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update config
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@baoachun
baoachun committed Dec 30, 2021
1 parent 7d02765 commit 1948797
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Showing 3 changed files with 199 additions and 51 deletions.
207 changes: 160 additions & 47 deletions paddle/fluid/framework/ir/mkldnn/quant_dequant_mkldnn_fuse_pass.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@
#include <string>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/enforce.h"

namespace paddle {
namespace framework {
Expand Down Expand Up @@ -55,7 +54,7 @@ void QuantDequantMkldnnFusePass::MarkSkipQuantizedOps(
void QuantDequantMkldnnFusePass::GatherInfoFromFake(
ir::Graph* graph, Scope* scope,
std::unordered_set<std::string> fake_dequantize_types,
std::unordered_map<std::string, std::vector<float>> weight_thresholds)
std::unordered_map<std::string, std::vector<float>>& weight_thresholds)
const {
for (auto* op_node :
ir::TopologyVarientSort(*graph, static_cast<ir::SortKind>(0))) {
Expand All @@ -65,15 +64,19 @@ void QuantDequantMkldnnFusePass::GatherInfoFromFake(
auto* op_desc = op_node->Op();
auto x_var_name = op_desc->Input("X")[0];
if (op_desc->HasAttr("max_range")) {
float max_range = BOOST_GET_CONST(float, op_desc->GetAttr("max_range"));
const float max_range =
BOOST_GET_CONST(float, op_desc->GetAttr("max_range"));
weight_thresholds[x_var_name].push_back(127 * 127 / max_range);
} else {
auto scale_name = op_desc->Input("Scales")[0];
// scope->FindVar(scale_name)判空？
const LoDTensor& scale_tensor =
scope->FindVar(scale_name)->Get<LoDTensor>();
const float* scale_data = scale_tensor.data<float>();
for (int i = 0; i < scale_tensor.numel(); i++) {
auto* var = scope->FindVar(scale_name);
PADDLE_ENFORCE_NOT_NULL(
var, "The Scales variable of dequantize op is not found.");

auto* scale_tensor = var->GetMutable<LoDTensor>();
auto scale_data =
scale_tensor->mutable_data<float>(platform::CPUPlace());
for (int i = 0; i < scale_tensor->numel(); i++) {
weight_thresholds[x_var_name].push_back(scale_data[i]);
}
}
Expand All @@ -84,7 +87,7 @@ void QuantDequantMkldnnFusePass::GatherInfoFromFake(
void QuantDequantMkldnnFusePass::GatherInputScalesFromFake(
ir::Graph* graph, Scope* scope,
std::unordered_set<std::string> fake_quantize_types,
std::unordered_map<std::string, std::pair<int, std::vector<float>>>
std::unordered_map<std::string, std::pair<int, std::vector<float>>>&
var_quant_scales) const {
for (auto* op_node :
ir::TopologyVarientSort(*graph, static_cast<ir::SortKind>(0))) {
Expand All @@ -93,7 +96,8 @@ void QuantDequantMkldnnFusePass::GatherInputScalesFromFake(
if (op_node->Name() == "fake_quantize_dequantize_moving_average_abs_max" ||
fake_quantize_types.count(op_node->Name())) {
auto* op_desc = op_node->Op();
int bit_length = BOOST_GET_CONST(int, op_desc->GetAttr("bit_length"));
const int bit_length =
BOOST_GET_CONST(int, op_desc->GetAttr("bit_length"));
PADDLE_ENFORCE_EQ(bit_length, 8, platform::errors::InvalidArgument(
"Unsupported number quantization "
"bits: %d, only 8 is supported now.",
Expand All @@ -102,7 +106,11 @@ void QuantDequantMkldnnFusePass::GatherInputScalesFromFake(
auto x_var_name = op_desc->Input("X")[0];
auto scale_name = op_desc->Input("InScale")[0];
auto out_var_name = op_desc->Output("Out")[0];
auto* scale_tensor = scope->FindVar(scale_name)->GetMutable<LoDTensor>();
auto* var = scope->FindVar(scale_name);
PADDLE_ENFORCE_NOT_NULL(
var, "The InScale variable of quantize op is not found.");

auto* scale_tensor = var->GetMutable<LoDTensor>();
auto scale_data = scale_tensor->mutable_data<float>(platform::CPUPlace());
float scale = 1.0 / scale_data[0];

