Trigger CI

CODEOWNERS

@@ -42,8 +42,8 @@
 /plugin/          @pllarroy
 
 # CMake
-CMakeLists.txt    @szha @rahul003 @pllarroy
-/cmake/           @szha @rahul003 @pllarroy
+CMakeLists.txt    @szha @pllarroy
+/cmake/           @szha @pllarroy
 
 # MXNet CI
 dev_menu.py         @pllarroy
@@ -71,4 +71,3 @@ prepare_mkl.sh    @szha
 
 # Github templates
 /.github/         @szha
-

benchmark/opperf/README.md

@@ -75,7 +75,7 @@ For example, you want to run benchmarks for all NDArray Broadcast Binary Operato
 
 ```
 #!/usr/bin/python
-from benchmark.opperf.tensor_operations.binary_broadcast_operators import run_mx_binary_broadcast_operators_benchmarks
+from benchmark.opperf.nd_operations.binary_broadcast_operators import run_mx_binary_broadcast_operators_benchmarks
 
 # Run all Binary Broadcast operations benchmarks with default input values
 print(run_mx_binary_broadcast_operators_benchmarks())

benchmark/opperf/nd_operations/README.md

@@ -28,17 +28,14 @@
 6. reshape
 7. one_hot
 8. linalg_potri
-9. mp_sgd_update
 10. multi_sgd_update
-11. signum_update
 12. Convolution_v1
 13. repeat
 14. Custom
 15. softmax_cross_entropy
 16. SwapAxis
 17. norm
 18. Softmax
-19. rmspropalex_update
 20. fill_element_0index
 21. cast
 22. UpSampling
@@ -52,37 +49,29 @@
 30. Activation
 31. LinearRegressionOutput
 32. Pooling_v1
-33. ftml_update
 34. Crop
 35. ElementWiseSum
 36. diag
 37. Reshape
 38. Pad
 39. linalg_gemm2
 40. crop
-41. rmsprop_update
 43. RNN
 45. SoftmaxOutput
 46. linalg_extractdiag
-47. sgd_mom_update
 48. SequenceLast
-50. flip
 51. SequenceReverse
-52. swapaxes
 53. SVMOutput
 54. linalg_trsm
 55. where
 56. SoftmaxActivation
-57. signsgd_update
 58. slice
 59. linalg_gelqf
 60. softmin
 61. linalg_gemm
 62. BilinearSampler
-63. mp_sgd_mom_update
 64. choose_element_0index
 65. tile
-66. space_to_depth
 67. gather_nd
 69. SequenceMask
 70. reshape_like
@@ -110,14 +99,11 @@
 94. broadcast_like
 95. Embedding
 96. linalg_makediag
-97. transpose
 98. linalg_syrk
 99. squeeze
 101. ROIPooling
-102. ftrl_update
 103. SliceChannel
 104. slice_like
-105. depth_to_space
 106. linalg_maketrian
 108. pad
 109. LayerNorm
@@ -132,6 +118,4 @@
 119. normal
 120. take
 121. MakeLoss
-122. sgd_update
-123. adam_update
-124. concat
+124. concat

benchmark/opperf/nd_operations/array_rearrange.py

@@ -0,0 +1,57 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
+from benchmark.opperf.utils.op_registry_utils import get_all_rearrange_operators
+
+"""Performance benchmark tests for MXNet NDArray Rearrange Operators.
+
+1. transpose
+2. swapaxes
+3. flip
+4. depth_to_space
+5. space_to_depth
+"""
+
+
+def run_rearrange_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+    """Runs benchmarks with the given context and precision (dtype) for all the
+    rearrange operators  in MXNet.
+
+    Parameters
+    ----------
+    ctx: mx.ctx
+        Context to run benchmarks
+    dtype: str, default 'float32'
+        Precision to use for benchmarks
+    warmup: int, default 25
+        Number of times to run for warmup
+    runs: int, default 100
+        Number of runs to capture benchmark results
+
+    Returns
+    -------
+    Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
+
+    """
+    # Fetch all optimizer operators
+    mx_rearrange_ops = get_all_rearrange_operators()
+
+    # Run benchmarks
+    mx_rearrange_op_results = run_op_benchmarks(mx_rearrange_ops, dtype, ctx, warmup, runs)
+    return mx_rearrange_op_results

benchmark/opperf/nd_operations/binary_operators.py

@@ -39,7 +39,7 @@
 
 
 def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
-    """Runs benchmarks with the given context and precision (dtype)for all the binary
+    """Runs benchmarks with the given context and precision (dtype) for all the binary
     broadcast operators in MXNet.
 
     Parameters
@@ -66,7 +66,7 @@ def run_mx_binary_broadcast_operators_benchmarks(ctx=mx.cpu(), dtype='float32',
 
 
 def run_mx_binary_element_wise_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
