Merge branch 'activation_offloading' into 'release_v2.7'

support activation offloading

See merge request dcutoolkit/deeplearing/TransformerEngine!41

transformer_engine/pytorch/module/batched_linear.py

@@ -157,6 +157,29 @@ class _BatchLinear(torch.autograd.Function):
                     for t in saved_inputmats:
                         if t is not None:
                             t.activation_offloading = True
+
+            offload_activation = False
+            if hasattr(inp, "offloading_activation"):
+                offload_activation = True
+                for i in range(num_gemms):
+                    saved_inputmats[i].offloading_activation = inp.offloading_activation
+            ctx.offload_activation = offload_activation
+
+            if offload_activation and cpu_offloading:
+                raise ValueError(
+                    f"Do not use offload_activation and cpu_offloading at the same time."
+                )
+
+            if offload_activation and weights[0].requires_grad and fuse_wgrad_accumulation:
+                grad_added_to_main_grad_list = []
+                for weight in weights:
+                    if weight.requires_grad and hasattr(weight, "grad_added_to_main_grad"):
+                        grad_added_to_main_grad_list.append(weight.grad_added_to_main_grad)
+                        weight.grad_added_to_main_grad = True
+                    else:
+                        grad_added_to_main_grad_list.append(None)
+                ctx.grad_added_to_main_grad_list = grad_added_to_main_grad_list
+
             ctx.save_for_backward(
                 None,
                 *saved_inputmats,
@@ -164,7 +187,7 @@ class _BatchLinear(torch.autograd.Function):
                 *weights,
                 *weights_fp8,
                 *[
-                    w.main_grad if cpu_offloading and fuse_wgrad_accumulation else None
+                    w.main_grad if (cpu_offloading or offload_activation) and fuse_wgrad_accumulation else None
                     for w in weights
                 ],
             )
@@ -203,11 +226,14 @@ class _BatchLinear(torch.autograd.Function):
             weights = saved_tensors[2 * ctx.num_gemms : 3 * ctx.num_gemms]
             weights_fp8 = saved_tensors[3 * ctx.num_gemms : 4 * ctx.num_gemms]
             main_grads = saved_tensors[4 * ctx.num_gemms :]
-            if ctx.cpu_offloading and ctx.fuse_wgrad_accumulation:
+            if (ctx.cpu_offloading or ctx.offload_activation) and ctx.fuse_wgrad_accumulation:
                 for i in range(ctx.num_gemms):
-                    w = torch.nn.Parameter(weights[i], False)
+                    w = torch.nn.Parameter(weights[i], weights[i].requires_grad)
                     w.main_grad = main_grads[i]
                     weights[i] = w
+                    if ctx.offload_activation and weights[i].requires_grad:
+                        weights[i].grad_added_to_main_grad = ctx.grad_added_to_main_grad_list[i]
+
             global _GEMM_INPUT, _GEMM_WEIGHT, _GRAD_OUTPUT
             grad_output = grad_output.contiguous()
             grad_output_mats = torch.split(

transformer_engine/pytorch/module/grouped_linear.py

@@ -211,6 +211,28 @@ class _GroupedLinear(torch.autograd.Function):
                     if isinstance(weight, QuantizedTensorBase):
                         weight.update_usage(columnwise_usage=True)
 
+            offload_activation = False
+            if hasattr(inp, "offloading_activation"):
+                offload_activation = True
+                for i in range(num_gemms):
+                    inputmats[i].offloading_activation = inp.offloading_activation
+            ctx.offload_activation = offload_activation
+
+            if offload_activation and cpu_offloading:
+                raise ValueError(
+                    f"Do not use offload_activation and cpu_offloading at the same time."
+                )
+
+            if offload_activation and weights[0].requires_grad and fuse_wgrad_accumulation:
+                grad_added_to_main_grad_list = []
+                for weight in weights:
+                    if weight.requires_grad and hasattr(weight, "grad_added_to_main_grad"):
+                        grad_added_to_main_grad_list.append(weight.grad_added_to_main_grad)
+                        weight.grad_added_to_main_grad = True
+                    else:
+                        grad_added_to_main_grad_list.append(None)
+                ctx.grad_added_to_main_grad_list = grad_added_to_main_grad_list
+
             tensors_to_save, tensor_objects = prepare_for_saving(
                 *inputmats,
                 *weights_fp8,
@@ -273,11 +295,13 @@ class _GroupedLinear(torch.autograd.Function):
             biases = saved_tensors[3 * N : 4 * N]
             main_grads = [main_grad_func() for main_grad_func in ctx.main_grad_funcs]
 
-            if ctx.cpu_offloading and ctx.fuse_wgrad_accumulation:
+            if (ctx.cpu_offloading or ctx.offload_activation) and ctx.fuse_wgrad_accumulation:
                 for i in range(ctx.num_gemms):
                     w = torch.nn.Parameter(weights[i], weights[i].requires_grad)
-                    w.main_grad = main_grads[i]
                     weights[i] = w
+                    weights[i].main_grad = main_grads[i]
+                    if ctx.offload_activation and weights[0].requires_grad:
+                        weights[i].grad_added_to_main_grad = ctx.grad_added_to_main_grad_list[i]
 
             # Preprocess grad output
             grad_output_view = grad_output.contiguous().view(-1, grad_output.shape[-1])