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Commit 304caae0 authored by yuguo's avatar yuguo
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Merge branch 'release_v2.7' into 'release_v2.7' 

Release v2.7

See merge request dcutoolkit/deeplearing/TransformerEngine!50
parents 6d43cba8 bdc64fdf
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Showing with 105 additions and 71 deletions
transformer_engine/pytorch/module/batched_linear.py
View file @ 304caae0
......@@ -13,6 +13,7 @@ import transformer_engine_torch as tex
from .base import (
get_multi_stream_cublas_batchgemm_workspace,
get_dummy_wgrad,
TransformerEngineBaseModule,
_2X_ACC_FPROP,
_2X_ACC_DGRAD,
......@@ -94,6 +95,7 @@ class _BatchLinear(torch.autograd.Function):
activation_dtype: torch.dtype,
parallel_mode: Union[str, None],
is_grad_enabled: bool,
fine_grained_activation_offloading,
*weights_and_biases: Union[Float8Tensor, torch.Tensor, None],
) -> torch.Tensor:
batch_num = int(os.getenv("NVTE_MOE_BATCHCOUNT", "2"))
......@@ -158,19 +160,24 @@ class _BatchLinear(torch.autograd.Function):
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
weights[i].offloading_activation = False
weights[i].main_grad.offloading_activation = False
if weights_fp8[i] is not None:
weights_fp8[i].offloading_activation = False
if offload_activation and cpu_offloading:
ctx.fine_grained_activation_offloading = fine_grained_activation_offloading
if fine_grained_activation_offloading and cpu_offloading:
raise ValueError(
f"Do not use offload_activation and cpu_offloading at the same time."
f"Do not use fine_grained_activation_offloading and cpu_offloading at the same time."
)
if offload_activation and weights[0].requires_grad and fuse_wgrad_accumulation:
if (
fine_grained_activation_offloading
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"):
......@@ -187,7 +194,7 @@ class _BatchLinear(torch.autograd.Function):
*weights,
*weights_fp8,
*[
w.main_grad if (cpu_offloading or offload_activation) and fuse_wgrad_accumulation else None
w.main_grad if (cpu_offloading or fine_grained_activation_offloading) and fuse_wgrad_accumulation else None
for w in weights
],
)
......@@ -226,12 +233,12 @@ 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 or ctx.offload_activation) and ctx.fuse_wgrad_accumulation:
if (ctx.cpu_offloading or ctx.fine_grained_activation_offloading) 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[i].requires_grad:
if ctx.fine_grained_activation_offloading 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
......@@ -304,18 +311,15 @@ class _BatchLinear(torch.autograd.Function):
if ctx.fuse_wgrad_accumulation and hasattr(w, "grad_added_to_main_grad"):
w.grad_added_to_main_grad = True
if getattr(w, "zero_out_wgrad", False):
wgrad = torch.zeros(
w.main_grad.shape,
dtype=w.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
wgrad = get_dummy_wgrad(
list(w.main_grad.shape),
w.dtype,
zero=True,
)
else:
wgrad = torch.empty(
w.main_grad.shape,
dtype=w.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
wgrad = get_dummy_wgrad(
list(w.main_grad.shape),
w.dtype,
)
elif ctx.fuse_wgrad_accumulation:
wgrad = None
......@@ -367,6 +371,7 @@ class _BatchLinear(torch.autograd.Function):
None, # activation_dtype
None, # parallel_mode
None, # is_grad_enabled
None, # fine_grained_activation_offloading
*wgrad_list,
*([None] * ctx.num_gemms), # weights_fp8
*grad_biases,
......@@ -457,6 +462,7 @@ class BatchedLinear(TransformerEngineBaseModule):
device: Union[torch.device, str] = "cuda",
ub_overlap_rs: bool = False,
ub_overlap_ag: bool = False,
fine_grained_activation_offloading: bool = False,
ub_name: Optional[str] = None,
delay_wgrad_compute: bool = False,
) -> None:
......@@ -480,6 +486,8 @@ class BatchedLinear(TransformerEngineBaseModule):
self.get_rng_state_tracker = get_rng_state_tracker
self.rng_tracker_name = rng_tracker_name
self.fine_grained_activation_offloading = fine_grained_activation_offloading
self.wgrad_store = WeightGradStore(delay_wgrad_compute)
global _GEMM_INPUT, _GEMM_WEIGHT, _GEMM_OUTPUT
......@@ -657,6 +665,7 @@ class BatchedLinear(TransformerEngineBaseModule):
self.activation_dtype,
self.parallel_mode,
torch.is_grad_enabled(),
self.fine_grained_activation_offloading,
*weight_tensors,
*weight_tensors_fp8,
*bias_tensors,
......