Expand All @@ -123,15 +131,15 @@ void QuantDequantMkldnnFusePass::GatherInputScalesFromFake(

void QuantDequantMkldnnFusePass::GatherOutputScalesFromAttr(
ir::Graph* graph,
std::unordered_map<std::string, std::pair<int, std::vector<float>>>
std::unordered_map<std::string, std::pair<int, std::vector<float>>>&
var_quant_scales) const {
for (auto* op_node :
ir::TopologyVarientSort(*graph, static_cast<ir::SortKind>(0))) {
if (!op_node->IsOp()) continue;

auto* op_desc = op_node->Op();
if (op_desc->HasAttr("out_threshold")) {
float attr_scale =
const float attr_scale =
BOOST_GET_CONST(float, op_desc->GetAttr("out_threshold"));
if (attr_scale == 0.0) continue;
float scale = 1.0 / attr_scale;
Expand Down Expand Up @@ -180,6 +188,10 @@ void QuantDequantMkldnnFusePass::RemoveFakeOps(
}
}

PADDLE_ENFORCE_NOT_NULL(fake_quant_in,
"The input var of quantize op is not found.");
PADDLE_ENFORCE_NOT_NULL(fake_quant_out,
"The output var of quantize op is not found.");
std::string input_act_name = fake_quant_in->Var()->Name();
std::string output_act_name = fake_quant_out->Var()->Name();
auto outlinks = fake_quant_out->outputs;
Expand Down Expand Up @@ -215,6 +227,10 @@ void QuantDequantMkldnnFusePass::RemoveFakeOps(
}
}

PADDLE_ENFORCE_NOT_NULL(fake_dequant_in,
"The input var of dequantize op is not found.");
PADDLE_ENFORCE_NOT_NULL(fake_dequant_out,
"The output var of dequantize op is not found.");
std::string input_act_name = fake_dequant_in->Var()->Name();
std::string output_act_name = fake_dequant_out->Var()->Name();
auto outlinks = fake_dequant_out->outputs;
Expand Down Expand Up @@ -246,65 +262,160 @@ void QuantDequantMkldnnFusePass::RemoveFakeOps(

void QuantDequantMkldnnFusePass::DequantizeWeights(
ir::Graph* graph, Scope* scope,
std::unordered_map<std::string, std::vector<float>> weight_thresholds)
std::unordered_map<std::string, std::vector<float>>& weight_thresholds)
const {
auto is_int8_weights = [&](Node* op_node, Scope* scope,
std::string weight_name) -> bool {
auto* op_desc = op_node->Op();
auto var_name = op_desc->Input(weight_name)[0];
std::cout << "var_name: " << var_name << std::endl;
if (scope->FindVar(var_name) == nullptr) {
std::cout << "eeeeeeeeeeeeeee" << std::endl;
return false;
auto* var = scope->FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(var,
"The input persistable var of %s op is not found.",
op_desc->Type());

auto* weight_tensor = var->GetMutable<LoDTensor>();
auto weight_data = weight_tensor->mutable_data<float>(platform::CPUPlace());
bool is_int8 = true;
for (int i = 0; i < weight_tensor->numel(); i++) {
if (weight_data[i] - static_cast<int>(weight_data[i]) != 0) {
is_int8 = false;
break;
}
}
return is_int8;
};

auto transpose_weight = [&](Tensor* input) {
const auto input_dims = input->dims();
std::vector<int> orders;
for (int i = input_dims.size() - 1; i >= 0; i--) {
orders.push_back(i);
}

