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!2905 two level reduce sparse gradient

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Merge pull request !2905 from kisnwang/parallel-reduce-sparse-gradient
pull/2905/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 0a375743d1 a719a9fe3a
commit b82926138d
8 changed files with 140 additions and 20 deletions
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69
mindspore/ccsrc/kernel/common_utils.cc
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@ -632,6 +632,75 @@ void ReduceSparseGradient(const SparseGradient &origin_sparse_grad, SparseGradie
unique_grad->indices_size_ = slice_positions.size();
}
void ReduceMultiSparseGradient(const std::vector<std::shared_ptr<SparseGradient>> &unique_slice_grads,
SparseGradient *tmp_grad, SparseGradient *unique_grad, size_t first_dim,
size_t outer_dim) {
if (unique_slice_grads.empty()) {
return;
}
size_t index_data_size = outer_dim * sizeof(float);
size_t unique_indices_size = 0;
for (size_t i = 0; i < unique_slice_grads.size(); ++i) {
auto &slice_grad = unique_slice_grads[i];
auto ret_code = memcpy_s(tmp_grad->value_ + unique_indices_size * outer_dim,
(tmp_grad->indices_size_ - unique_indices_size) * index_data_size, slice_grad->value_,
slice_grad->indices_size_ * index_data_size);
if (ret_code != EOK) {
MS_LOG(EXCEPTION) << "Failed to copy data!";
}
ret_code =
memcpy_s(tmp_grad->indices_ + unique_indices_size, (tmp_grad->indices_size_ - unique_indices_size) * sizeof(int),
slice_grad->indices_, slice_grad->indices_size_ * sizeof(int));
if (ret_code != EOK) {
MS_LOG(EXCEPTION) << "Failed to copy data!";
}
unique_indices_size += slice_grad->indices_size_;
}
tmp_grad->indices_size_ = unique_indices_size;
ReduceSparseGradient(*tmp_grad, unique_grad, first_dim, outer_dim);
}
void TwoLevelReduceSparseGradient(const SparseGradient &origin_sparse_grad, SparseGradient *tmp_grad,
SparseGradient *unique_grad, size_t first_dim, size_t outer_dim) {
MS_EXCEPTION_IF_NULL(origin_sparse_grad.value_);
MS_EXCEPTION_IF_NULL(origin_sparse_grad.indices_);
MS_EXCEPTION_IF_NULL(unique_grad);
MS_EXCEPTION_IF_NULL(unique_grad->value_);
MS_EXCEPTION_IF_NULL(unique_grad->indices_);
MS_EXCEPTION_IF_NULL(tmp_grad);
MS_EXCEPTION_IF_NULL(tmp_grad->value_);
MS_EXCEPTION_IF_NULL(tmp_grad->indices_);
size_t thread_num = 24;
if (origin_sparse_grad.indices_size_ < thread_num) {
thread_num = origin_sparse_grad.indices_size_;
}
size_t thread_indices_size = origin_sparse_grad.indices_size_ / thread_num;
size_t left_indices_size = origin_sparse_grad.indices_size_ % thread_num;
std::vector<std::thread> threads;
threads.reserve(thread_num);
std::vector<std::shared_ptr<SparseGradient>> unique_slice_grads;
for (size_t i = 0; i < thread_num; ++i) {
size_t indices_size = thread_indices_size;
if (i == thread_num - 1) {
indices_size = thread_indices_size + left_indices_size;
}
size_t value_offset = i * thread_indices_size * outer_dim;
size_t indices_offset = i * thread_indices_size;
auto slice_grad = SparseGradient(
{origin_sparse_grad.value_ + value_offset, origin_sparse_grad.indices_ + indices_offset, indices_size});
unique_slice_grads.emplace_back(std::make_shared<SparseGradient>());
unique_slice_grads[i]->value_ = unique_grad->value_ + value_offset;
unique_slice_grads[i]->indices_ = unique_grad->indices_ + indices_offset;
unique_slice_grads[i]->indices_size_ = indices_size;
threads.emplace_back(
std::thread(ReduceSparseGradient, slice_grad, unique_slice_grads[i].get(), first_dim, outer_dim));
}
for (size_t i = 0; i < thread_num; ++i) {
threads[i].join();
}
ReduceMultiSparseGradient(unique_slice_grads, tmp_grad, unique_grad, first_dim, outer_dim);
}
std::pair<AnfNodePtr, size_t> GetKernelInput(const AnfNodePtr &anf_node, size_t index) {
MS_EXCEPTION_IF_NULL(anf_node);

