[PyTorch] Deprecate the weight offloading (#1678)

* drop
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

* fix
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>

---------
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

transformer_engine/pytorch/attention.py

@@ -81,7 +81,7 @@ import transformer_engine.pytorch.dot_product_attention.utils as dpa_utils
 from transformer_engine.pytorch.dot_product_attention.utils import FlashAttentionUtils as fa_utils
 from transformer_engine.pytorch.dot_product_attention.utils import AttentionLogging as attn_log
 from transformer_engine.pytorch.dot_product_attention.rope import apply_rotary_pos_emb
-from .cpu_offload import set_offloading_param
+from .cpu_offload import mark_activation_offload
 
 
 # Setup Attention Logging
@@ -4323,10 +4323,9 @@ class FlashAttention(torch.nn.Module):
             from .cpu_offload import CPUOffloadEnabled
 
             if CPUOffloadEnabled:
-                tensor_list = [query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv]
-                for tensor in tensor_list:
-                    if tensor is not None:
-                        set_offloading_param(tensor, "activation_offloading", True)
+                mark_activation_offload(
+                    query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv
+                )
 
             with self.attention_dropout_ctx():
                 #       | API                     | use cases
@@ -4729,13 +4728,8 @@ class FusedAttnFunc(torch.autograd.Function):
                 tensor_list = [q, k, v, out_save]
 
             qkv_layout = "sbhd_sbhd_sbhd"
-            for tensor in tensor_list:
-                if tensor is not None:
-                    set_offloading_param(tensor, "activation_offloading", True)
-
-            for tensor in aux_ctx_tensors:
-                if tensor is not None:
-                    set_offloading_param(tensor, "activation_offloading", True)
+            mark_activation_offload(*tensor_list)
+            mark_activation_offload(*aux_ctx_tensors)
 
         ctx.is_input_fp8 = is_input_fp8
         ctx.is_output_fp8 = is_output_fp8

transformer_engine/pytorch/cpu_offload.py

@@ -16,18 +16,22 @@ __all__ = ["get_cpu_offload_context"]
 CPUOffloadEnabled = False
 
 
-def set_offloading_param(tensor, param_name, value):
+def mark_activation_offload(*tensors):
     """Set the type of the offloading needed for a tensor."""
-    assert param_name in ["weight_offloading", "activation_offloading"]
-    if tensor is None:
-        return
-    if type(tensor) in [torch.Tensor, torch.nn.Parameter]:
-        setattr(tensor, param_name, value)
-    else:
-        data_tensors = tensor.get_data_tensors()
-        for tensor in data_tensors:
-            if tensor is not None:
-                setattr(tensor, param_name, value)
+    for tensor in tensors:
+        if tensor is None:
+            continue
+        if type(tensor) in [torch.Tensor, torch.nn.Parameter]:
+            tensor.activation_offloading = True
+        else:
+            data_tensors = tensor.get_data_tensors()
+            for tensor in data_tensors:
+                if tensor is not None:
+                    tensor.activation_offloading = True
+                    # This is a hack to force clear the tensor after it is offloaded.
+                    # It is needed, because .*TensorBase classes are saved in the ctx,
+                    # and they contain the reference to their data tensors.
+                    tensor.needs_force_clear = True
 
 
 def is_cpu_offload_enabled() -> bool:
@@ -459,8 +463,15 @@ class AsyncDoubleBufferGroupOffloadHandler(SynchronizedGroupOffloadHandler):
             torch.cuda.current_stream().wait_stream(self.d2h_stream)
 
             # Time to free the activation memory after usage
-            for tensor_tag, _ in self.tensor_tag_to_buf.items():
+            for tensor_tag, tensor_buf in self.tensor_tag_to_buf.items():
                 if tensor_tag[0] == self.offloaded_group_count:
+                    if hasattr(tensor_buf, "needs_force_clear"):
+                        # Need to clear activation tensor - sometimes references persist in the code.
+                        # This is the case for example with the Float8TensorBase class,
+                        # which is saved directly inside the ctx while its internal tensors are
+                        # saved inside save_for_backward.
+                        tensor_buf.data = torch.Tensor()
+                    # Release the pointer to the tensor
                     self.tensor_tag_to_buf[tensor_tag] = None
 
             # Time to offload the next group
@@ -538,7 +549,7 @@ def get_cpu_offload_context(
     num_layers: int = 1,
     model_layers: int = 1,
     offload_activations: bool = True,
-    offload_weights: bool = True,
+    offload_weights: bool = False,
 ):
     """
     This function returns the CPU Offload context and the synchronizer function that needs to be
@@ -570,28 +581,30 @@ def get_cpu_offload_context(
 
     """
 
-    def tensor_need_offloading_checker_activations(tensor):
-        return hasattr(tensor, "activation_offloading")
-
-    # This includes the Gradient Accumulation Buffer
-    def tensor_need_offloading_checker_weights(tensor):
-        return hasattr(tensor, "weight_offloading")
-
-    def tensor_need_offloading_checker_all(tensor):
-        return hasattr(tensor, "activation_offloading") or hasattr(tensor, "weight_offloading")
-
-    if offload_activations and offload_weights:
-        tensor_need_offloading_checker = tensor_need_offloading_checker_all
-    elif offload_activations:
-        tensor_need_offloading_checker = tensor_need_offloading_checker_activations
-    elif offload_weights:
-        tensor_need_offloading_checker = tensor_need_offloading_checker_weights
-    else:
+    if not offload_weights and not offload_activations:
         raise ValueError(
             "CPU Offloading is enabled while it is not "
             "mentioned what to offload (weights/activations)"
         )
 
