not supported when not compiled with XPU"));
|
|
#endif
|
|
} else {
|
|
os.write(static_cast<const char*>(data_ptr),
|
|
static_cast<std::streamsize>(size));
|
|
}
|
|
}
|
|
}
|
|
|
|
struct DeserializedDataFunctor {
|
|
DeserializedDataFunctor(void** buf, Tensor* tensor,
|
|
const platform::Place& place)
|
|
: buf_(buf), tensor_(tensor), place_(place) {}
|
|
|
|
template <typename T>
|
|
void apply() {
|
|
*buf_ = tensor_->mutable_data<T>(place_);
|
|
}
|
|
|
|
void** buf_;
|
|
Tensor* tensor_;
|
|
platform::Place place_;
|
|
};
|
|
|
|
void TensorFromStream(std::istream& is, Tensor* tensor,
|
|
const platform::DeviceContext& dev_ctx,
|
|
const size_t& seek, const std::vector<int64_t>& shape) {
|
|
uint32_t version;
|
|
is.read(reinterpret_cast<char*>(&version), sizeof(version));
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
version, 0U,
|
|
platform::errors::InvalidArgument(
|
|
"tensor version %u is not supported, Only version 0 is supported",
|
|
version));
|
|
|
|
proto::VarType::TensorDesc desc;
|
|
{ // int32_t size
|
|
// proto buffer
|
|
int32_t size;
|
|
is.read(reinterpret_cast<char*>(&size), sizeof(size));
|
|
std::unique_ptr<char[]> buf(new char[size]);
|
|
is.read(reinterpret_cast<char*>(buf.get()), size);
|
|
PADDLE_ENFORCE_EQ(
|
|
desc.ParseFromArray(buf.get(), size), true,
|
|
platform::errors::InvalidArgument("Cannot parse tensor desc"));
|
|
}
|
|
{ // read tensor
|
|
tensor->Resize(framework::make_ddim(shape));
|
|
size_t seekg = seek * framework::SizeOfType(desc.data_type());
|
|
is.seekg(seekg, is.cur);
|
|
|
|
void* buf;
|
|
auto ctx = platform::CPUDeviceContext();
|
|
size_t size = tensor->numel() * framework::SizeOfType(desc.data_type());
|
|
if (platform::is_gpu_place(dev_ctx.GetPlace()) ||
|
|
platform::is_xpu_place(dev_ctx.GetPlace())) {
|
|
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) || \
|
|
defined(PADDLE_WITH_XPU)
|
|
Tensor cpu_tensor;
|
|
cpu_tensor.Resize(framework::make_ddim(shape));
|
|
framework::VisitDataType(
|
|
desc.data_type(),
|
|
DeserializedDataFunctor(&buf, &cpu_tensor, ctx.GetPlace()));
|
|
is.read(static_cast<char*>(buf), size);
|
|
auto dst_place = dev_ctx.GetPlace();
|
|
framework::TensorCopy(cpu_tensor, dst_place, dev_ctx, tensor);
|
|
#else
|
|
if (platform::is_gpu_place(dev_ctx.GetPlace())) {
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"CUDAPlace is not supported when not compiled with CUDA"));
|
|
} else {
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"XPUPlace is not supported when not compiled with XPU"));
|
|
}
|
|
#endif
|
|
} else {
|
|
framework::VisitDataType(
|
|
desc.data_type(),
|
|
DeserializedDataFunctor(&buf, tensor, ctx.GetPlace()));
|
|
is.read(static_cast<char*>(buf), size);
|
|
}
|
|
}
|
|
}
|
|
|
|
void TensorFromStream(std::istream& is, Tensor* tensor,
|
|
const platform::DeviceContext& dev_ctx) {
|
|
uint32_t version;
|
|
is.read(reinterpret_cast<char*>(&version), sizeof(version));
|
|
PADDLE_ENFORCE_EQ(
|
|
version, 0U,
|
|
platform::errors::InvalidArgument(
|
|
"tensor version %u is not supported, Only version 0 is supported",
|
|
version));
|
|
proto::VarType::TensorDesc desc;
|
|
{ // int32_t size
|
|
// proto buffer
|
|
int32_t size;
|
|
is.read(reinterpret_cast<char*>(&size), sizeof(size));
|
|
std::unique_ptr<char[]> buf(new char[size]);
|
|
is.read(reinterpret_cast<char*>(buf.get()), size);
|
|
PADDLE_ENFORCE_EQ(
|
|
desc.ParseFromArray(buf.get(), size), true,
|
|
platform::errors::InvalidArgument("Cannot parse tensor desc"));
|
|
}
|
|
{ // read tensor
|
|
std::vector<int64_t> dims;
|
|
dims.reserve(static_cast<size_t>(desc.dims().size()));
|
|
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
|
|
tensor->Resize(framework::make_ddim(dims));
|
|
void* buf;
|
|
auto ctx = platform::CPUDeviceContext();
|
|
size_t size = tensor->numel() * framework::SizeOfType(desc.data_type());
|
|
if (platform::is_gpu_place(dev_ctx.GetPlace()) ||
|
|
platform::is_xpu_place(dev_ctx.GetPlace())) {
|
|
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) || \
|
|
defined(PADDLE_WITH_XPU)
|
|
Tensor cpu_tensor;
|
|
cpu_tensor.Resize(framework::make_ddim(dims));
