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rsp push and rsp pull for comm device, used in kvstore('device') #8732
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| Original file line number | Diff line number | Diff line change |
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@@ -489,11 +489,7 @@ class CommDevice : public Comm { | |
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| void Init(int key, const NDArrayStorageType stype, const TShape& shape, | ||
| int dtype = mshadow::kFloat32) override { | ||
| sorted_key_attrs_.push_back(std::make_tuple(key, shape, dtype, stype)); | ||
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There was a problem hiding this comment. Using |
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| } | ||
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| void InitBuffersAndComm(const std::vector<NDArray>& src) { | ||
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@@ -526,26 +522,66 @@ class CommDevice : public Comm { | |
| InitBuffersAndComm(src); | ||
| auto& buf = merge_buf_[key]; | ||
| std::vector<NDArray> reduce(src.size()); | ||
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| if (buf.merged.storage_type() == kDefaultStorage) { | ||
| CopyFromTo(src[0], &(buf.merged), priority); | ||
| reduce[0] = buf.merged; | ||
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| if (buf.copy_buf.empty()) { | ||
| // TODO(mli) this results in large device memory usage for huge ndarray, | ||
| // such as the largest fullc in VGG. consider to do segment reduce with | ||
| // NDArray.Slice or gpu direct memory access. for the latter, we need to | ||
| // remove some ctx check, and also it reduces 20% perf | ||
| buf.copy_buf.resize(src.size()-1); | ||
| for (size_t i = 0; i < src.size()-1; ++i) { | ||
| buf.copy_buf[i] = NDArray( | ||
| buf.merged.shape(), buf.merged.ctx(), false, buf.merged.dtype()); | ||
| } | ||
| } | ||
| for (size_t i = 0; i < src.size()-1; ++i) { | ||
| CopyFromTo(src[i+1], &(buf.copy_buf[i]), priority); | ||
| reduce[i+1] = buf.copy_buf[i]; | ||
| } | ||
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| ElementwiseSum(reduce, &buf.merged); | ||
| } else { | ||
| std::vector<Engine::VarHandle> const_vars(src.size()); | ||
| if (buf.copy_buf.empty()) { | ||
| buf.copy_buf.resize(src.size()); | ||
| for (size_t j = 0; j < src.size(); ++j) { | ||
| buf.copy_buf[j] = NDArray( | ||
| buf.merged.storage_type(), buf.merged.shape(), buf.merged.ctx(), | ||
| true, buf.merged.dtype()); | ||
| } | ||
| } | ||
| for (size_t i = 0; i < src.size(); ++i) { | ||
| CopyFromTo(src[i], &(buf.copy_buf[i]), priority); | ||
| reduce[i] = buf.copy_buf[i]; | ||
| const_vars[i] = reduce[i].var(); | ||
| } | ||
| auto result = buf.merged; | ||
| Engine::Get()->PushAsync( | ||
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There was a problem hiding this comment. Should this be moved to ndarray.cc instead? There was a problem hiding this comment. Why this should move into There was a problem hiding this comment. Can we extend the |
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| [reduce, result, this](RunContext rctx, Engine::CallbackOnComplete on_complete) { | ||
| NDArray out = result; | ||
| Resource rsc = ResourceManager::Get()->Request(rctx.ctx, | ||
| ResourceRequest(ResourceRequest::kTempSpace)); | ||
| switch (result.ctx().dev_mask()) { | ||
| case cpu::kDevMask: { | ||
| mxnet::ndarray::ElementwiseSum(rctx.get_stream<cpu>(), rsc, reduce, &out); | ||
| break; | ||
| } | ||
| #if MXNET_USE_CUDA | ||
| case gpu::kDevMask: { | ||
| mxnet::ndarray::ElementwiseSum(rctx.get_stream<gpu>(), rsc, reduce, &out); | ||
| break; | ||
| } | ||
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There was a problem hiding this comment. Is There was a problem hiding this comment.
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| #endif | ||
| default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR; | ||
| } | ||
| on_complete(); | ||
| }, result.ctx(), const_vars, {result.var()}, | ||
| FnProperty::kNormal, priority, PROFILER_MESSAGE("KVStoreReduce")); | ||
| } | ||
| return buf.merged; | ||
| } | ||
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@@ -619,7 +655,62 @@ class CommDevice : public Comm { | |
| const std::vector<std::pair<NDArray*, NDArray>>& dst, | ||
| const bool use_copy, | ||
| const int priority) override { | ||
| using namespace mshadow; | ||
| CHECK_EQ(src.storage_type(), kRowSparseStorage) | ||
| << "BroadcastRowSparse expects row-sparse src NDArray"; | ||
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| bool is_same_rowid = true; | ||
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There was a problem hiding this comment. Please add some brief description explaining the optimization |
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| for (size_t i = 1; i < dst.size(); ++i) { | ||
| if (dst[i].second.var() != dst[0].second.var()) { | ||
| is_same_rowid = false; | ||
| } | ||
| } | ||
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| for (size_t i = 0; i < dst.size(); ++i) { | ||
| if (is_same_rowid && i != 0) { | ||
| CopyFromTo(*dst[0].first, dst[i].first, priority); | ||
| continue; | ||
| } | ||
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| NDArray* out = dst[i].first; | ||
| NDArray row_id = dst[i].second; | ||
| if (use_copy) { | ||
| CopyFromTo(src, out, priority); | ||
| } else { | ||
| CHECK_EQ(out->storage_type(), kRowSparseStorage) | ||
| << "BroadcastRowSparse expects row_sparse dst NDArray"; | ||
| const bool is_diff_ctx = out->ctx() != src.ctx(); | ||