+    if offload_weights:
+        import warnings
+
+        warnings.warn(
+            "Offloading weights is deprecated. Using offload_weights=True does not have any"
+            " effect.",
+            DeprecationWarning,
+        )
+
+        # Weights offloading is deprecated but we maintain backward compatibility by doing nothing.
+        if not offload_activations:
+            return nullcontext(), lambda x: x
+
+    def tensor_need_offloading_checker_activations(tensor):
+        return hasattr(tensor, "activation_offloading")
+
+    tensor_need_offloading_checker = tensor_need_offloading_checker_activations
+
     cpu_offload_handler = AsyncDoubleBufferGroupOffloadHandler(
         num_offload_group=num_layers,
         num_model_group=model_layers,

transformer_engine/pytorch/module/layernorm_linear.py

@@ -63,7 +63,7 @@ from ..tensor.float8_tensor import Float8CurrentScalingQuantizer, Float8Quantize
 from ..tensor.float8_blockwise_tensor import Float8BlockQuantizer
 from ..tensor.mxfp8_tensor import MXFP8Quantizer
 from ..tensor._internal.mxfp8_tensor_base import MXFP8TensorBase
-from ..cpu_offload import is_cpu_offload_enabled, set_offloading_param
+from ..cpu_offload import is_cpu_offload_enabled, mark_activation_offload
 
 from ..cpp_extensions import (
     general_gemm,
@@ -355,15 +355,7 @@ class _LayerNormLinear(torch.autograd.Function):
                 weightmat.update_usage(columnwise_usage=True)
 
             if cpu_offloading:
-                if fp8 and weightmat is not None:
-                    set_offloading_param(weightmat, "weight_offloading", True)
-                set_offloading_param(ln_weight, "weight_offloading", True)
-                set_offloading_param(weight, "weight_offloading", True)
-
-                set_offloading_param(inputmat, "activation_offloading", True)
-                set_offloading_param(mu, "activation_offloading", True)
-                set_offloading_param(rsigma, "activation_offloading", True)
-                set_offloading_param(ln_out, "activation_offloading", True)
+                mark_activation_offload(inputmat, mu, rsigma, ln_out)
 
             # Scatter intermediate/activation tensors saved for the backward pass
             # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already

transformer_engine/pytorch/module/layernorm_mlp.py

@@ -64,7 +64,7 @@ from ..tensor.float8_tensor import (
 from ..tensor.mxfp8_tensor import MXFP8Quantizer
 from ..tensor.float8_blockwise_tensor import Float8BlockQuantizer
 from ._common import apply_normalization, _fix_gathered_fp8_transpose
-from ..cpu_offload import is_cpu_offload_enabled, set_offloading_param
+from ..cpu_offload import is_cpu_offload_enabled, mark_activation_offload
 from ..tensor.quantized_tensor import (
     QuantizedTensor,
     Quantizer,
@@ -473,23 +473,9 @@ class _LayerNormMLP(torch.autograd.Function):
             clear_tensor_data(act_out, fc1_out_without_bias, fc1_out)
         else:
             if cpu_offloading:
-                if fp8 and fc1_weight_final is not None:
-                    set_offloading_param(fc1_weight_final, "weight_offloading", True)
-                if fp8 and fc2_weight_final is not None:
-                    set_offloading_param(fc2_weight_final, "weight_offloading", True)
-                set_offloading_param(ln_weight, "weight_offloading", True)
-                set_offloading_param(fc1_weight, "weight_offloading", True)
-                set_offloading_param(fc2_weight, "weight_offloading", True)
-                set_offloading_param(fc1_bias, "weight_offloading", True)
-
-                set_offloading_param(inputmat, "activation_offloading", True)
-                set_offloading_param(mu, "activation_offloading", True)
-                set_offloading_param(rsigma, "activation_offloading", True)
-                set_offloading_param(mu, "activation_offloading", True)
-                set_offloading_param(ln_out, "activation_offloading", True)
-                set_offloading_param(fc1_out, "activation_offloading", True)
-                set_offloading_param(fc1_out_without_bias, "activation_offloading", True)
-                set_offloading_param(act_out, "activation_offloading", True)
+                mark_activation_offload(
+                    inputmat, mu, rsigma, ln_out, fc1_out, fc1_out_without_bias, act_out
+                )
 
             # Scatter intermediate/activation tensors saved for the backward pass
             # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already

transformer_engine/pytorch/module/linear.py

@@ -62,7 +62,7 @@ from ..tensor.float8_tensor import Float8CurrentScalingQuantizer, Float8Quantize
 from ..tensor.mxfp8_tensor import MXFP8Quantizer
 from ..tensor._internal.mxfp8_tensor_base import MXFP8TensorBase
 from ..tensor.float8_blockwise_tensor import Float8BlockQuantizer
-from ..cpu_offload import is_cpu_offload_enabled, set_offloading_param
+from ..cpu_offload import is_cpu_offload_enabled, mark_activation_offload
 from ...debug.pytorch.debug_state import TEDebugState
 from ...debug.pytorch.utils import any_feature_enabled
 
@@ -307,11 +307,8 @@ class _Linear(torch.autograd.Function):
                 if isinstance(weightmat, QuantizedTensor):
                     weightmat.update_usage(columnwise_usage=True)
 
-            if cpu_offloading:
-                set_offloading_param(weight, "weight_offloading", True)
-                set_offloading_param(weightmat, "weight_offloading", True)
-                if saved_inputmat is not None:
-                    set_offloading_param(saved_inputmat, "activation_offloading", True)
+            if cpu_offloading and saved_inputmat is not None:
+                mark_activation_offload(saved_inputmat)
 
             # Scatter intermediate/activation tensors saved for the backward pass
             # NOTE: FSDP sharding is not valid for models initialized with primary Fp8 weights