[OpPerf] Implement remaining random sampling ops (apache#17502)

* Added support for remaining random sampling ops, removed exception for sample_multinomial as it does work with random data

* Dropped unused unique_ops variable

* Added affine transform, dropped parentheses, changed 19 to 18

* Dropped unique_ops condition - no longer in use

* Fixed indentation

* Dropped unique_ops

benchmark/opperf/nd_operations/random_sampling_operators.py

@@ -19,12 +19,12 @@
 1. Operators are automatically fetched from MXNet operator registry.
 2. Default Inputs are generated. See rules/default_params.py. You can override the default values.
 
-Below 16 random sampling Operators are covered:
+Below 18 random sampling Operators are covered:
 
 ['random_exponential', 'random_gamma', 'random_generalized_negative_binomial', 'random_negative_binomial',
 'random_normal', 'random_poisson', 'random_randint', 'random_uniform', 'sample_exponential', 'sample_gamma',
 'sample_generalized_negative_binomial', 'sample_multinomial', 'sample_negative_binomial', 'sample_normal',
-'sample_poisson', 'sample_uniform']
+'sample_poisson', 'sample_uniform', 'GridGenerator', 'BilinearSampler']
 
 """
 

benchmark/opperf/rules/default_params.py

@@ -63,6 +63,12 @@
 DEFAULT_LAM = [[1.0, 8.5]]
 DEFAULT_K_ND = [[20, 49]]
 DEFAULT_P_ND = [[0.4, 0.77]]
+DEFAULT_GRID = [(32, 2, 256, 256)]
+DEFAULT_DATA_BILINEAR = [(32, 2, 256, 256)]
+DEFAULT_TRANSFORM_TYPE = ['warp', 'affine']
+DEFAULT_DATA_GRIDGEN = [(32, 2, 256, 256), (256, 6)]
+DEFAULT_TARGET_SHAPE = [(256, 6)]
+DEFAULT_DATA_SM = [(32, 32), (64, 64)]
 
 # For reduction operators
 # NOTE: Data used is DEFAULT_DATA
@@ -194,7 +200,13 @@
  "data_3d": DEFAULT_DATA_3d,
  "label_smce": DEFAULT_LABEL_SMCE,
  "label": DEFAULT_LABEL,
- "index": DEFAULT_INDEX}
+ "index": DEFAULT_INDEX,
+ "grid": DEFAULT_GRID,
+ "data_bilinearsampler": DEFAULT_DATA_BILINEAR,
+ "transform_type": DEFAULT_TRANSFORM_TYPE,
+ "data_gridgenerator": DEFAULT_DATA_GRIDGEN,
+ "target_shape_gridgenerator": DEFAULT_TARGET_SHAPE,
+ "data_sample_multinomial": DEFAULT_DATA_SM}
 
 
 # These are names of MXNet operator parameters that is of type NDArray.
@@ -207,4 +219,4 @@
  "low", "high", "weight", "bias", "moving_mean", "moving_var",
  "weight", "weight32", "grad", "mean", "var", "mom", "n", "d",
  "v", "z", "g", "delta", "args", "indices", "shape_like", "y",
- "x", "condition", "a", "index", "raveL_data", "label"]
+ "x", "condition", "a", "index", "raveL_data", "label", "grid"]

benchmark/opperf/utils/op_registry_utils.py

@@ -22,10 +22,6 @@
 
 from benchmark.opperf.rules.default_params import DEFAULTS_INPUTS, MX_OP_MODULE
 
-# Operators where parameter have special criteria that cannot be cleanly automated.
-# Example: sample_multinomial operator has a parameter 'data'. It expects values to sum up to 1.
-unique_ops = ("sample_multinomial",)
-
 
 def _select_ops(operator_names, filters=("_contrib", "_"), merge_op_forward_backward=True):
  """From a given list of operators, filter out all operator names starting with given filters and prepares
@@ -121,13 +117,16 @@ def prepare_op_inputs(op, arg_params):
 
  # 3d tensor is needed by following ops
  ops_3d = ['CTCLoss', 'ctc_loss']
+ custom_data = ['BilinearSampler', 'GridGenerator', 'sample_multinomial']
 