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There was a problem hiding this comment. Are we assuming src is always on GPU? There was a problem hiding this comment. src is not assumed to be on gpu. Actually src is always on cpu. As you can see in https://github.com/apache/incubator-mxnet/blob/master/src/kvstore/kvstore_local.h#L233, src is There was a problem hiding this comment. That's true at the beginning. But as soon as you push some gradients on GPU, it copies the weight from pinned_ctx to GPU. See There was a problem hiding this comment. Nonetheless, I think performing sparse retain before the copy makes more sense since the source array is usually very large. |
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| NDArray src_gpu = is_diff_ctx? NDArray(kRowSparseStorage, src.shape(), | ||
| out->ctx(), true, src.dtype(), src.aux_types()) : src; | ||
| if (is_diff_ctx) { | ||
| CopyFromTo(src, &src_gpu, priority); | ||
| } | ||
| NDArray row_id_gpu = NDArray(row_id.shape(), out->ctx(), false, kInt64); | ||
| const TBlob& indices = row_id_gpu.data(); | ||
| CopyFromTo(row_id, &row_id_gpu, priority); | ||
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| Engine::Get()->PushAsync([=](RunContext rctx, Engine::CallbackOnComplete on_complete) { | ||
| NDArray temp = *out; | ||
| switch (temp.ctx().dev_mask()) { | ||
| case cpu::kDevMask: { | ||
| mxnet::common::SparseRetainOpForwardRspWrapper<cpu>(rctx.get_stream<cpu>(), | ||
| src_gpu, indices, kWriteTo, &temp); | ||
| break; | ||
| } | ||
| #if MXNET_USE_CUDA | ||
| case gpu::kDevMask: { | ||
| mxnet::common::SparseRetainOpForwardRspWrapper<gpu>(rctx.get_stream<gpu>(), | ||
| src_gpu, indices, kWriteTo, &temp); | ||
| break; | ||
| } | ||
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There was a problem hiding this comment. is |
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| #endif | ||
| default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR; | ||
| } | ||
| on_complete(); | ||
| }, out->ctx(), {src_gpu.var(), row_id_gpu.var()}, {out->var()}, | ||
| FnProperty::kNormal, priority, PROFILER_MESSAGE("KVStoreSparseRetain")); | ||
| } | ||
| } | ||
| } | ||
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| private: | ||
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@@ -665,7 +756,7 @@ class CommDevice : public Comm { | |
| #endif | ||
| } | ||
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| using KeyAttrs = std::tuple<int, TShape, int, NDArrayStorageType>; | ||
| // try to allocate buff on device evenly | ||
| void InitMergeBuffer(const std::vector<Context>& devs) { | ||
| std::sort(sorted_key_attrs_.begin(), sorted_key_attrs_.end(), []( | ||
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@@ -679,8 +770,9 @@ class CommDevice : public Comm { | |
| } | ||
| for (size_t i = 0; i < sorted_key_attrs_.size(); ++i) { | ||
| int key = std::get<0>(sorted_key_attrs_[i]); | ||
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| TShape shape = std::get<1>(sorted_key_attrs_[i]); | ||
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| int type = std::get<2>(sorted_key_attrs_[i]); | ||
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| NDArrayStorageType stype = std::get<3>(sorted_key_attrs_[i]); | ||
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| auto& buf = merge_buf_[key]; | ||
| Context ctx; | ||
| size_t min_size = std::numeric_limits<size_t>::max(); | ||
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@@ -691,8 +783,12 @@ class CommDevice : public Comm { | |
| min_size = size; | ||
| } | ||
| } | ||
| if (stype == kDefaultStorage) { | ||
| buf.merged = NDArray(shape, ctx, false, type); | ||
| } else { | ||
| buf.merged = NDArray(stype, shape, ctx, true, type); | ||
| } | ||
| ctx_info[ctx.dev_id].second += shape.Size(); | ||
| } | ||
| inited_ = true; | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
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@@ -26,21 +26,20 @@ | |
| str_keys = ['b', 'c', 'd'] | ||
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| def init_kv_with_str(stype='default', kv_type='local'): | ||
| """init kv """ | ||
| kv = mx.kv.create(kv_type) | ||
| # single | ||
| kv.init('a', mx.nd.zeros(shape, stype=stype)) | ||
| # list | ||
| kv.init(str_keys, [mx.nd.zeros(shape=shape, stype=stype)] * len(keys)) | ||
| return kv | ||
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There was a problem hiding this comment. The test should be fixed in #8838 |
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| def test_row_sparse_pull(kv_type='device'): | ||
| kv = init_kv_with_str('row_sparse', kv_type) | ||
| kv.init('e', mx.nd.ones(shape).tostype('row_sparse')) | ||
| kv.push('e', [mx.nd.ones(shape, ctx=mx.cpu(i)).tostype('row_sparse') for i in range(2)]) | ||
| def check_row_sparse_pull(kv, count, ctx=default_context()): | ||
| num_rows = shape[0] | ||
| vals = [] | ||
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@@ -59,7 +58,7 @@ def check_row_sparse_pull(kv, count, ctx=default_context()): | |
| excluded_row_ids = np.setdiff1d(all_row_ids, row_id.asnumpy()) | ||
| for row in range(num_rows): | ||
| expected_val = np.zeros_like(retained[row]) | ||
| expected_val += 0 if row in excluded_row_ids else 2 | ||
| assert_almost_equal(retained[row], expected_val) | ||
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| check_row_sparse_pull(kv, 1, mx.gpu(0)) | ||
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Let's make sure functions in
.hare documented. Should add some description forCastStorageDispatchtoo...