|
|
framework::VisitDataType(
|
|
desc.data_type(),
|
|
DeserializedDataFunctor(&buf, &cpu_tensor, ctx.GetPlace()));
|
|
is.read(static_cast<char*>(buf), size);
|
|
auto dst_place = dev_ctx.GetPlace();
|
|
framework::TensorCopy(cpu_tensor, dst_place, dev_ctx, tensor);
|
|
#else
|
|
if (platform::is_gpu_place(dev_ctx.GetPlace())) {
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"CUDAPlace is not supported when not compiled with CUDA"));
|
|
} else {
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"XPUPlace is not supported when not compiled with XPU"));
|
|
}
|
|
#endif
|
|
} else {
|
|
framework::VisitDataType(
|
|
desc.data_type(),
|
|
DeserializedDataFunctor(&buf, tensor, ctx.GetPlace()));
|
|
is.read(static_cast<char*>(buf), size);
|
|
}
|
|
}
|
|
}
|
|
|
|
// get tensor data point by DLDataType
|
|
void* GetDstPtrByDLDataType(DLDataType type, framework::Tensor* dst,
|
|
const platform::Place& dst_place) {
|
|
// vector types not currently supported
|
|
PADDLE_ENFORCE_LE(type.lanes, 1,
|
|
platform::errors::Unimplemented(
|
|
"Vector type is not supported currently."));
|
|
|
|
switch (type.bits) {
|
|
case 8:
|
|
if (type.code == kDLInt)
|
|
return static_cast<void*>(dst->mutable_data<int8_t>(dst_place));
|
|
if (type.code == kDLUInt)
|
|
return static_cast<void*>(dst->mutable_data<uint8_t>(dst_place));
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
|
|
type.code, type.bits));
|
|
case 16:
|
|
if (type.code == kDLInt)
|
|
return static_cast<void*>(dst->mutable_data<int16_t>(dst_place));
|
|
if (type.code == kDLFloat)
|
|
return static_cast<void*>(
|
|
dst->mutable_data<paddle::platform::float16>(dst_place));
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
|
|
type.code, type.bits));
|
|
case 32:
|
|
if (type.code == kDLInt)
|
|
return static_cast<void*>(dst->mutable_data<int32_t>(dst_place));
|
|
if (type.code == kDLFloat)
|
|
return static_cast<void*>(dst->mutable_data<float>(dst_place));
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
|
|
type.code, type.bits));
|
|
case 64:
|
|
if (type.code == kDLInt)
|
|
return static_cast<void*>(dst->mutable_data<int64_t>(dst_place));
|
|
if (type.code == kDLFloat)
|
|
return static_cast<void*>(dst->mutable_data<double>(dst_place));
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"DLDataType code <%d> is illegal when DLDataType.bits is <%d>.",
|
|
type.code, type.bits));
|
|
default:
|
|
PADDLE_THROW(platform::errors::Unimplemented(
|
|
"Unsupported DLDataType.bits %d.", type.bits));
|
|
}
|
|
}
|
|
|
|
void TensorFromDLPack(const ::DLTensor& dl_tensor, framework::Tensor* dst) {
|
|
platform::CPUPlace dst_place = platform::CPUPlace();
|
|
platform::CPUPlace src_place = platform::CPUPlace();
|
|
|
|
std::vector<int64_t> vec;
|
|
std::copy(dl_tensor.shape, dl_tensor.shape + dl_tensor.ndim,
|
|
std::back_inserter(vec));
|
|
|
|
framework::DDim vddim = framework::make_ddim(vec);
|
|
|
|
dst->Resize(vddim);
|
|
::DLDataType type = dl_tensor.dtype;
|
|
void* dst_ptr = GetDstPtrByDLDataType(type, dst, dst_place);
|
|
|
|
auto src_ptr = static_cast<const void*>(dl_tensor.data);
|
|
auto size = paddle::framework::product(vddim) * type.bits / 8;
|
|
|
|
if (dl_tensor.ctx.device_type == kDLCPU) {
|
|
memory::Copy(dst_place, dst_ptr, src_place, src_ptr, size);
|
|
}
|
|
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
|
|
if (dl_tensor.ctx.device_type == kDLGPU) {
|
|
platform::CUDAPlace dst_place =
|
|
platform::CUDAPlace(dl_tensor.ctx.device_id);
|
|
platform::CUDAPlace src_place =
|
|
platform::CUDAPlace(dl_tensor.ctx.device_id);
|
|
dst_ptr = GetDstPtrByDLDataType(type, dst, dst_place);
|
|
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(dst_place);
|
|
memory::Copy(
|
|
dst_place, dst_ptr, src_place, src_ptr, size,
|
|
reinterpret_cast<const platform::CUDADeviceContext&>(*ctx).stream());
|
|
}
|
|
#endif
|
|
#ifdef PADDLE_WITH_XPU
|
|
PADDLE_THROW(platform::errors::Unimplemented("XPUPlace is not supported"));
|
|
#endif
|
|
}
|
|
|
|
template <typename T>
|
|
std::string format_tensor(const framework::Tensor& tensor) {
|
|
// TODO(zhiqiu): use the print option to format tensor.