  # Prepare op to default input mapping
  arg_values = {}
  for arg_name, arg_type in zip(arg_params["params"]["arg_names"],
  arg_params["params"]["arg_types"]):
  if "NDArray" in arg_type and op == "ravel_multi_index":
  arg_values[arg_name] = DEFAULTS_INPUTS["ravel_data"]
+ elif op in custom_data and arg_name + "_" + op.lower() in DEFAULTS_INPUTS:
+ arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_" + op.lower()]
  elif "NDArray" in arg_type and arg_name + "_nd" in DEFAULTS_INPUTS:
  arg_values[arg_name] = DEFAULTS_INPUTS[arg_name + "_nd"]
  elif "NDArray" in arg_type and op in ops_4d and arg_name + "_4d" in DEFAULTS_INPUTS:
@@ -254,13 +253,16 @@ def get_all_random_sampling_operators():
  -------
  {"operator_name": {"has_backward", "nd_op_handle", "params"}}
  """
+ # Additional Random Sampling ops which do not start with "random_" or "sample_"
+ additional_random_sampling_ops = ['GridGenerator', 'BilinearSampler']
+
  # Get all mxnet operators
  mx_operators = _get_all_mxnet_operators()
 
  # Filter for Random Sampling operators
  random_sampling_mx_operators = {}
  for op_name, _ in mx_operators.items():
- if op_name.startswith(("random_", "sample_")) and op_name not in unique_ops:
+ if op_name.startswith(("random_", "sample_")) or op_name in additional_random_sampling_ops:
  random_sampling_mx_operators[op_name] = mx_operators[op_name]
  return random_sampling_mx_operators
 
@@ -279,8 +281,7 @@ def get_all_reduction_operators():
  reduction_mx_operators = {}
  for op_name, op_params in mx_operators.items():
  if op_params["params"]["narg"] == 4 and \
- set(["data", "axis", "exclude", "keepdims"]).issubset(set(op_params["params"]["arg_names"])) \
- and op_name not in unique_ops:
+ set(["data", "axis", "exclude", "keepdims"]).issubset(set(op_params["params"]["arg_names"])):
  reduction_mx_operators[op_name] = mx_operators[op_name]
  return reduction_mx_operators
 
@@ -301,8 +302,8 @@ def get_all_optimizer_operators():
 
  # Filter for Optimizer operators
  optimizer_mx_operators = {}
- for op_name, _ in mx_operators.items():
- if op_name in optimizer_ops and op_name not in unique_ops:
+ for op_name, op_params in mx_operators.items():
+ if op_name in optimizer_ops:
  optimizer_mx_operators[op_name] = mx_operators[op_name]
  return optimizer_mx_operators
 
@@ -320,8 +321,8 @@ def get_all_sorting_searching_operators():
 
  # Filter for Sort and search operators
  sort_search_mx_operators = {}
- for op_name, _ in mx_operators.items():
- if op_name in sort_search_ops and op_name not in unique_ops:
+ for op_name, op_params in mx_operators.items():
+ if op_name in sort_search_ops:
  sort_search_mx_operators[op_name] = mx_operators[op_name]
  return sort_search_mx_operators
 
@@ -340,8 +341,8 @@ def get_all_rearrange_operators():
 
  # Filter for Array Rearrange operators
  rearrange_mx_operators = {}
- for op_name, _ in mx_operators.items():
- if op_name in rearrange_ops and op_name not in unique_ops:
+ for op_name, op_params in mx_operators.items():
+ if op_name in rearrange_ops:
  rearrange_mx_operators[op_name] = mx_operators[op_name]
  return rearrange_mx_operators
 
@@ -366,7 +367,7 @@ def get_all_indexing_routines():
  # Filter for Indexing routines
  indexing_mx_routines = {}
  for op_name, _ in mx_operators.items():
- if op_name in indexing_routines and op_name not in unique_ops:
+ if op_name in indexing_routines:
  indexing_mx_routines[op_name] = mx_operators[op_name]
  return indexing_mx_routines
 
@@ -386,7 +387,7 @@ def get_all_loss_operators():
  # Filter for NN Loss operators
  loss_mx_operators = {}
  for op_name, op_params in mx_operators.items():
- if op_name in loss_ops and op_name not in unique_ops:
+ if op_name in loss_ops:
  loss_mx_operators[op_name] = mx_operators[op_name]
  return loss_mx_operators