-    """Runs benchmarks with the given context and precision (dtype)for all the binary
+    """Runs benchmarks with the given context and precision (dtype) for all the binary
     element_wise operators in MXNet.
 
     Parameters

benchmark/opperf/nd_operations/nn_optimizer_operators.py

@@ -0,0 +1,64 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
+from benchmark.opperf.utils.op_registry_utils import get_all_optimizer_operators
+
+"""Performance benchmark tests for MXNet Neural Network Optimizer Update Operators.
+
+1. Stochastic Gradient Descent (SGD)
+    1.1 mp_sgd_update
+    1.2 sgd_mom_update
+    1.3 signsgd_update
+    1.4 mp_sgd_mom_update
+    1.5 sgd_update
+2. signum_update
+3. rmspropalex_update
+4. ftml_update
+5. rmsprop_update
+6. ftrl_update
+7. adam_update
+"""
+
+
+def run_optimizer_operators_benchmarks(ctx=mx.cpu(), dtype='float32', warmup=25, runs=100):
+    """Runs benchmarks with the given context and precision (dtype) for all the neural network
+    optimizer update operators in MXNet.
+
+    Parameters
+    ----------
+    ctx: mx.ctx
+        Context to run benchmarks
+    dtype: str, default 'float32'
+        Precision to use for benchmarks
+    warmup: int, default 25
+        Number of times to run for warmup
+    runs: int, default 100
+        Number of runs to capture benchmark results
+
+    Returns
+    -------
+    Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
+
+    """
+    # Fetch all optimizer operators
+    mx_optimizer_ops = get_all_optimizer_operators()
+
+    # Run benchmarks
+    mx_optimizer_op_results = run_op_benchmarks(mx_optimizer_ops, dtype, ctx, warmup, runs)
+    return mx_optimizer_op_results

benchmark/opperf/opperf.py

@@ -39,6 +39,8 @@
 from benchmark.opperf.nd_operations.nn_conv_operators import run_pooling_operators_benchmarks, \
     run_convolution_operators_benchmarks, run_transpose_convolution_operators_benchmarks
 from benchmark.opperf.nd_operations.nn_basic_operators import run_nn_basic_operators_benchmarks
+from benchmark.opperf.nd_operations.nn_optimizer_operators import run_optimizer_operators_benchmarks
+from benchmark.opperf.nd_operations.array_rearrange import run_rearrange_operators_benchmarks
 
 from benchmark.opperf.utils.common_utils import merge_map_list, save_to_file
 from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark, \
@@ -78,6 +80,9 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # Run all Sorting and Searching operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_sorting_searching_operators_benchmarks(ctx=ctx, dtype=dtype))
 
+    # Run all Array Rearrange operations benchmarks with default input values
+    mxnet_operator_benchmark_results.append(run_rearrange_operators_benchmarks(ctx=ctx, dtype=dtype))
+
     # ************************ MXNET NN OPERATOR BENCHMARKS ****************************
 
     # Run all basic NN operations benchmarks with default input values
@@ -92,6 +97,8 @@ def run_all_mxnet_operator_benchmarks(ctx=mx.cpu(), dtype='float32'):
     # Run all Convolution operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
 
+    # Run all Optimizer operations benchmarks with default input values
+    mxnet_operator_benchmark_results.append(run_optimizer_operators_benchmarks(ctx=ctx, dtype=dtype))
     # Run all Transpose Convolution operations benchmarks with default input values
     mxnet_operator_benchmark_results.append(run_transpose_convolution_operators_benchmarks(ctx=ctx, dtype=dtype))
 

benchmark/opperf/rules/default_params.py

@@ -34,6 +34,7 @@
 
 # For operators like - random_uniform, random_normal etc..
 DEFAULT_SHAPE = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_SAMPLE = [(2,)]
 DEFAULT_LOW = [0]
 DEFAULT_HIGH = [5]
 DEFAULT_K = [1]
@@ -62,8 +63,44 @@
 # NOTE: Data used is DEFAULT_DATA
 DEFAULT_AXIS = [0]
 
+# For optimizer operators