Tensor trans_tensor;
trans_tensor.Resize(input_dims);
float* trans_data = trans_tensor.mutable_data<float>(platform::CPUPlace());
float* in_data = input->mutable_data<float>(platform::CPUPlace());

auto in_dims = input->dims();
auto out_dims = trans_tensor.dims();
int num_axes = in_dims.size();
int count = 1;
for (int i = 0; i < num_axes; i++) {
count *= in_dims[i];
}

std::vector<int> old_steps(
{static_cast<int>(in_dims[1] * in_dims[2] * in_dims[3]),
static_cast<int>(in_dims[2] * in_dims[3]),
static_cast<int>(in_dims[3]), 1});
std::vector<int> new_steps(
{static_cast<int>(out_dims[1] * out_dims[2] * out_dims[3]),
static_cast<int>(out_dims[2] * out_dims[3]),
static_cast<int>(out_dims[3]), 1});

for (int i = 0; i < count; ++i) {
int old_idx = 0;
int idx = i;
for (int j = 0; j < num_axes; ++j) {
int order = orders[j];
old_idx += (idx / new_steps[j]) * old_steps[order];
idx %= new_steps[j];
}
trans_data[i] = in_data[old_idx];
}

for (int i = 0; i < input->numel(); i++) {
in_data[i] = trans_data[i];
}
auto* weight_tensor = scope->FindVar(var_name)->GetMutable<LoDTensor>();
return weight_tensor->type() == framework::proto::VarType::INT8;
};

auto dequantize_op_weights = [&](Node* op_node, Scope* scope,
std::string weight_name,
std::string output_name) {
auto dequantize_op_weights = [&](
Node* op_node, Scope* scope, std::string weight_name,
std::string output_name,
std::unordered_map<std::string, std::vector<float>>& weight_thresholds) {
auto* op_desc = op_node->Op();
auto weight_var_name = op_desc->Input(weight_name)[0];
auto output_var_name = op_desc->Output(output_name)[0];
std::string weight_var_name = op_desc->Input(weight_name)[0];
std::string output_var_name = op_desc->Output(output_name)[0];
std::vector<float> scales = weight_thresholds[output_var_name];
auto* weight_tensor =
scope->FindVar(weight_var_name)->GetMutable<LoDTensor>();
const auto weight_dims = weight_tensor->dims();

const int size = scales.size();
if (size == 1 || size == weight_dims[0]) {
auto weight_data =
weight_tensor->mutable_data<float>(platform::CPUPlace());
for (int i = 0; i < weight_tensor->numel(); i++) {
weight_data[i] /= 127;
}

transpose_weight(weight_tensor);

int size = scales.size();
if (size == 1 || size == weight_tensor->dims()[0]) {
if (size == 1) {
for (int i = 0; i < weight_tensor->numel(); i++) {
weight_data[i] *= scales[0];
}
} else {
int step = 1;
for (int i = 1; i < weight_dims.size(); i++) {
step *= weight_dims[i];
}

for (int i = 0; i < size; i++) {
int begin = i * step;
for (int j = begin; j < begin + step; j++) {
weight_data[j] *= scales[i];
}
}
}

transpose_weight(weight_tensor);
} else if (weight_dims.size() > 1 && size == weight_dims[1]) {
auto weight_data =
weight_tensor->mutable_data<int8_t>(platform::CPUPlace());
for (int i = 0; i < weight_tensor->numel(); i++) {
weight_data[i] /= 127;
}
// } else if (weight_tensor->dims().size() > 1 && scales.size() ==
// weight_tensor->dims()[1]) {

int step_n = 1;
for (int i = 1; i < weight_dims.size(); i++) {
step_n *= weight_dims[i];
}
int step_c = step_n / size;
for (int i = 0; i < weight_dims[0]; i++) {
int begin_n = i * step_n;
for (int j = begin_n; j < begin_n + step_n; j++) {
for (int k = 0; k < size; k++) {
int begin_c = k * step_c;
for (int m = begin_c; m < begin_c + step_c; m++) {
weight_data[m] *= scales[k];
}
}
}
}
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The size of weight scales vector (%d) does not "
"match the dimensions (%d) of the weights tensor %s.",
size, weight_tensor->dims().size(), weight_var_name));
}

