srkreddy1238 commented on a change in pull request #5617:
URL: https://github.com/apache/incubator-tvm/pull/5617#discussion_r429331590
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -2896,15 +2903,29 @@ def _parse_import_prerequisites(self, graph):
"""
missing_operators = set()
for node in graph.node:
+ try:
+ from tensorflow.python.framework import op_def_registry
Review comment:
Can you confirm is op_def_registry is not part of all TF versions ? pls
confirn.
If this is not in 1.x we shouldn't error as the front end is compatible to
TF 1.x now.
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -3179,10 +3183,342 @@ def test_forward_isfinite():
_verify_infiniteness_ops(tf.is_finite, "isfinite")
+def _test_spop_placeholder_one():
+ with tf.Graph().as_default():
Review comment:
Advice to use appropriate name instead of _one / _two ...etc.
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -3155,6 +3176,91 @@ def _convert_control_flow_operator(self, node, inputs,
attrs, control_flow_node_
return op
+ def _partition_call_operator(self, inputs, attr):
+ """
+ Convert the Relay Partition call ops into Relay Function calls and
+ function definitions from Tensorflow graph library attribute to Relay
global
+ functions
+
+ Parameters
+ ----------
+ node: TensorFlow graph node object.
+ A TensorFlow graph node object.
+
+ inputs : List[tvm.relay.Expr]
+ List of input symbols.
+
+ attrs : Dict[tvm.Attrs]
+ Dict of operator attributes.
+
+ Returns
+ -------
+ op : tvm.relay.Expr
+ Converted relay expression.
+ """
+
+ try:
+ from tensorflow.python.framework import function_def_to_graph
+ except ImportError as e:
+ raise ImportError(
+ "Unable to import tensorflow which is required {}".format(e))
+
+ main_graph_proto = self._main_graph_proto
+ outer_graph_def = main_graph_proto._graph
+
+ node_func_name = attr.get('f').name
+ func = next((f for f in outer_graph_def.library.function
+ if f.signature.name == node_func_name), None)
+ if func:
+ devices = set(node.device for node in func.node_def)
+ if len(devices) > 1:
+ raise Exception("Found inconsistent Device assignment in the "\
+ "Stateful Partitioned SubGraph. Rejecting "\
+ "the subgraph ")
+ # Convert function definition to graph
+ func_input_shapes = func.attr["_input_shapes"].list.shape
+ subgraph, _ = function_def_to_graph.\
+ function_def_to_graph_def(func, func_input_shapes)
+
+ # Computing subgraph's input shape dictionary
+ subgraph_shape_dict, input_expr_dict = {}, {}
+ for f_arg, input in zip(func.signature.input_arg, inputs):
+ input_expr_dict[f_arg.name] = input
+ subgraph_shape_dict[f_arg.name] = _infer_shape(input,
main_graph_proto._mod)
+
+ func_name = 'func_{}'.format(func.signature.name)
+ try:
+ global_func = main_graph_proto._mod[func_name]
Review comment:
Is this the case where the function is called multiple times with in a
graph ?
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -2896,15 +2903,29 @@ def _parse_import_prerequisites(self, graph):
"""
missing_operators = set()
for node in graph.node:
+ try:
+ from tensorflow.python.framework import op_def_registry
+ except ImportError as e:
+ raise ImportError(
+ "Unable to import tensorflow which is required
{}".format(e))
+ getOpDef = op_def_registry._registered_ops.get if
hasattr(op_def_registry,\
+ "_registered_ops") else op_def_registry.get
+ op_def = getOpDef(node.op)
if node.op == "Placeholder" or node.op == 'PlaceholderWithDefault':
pass
elif node.op == "Const":
pass
+ elif node.op in ["PartitionedCall", "StatefulPartitionedCall"]:
+ pass
else:
if any([node.op in t for t in [_identity_list, _convert_map,
_convert_map_rnn,
_control_flow_nodes]]):
pass
+ elif op_def is not None and op_def.is_stateful:
+ raise Exception("Found a stateful operator in this graph
{}. "\
Review comment:
Better to add this to missing op list (with extended info if needed)
still instead of exception as we may miss over all missing ops list.
