Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into int8_quantize

test/py/test_cpu.py

 import migraphx
 
-p = migraphx.parse_onnx("conv_relu_maxpool.onnx")
+p = migraphx.parse_onnx("conv_relu_maxpool_test.onnx")
 print(p)
 s1 = p.get_shape()
 print("Compiling ...")

test/py/test_gpu.py

 import migraphx
 
-p = migraphx.parse_onnx("conv_relu_maxpool.onnx")
+p = migraphx.parse_onnx("conv_relu_maxpool_test.onnx")
 print(p)
 print("Compiling ...")
 p.compile(migraphx.get_target("gpu"))

test/tf/gen_tf_pb.py

+import numpy as np
+import tensorflow as tf
+
+
+def tf_test(op_test):
+    def run_test():
+        g1 = tf.Graph()
+        op_test(g1)
+        tf.io.write_graph(g1,
+                          '.',
+                          '{}.pb'.format(op_test.__name__),
+                          as_text=False)
+
+    return run_test
+
+
+@tf_test
+def add_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
+        tf.add(g1_input, g2_input, name='add1')
+
+
+@tf_test
+def add_bcast_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(2, 1), name='1')
+        tf.math.add(g1_input, g2_input, name='add_bcast1')
+
+
+@tf_test
+def assert_less_equal_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(2, 3), name='1')
+        with tf.control_dependencies(
+            [tf.assert_less_equal(g1_input, g2_input)]):
+            tf.add(g1_input, g2_input, name='add1')
+
+
+@tf_test
+def batchmatmul_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 2, 8, 4), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 2, 4, 8), name='1')
+        tf.matmul(g1_input,
+                  g2_input,
+                  transpose_a=True,
+                  transpose_b=True,
+                  name='batchmatmul1')
+
+
+@tf_test
+def batchnorm_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name='0')
+        g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='1')
+        g1_offset = tf.placeholder(tf.float32, shape=(32), name='2')
+        g1_mean = tf.placeholder(tf.float32, shape=(32), name='3')
+        g1_variance = tf.placeholder(tf.float32, shape=(32), name='4')
+        tf.nn.fused_batch_norm(g1_input,
+                               g1_scale,
+                               g1_offset,
+                               g1_mean,
+                               g1_variance,
+                               epsilon=0.00001,
+                               is_training=False,
+                               name='batchnorm1')
+
+
+@tf_test
+def biasadd_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 500), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(500), name='1')
+        tf.nn.bias_add(g1_input, g2_input, name='bias_add1')
+
+
+@tf_test
+def cast_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.cast(g1_input, dtype=tf.int32, name='cast1')
+
+
+@tf_test
+def concat_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(4, 7, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(4, 2, 3), name='1')
+        tf.concat([g1_input, g2_input], axis=1, name='concat1')
+
+
+@tf_test
+def const_test(g1):
+    with g1.as_default():
+        tf.constant(1.0, dtype=tf.float32, name='constant1')
+
+
+@tf_test
+def conv_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
+        g1_weights = tf.constant(value=1.0,
+                                 dtype=tf.float32,
+                                 shape=(3, 3, 3, 32),
+                                 name='1')
+        tf.nn.conv2d(g1_input, g1_weights, [1, 1, 1, 1], "SAME", name='conv1')
+
+
+@tf_test
+def depthwiseconv_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
+        g1_weights = tf.constant(value=1.0,
+                                 dtype=tf.float32,
+                                 shape=(3, 3, 3, 1),
+                                 name='1')
+        tf.nn.depthwise_conv2d_native(g1_input,
+                                      g1_weights, [1, 1, 1, 1],
+                                      "SAME",
+                                      name='depthwiseconv1')
+
+
+@tf_test
+def expanddims_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2, 3, 4), name='0')
+        tf.expand_dims(g1_input, axis=-1, name='expanddims_neg')
+
+
+@tf_test
+def gather_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2, 4), name='0')
+        tf.gather(g1_input, [1, 1], axis=1, name='gather1')
+
+
+@tf_test
+def identity_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.identity(g1_input, 'identity')
+
+
+@tf_test
+def matmul_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(8, 4), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(4, 8), name='1')
