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Cleaned up GEMM op inputs
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@connorgoggins
connorgoggins committed Feb 28, 2020
1 parent 52e0aea commit fec6fb2
Showing 1 changed file with 65 additions and 72 deletions.
137 changes: 65 additions & 72 deletions benchmark/opperf/nd_operations/gemm_operators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,83 +59,76 @@ def run_gemm_operators_benchmarks(ctx=mx.cpu(), dtype='float32', profiler='nativ
Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
"""
# Benchmark tests for dot and batch_dot operators
if int64_tensor == "on":
dot_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "dot")], run_backward=True,
dtype=dtype, ctx=ctx,
inputs=[{"lhs": (2**16, 2**16),
"rhs": (2**16, 2**16)},
{"lhs": (4, 2**30),
"rhs": (4, 2**30),
"transpose_b": True},
{"lhs": (2**28, 16),
"rhs": (16, 2**28),
"transpose_a": True,
"transpose_b": True}],
warmup=warmup, runs=runs, profiler=profiler)
standard_inputs_dot = [{"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}]
int64_tensor_inputs_dot = [{"lhs": (2**16, 2**16),
"rhs": (2**16, 2**16)},
{"lhs": (4, 2**30),
"rhs": (4, 2**30),
"transpose_b": True},
{"lhs": (2**28, 16),
"rhs": (16, 2**28),
"transpose_a": True,
"transpose_b": True}]
standard_inputs_batch_dot = [{"lhs": (32, 1024, 1024),
"rhs": (32, 1024, 1024)},
{"lhs": (32, 1000, 10),
"rhs": (32, 1000, 10),
"transpose_b": True},
{"lhs": (32, 1000, 1),
"rhs": (32, 100, 1000),
"transpose_a": True,
"transpose_b": True}]
int64_tensor_inputs_batch_dot = [{"lhs": (1, 2**16, 2**16),
"rhs": (1, 2**16, 2**16)},
{"lhs": (1, 4, 2**30),
"rhs": (1, 4, 2**30),
"transpose_b": True},
{"lhs": (1, 2**28, 16),
"rhs": (1, 16, 2**28),
"transpose_a": True,
"transpose_b": True}]
standard_inputs_khatri_rao = [{"args": [(32, 32), (32, 32)]},
{"args": [(64, 64), (64, 64)]}]
int64_tensor_inputs_khatri_rao = [{"args": [(2**32, 1), (2**32, 1)]}]

batch_dot_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "batch_dot")], run_backward=True,
dtype=dtype, ctx=ctx,
inputs=[{"lhs": (1, 2**16, 2**16),
"rhs": (1, 2**16, 2**16)},
{"lhs": (1, 4, 2**30),
"rhs": (1, 4, 2**30),
"transpose_b": True},
{"lhs": (1, 2**28, 16),
"rhs": (1, 16, 2**28),
"transpose_a": True,
"transpose_b": True}],
warmup=warmup, runs=runs, profiler=profiler)
# Operator khatri_rao is not yet implemented for GPU
khatri_rao_benchmark_res = []
if ctx != mx.gpu():
# Benchmark tests for khatri_rao operator
khatri_rao_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "khatri_rao")], run_backward=False,
dtype=dtype, ctx=ctx,
inputs=[{"args": [(32, 32), (32, 32)]},
{"args": [(64, 64), (64, 64)]}],
warmup=warmup, runs=runs, profiler=profiler)
if int64_tensor == 'on':
inputs_dot = int64_tensor_inputs_dot
inputs_batch_dot = int64_tensor_inputs_batch_dot
inputs_khatri_rao = int64_tensor_inputs_khatri_rao
else:
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)
inputs_dot = standard_inputs_dot
inputs_batch_dot = standard_inputs_batch_dot
inputs_khatri_rao = standard_inputs_khatri_rao

# 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=inputs_dot,
warmup=warmup, runs=runs, profiler=profiler)

batch_dot_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "batch_dot")], run_backward=True,
batch_dot_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "batch_dot")], run_backward=True,
dtype=dtype, ctx=ctx,
inputs=inputs_batch_dot,
warmup=warmup, runs=runs, profiler=profiler)
# Operator khatri_rao is not yet implemented for GPU
khatri_rao_benchmark_res = []
if ctx != mx.gpu():
# Benchmark tests for khatri_rao operator
khatri_rao_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "khatri_rao")], run_backward=False,
dtype=dtype, ctx=ctx,
inputs=[{"lhs": (32, 1024, 1024),
"rhs": (32, 1024, 1024)},
{"lhs": (32, 1000, 10),
"rhs": (32, 1000, 10),
"transpose_b": True},
{"lhs": (32, 1000, 1),
"rhs": (32, 100, 1000),
"transpose_a": True,
"transpose_b": True}],
inputs=inputs_khatri_rao,
warmup=warmup, runs=runs, profiler=profiler)
# Operator khatri_rao is not yet implemented for GPU
khatri_rao_benchmark_res = []
if ctx != mx.gpu():
# Benchmark tests for khatri_rao operator
khatri_rao_benchmark_res = run_performance_test(
[getattr(MX_OP_MODULE, "khatri_rao")], run_backward=False,
dtype=dtype, ctx=ctx,
inputs=[{"args": [(32, 32), (32, 32)]},
{"args": [(64, 64), (64, 64)]}],
warmup=warmup, runs=runs, profiler=profiler)

# Prepare combined results for GEMM operators
mx_gemm_op_results = merge_map_list(dot_benchmark_res + batch_dot_benchmark_res + khatri_rao_benchmark_res)
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