weight_tensor->Resize(weight_dims);
};

std::cout << "scope11111: " << static_cast<void*>(scope) << std::endl;
for (auto* op_node :
ir::TopologyVarientSort(*graph, static_cast<ir::SortKind>(0))) {
if (!op_node->IsOp()) continue;
std::cout << "8888888888" << std::endl;
if (op_node->Name() == "conv2d" || op_node->Name() == "depthwise_conv2d") {
std::cout << "9999999999" << std::endl;
if (is_int8_weights(op_node, scope, "Filter")) {
std::cout << "95555555555" << std::endl;
dequantize_op_weights(op_node, scope, "Filter", "Output");
dequantize_op_weights(op_node, scope, "Filter", "Output",
weight_thresholds);
}
} else if (op_node->Name() == "mul" || op_node->Name() == "matmul" ||
op_node->Name() == "matmul_v2") {
std::cout << "qqqqqqqqq" << std::endl;
if (is_int8_weights(op_node, scope, "Y")) {
dequantize_op_weights(op_node, scope, "Y", "Out");
dequantize_op_weights(op_node, scope, "Y", "Out", weight_thresholds);
}
}
}
Expand All @@ -320,10 +431,11 @@ void QuantDequantMkldnnFusePass::UpdateActivations(ir::Graph* graph) const {
if (!op_desc->HasAttr("fuse_activation")) {
std::string activation;
if (op_desc->HasAttr("fuse_relu")) {
bool fuse_relu = BOOST_GET_CONST(bool, op_desc->GetAttr("fuse_relu"));
const bool fuse_relu =
BOOST_GET_CONST(bool, op_desc->GetAttr("fuse_relu"));
if (fuse_relu) activation = "relu";
} else if (op_desc->HasAttr("fuse_brelu")) {
bool fuse_brelu =
const bool fuse_brelu =
BOOST_GET_CONST(bool, op_desc->GetAttr("fuse_relu"));
if (fuse_brelu) {
activation = "relu6";
Expand Down Expand Up @@ -354,6 +466,7 @@ void QuantDequantMkldnnFusePass::RemoveCtrlVars(ir::Graph* graph) const {
}

void QuantDequantMkldnnFusePass::ApplyImpl(ir::Graph* graph) const {
VLOG(3) << "Convert paddle slim quantized model to mkldnn quantized model.";
const std::string pattern_name = "quant_dequant_mkldnn_fuse_pass";
FusePassBase::Init(pattern_name, graph);

Expand All @@ -373,24 +486,24 @@ void QuantDequantMkldnnFusePass::ApplyImpl(ir::Graph* graph) const {
var_quant_scales;

auto* scope = param_scope();
std::cout << "scope: " << static_cast<void*>(scope) << std::endl;

MarkSkipQuantizedOps(graph, skip_ops);
std::cout << "11111111" << std::endl;
GatherInfoFromFake(graph, scope, fake_dequantize_types, weight_thresholds);
std::cout << "2222222" << std::endl;
for (auto iter = weight_thresholds.begin(); iter != weight_thresholds.end();
++iter) {
std::cout << iter->first << std::endl;
}
std::cout << "111111" << std::endl;
GatherInputScalesFromFake(graph, scope, fake_quantize_types,
var_quant_scales);
std::cout << "333333333" << std::endl;
GatherOutputScalesFromAttr(graph, var_quant_scales);
std::cout << "444444444" << std::endl;
for (auto iter = var_quant_scales.begin(); iter != var_quant_scales.end();
++iter) {
std::cout << iter->first << std::endl;
}
RemoveFakeOps(graph, fake_quantize_types, fake_dequantize_types,
fake_quantize_dequantize_types);
std::cout << "555555555" << std::endl;
DequantizeWeights(graph, scope, weight_thresholds);
std::cout << "666666666" << std::endl;
UpdateActivations(graph);
std::cout << "77777777" << std::endl;
RemoveCtrlVars(graph);
}

