def __named_apply_prep(self, ctx, name, fn, attrs, do_apply):
if attrs and '_ellipsis' in attrs:
raise Exception("Can't use attribute ellipsis in function apply")
fn = unwrap_bag(fn)
base_scope_name = None
if hasattr(fn, '_name'):
base_scope_name = fn._name()
if not base_scope_name:
base_scope_name = 'fnc'
g = tf.get_default_graph()
scope_name = "q___%s" % g.unique_name(base_scope_name, False).split("/")[-1]
if not hasattr(fn, 'apply') and hasattr(fn, 'apply_attrs'):
fn = fn.apply_attrs(self, attrs)
fn = unwrap_bag(fn)
attrs = None
result = do_apply(fn, scope_name)
ctx.possible_leaf(result)
if name != None:
result = ctx.define_local(name, result)
# HACK(adamb) The tf.identity call below just demands that the result is a Tensor.
if isinstance(result, RetvalBag) and result.len() == 1:
result = result.get(None)
if isinstance(result, tf.Tensor):
tf.identity(result, name=name)
return result
def keyword_apply(unwrapped_fn, scope_name):
unwrapped_kwargs = {}
for key, value in kwargs.items():
value = unwrap_bag(value)
ctx.eliminate_leaf(value)
unwrapped_kwargs[key] = value
return unwrapped_fn.apply_kw(self, ctx, name, attrs, unwrapped_kwargs)
def _maybe_export_function(self, package_name, subctx, name, value):
if not name[0].isupper():
eprint("not capitalized", name)
return
value = unwrap_bag(value)
eprint("considering", name)
if not isinstance(value, graph_function.DeclaredFunction):
eprint("isn't a declared function", type(value))
return
fn = value
if fn.has_attrs():
eprint("has attributes, skipping.")
return
g = tf.get_default_graph()
var_collection_name = "%s:variable_names" % (g.unique_name(name, False))
var_set = set()
def on_var(var):
var_set.add(var.name)
self.add_variable_listener(on_var)
eprint("exporting", name, fn)
with tf.variable_scope(name):
with tf.variable_scope("inputs"):
args = [tf.placeholder(arg_dtype, arg_shape, arg_name) for (arg_name, arg_shape, arg_dtype) in fn._arg_specs()]
subctx2 = subctx.subcontext()
fn.apply(self, subctx2, "_", None, args)
with tf.variable_scope("outputs"):
g = tf.get_default_graph()
for (retval_name, retval_inner_name) in fn._retval_specs():
tensor_prefix = "%s/%s" % (package_name, name)
try:
returned_tensor = g.get_tensor_by_name("%s/_/%s:0" % (tensor_prefix, retval_inner_name))
except KeyError as ke:
eprint("repeating lookup of %s in prefix %s for retval %s" % (retval_inner_name, tensor_prefix, retval_name))
# If we fail to find the tensor above, perhaps it was just an input.
try:
returned_tensor = g.get_tensor_by_name("%s/inputs/%s:0" % (tensor_prefix, retval_inner_name))
except KeyError:
nodes = [n.name for n in tf.get_default_graph().as_graph_def().node]
nodes.sort()
eprint('error, but got nodes', nodes)
raise ke
tf.identity(returned_tensor, name=retval_name)
for var_name in var_set:
g.add_to_collection(var_collection_name, var_name)
self.remove_variable_listener(on_var)
def positonal_apply(unwrapped_fn, scope_name):
unwrapped_args = []
for arg in args:
arg = unwrap_bag(arg)
ctx.eliminate_leaf(arg)
unwrapped_args.append(arg)
if hasattr(unwrapped_fn, 'apply'):
return unwrapped_fn.apply(self, ctx, scope_name, attrs, unwrapped_args)
else:
raise Exception("Can't apply non-function %s with unwrapped args %s" % (unwrapped_fn, unwrapped_args))
def _sf_while_inner(use_device, visitor_class, ctx, exprs):
with tf.Graph().as_default() as g:
with tf.device(use_device):
visitor = visitor_class()
final_tensor = visitor._visit_exprs(ctx, exprs)
