connorgoggins commented on a change in pull request #17449: Implemented large
tensor flag for opperf testing
URL: https://github.com/apache/incubator-mxnet/pull/17449#discussion_r378430775
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File path: benchmark/opperf/nd_operations/gemm_operators.py
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@@ -55,33 +57,62 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(),
dtype='float32', profiler='nativ
"""
# Benchmark tests for dot and batch_dot operators
- dot_benchmark_res = run_performance_test(
- [getattr(MX_OP_MODULE, "dot")], run_backward=True,
- dtype=dtype, ctx=ctx,
- inputs=[{"lhs": (1024, 1024),
- "rhs": (1024, 1024)},
- {"lhs": (1000, 10),
- "rhs": (1000, 10),
- "transpose_b": True},
- {"lhs": (1000, 1),
- "rhs": (100, 1000),
- "transpose_a": True,
- "transpose_b": True}],
- warmup=warmup, runs=runs, profiler=profiler)
+ if large_tensor == "on":
Review comment:
The purpose of this flag wouldn't be for use on user-specified shapes, it
would be for general category and full suite testing of operator performance on
input data with dimensions >= 2^32. If the user wanted to test individual
operators with custom shapes, they would use `run_performance_test()` and add
their custom data as input - they wouldn't use the flag in that case, as the
`run_performance_test()` function doesn't take in the `large_tensor` flag as an
argument.
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