Expand Down
8 changes: 4 additions & 4 deletions paddle/fluid/framework/ir/mkldnn/quant_dequant_mkldnn_fuse_pass.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -35,18 +35,18 @@ class QuantDequantMkldnnFusePass : public FusePassBase {

void GatherInfoFromFake(ir::Graph* graph, Scope* scope,
std::unordered_set<std::string> fake_dequantize_types,
std::unordered_map<std::string, std::vector<float>>
std::unordered_map<std::string, std::vector<float>>&
weight_thresholds) const;

void GatherInputScalesFromFake(
ir::Graph* graph, Scope* scope,
std::unordered_set<std::string> fake_quantize_types,
std::unordered_map<std::string, std::pair<int, std::vector<float>>>
std::unordered_map<std::string, std::pair<int, std::vector<float>>>&
var_quant_scales) const;

void GatherOutputScalesFromAttr(
ir::Graph* graph,
std::unordered_map<std::string, std::pair<int, std::vector<float>>>
std::unordered_map<std::string, std::pair<int, std::vector<float>>>&
var_quant_scales) const;

void RemoveFakeOps(
Expand All @@ -55,7 +55,7 @@ class QuantDequantMkldnnFusePass : public FusePassBase {
std::unordered_set<std::string> fake_quantize_dequantize_types) const;

void DequantizeWeights(ir::Graph* graph, Scope* scope,
std::unordered_map<std::string, std::vector<float>>
std::unordered_map<std::string, std::vector<float>>&
weight_thresholds) const;

void UpdateActivations(ir::Graph* graph) const;
Expand Down
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2 comments on commit 1948797

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@paddle-bot-old paddle-bot-old bot commented on 1948797 Dec 30, 2021 •
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🕵️ CI failures summary

🔍 PR: #38437 Commit ID: 1948797 contains failed CI.

🔹 Failed: PR-CI-APPROVAL

approve_failed
2021-12-30 14:27:12 ****************

2021-12-30 14:27:12 0. The error message you wrote in PADDLE_ENFORCE{**} or PADDLE_THROW does not meet our error message writing specification. Possible errors include 1. the error message is empty / 2. the error message is too short / 3. the error type is not specified. Please read the specification [ https://github.com/PaddlePaddle/Paddle/wiki/Paddle-Error-Message-Writing-Specification ], then refine the error message. If it is a mismatch, please request chenwhql (Recommend), luotao1 or lanxianghit review and approve.

2021-12-30 14:27:12 The PADDLE_ENFORCE{**} or PADDLE_THROW entries that do not meet the specification are as follows:

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL( + var, "The Scales variable of dequantize op is not found.");

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL( + var, "The InScale variable of quantize op is not found.");

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL(fake_quant_in, + "The input var of quantize op is not found.");

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL(fake_quant_out, + "The output var of quantize op is not found.");

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL(fake_dequant_in, + "The input var of dequantize op is not found.");

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL(fake_dequant_out, + "The output var of dequantize op is not found.");

2021-12-30 14:27:12 PADDLE_ENFORCE_NOT_NULL(var, + "The input persistable var of %s op is not found.", + op_desc->Type());

2021-12-30 14:27:12 There are 1 approved errors.

2021-12-30 14:27:12 ****************

2021-12-30 14:27:12 + EXCODE=6

2021-12-30 14:27:12 + echo 'EXCODE: 6'

2021-12-30 14:27:12 EXCODE: 6

2021-12-30 14:27:12 + echo 'ipipe_log_param_EXCODE: 6'

2021-12-30 14:27:12 ipipe_log_param_EXCODE: 6

2021-12-30 14:27:12 + exit 6

🔹 Failed: PR-CI-Windows-OPENBLAS

Unknown Failed
2021-12-30 15:10:36 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>taskkill /f /im git-remote-https.exe  2>NUL

2021-12-30 15:10:36 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>taskkill /f /im vctip.exe  2>NUL

2021-12-30 15:10:36 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>taskkill /f /im cvtres.exe  2>NUL

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>taskkill /f /im rc.exe  2>NUL

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>wmic process where name="op_function_generator.exe" call terminate

2021-12-30 15:10:37 No Instance(s) Available.