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -3179,10 +3183,342 @@ def test_forward_isfinite():
_verify_infiniteness_ops(tf.is_finite, "isfinite")
+def _test_spop_placeholder_one():
+ with tf.Graph().as_default():
+
+ @function.Defun(*[tf.int32]*2)
+ def Forward(x,y):
+ print(x.name)
+ print(y.name)
+ b = tf.add(x, y)
+ return b
+ pl1 = tf.placeholder(tf.int32,name="pl1")
+ pl2 = tf.placeholder(tf.int32,name="pl2")
+ pl3 = tf.placeholder(tf.int32, name="pl3")
+ data = np.array([[-1, 1], [2, -2]], dtype=np.int32)
+ data2 = np.array([[-2, 3], [4, -6]], dtype=np.int32)
+ data3 = np.array([[-2, 3], [4, -6]], dtype=np.int32)
+ z1 = gen_functional_ops.StatefulPartitionedCall(args=[pl1,pl2],
Tout=[tf.int32],f=Forward)
+ z2 = z1 + pl3
+ compare_tf_with_tvm([data, data2, data3], ['pl1:0', 'pl2:0', 'pl3:0'],
+ ['StatefulPartitionedCall:0',z2.name], mode='vm',
init_global_variables=True)
+
+
+def _test_spop_placeholder_two():
+ with tf.Graph().as_default():
+ data = np.ones([1], dtype=int).astype(np.int32)
+ dataVar = tf.Variable(data, shape=data.shape)
+ pl1 =
array_ops.placeholder_with_default(dataVar,shape=data.shape,name="pl1")
+ tpl = tf.convert_to_tensor(pl1, dtype=tf.int32)
+
+ @function.Defun(*[tf.int32])
+ def pl_with_default(pl):
+ return tf.expand_dims(tf.multiply(pl, pl), 0)
+
+ z = gen_functional_ops.StatefulPartitionedCall(args=[tpl],
Tout=[tf.int32], f=pl_with_default)
+ compare_tf_with_tvm(data, ['pl1:0'], 'StatefulPartitionedCall:0',
mode='vm', init_global_variables=True)
+
+
+def _test_spop_placeholder_three():
+ with tf.Graph().as_default():
+ t1 = tf.placeholder(tf.int32, (3, 3, 3), "t1")
+ t1_data = np.arange(27, dtype=np.int32).reshape((3, 3, 3))
+ t2 = tf.placeholder(tf.int32, (3, 3, 3), "t2")
+ t2_data = np.arange(27, dtype=np.int32).reshape((3, 3, 3))
+
+ @tf.function
+ def add(x, y):
+ return tf.add(x, y, "add_t1_t2")
+
+ t3 = add(t1, t2)
+ compare_tf_with_tvm([t1_data, t2_data], ['t1:0', 't2:0'], [t3.name],
mode='vm', init_global_variables=True)
+
+
+def _test_spop_placeholder_four():
+ with tf.Graph().as_default():
+ t1_data = np.array([[-1, 1, 3], [2, -2, 4], [2, -3, 14]],
dtype=np.int32)
+ t2_data = np.array([[-2, 1, 2], [12, -2, 14], [12, -3, 4]],
dtype=np.int32)
+ t1 = tf.placeholder(tf.int32, name="t1")
+ t2 = tf.placeholder(tf.int32, name="t2")
+
+ @tf.function
+ def add(x, y):
+ return tf.add(x, y, "add_t1_t2")
+
+ t3 = add(t1, t2)
+ compare_tf_with_tvm([t1_data, t2_data], ['t1:0', 't2:0'], [t3.name],
mode='vm', init_global_variables=True)
+
+
+def _test_spop_function_invocation_basic():
+ with tf.Graph().as_default():
+
+ def fun1(a):
+ return tf.multiply(a,a)
+
+ def fun2(b):
+ return tf.multiply(b,10)
+
+ @tf.function
+ def fun3(x,y):
+ x = fun2(x)
+ y = fun1(y)
+ z = tf.add(x,y)
+ return z
+
+ t3 = fun3(tf.constant(10.5), tf.constant(20.4))
+
+ compare_tf_with_tvm([], [], [t3.name], mode='vm',
init_global_variables=True)
+
+
+def _test_spop_function_invocation_nested():
+ with tf.Graph().as_default():
+ t1 = tf.placeholder(tf.int32, (3, 3, 3), name="t1")
+ t1_data = np.arange(27, dtype=np.int32).reshape((3, 3, 3))
+ t2 = tf.placeholder(tf.int32, name="t2")
+ t2_data = np.arange(27, dtype=np.int32).reshape((3, 3, 3))
+
+ @tf.function
+ def myfunc(x, y):
+ return tf.add(x, y, "myfunc")
+
+ @tf.function
+ def myfunc2(x, y):
+ z = myfunc(x, y)
+ l = myfunc(z, y)
+ m = myfunc(l,z)
+ return tf.add(l, m, "myfunc2")
+
+ res1 = myfunc(t1, t2)
+ res2 = myfunc2(res1, t1)
+
+ compare_tf_with_tvm([t1_data, t2_data], ['t1:0', 't2:0'], [res2.name],
mode='vm', init_global_variables=True)
