[GitHub] [incubator-mxnet] connorgoggins commented on a change in pull request #17449: Implemented large tensor flag for opperf testing

[GitBox <gi...@apache.org>]

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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