5
mindspore/ccsrc/kernel/common_utils.h
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@ -130,6 +130,11 @@ void GetGraphRealOutput(const FuncGraphPtr &func_graph, std::vector<std::pair<An
bool IsWeightBoundary(const AnfNodePtr &node);
void MultiThreadCompute(const MultiThreadComputeFunc &func, MultiThreadComputeParams *params,
size_t total_compute_size);
void ReduceMultiSparseGradient(const std::vector<std::shared_ptr<SparseGradient>> &unique_slice_grads,
SparseGradient *tmp_grad, SparseGradient *unique_grad, size_t first_dim,
size_t outer_dim);
void TwoLevelReduceSparseGradient(const SparseGradient &origin_sparse_grad, SparseGradient *tmp_grad,
SparseGradient *unique_grad, size_t first_dim, size_t outer_dim);
} // namespace kernel
} // namespace mindspore

9
mindspore/ccsrc/kernel/cpu/sparse_apply_ftrl_cpu_kernel.cc
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@ -66,6 +66,8 @@ void SparseApplyFtrlCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node)
MS_EXCEPTION_IF_NULL(kernel_node);
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
}
void SparseApplyFtrlCPUKernel::InitKernel(const CNodePtr &kernel_node) {
@ -130,9 +132,12 @@ bool SparseApplyFtrlCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inp
auto indices = reinterpret_cast<int *>(inputs[4]->addr);
auto new_grad = reinterpret_cast<float *>(workspace[0]->addr);
auto new_indices = reinterpret_cast<int *>(workspace[1]->addr);
auto tmp_grad = reinterpret_cast<float *>(workspace[2]->addr);
auto tmp_indices = reinterpret_cast<int *>(workspace[3]->addr);
SparseGradient unique_sparse_grad({new_grad, new_indices, indices_size_});
ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
var_outer_dim_size_);
SparseGradient tmp_sparse_grad({tmp_grad, tmp_indices, indices_size_});
TwoLevelReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &tmp_sparse_grad, &unique_sparse_grad,
var_first_dim_size_, var_outer_dim_size_);
MultiThreadComputeParams input_params;
input_params.var_ = var;

9
mindspore/ccsrc/kernel/cpu/sparse_apply_lazy_adam_cpu_kernel.cc
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@ -61,6 +61,8 @@ void SparseApplyLazyAdamCPUKernel::InitInputOutputSize(const CNodePtr &kernel_no
MS_EXCEPTION_IF_NULL(kernel_node);
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
}
void SparseApplyLazyAdamCPUKernel::InitKernel(const CNodePtr &kernel_node) {
@ -121,10 +123,13 @@ bool SparseApplyLazyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr>
auto indices = reinterpret_cast<int *>(inputs[10]->addr);
auto new_grad = reinterpret_cast<float *>(workspace[0]->addr);
auto new_indices = reinterpret_cast<int *>(workspace[1]->addr);
auto tmp_grad = reinterpret_cast<float *>(workspace[2]->addr);
auto tmp_indices = reinterpret_cast<int *>(workspace[3]->addr);
SparseGradient unique_sparse_grad({new_grad, new_indices, indices_size_});
ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
var_outer_dim_size_);
SparseGradient tmp_sparse_grad({tmp_grad, tmp_indices, indices_size_});
TwoLevelReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &tmp_sparse_grad, &unique_sparse_grad,
var_first_dim_size_, var_outer_dim_size_);
lr = lr * std::sqrt(1 - beta2_power) / (1 - beta1_power);
MultiThreadComputeParams input_params;

17
tests/ut/cpp/kernel/cpu/sparse_apply_adam_cpu_kernel_test.cc
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@ -58,9 +58,12 @@ class SparseApplyAdamCpuKernelTest : public UT::Common {
inputs_.push_back(CreateKernelAddress(indices.data()));
}
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices, std::vector<float> &m_t) {
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices, std::vector<float> &tmp_grad,
std::vector<int> &tmp_indices, std::vector<float> &m_t) {
workspace_.push_back(CreateKernelAddress(new_grad.data()));
workspace_.push_back(CreateKernelAddress(new_indices.data()));
workspace_.push_back(CreateKernelAddress(tmp_grad.data()));
workspace_.push_back(CreateKernelAddress(tmp_indices.data()));
workspace_.push_back(CreateKernelAddress(m_t.data()));
}
@ -95,8 +98,10 @@ TEST_F(SparseApplyAdamCpuKernelTest, dense_test) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
std::vector<float> m_t(3 * 3 * 3);
CreateWorkspaceAddress(new_grad, new_indices, m_t);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices, m_t);
sparse_adam_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.999684) < 1e-6);
@ -120,8 +125,10 @@ TEST_F(SparseApplyAdamCpuKernelTest, sparse_test1) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
std::vector<float> m_t(3 * 3 * 3);
CreateWorkspaceAddress(new_grad, new_indices, m_t);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices, m_t);
sparse_adam_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.999684) < 1e-6);
@ -149,8 +156,10 @@ TEST_F(SparseApplyAdamCpuKernelTest, sparse_test2) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
std::vector<float> m_t(3 * 3 * 3);
CreateWorkspaceAddress(new_grad, new_indices, m_t);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices, m_t);
sparse_adam_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.999715) < 1e-6);

