Move workspace memory-allocation to PyTorch (#661)

* move workspace memory-allocation to PyTorch

* refine the code based on the comments

* remove unnecessary options

* remove bsz from set_seq_len function

csrc/includes/context.h

@@ -64,17 +64,10 @@ public:
         return _ctx;
     }
 
-    void GenWorkSpace(size_t size)
+    void SetWorkSpace(void* workspace)
     {
-        if (!_workspace) {
-            assert(_workspace == nullptr);
-            cudaMalloc(&_workspace, size);
-        } else if (_workSpaceSize < size) {
-            cudaFree(_workspace);
-            cudaMalloc(&_workspace, size);
-        }
-
-        _workSpaceSize = size;
+        if (!workspace) { throw std::runtime_error("Workspace is null."); }
+        _workspace = workspace;
     }
 
     void* GetWorkSpace() { return _workspace; }
@@ -172,6 +165,5 @@ private:
     void* _workspace;
     uint64_t _seed;
     uint64_t _curr_offset;
-    size_t _workSpaceSize;
     std::vector<std::array<int, 3>> _gemm_algos;
 };

csrc/includes/ds_transformer_cuda.h

@@ -130,10 +130,13 @@ public:
     inline int GetBatchSize() const { return _batch_size; }
     inline int GetNumHeads() const { return _heads; }
     inline int GetSeqLength() const { return _seq_length; }
+    inline int GetIntermediateSize() const { return _intermediate_size; }
 
-    void SetSeqLength(int seq_len, int bsz);
+    void SetSeqLength(int seq_len);
     inline int GetHiddenSize() const { return _hidden_size; }
     void SetTrainingMode(bool training);
+    inline bool IsTrainingMode() const { return _training; }
+    inline bool GeluCheckpoint() const { return _gelu_checkpoint; }
 
 private:
     void Initialize();

csrc/transformer/ds_transformer_cuda.cpp

@@ -33,7 +33,7 @@ size_t get_workspace_size(int maxBatchSize,
                                      2 * (size_t(maxBatchSize) * heads * seq_len * seq_len)));
         if (gelu_checkpoint) workSpacesize += 2 * (size_t(maxBatchSize) * seq_len * hidden_size);
     }
-    return workSpacesize * sizeof(T);
+    return workSpacesize;  // * sizeof(T);
 }
 
 // NOTE: AT_ASSERT has become AT_CHECK on master after 0.4.
@@ -123,7 +123,6 @@ BertTransformerLayer<T>::BertTransformerLayer(int layer_id,
                                                          gemm_algos[4]))
 {
     assert(_hidden_size % _heads == 0);
-    assert(_seq_length <= 1024);
 
     Initialize();
 }
@@ -136,14 +135,6 @@ BertTransformerLayer<T>::~BertTransformerLayer()
 template <typename T>
 void BertTransformerLayer<T>::Initialize()
 {
-    Context::Instance().GenWorkSpace(get_workspace_size<T>(_batch_size,
-                                                           _seq_length,
-                                                           _hidden_size,
-                                                           _intermediate_size,
-                                                           _heads,
-                                                           _training,
-                                                           _gelu_checkpoint));
-
     if (std::is_same<T, __half>::value) cublasSetMathMode(_cublasHandle, CUBLAS_TENSOR_OP_MATH);
 }
 
@@ -574,7 +565,7 @@ void BertTransformerLayer<T>::SetIntermediateBuffers(uint8_t* attn_prob_dropout_
 }
 
 template <typename T>
-void BertTransformerLayer<T>::SetSeqLength(int seq_len, int bsz)
+void BertTransformerLayer<T>::SetSeqLength(int seq_len)
 {
     _seq_length = seq_len;
 
@@ -582,9 +573,6 @@ void BertTransformerLayer<T>::SetSeqLength(int seq_len, int bsz)
     _attn_prob_dropout.SetDimension(_seq_length);
     _attn_scores.SetConfig(_seq_length, _seq_length, _hidden_size / _heads);
     _attn_context.SetConfig(_hidden_size / _heads, _seq_length, _seq_length);
-
-    Context::Instance().GenWorkSpace(get_workspace_size<T>(
-        bsz, _seq_length, _hidden_size, _intermediate_size, _heads, _training, _gelu_checkpoint));
 }
 
 template <typename T>
@@ -707,9 +695,19 @@ std::vector<torch::Tensor> ds_transformer_forward(int layer_id,
     int seq_len = layer->GetSeqLength();
     if (input.size(1) != seq_len) {
         seq_len = input.size(1);
-        layer->SetSeqLength(seq_len, bsz);
+        layer->SetSeqLength(seq_len);
     }
 
+    auto workspace = torch::empty({get_workspace_size<T>(bsz,
+                                                         seq_len,
+                                                         layer->GetHiddenSize(),
+                                                         layer->GetIntermediateSize(),
+                                                         layer->GetNumHeads(),
+                                                         layer->IsTrainingMode(),
+                                                         layer->GeluCheckpoint())},
+                                  options);
+    Context::Instance().SetWorkSpace((T*)workspace.data_ptr());
+
     auto inp_norm = ((prelayernorm || !normalize_invertible) ? torch::empty_like(input) : output);
     auto add_res = (normalize_invertible ? inp_norm : torch::empty_like(input));
     auto attn_o_inp = torch::empty_like(input);
@@ -877,9 +875,19 @@ std::vector<torch::Tensor> ds_transformer_backward(int layer_id,
     int seq_len = layer->GetSeqLength();
     if (g_output.size(1) != seq_len) {
         seq_len = g_output.size(1);
-        layer->SetSeqLength(seq_len, bsz);
+        layer->SetSeqLength(seq_len);
     }
 
+    auto workspace = torch::empty({get_workspace_size<T>(bsz,
+                                                         seq_len,
+                                                         layer->GetHiddenSize(),
+                                                         layer->GetIntermediateSize(),
+                                                         layer->GetNumHeads(),
+                                                         layer->IsTrainingMode(),
+                                                         layer->GeluCheckpoint())},
+                                  grad_output.options());
+    Context::Instance().SetWorkSpace((T*)workspace.data_ptr());
+
     auto grad_input = torch::empty_like(input);
     auto grad_attn_qkvw = torch::empty_like(attn_qkvw);
     auto grad_attn_qkvb = torch::empty_like(attn_qkvb);