transformer_engine/pytorch/module/grouped_linear.py
View file @ 304caae0
......@@ -15,6 +15,7 @@ import transformer_engine_torch as tex
from transformer_engine.common.recipe import Recipe
from .base import (
get_multi_stream_cublas_workspace,
get_dummy_wgrad,
TransformerEngineBaseModule,
_2X_ACC_FPROP,
_2X_ACC_DGRAD,
......@@ -82,6 +83,7 @@ class _GroupedLinear(torch.autograd.Function):
module,
skip_fp8_weight_update,
save_original_input,
fine_grained_activation_offloading,
*weights_and_biases,
) -> torch.Tensor:
# pylint: disable=missing-function-docstring
......@@ -211,19 +213,22 @@ 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
weights[i].offloading_activation = False
weights_fp8[i].offloading_activation = False
biases[i].offloading_activation = False
ctx.fine_grained_activation_offloading = fine_grained_activation_offloading
if offload_activation and cpu_offloading:
if fine_grained_activation_offloading and cpu_offloading:
raise ValueError(
f"Do not use offload_activation and cpu_offloading at the same time."
f"Do not use fine_grained_activation_offloading and cpu_offloading at the same time."
)
if offload_activation and weights[0].requires_grad and fuse_wgrad_accumulation:
if (
fine_grained_activation_offloading
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"):
......@@ -295,12 +300,12 @@ 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 or ctx.offload_activation) and ctx.fuse_wgrad_accumulation:
if (ctx.cpu_offloading or ctx.fine_grained_activation_offloading) 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:
if ctx.fine_grained_activation_offloading and weights[0].requires_grad:
weights[i].grad_added_to_main_grad = ctx.grad_added_to_main_grad_list[i]
# Preprocess grad output
......@@ -452,18 +457,15 @@ class _GroupedLinear(torch.autograd.Function):
):
weight.grad_added_to_main_grad = True
if getattr(weight, "zero_out_wgrad", False):
wgrad = torch.zeros(
weight.main_grad.shape,
dtype=weight.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
wgrad = get_dummy_wgrad(
list(weight.main_grad.shape),
weight.dtype,
zero=True,
)
else:
wgrad = torch.empty(
weight.main_grad.shape,
dtype=weight.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
wgrad = get_dummy_wgrad(
list(weight.main_grad.shape),
weight.dtype,
)
elif ctx.fuse_wgrad_accumulation:
wgrad = None
......@@ -514,6 +516,7 @@ class _GroupedLinear(torch.autograd.Function):
None,
None,
None,
None,
*wgrad_list,
*grad_biases,
)
......@@ -595,6 +598,7 @@ class GroupedLinear(TransformerEngineBaseModule):
ub_overlap_rs: bool = False,
ub_overlap_ag: bool = False,
ub_name: Optional[str] = None,
fine_grained_activation_offloading: bool = False,
delay_wgrad_compute: bool = False,
save_original_input: bool = False,
) -> None:
......@@ -617,6 +621,7 @@ class GroupedLinear(TransformerEngineBaseModule):
), "GroupedLinear doesn't support Userbuffer overlap."
self.get_rng_state_tracker = get_rng_state_tracker
self.rng_tracker_name = rng_tracker_name
self.fine_grained_activation_offloading = fine_grained_activation_offloading
self.wgrad_store = WeightGradStore(delay_wgrad_compute)
......@@ -836,6 +841,7 @@ class GroupedLinear(TransformerEngineBaseModule):
self,
skip_fp8_weight_update,
self.save_original_input,
self.fine_grained_activation_offloading,
*weight_tensors,
*bias_tensors,
)
......
transformer_engine/pytorch/module/layernorm_linear.py
View file @ 304caae0
......@@ -130,6 +130,7 @@ class _LayerNormLinear(torch.autograd.Function):
ub_bulk_wgrad: bool,
ub_bulk_dgrad: bool,
ub_name: str,
fine_grained_activation_offloading: bool,
fsdp_group: Union[dist_group_type, None],
module: torch.nn.Module,
skip_fp8_weight_update: bool,
......@@ -432,21 +433,24 @@ 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
# Do not offload weights and biases
weight.offloading_activation = False
weightmat.offloading_activation = False
if bias is not None:
bias.offloading_activation = False
ln_weight.offloading_activation = False
ctx.fine_grained_activation_offloading = fine_grained_activation_offloading
ctx.offload_activation = offload_activation
if offload_activation and cpu_offloading:
if fine_grained_activation_offloading and cpu_offloading:
raise ValueError(
f"Do not use offload_activation and cpu_offloading at the same time."
f"Do not use fine_grained_activation_offloading and cpu_offloading at the same time."
