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enable detailed memory breakdown for fixed number of iterations #5170

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enable detailed memory breakdown for fixed number of iterations #5170

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18 changes: 11 additions & 7 deletions fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1430,6 +1430,10 @@ def __init__( # noqa C901
self.step = 0
self.last_reported_step = 0
self.last_reported_uvm_stats: list[float] = []
# Track number of times detailed memory breakdown has been reported
self.detailed_mem_breakdown_report_count = 0
# Set max number of reports for detailed memory breakdown
self.max_detailed_mem_breakdown_reports = 10

# Check whether to use TBE v2
is_experimental = False
Expand Down Expand Up @@ -1885,14 +1889,14 @@ def _report_tbe_mem_usage(self) -> None:
tbe_id=self.uuid,
)

# Check if detailed memory breakdown is enabled via environment variable
# Set FBGEMM_TBE_MEM_BREAKDOWN=1 to enable expensive detailed breakdown
enable_detailed_breakdown = (
int(os.environ.get("FBGEMM_TBE_MEM_BREAKDOWN", "0")) == 1
)

if not enable_detailed_breakdown:
# Only report detailed breakdown for the first max_detailed_mem_breakdown_reports reportable
# steps since static sparse memory (weights, optimizer states, cache) is constant
if (
self.detailed_mem_breakdown_report_count
>= self.max_detailed_mem_breakdown_reports
):
return
self.detailed_mem_breakdown_report_count += 1

# Tensor groups for sparse memory categorization
weight_tensors = ["weights_dev", "weights_host", "weights_uvm"]
Expand Down
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