Fix Stable-Diffusion: Add correct memory-allocation at DeepSpeed-Attention

csrc/transformer/inference/csrc/pt_binding.cpp

@@ -849,20 +849,6 @@ at::Tensor ds_linear_layer(at::Tensor& input,
     int head_size = input_cont.size(2) / num_heads;
     int bsz = input.size(0) * input.size(1);
     T* workspace = (T*)Context::Instance().GetWorkSpace();
-    // Reallocate memory if we received a new prompt
-    if (!workspace) {
-        cublasSetStream(Context::Instance().GetCublasHandle(),
-                        Context::Instance().GetCurrentStream());
-        allocate_workspace<T>(input.size(2),
-                              input.size(0),
-                              input.size(1),
-                              num_layers,
-                              num_heads,
-                              1,
-                              external_cache,
-                              0);
-        workspace = (T*)Context::Instance().GetWorkSpace();
-    }
     auto output = at::from_blob(workspace, {input.size(0), input.size(1), weight.size(1)}, options);
 
     float alpha = (T)1.0;

deepspeed/module_inject/replace_module.py

@@ -210,6 +210,7 @@ def replace_attn(child, policy, layer_id):
             heads=heads,
             fp16=fp16,
             triangular_masking=False,
+            max_out_tokens=4096,
         )
         attn_module = transformer_inference.DeepSpeedAttention(config)
 

deepspeed/ops/transformer/inference/attention.py

@@ -233,6 +233,9 @@ def __init__(
                                     inference_cuda_module.linear_layer_fp32
         self.cuda_graph_created = False
         self.enable_cuda_graph = False
+        self.allocate_workspace = inference_cuda_module.allocate_workspace_fp32 if (not config.fp16) else \
+                                inference_cuda_module.allocate_workspace_fp16
+        self.iter = 0
 
     def _graph_replay(self, *inputs, **kwargs):
         for i in range(len(inputs)):
@@ -275,6 +278,18 @@ def forward(self, *inputs, **kwargs):
         return outputs
 
     def _forward(self, input, context=None, input_mask=None):
+        # Allocate memory only on first layer forward
+        if self.config.layer_id == 0 and self.iter == 0:
+            self.iter += 1
+            self.allocate_workspace(self.config.hidden_size,
+                                    input.size()[0],
+                                    input.size()[1],
+                                    DeepSpeedAttention.layer_id,
+                                    self.config.heads,
+                                    self.config.mp_size,
+                                    self.config.bigscience_bloom,
+                                    0,
+                                    self.config.max_out_tokens)
         output = DeepSpeedAttentionFunction.apply(input,
                                                   context,
                                                   input_mask,