+DEFAULT_WEIGHT = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_GRAD = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_MOM = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_MEAN = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_VAR = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_N = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_D = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_V = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_Z = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_G = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_DELTA = [(1024, 1024), (10000, 1), (10000, 100)]
+DEFAULT_LRS = [(0.1,0.1)]
+DEFAULT_LR = [0.1,0.5,0.9]
+DEFAULT_GAMMA_1 = [0.1,0.5,0.9]
+DEFAULT_GAMMA_2 = [0.1,0.5,0.9]
+DEFAULT_EPSILON = [1e-08]
+DEFAULT_BETA_1 = [0.1,0.5,0.9]
+DEFAULT_BETA_2 = [0.1,0.5,0.9]
+DEFAULT_T = [1,5]
+DEFAULT_RESCALE_GRAD = [0.4, 0.77]
+DEFAULT_CLIP_GRADIENT = [-1.0,0.8]
+DEFAULT_CLIP_WEIGHTS = [-1.0,0.8]
+DEFAULT_LAZY_UPDATE = [0,1]
+
+# For rearrange operators
+# NOTE: Data needs to be a 4D tensor for  operators like space_to_depth and depth_to_space
+# Hence below we append 4d to mark the difference.
+# For depth_to_space, dimension 3 needs to be a multiple of 'block' and 1 should be a multiple of `block^2`
+DEFAULT_DATA_4d = [(1, 4, 2, 4), (10,25,10,100)]
+DEFAULT_DIM_1 = [0, 1, 2, 3]
+DEFAULT_DIM_2 = [1, 2, 3, 0]
+DEFAULT_BLOCK_SIZE = [2, 5]
+
+
 # Default Inputs. MXNet Op Param Name to Default Input mapping
 DEFAULTS_INPUTS = {"data": DEFAULT_DATA,
+                   "sample": DEFAULT_SAMPLE,
                    "lhs": DEFAULT_LHS,
                    "rhs": DEFAULT_RHS,
                    "shape": DEFAULT_SHAPE,
@@ -81,13 +118,43 @@
                    "k_nd": DEFAULT_K_ND,
                    "p_nd": DEFAULT_P_ND,
                    "axis_shape": DEFAULT_AXIS_SHAPE,
-                   "axis": DEFAULT_AXIS}
+                   "axis": DEFAULT_AXIS,
+                   "weight" : DEFAULT_WEIGHT,
+                   "weight32" : DEFAULT_WEIGHT,
+                   "grad" : DEFAULT_GRAD,
+                   "mean" : DEFAULT_MEAN,
+                   "var" : DEFAULT_VAR,
+                   "mom" : DEFAULT_MOM,
+                   "n" : DEFAULT_N,
+                   "d" : DEFAULT_D,
+                   "v" : DEFAULT_V,
+                   "z" : DEFAULT_Z,
+                   "g" : DEFAULT_G,
+                   "delta" : DEFAULT_DELTA,
+                   "lr" : DEFAULT_LR,
+                   "lrs" : DEFAULT_LRS,
+                   "wds" : DEFAULT_LRS,
+                   "gamma1" : DEFAULT_GAMMA_1,
+                   "gamma2" : DEFAULT_GAMMA_2,
+                   "epsilon" : DEFAULT_EPSILON,
+                   "beta1" : DEFAULT_BETA_1,
+                   "beta2" : DEFAULT_BETA_2,
+                   "t" : DEFAULT_T,
+                   "rescale_grad" : DEFAULT_RESCALE_GRAD,
+                   "clip_grad" : DEFAULT_CLIP_GRADIENT,
+                   "lazy_update" : DEFAULT_LAZY_UPDATE,
+                   "data_4d": DEFAULT_DATA_4d,
+                   "dim1": DEFAULT_DIM_1,
+                   "dim2": DEFAULT_DIM_2,
+                   "block_size": DEFAULT_BLOCK_SIZE}
+
 
 # These are names of MXNet operator parameters that is of type NDArray.
 # We maintain this list to automatically recognize these parameters are to be
 # given as NDArray and translate users inputs such as a shape tuple, Numpy Array or
 # a list to MXNet NDArray. This is just a convenience added so benchmark utility users
 # can just say shape of the tensor, and we automatically create Tensors.
-PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp",
+PARAMS_OF_TYPE_NDARRAY = ["lhs", "rhs", "data", "base", "exp", "sample",
                           "mu", "sigma", "lam", "alpha", "beta", "gamma", "k", "p",
-                          "low", "high", "weight", "bias", "moving_mean", "moving_var"]
+                          "low", "high", "weight", "bias", "moving_mean", "moving_var",
+                          "weight", "weight32", "grad", "mean", "var", "mom", "n", "d", "v", "z", "g", "delta"]