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Improve CPU benchmark for KJT #2001

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14 changes: 10 additions & 4 deletions torchrec/distributed/benchmark/benchmark_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,14 +106,20 @@ class BenchmarkResult:
max_mem_allocated: List[int] # megabytes
rank: int = -1

def runtime_percentile(self, percentile: int = 50) -> torch.Tensor:
def runtime_percentile(
self, percentile: int = 50, interpolation: str = "nearest"
) -> torch.Tensor:
return torch.quantile(
self.elapsed_time, percentile / 100.0, interpolation="nearest"
self.elapsed_time,
percentile / 100.0,
interpolation=interpolation,
)

def max_mem_percentile(self, percentile: int = 50) -> torch.Tensor:
def max_mem_percentile(
self, percentile: int = 50, interpolation: str = "nearest"
) -> torch.Tensor:
max_mem = torch.tensor(self.max_mem_allocated, dtype=torch.float)
return torch.quantile(max_mem, percentile / 100.0, interpolation="nearest")
return torch.quantile(max_mem, percentile / 100.0, interpolation=interpolation)


class ECWrapper(torch.nn.Module):
Expand Down
61 changes: 41 additions & 20 deletions torchrec/distributed/test_utils/test_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,8 @@ def generate(
long_indices: bool = True,
tables_pooling: Optional[List[int]] = None,
weighted_tables_pooling: Optional[List[int]] = None,
randomize_indices: bool = True,
device: Optional[torch.device] = None,
) -> Tuple["ModelInput", List["ModelInput"]]:
"""
Returns a global (single-rank training) batch
Expand Down Expand Up @@ -132,15 +134,16 @@ def _validate_pooling_factor(
idlist_pooling_factor[idx],
idlist_pooling_factor[idx] / 10,
[batch_size * world_size],
device=device,
),
torch.tensor(1.0),
torch.tensor(1.0, device=device),
).int()
else:
lengths_ = torch.abs(
torch.randn(batch_size * world_size) + pooling_avg
torch.randn(batch_size * world_size, device=device) + pooling_avg,
).int()
if variable_batch_size:
lengths = torch.zeros(batch_size * world_size).int()
lengths = torch.zeros(batch_size * world_size, device=device).int()
for r in range(world_size):
lengths[r * batch_size : r * batch_size + batch_size_by_rank[r]] = (
lengths_[
Expand All @@ -150,12 +153,20 @@ def _validate_pooling_factor(
else:
lengths = lengths_
num_indices = cast(int, torch.sum(lengths).item())
indices = torch.randint(
0,
ind_range,
(num_indices,),
dtype=torch.long if long_indices else torch.int32,
)
if randomize_indices:
indices = torch.randint(
0,
ind_range,
(num_indices,),
dtype=torch.long if long_indices else torch.int32,
device=device,
)
else:
indices = torch.zeros(
(num_indices),
dtype=torch.long if long_indices else torch.int32,
device=device,
)
global_idlist_lengths.append(lengths)
global_idlist_indices.append(indices)
global_idlist_kjt = KeyedJaggedTensor(
Expand All @@ -167,15 +178,15 @@ def _validate_pooling_factor(
for idx in range(len(idscore_ind_ranges)):
ind_range = idscore_ind_ranges[idx]
lengths_ = torch.abs(
torch.randn(batch_size * world_size)
torch.randn(batch_size * world_size, device=device)
+ (
idscore_pooling_factor[idx]
if idscore_pooling_factor
else pooling_avg
)
).int()
if variable_batch_size:
lengths = torch.zeros(batch_size * world_size).int()
lengths = torch.zeros(batch_size * world_size, device=device).int()
for r in range(world_size):
lengths[r * batch_size : r * batch_size + batch_size_by_rank[r]] = (
lengths_[
Expand All @@ -185,13 +196,21 @@ def _validate_pooling_factor(
else:
lengths = lengths_
num_indices = cast(int, torch.sum(lengths).item())
indices = torch.randint(
0,
ind_range,
(num_indices,),
dtype=torch.long if long_indices else torch.int32,
)
weights = torch.rand((num_indices,))
if randomize_indices:
indices = torch.randint(
0,
ind_range,
(num_indices,),
dtype=torch.long if long_indices else torch.int32,
device=device,
)
else:
indices = torch.zeros(
(num_indices),
dtype=torch.long if long_indices else torch.int32,
device=device,
)
weights = torch.rand((num_indices,), device=device)
global_idscore_lengths.append(lengths)
global_idscore_indices.append(indices)
global_idscore_weights.append(weights)
Expand All @@ -206,8 +225,10 @@ def _validate_pooling_factor(
else None
)

global_float = torch.rand((batch_size * world_size, num_float_features))
global_label = torch.rand(batch_size * world_size)
global_float = torch.rand(
(batch_size * world_size, num_float_features), device=device
)
global_label = torch.rand(batch_size * world_size, device=device)

# Split global batch into local batches.
local_inputs = []
Expand Down
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