+
+
+def _test_spop_function_invocation_no_autograph():
+ with tf.Graph().as_default():
+
+ @tf.function(autograph=False)
+ def fun1(a):
+ return tf.multiply(a,a)
+
+ @tf.function(autograph=False)
+ def fun2(b):
+ return tf.multiply(b,10)
+
+ @tf.function
+ def fun3(x,y):
+ x = fun2(x)
+ y = fun1(y)
+ z = tf.add(x,y)
+ return z
+
+ t3 = fun3(tf.constant(10.5), tf.constant(20.4))
+
+ compare_tf_with_tvm([], [], [t3.name], mode='vm',
init_global_variables=True)
+
+
+def _test_spop_function_invocation_defun():
+ with tf.Graph().as_default():
+
+ def fun1(a):
+ return tf.multiply(a,a)
+
+ def fun2(b):
+ return tf.multiply(b,b)
+
+ @function.Defun(dtypes.float32, dtypes.float32, func_name="Fun3")
+ def fun3(x,y):
+ x = fun2(x)
+ y = fun1(y)
+ z = tf.add(x,y)
+ return z
+
+ op =
gen_functional_ops.StatefulPartitionedCall(args=[tf.constant(10.5),tf.constant(20.4)],
+ Tout=[dtypes.float32],
f=fun3, name="SpopFnInvocation")
+ compare_tf_with_tvm([],[], 'SpopFnInvocation:0', mode='vm',
init_global_variables=True)
+
+
+def _test_spop_arithmetic():
+ with tf.Graph().as_default():
+ @function.Defun(*[dtypes.int32]*3)
+ def arithmetic(m,x,c):
+ z = tf.add(tf.multiply(m, x), c)
+ return z
+
+ m = tf.constant(10)
+ x = tf.constant(20)
+ c = tf.constant(2)
+ spopFn =
gen_functional_ops.StatefulPartitionedCall(args=[m,x,c],Tout=[tf.int32],
f=arithmetic)
+
+ compare_tf_with_tvm([],[],'StatefulPartitionedCall:0', mode='vm',
init_global_variables=True)
+
+
+def _test_spop_control_flow():
+ with tf.Graph().as_default():
+
+ @function.Defun(*[dtypes.float32] * 2)
+ def Body1(x, y):
+ with ops.device("/job:localhost/replica:0/task:0/device:CPU:0"):
+ z = math_ops.multiply(x, y)
+ i = 0
+ while i<10 :
+ i +=1
+ if i == 5:
+ continue
+ z = math_ops.multiply(x, y*i)
+ return z
+
+ op = gen_functional_ops.StatefulPartitionedCall(
+ args=[constant_op.constant(32.), constant_op.constant(100.)],
+ Tout=[dtypes.float32], f=Body1)
+ compare_tf_with_tvm([], [], 'StatefulPartitionedCall:0', mode='vm',
init_global_variables=True)
+
+
+def _test_spop_variables():
+ with tf.Graph().as_default():
+ const1 = tf.constant(10)
+ const2 = tf.constant(20)
+ var1 = tf.Variable(const1, dtype=tf.int32)
+ var2 = tf.Variable(const2, dtype=tf.int32)
+
+ @function.Defun(tf.int32,tf.int32)
+ def Forward(x,y):
+ return tf.multiply(x,y)
+
+ z =
gen_functional_ops.StatefulPartitionedCall(args=[var1,var2],Tout=[tf.int32],
f=Forward)
+ compare_tf_with_tvm([], [], 'StatefulPartitionedCall:0',
init_global_variables=True, mode="vm")
+
+
+def _test_spop_constants():
+ with tf.Graph().as_default():
+ @function.Defun(*[dtypes.int32] * 2)
+ def constantsFn(x, y):
+ vv = tf.constant([2, 3, 4], name="vv")
+ z = tf.add(vv + x, y)
+ return z
+
+ a = tf.constant(20000, name = "a")
+ b = tf.constant(40000, name = "b")
+ spopFn = gen_functional_ops.StatefulPartitionedCall(args=[a, b],
Tout=[tf.int32], f=constantsFn)
+
+ compare_tf_with_tvm([], [], 'StatefulPartitionedCall:0', mode='vm',
init_global_variables=True)
+
+
+def _test_spop_stateful():
+
+ tf.reset_default_graph()
+ with tf.Graph().as_default():
+
+ @tf.function
+ def FunctionWithStatefulOp_One(i):
+ b = tf.random.uniform(shape=[2, 4], maxval=10, dtype=tf.float32,
seed=10)
+ y = tf.multiply(b, i)
+ return y
+
+ @tf.function
+ def FunctionWithStatefulOp(m, n):
+ a = tf.random.uniform(shape=[2, 4], maxval=10, dtype=tf.float32,
seed = 10)
+ x = tf.multiply(a,m)
+ y = FunctionWithStatefulOp_One(n)
+ z = tf.multiply(x,y)
+ return z
+
+ op = FunctionWithStatefulOp(constant_op.constant(1.),
constant_op.constant(2.))
