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preserve tensor striding during compute estimation #37

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Merged
merged 2 commits into from
Jul 17, 2025
Merged

preserve tensor striding during compute estimation #37

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afa0050
preserve tensor striding during compute estimation
bdhirsh Jul 10, 2025
0208656
Fix other callsites
wconstab Jul 17, 2025
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17 changes: 12 additions & 5 deletions autoparallel/compute_estimation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,7 @@ def _get_device_tflops(dtype):
return device_limit.gemm_tflops[dtype]


def _get_sharded_shape(spec):
def _get_sharded_shape_stride(spec):
mesh = spec.mesh
tensor_shape = spec.tensor_meta.shape
# TODO: take dtype into account as well
Expand All @@ -164,11 +164,15 @@ def _get_sharded_shape(spec):
# TODO: find a better heuristic other than
# running DTensor
new_tensor_shape = list(tensor_shape)
new_tensor_stride = list(spec.tensor_meta.stride)
for mesh_size, placement in zip(mesh.shape, placements):
if placement.is_shard():
dim = placement.dim
new_tensor_shape[dim] = (new_tensor_shape[dim] + mesh_size - 1) // mesh_size
return new_tensor_shape
new_tensor_stride[dim] = (
new_tensor_stride[dim] + mesh_size - 1
) // mesh_size
return new_tensor_shape, new_tensor_stride


def estimate_strategy_runtime_cost(node, strategy):
Expand All @@ -191,15 +195,18 @@ def estimate_strategy_runtime_cost(node, strategy):
if len(kwargs) > 0:
for k, v in kwargs.items():
assert not isinstance(v, torch.Tensor), f"{node} {v}"
args_shapes = tuple(_get_sharded_shape(spec) for spec in strategy.input_specs)
args_sizes_strides = tuple(
_get_sharded_shape_stride(spec) for spec in strategy.input_specs
)

counter = 0
args = list(args)
for i, arg in enumerate(args):
if isinstance(arg, torch.Tensor):
with fake_mode:
args[i] = torch.empty(
args_shapes[counter], device=arg.device, dtype=arg.dtype
sizes, strides = args_sizes_strides[counter]
args[i] = torch.empty_strided(
sizes, strides, device=arg.device, dtype=arg.dtype
)
counter += 1

Expand Down
7 changes: 5 additions & 2 deletions autoparallel/optimize_sharding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,10 @@
from torch.distributed.tensor.placement_types import Replicate, Shard
from torch.utils._pytree import tree_flatten, tree_map_only

from .compute_estimation import _get_sharded_shape, estimate_strategy_runtime_cost
from .compute_estimation import (
_get_sharded_shape_stride,
estimate_strategy_runtime_cost,
)
from .propagation_rules import _create_all_options
from .utils import get_placement_options

Expand Down Expand Up @@ -488,7 +491,7 @@ def add_parameter_memory_constraint(self, memory_factor_low, memory_factor_high)
data = self.ds[(s_i, 0, ii, 0)]
spec = data["inp_strat"]
tensor_shape = spec.tensor_meta.shape
new_tensor_shape = _get_sharded_shape(spec)
new_tensor_shape, _ = _get_sharded_shape_stride(spec)
new_size = math.prod(new_tensor_shape)
old_size = math.prod(tensor_shape)
elms.append(data["va"] * new_size / old_size)
Expand Down