Merge branch 'main' into 'main'

support activation offloading

See merge request dcutoolkit/deeplearing/TransformerEngine!44

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
+                    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])

transformer_engine/pytorch/module/layernorm_linear.py

@@ -38,6 +38,7 @@ from ..utils import (
     nvtx_range_push,
     requires_grad,
     needs_quantized_gemm,
+    get_activation_offloading,
 )
 from ..distributed import (
     set_tensor_model_parallel_attributes,
@@ -431,10 +432,33 @@ class _LayerNormLinear(torch.autograd.Function):
             )
             nvtx_range_pop(f"{nvtx_label}.fsdp_scatter")
 
+            offload_activation = False
+            if get_activation_offloading():
+                offload_activation = True
+                if not inputmat.is_contiguous():
+                    inputmat = inputmat.contiguous()
+                inputmat.offloading_activation = True
+
+            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 weight.requires_grad and fuse_wgrad_accumulation:
+                if hasattr(weight, "grad_added_to_main_grad"):
+                    ctx.has_grad_added_to_main_grad = True
+                    ctx.grad_added_to_main_grad = weight.grad_added_to_main_grad
+                    weight.grad_added_to_main_grad = True
+                    ctx.weight_object = weight
+                else:
+                    ctx.has_grad_added_to_main_grad = False
+
             if cpu_offloading or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
-                ctx.grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")
+                ctx.has_grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")
 
-                if ctx.grad_added_to_main_grad:
+                if ctx.has_grad_added_to_main_grad:
                     # If you are passing torch.nn.Parameter through the Torch hooks, you will
                     # get back torch.Tensor. Torch rips off the Parameter wrapper.
                     # You need to preserve the weight object to have all the attributes user
@@ -567,9 +591,11 @@ class _LayerNormLinear(torch.autograd.Function):
 
             # For CPU offloading, we offloaded weight and weight.main_grad to different tensors,
             # we need to connect them into one.
-            if ctx.cpu_offloading or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
-                if ctx.grad_added_to_main_grad:
+            if ctx.cpu_offloading or ctx.offload_activation or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
+                if ctx.has_grad_added_to_main_grad:
                     origin_weight = ctx.weight_object
+                    if ctx.offload_activation:
+                        origin_weight.grad_added_to_main_grad = ctx.grad_added_to_main_grad
                 if ctx.requires_wgrad and ctx.fuse_wgrad_accumulation:
                     origin_weight.main_grad = main_grad
 

transformer_engine/pytorch/module/linear.py

@@ -38,6 +38,7 @@ from ..utils import (
     assert_dim_for_fp8_exec,
     nvtx_range_pop,
     nvtx_range_push,
+    get_activation_offloading,
 )
 from ..distributed import (
     set_tensor_model_parallel_attributes,
@@ -396,10 +397,30 @@ class _Linear(torch.autograd.Function):
             )
             nvtx_range_pop(f"{nvtx_label}.fsdp_scatter")
 
+            offload_activation = False
+            if get_activation_offloading():
+                offload_activation = True
+                if not saved_inputmat.is_contiguous():
+                    saved_inputmat = saved_inputmat.contiguous()
+                saved_inputmat.offloading_activation = True
+            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 weight.requires_grad and fuse_wgrad_accumulation:
+                ctx.has_grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")
+                if ctx.has_grad_added_to_main_grad:
+                    ctx.grad_added_to_main_grad = weight.grad_added_to_main_grad
+                    ctx.weight_object = weight
+                    weight.grad_added_to_main_grad = True
+
             if cpu_offloading or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
-                ctx.grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")
+                ctx.has_grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")
 
-                if ctx.grad_added_to_main_grad:
+                if ctx.has_grad_added_to_main_grad:
                     # If you are passing torch.nn.Parameter through the Torch hooks, you will
                     # get back torch.Tensor. Torch rips off the Parameter wrapper.
                     # You need to preserve the weight object to have all the attributes user
@@ -494,9 +515,11 @@ class _Linear(torch.autograd.Function):
                 else None
             )
 
-            if ctx.cpu_offloading or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
-                if ctx.grad_added_to_main_grad:
+            if ctx.cpu_offloading or ctx.offload_activation or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
+                if ctx.has_grad_added_to_main_grad:
                     weight = ctx.weight_object
+                    if ctx.offload_activation:
+                        weight.grad_added_to_main_grad = ctx.grad_added_to_main_grad
                 if ctx.requires_wgrad and ctx.fuse_wgrad_accumulation:
                     weight.main_grad = main_grad
 

transformer_engine/pytorch/utils.py

@@ -764,3 +764,30 @@ def make_weak_ref(x):
     if x is None:
         return None
     raise TypeError(f"Invalid type {type(x)} to make weak ref")
+
+
+ActivationOffloadEnabled = False
+
+def get_activation_offloading():
+    global ActivationOffloadEnabled
+    return ActivationOffloadEnabled
+
+
+def set_activation_offloading(activation_offloading):
+    global ActivationOffloadEnabled
+    ActivationOffloadEnabled = activation_offloading
+
+
+class ActivationOffloadContextManager:
+    """A reusable context manager for switch ActivationOffloadEnabled"""
+
+    def __init__(self, activation_offloading):
+        self.activation_offloading = activation_offloading
+
+    def __enter__(self):
+        self.origin_cpu_offloading = get_activation_offloading()
+        set_activation_offloading(self.activation_offloading)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        set_activation_offloading(self.origin_cpu_offloading)