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>wmic process where name="python.exe" call terminate  2>NUL

2021-12-30 15:10:37 No Instance(s) Available.

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>taskkill /f /im python.exe   2>NUL

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>taskkill /f /im rc.exe  2>NUL

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>if not exist C:\home\workspace mkdir C:\home\workspace

2021-12-30 15:10:37 C:\Users\Administrator\Downloads\workspace\b18d14d7-9e78-4d7b-b3a0-12a89c7c25eb>cd /d C:\home\workspace

2021-12-30 15:10:37 C:\home\workspace>rmdir Paddle /s/q

2021-12-30 15:10:52 C:\home\workspace>git clone -b develop --depth=1000 https://github.com/PaddlePaddle/Paddle.git

2021-12-30 15:10:52 Cloning into 'Paddle'...

2021-12-30 15:21:30 fatal: the remote end hung up unexpectedly

2021-12-30 15:21:30 fatal: early EOF

2021-12-30 15:21:30 fatal: index-pack failed

2021-12-30 15:21:30 C:\home\workspace>if 128 NEQ 0 exit /b 1

🔹 Failed: PR-CI-Model-benchmark

Unknown Failed
2021-12-30 17:52:13 loss result:4.18039

2021-12-30 17:52:13 loss standard result:4.29

2021-12-30 17:52:13 ResNet50_bs32_dygraph, Precision_test, SUCCESS

2021-12-30 17:52:13 model benchmark ci job failed!

2021-12-30 17:52:13 See https://github.com/PaddlePaddle/Paddle/wiki/PR-CI-Model-benchmark-Manual for details.

2021-12-30 17:52:13 Or you can apply for one QA(xiegegege(Recommend), hysunflower) approval to pass this PR.

2021-12-30 17:52:13 + EXCODE=24

2021-12-30 17:52:13 + '[' 24 -eq 0 ']'

2021-12-30 17:52:13 + [[ 24 -eq 22 ]]

2021-12-30 17:52:13 + [[ 24 -eq 0 ]]

2021-12-30 17:52:13 + [[ 24 -eq 4 ]]

2021-12-30 17:52:13 + [[ 24 -eq 5 ]]

2021-12-30 17:52:13 + [[ 24 -eq 6 ]]

2021-12-30 17:52:13 + [[ 24 -eq 7 ]]

2021-12-30 17:52:13 + [[ 24 -eq 8 ]]

2021-12-30 17:52:13 + [[ 24 -eq 9 ]]

2021-12-30 17:52:13 + set -x

2021-12-30 17:52:13 + exit 24

2021-12-30 17:52:13 {build code state=24}

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🕵️ CI failures summary

🔍 PR: #38437 Commit ID: 1948797 contains failed CI.

🔹 Failed: PR-CI-Windows

build_failed
2021-12-30 14:27:43 Build Paddle failed, will retry

2021-12-30 14:27:44 没有可用实例。

2021-12-30 14:27:44 没有可用实例。

2021-12-30 14:27:44 没有可用实例。

2021-12-30 14:27:44 没有可用实例。

2021-12-30 14:27:44 没有可用实例。

2021-12-30 14:27:45 没有可用实例。

2021-12-30 14:27:45 Build Paddle the 3 time:

2021-12-30 14:27:45 ninja: error: 'C:/home/workspace/cache/third_party/cuda102/3c382dcb436f28a48d9cb4ad5f7e7149/install/protobuf/lib/libprotobuf.lib', needed by 'paddle/testing/small_vector_test.exe', missing and no known rule to make it

2021-12-30 14:27:45 7

2021-12-30 14:27:45 Build Paddle failed, will exit

2021-12-30 14:27:45 EXCODE: 7

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