+        tf.matmul(g1_input,
+                  g2_input,
+                  transpose_a=True,
+                  transpose_b=True,
+                  name='matmul1')
+
+
+@tf_test
+def mean_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.math.reduce_mean(g1_input, axis=(2, 3), keepdims=True, name='mean1')
+        tf.math.reduce_mean(g1_input,
+                            axis=(2, 3),
+                            keepdims=False,
+                            name='mean2')
+
+
+@tf_test
+def mean_test_nhwc(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
+        tf.math.reduce_mean(g1_input, axis=(1, 2), keepdims=True, name='mean1')
+        tf.math.reduce_mean(g1_input,
+                            axis=(1, 2),
+                            keepdims=False,
+                            name='mean2')
+
+
+@tf_test
+def mul_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 16), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 16), name='1')
+        tf.multiply(g1_input, g2_input, name='mul1')
+
+
+@tf_test
+def pack_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(2), name='1')
+        g3_input = tf.placeholder(tf.float32, shape=(2), name='2')
+        tf.stack([g1_input, g2_input, g3_input], axis=1, name='pack1')
+
+
+@tf_test
+def pack_test_nhwc(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='1')
+        g3_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 2), name='2')
+        tf.stack([g1_input, g2_input, g3_input], axis=3, name='pack1')
+
+
+@tf_test
+def pooling_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 3), name='0')
+        tf.nn.avg_pool(value=g1_input,
+                       ksize=(1, 2, 2, 1),
+                       strides=(1, 2, 2, 1),
+                       padding='VALID',
+                       data_format='NHWC',
+                       name='avg_pooling')
+        tf.nn.max_pool(value=g1_input,
+                       ksize=(1, 2, 2, 1),
+                       strides=(1, 2, 2, 1),
+                       padding='VALID',
+                       data_format='NHWC',
+                       name='max_pooling')
+
+
+@tf_test
+def pow_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
+        tf.pow(g1_input, g2_input, name='pow1')
+
+
+@tf_test
+def relu_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.nn.relu(g1_input, 'relu')
+
+
+@tf_test
+def relu6_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.nn.relu6(g1_input, 'relu6')
+
+
+@tf_test
+def reshape_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(16), name='0')
+        tf.reshape(g1_input, (1, 1, 1, 16), 'reshape')
+
+
+@tf_test
+def rsqrt_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.math.rsqrt(g1_input, 'rsqrt')
+
+
+@tf_test
+def slice_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(5, 10), name='0')
+        tf.slice(g1_input, [1, 0], [2, -1], name='slice1')
+
+
+@tf_test
+def softmax_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3), name='0')
+        tf.nn.softmax(g1_input, name='softmax')
+
+
+@tf_test
+def sqdiff_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
+        tf.squared_difference(g1_input, g2_input, name='sqdiff')
+
+
+@tf_test
+def squeeze_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 2, 3, 1), name='0')
+        tf.squeeze(g1_input, name='squeeze')
+
+
+@tf_test
+def stopgradient_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.stop_gradient(g1_input, 'stopgradient')
+
+
+@tf_test
+def stridedslice_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 1, 1, 10), name='0')
+        tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1, 1, 1, 1],
+                         shrink_axis_mask=2,
+                         name='stridedslice1')
+
+
+@tf_test
+def stridedslice_masks_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 3, 10), name='0')
+        tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1, 1, 1, 1],
+                         begin_mask=9,
+                         end_mask=15,
+                         name='stridedslice1')
+
+
+@tf_test
+def sub_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='0')
+        g2_input = tf.placeholder(tf.float32, shape=(1, 2, 2, 3), name='1')
+        tf.subtract(g1_input, g2_input, name='sub1')
+
+
+@tf_test
+def tanh_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.tanh(g1_input, 'tanh')
+
+
+@tf_test
+def transpose_test(g1):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 3, 16, 16), name='0')
+        tf.transpose(g1_input, perm=[0, 2, 3, 1], name='transpose')