|
|
return "NOT IMPLEMENTED";
|
|
}
|
|
|
|
template <typename T>
|
|
std::ostream& print_tensor(std::ostream& os, const framework::Tensor& tensor) {
|
|
auto inspect = tensor.data<T>();
|
|
auto element_num = tensor.numel();
|
|
|
|
os << " - data: [";
|
|
// Note: int8_t && uint8_t is typedf of char, ostream unable to print properly
|
|
if (typeid(int8_t) == typeid(T) || typeid(uint8_t) == typeid(T)) {
|
|
if (element_num > 0) {
|
|
os << signed(inspect[0]);
|
|
for (int j = 1; j < element_num; ++j) {
|
|
os << " " << signed(inspect[j]);
|
|
}
|
|
}
|
|
} else {
|
|
if (element_num > 0) {
|
|
os << inspect[0];
|
|
for (int j = 1; j < element_num; ++j) {
|
|
os << " " << inspect[j];
|
|
}
|
|
}
|
|
}
|
|
os << "]";
|
|
return os;
|
|
}
|
|
|
|
template <>
|
|
std::ostream& print_tensor<paddle::platform::complex64>(
|
|
std::ostream& os, const framework::Tensor& tensor) {
|
|
auto inspect = tensor.data<paddle::platform::complex64>();
|
|
auto element_num = tensor.numel();
|
|
|
|
os << " - data: [";
|
|
if (element_num > 0) {
|
|
os << signed(inspect[0].real) << "+" << signed(inspect[0].imag) << "j";
|
|
for (int j = 1; j < element_num; ++j) {
|
|
os << " " << signed(inspect[j].real) << "+" << signed(inspect[j].imag)
|
|
<< "j";
|
|
}
|
|
}
|
|
os << "]";
|
|
return os;
|
|
}
|
|
|
|
template <>
|
|
std::ostream& print_tensor<paddle::platform::complex128>(
|
|
std::ostream& os, const framework::Tensor& tensor) {
|
|
auto inspect = tensor.data<paddle::platform::complex128>();
|
|
auto element_num = tensor.numel();
|
|
|
|
os << " - data: [";
|
|
if (element_num > 0) {
|
|
os << signed(inspect[0].real) << "+" << signed(inspect[0].imag) << "j";
|
|
for (int j = 1; j < element_num; ++j) {
|
|
os << " " << signed(inspect[j].real) << "+" << signed(inspect[j].imag)
|
|
<< "j";
|
|
}
|
|
}
|
|
os << "]";
|
|
return os;
|
|
}
|
|
|
|
std::ostream& operator<<(std::ostream& os, const Tensor& t) {
|
|
os << " - place: " << t.place() << "\n";
|
|
os << " - shape: [" << t.dims() << "]\n";
|
|
os << " - layout: " << DataLayoutToString(t.layout()) << "\n";
|
|
|
|
Tensor tensor;
|
|
tensor.Resize(t.dims());
|
|
if (platform::is_cpu_place(t.place())) {
|
|
tensor.ShareDataWith(t);
|
|
} else {
|
|
platform::CPUPlace place;
|
|
framework::TensorCopy(t, place, &tensor);
|
|
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
|
|
auto& dev_ctx = *pool.Get(t.place());
|
|
dev_ctx.Wait();
|
|
}
|
|
|
|
#define PrintTensorCallback(cpp_type, proto_type) \
|
|
do { \
|
|
if (tensor.type() == proto_type) { \
|
|
os << " - dtype: " << proto_type << "\n"; \
|
|
print_tensor<cpp_type>(os, tensor); \
|
|
return os; \
|
|
} \
|
|
} while (0)
|
|
|
|
_ForEachDataType_(PrintTensorCallback);
|
|
VLOG(1) << "PrintVar: unrecognized data type:" << t.type();
|
|
return os;
|
|
}
|
|
|
|
} // namespace framework
|
|
} // namespace paddle
|