17
tests/ut/cpp/kernel/cpu/sparse_apply_ftrl_cpu_kernel_test.cc
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@ -56,9 +56,12 @@ class SparseApplyFtrlCpuKernelTest : public UT::Common {
inputs_.push_back(CreateKernelAddress(indices.data()));
}
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices) {
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices, std::vector<float> &tmp_grad,
std::vector<int> &tmp_indices) {
workspace_.push_back(CreateKernelAddress(new_grad.data()));
workspace_.push_back(CreateKernelAddress(new_indices.data()));
workspace_.push_back(CreateKernelAddress(tmp_grad.data()));
workspace_.push_back(CreateKernelAddress(tmp_indices.data()));
}
std::vector<float> var_;
@ -86,7 +89,9 @@ TEST_F(SparseApplyFtrlCpuKernelTest, dense_test) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_ftrl_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.291479) < 1e-6);
@ -110,7 +115,9 @@ TEST_F(SparseApplyFtrlCpuKernelTest, sparse_test1) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_ftrl_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.291479) < 1e-6);
@ -138,7 +145,9 @@ TEST_F(SparseApplyFtrlCpuKernelTest, sparse_test2) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_ftrl_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_EQ(var_[i], 1.0);

17
tests/ut/cpp/kernel/cpu/sparse_apply_lazy_adam_cpu_kernel_test.cc
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@ -58,9 +58,12 @@ class SparseApplyLazyAdamCpuKernelTest : public UT::Common {
inputs_.push_back(CreateKernelAddress(indices.data()));
}
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices) {
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices, std::vector<float> &tmp_grad,
std::vector<int> &tmp_indices) {
workspace_.push_back(CreateKernelAddress(new_grad.data()));
workspace_.push_back(CreateKernelAddress(new_indices.data()));
workspace_.push_back(CreateKernelAddress(tmp_grad.data()));
workspace_.push_back(CreateKernelAddress(tmp_indices.data()));
}
std::vector<float> var_;
@ -94,7 +97,9 @@ TEST_F(SparseApplyLazyAdamCpuKernelTest, dense_test) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_lazy_adam_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.999684) < 1e-6);
@ -118,7 +123,9 @@ TEST_F(SparseApplyLazyAdamCpuKernelTest, sparse_test1) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_lazy_adam_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.999684) < 1e-6);
@ -146,7 +153,9 @@ TEST_F(SparseApplyLazyAdamCpuKernelTest, sparse_test2) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_lazy_adam_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_EQ(var_[i], 1.0);

17
tests/ut/cpp/kernel/cpu/sparse_apply_proximal_adagrad_cpu_kernel_test.cc
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@ -54,9 +54,12 @@ class SparseApplyProximalAdagradCpuKernelTest : public UT::Common {
inputs_.push_back(CreateKernelAddress(indices.data()));
}
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices) {
void CreateWorkspaceAddress(std::vector<float> &new_grad, std::vector<int> &new_indices, std::vector<float> &tmp_grad,
std::vector<int> &tmp_indices) {
workspace_.push_back(CreateKernelAddress(new_grad.data()));
workspace_.push_back(CreateKernelAddress(new_indices.data()));
workspace_.push_back(CreateKernelAddress(tmp_grad.data()));
workspace_.push_back(CreateKernelAddress(tmp_indices.data()));
}
std::vector<float> var_;
@ -85,7 +88,9 @@ TEST_F(SparseApplyProximalAdagradCpuKernelTest, dense_test) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_proximal_adagrad_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.9929289) < 1e-6);
@ -108,7 +113,9 @@ TEST_F(SparseApplyProximalAdagradCpuKernelTest, sparse_test1) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_proximal_adagrad_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_TRUE(std::fabs(var_[i] - 0.9929289) < 1e-6);
@ -135,7 +142,9 @@ TEST_F(SparseApplyProximalAdagradCpuKernelTest, sparse_test2) {
CreateInputAddress(indices);
std::vector<float> new_grad(3 * 3 * 3);
std::vector<int> new_indices(3);
CreateWorkspaceAddress(new_grad, new_indices);
std::vector<float> tmp_grad(3 * 3 * 3);
std::vector<int> tmp_indices(3);
CreateWorkspaceAddress(new_grad, new_indices, tmp_grad, tmp_indices);
sparse_proximal_adagrad_->Launch(inputs_, workspace_, outputs_);
for (size_t i = 0; i < 3 * 3; ++i) {
EXPECT_EQ(var_[i], 1.0);

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