+ with pytest.raises(Exception) as execinfo:
+ compare_tf_with_tvm([], [], [op.name], init_global_variables=True,
mode="vm")
+ assert execinfo.value.args[0].startswith("Found a stateful operator in
this graph")
+
+
+def _test_spop_device_assignment():
+
+ tf.reset_default_graph()
+ with tf.Graph().as_default():
+
+ def fun1(a):
+ with ops.device("/GPU:0"):
+ return tf.multiply(a,a)
+
+ def fun2(b):
+ with ops.device("/job:localhost/replica:0/task:0/device:CPU:1"):
+ return tf.multiply(b,b)
+
+ @function.Defun(dtypes.float32, dtypes.float32, func_name="Fun3")
+ def fun3(x,y):
+ with ops.device("/CPU:0"):
+ x = fun2(x)
+ with ops.device("/job:localhost/replica:0/task:0/device:CPU:2"):
+ y = fun1(y)
+ with ops.device("/job:localhost/replica:0/task:0/device:CPU:3"):
+ z = tf.add(x,y)
+ return z
+
+ op =
gen_functional_ops.StatefulPartitionedCall(args=[tf.constant(10.5),tf.constant(20.4)],
+ Tout=[dtypes.float32],
f=fun3)
+ with pytest.raises(Exception) as execinfo:
+ compare_tf_with_tvm([], [], 'StatefulPartitionedCall:0',
+ mode='vm', init_global_variables=True)
+ assert execinfo.value.args[0].startswith("Found inconsistent Device
assignment")
+
+
+def _test_spop_resource_variables():
+ tf.reset_default_graph()
+ with tf.Graph().as_default():
+
+ const1 = tf.constant(10)
+ const2 = tf.constant(20)
+ var1 = tf.Variable(const1, dtype=tf.int32, use_resource=True)
+ var2 = tf.Variable(const2, dtype=tf.int32, use_resource=True)
+
+ @tf.function
+ def resourceVariablesTest(x, y):
+ return tf.multiply(x, y)
+
+ op = resourceVariablesTest(var1,var2)
+ with pytest.raises(Exception) as execinfo:
+ compare_tf_with_tvm([], [], 'StatefulPartitionedCall:0',
+ mode='vm', init_global_variables=True)
+ assert execinfo.value.args[0].startswith("Found a stateful operator in
this graph")
+
+def test_forward_spop():
+ # This test case is to test that TVM rejects any TF stateful operations
Review comment:
Better to move these descriptions into the test case.
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -2896,15 +2903,29 @@ def _parse_import_prerequisites(self, graph):
"""
missing_operators = set()
for node in graph.node:
+ try:
+ from tensorflow.python.framework import op_def_registry
+ except ImportError as e:
+ raise ImportError(
+ "Unable to import tensorflow which is required
{}".format(e))
+ getOpDef = op_def_registry._registered_ops.get if
hasattr(op_def_registry,\
+ "_registered_ops") else op_def_registry.get
+ op_def = getOpDef(node.op)
if node.op == "Placeholder" or node.op == 'PlaceholderWithDefault':
pass
elif node.op == "Const":
pass
+ elif node.op in ["PartitionedCall", "StatefulPartitionedCall"]:
Review comment:
Should note TF version here to before supporting.
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