)
if offload_activation and weight.requires_grad and fuse_wgrad_accumulation:
if (
fine_grained_activation_offloading
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
......@@ -591,10 +595,10 @@ 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 ctx.offload_activation or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
if ctx.cpu_offloading or ctx.fine_grained_activation_offloading 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:
if ctx.fine_grained_activation_offloading:
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
......@@ -1061,6 +1065,7 @@ class _LayerNormLinear(torch.autograd.Function):
None, # ub_bulk_dgrad
None, # ub_bulk_wgrad
None, # ub_name
None, # fine_grained_activation_offloading
None, # fsdp_group
None, # debug
None, # module
......@@ -1196,6 +1201,7 @@ class LayerNormLinear(TransformerEngineBaseModule):
delay_wgrad_compute: bool = False,
symmetric_ar_type: Optional[str] = None,
name: str = None,
fine_grained_activation_offloading: bool = False,
) -> None:
super().__init__()
......@@ -1212,6 +1218,7 @@ class LayerNormLinear(TransformerEngineBaseModule):
self.return_layernorm_output_gathered = return_layernorm_output_gathered
self.zero_centered_gamma = zero_centered_gamma
self.symmetric_ar_type = symmetric_ar_type
self.fine_grained_activation_offloading = fine_grained_activation_offloading
self.wgrad_store = WeightGradStore(delay_wgrad_compute, ub_bulk_wgrad)
self.name = name
......@@ -1612,6 +1619,7 @@ class LayerNormLinear(TransformerEngineBaseModule):
self.ub_bulk_wgrad,
self.ub_bulk_dgrad,
self.ub_name,
self.fine_grained_activation_offloading,
self.fsdp_group,
self,
skip_fp8_weight_update,
......
transformer_engine/pytorch/module/linear.py
View file @ 304caae0
......@@ -111,6 +111,7 @@ class _Linear(torch.autograd.Function):
ub_bulk_dgrad: bool,
ub_bulk_wgrad: bool,
ub_name: str,
fine_grained_activation_offloading: bool,
fp8_output: bool, # pylint: disable=unused-argument
fsdp_group: Union[dist_group_type, None],
module: torch.nn.Module,
......@@ -397,25 +398,25 @@ 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
ctx.fine_grained_activation_offloading = fine_grained_activation_offloading
if offload_activation and cpu_offloading:
if fine_grained_activation_offloading and cpu_offloading:
raise ValueError(
f"Do not use offload_activation and cpu_offloading at the same time."
f"Do not use fine_grained_activation_offloading 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:
if (
fine_grained_activation_offloading
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
ctx.weight_object = weight
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.has_grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")
......@@ -428,6 +429,12 @@ class _Linear(torch.autograd.Function):
# weights if weights are externally touched outside this module
ctx.weight_object = weight
# Do not offload weights and biases
weight.offloading_activation = False
weightmat.offloading_activation = False
if bias is not None:
bias.offloading_activation = False
# TODO(ksivamani): Check memory usage
tensors_to_save, tensor_objects = prepare_for_saving(
saved_inputmat,
......@@ -514,10 +521,10 @@ class _Linear(torch.autograd.Function):
else None
)
if ctx.cpu_offloading or ctx.offload_activation or int(os.getenv("NVTE_SWAP_OVERLAP_GRAD", "0")):
if ctx.cpu_offloading or ctx.fine_grained_activation_offloading 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:
if ctx.fine_grained_activation_offloading:
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
......@@ -994,6 +1001,7 @@ class _Linear(torch.autograd.Function):
None, # ub_bulk_dgrad
None, # ub_bulk_wgrad
None, # ub_name
None, # fine_grained_activation_offloading
None, # fp8_output
None, # fsdp_group
None, # module
......@@ -1116,6 +1124,7 @@ class Linear(TransformerEngineBaseModule):
symmetric_ar_type: Optional[str] = None,
save_original_input: bool = False,
name: Optional[str] = None,
fine_grained_activation_offloading: bool = False,
) -> None:
super().__init__()
......@@ -1131,6 +1140,7 @@ class Linear(TransformerEngineBaseModule):
self.symmetric_ar_type = symmetric_ar_type
self.save_original_input = save_original_input
self.name = name
self.fine_grained_activation_offloading = fine_grained_activation_offloading
self.wgrad_store = WeightGradStore(delay_wgrad_compute, ub_bulk_wgrad)
......@@ -1474,6 +1484,7 @@ class Linear(TransformerEngineBaseModule):
self.ub_bulk_dgrad,
self.ub_bulk_wgrad,
self.ub_name,
self.fine_grained_activation_offloading,
fp8_output,
self.fsdp_group,
self,
......
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