# We do not want to include shapes, since inferred shapes will cause problems
# for shape inference upon import and re-export.
# HACK(adamb) Since TensorFlow uses __del__ to clean up py_funcs, we need to copy them.
cleanup_py_funcs_used_in_graph = []
if hasattr(g, "_cleanup_py_funcs_used_in_graph"):
cleanup_py_funcs_used_in_graph = g._cleanup_py_funcs_used_in_graph[:]
return (tf.train.export_meta_graph(),
cleanup_py_funcs_used_in_graph,
unwrap_bag(final_tensor).name)
def _sf_while_loop(visitor, ctx, cond_expr, body_exprs, body_retvals,
init_exprs):
# Need to evaluate body_exprs first, looking for all variables that will be created
# internally. Roll up into nested variable contexts. Unroll these contexts to be
# passed as part of var_list. Within def body(*a), repackage these variables into
# variable contexts. Use these contexts *instead of* creating variables directly.
# So we want to be able to indicate whether or not contexts should be allowed to
# create variables on the fly. Within a while cond/body, they should not. Otherwise,
# the can (e.g. when compiling a graph { ... } expression)
# track these so we can eventually remove them.
proxy_cruft = set()
proxied_placeholder_names = OrderedDict()
proxied_placeholders = OrderedDict()
# track replacements. focus on external entity to internal name.
def proxy(v):
# eprint("proxy", v)
if isinstance(v, RetvalBag):
if v.graph is None:
return v
if v.graph == tf.get_default_graph():
return v
return v.wrap(proxy)
if not isinstance(v, (tf.Operation, tf.Tensor, tf.Variable)):
return v
if v.graph == tf.get_default_graph():
return v
if v.name in proxied_placeholders:
return proxied_placeholders[v.name]
if v.name in proxied_placeholder_names:
placeholder_name = proxied_placeholder_names[v.name]
else:
placeholder_name = "Proxy_%d" % len(proxied_placeholder_names)
p = None
with tf.name_scope(None):
with tf.control_dependencies(None):
p_name = None
if isinstance(v, tf.Tensor) and v.dtype._is_ref_dtype:
p = tf.Variable(initial_value=zero_value_for_dtype(
v.dtype),
trainable=False,
collections=[],
name=placeholder_name,
dtype=v.dtype.base_dtype,
validate_shape=False)
p.set_shape(v.get_shape())
p_name = "%s" % p.op.name
proxy_cruft.add(p_name)
proxy_cruft.add("%s/read" % p.op.name)
proxy_cruft.add("%s/Assign" % p.op.name)
proxy_cruft.add("%s/initial_value" % p.op.name)
elif isinstance(v, tf.Variable):
p = tf.Variable(initial_value=zero_value_for_dtype(
v.dtype),
trainable=False,
collections=[],
name=placeholder_name,
dtype=v.dtype.base_dtype,
validate_shape=False)
p.set_shape(v.get_shape())
p_name = "%s:0" % p.op.name
p = tf.get_default_graph().get_tensor_by_name(p_name)
v = v.graph.get_tensor_by_name("%s:0" % v.op.name)
proxy_cruft.add(p_name)
proxy_cruft.add("%s/read" % p.op.name)
proxy_cruft.add("%s/Assign" % p.op.name)
proxy_cruft.add("%s/initial_value" % p.op.name)
else:
p = tf.placeholder(v.dtype,
shape=v.get_shape(),
name=placeholder_name)
p_name = p.op.name
proxy_cruft.add(p_name)
proxied_placeholders[v.name] = p
proxied_placeholder_names[v.name] = p_name
if placeholder_name and placeholder_name != p.op.name:
raise Exception(
"Created placeholder with unexpected name: %s vs %s" %
(placeholder_name, p.op.name))
return p
g = tf.get_default_graph()
while_loop_name = g.unique_name("while", False)
# eprint('init_exprs', init_exprs)
initial_value_ctx = ctx.subcontext()
initial_value_ctx._proxy = proxy
initial_tensor_list = None
initial_local_names = None
with tf.variable_scope('%s_init' % while_loop_name):
initial_tensor_list = [
unwrap_bag(visitor.visit(initial_value_ctx, expr))
for expr in init_exprs
]
initial_local_names = [define[1] for define in init_exprs]
local_name_by_tensor_name = dict(
zip([t.name for t in initial_tensor_list], initial_local_names))
device_stack = g._device_function_stack
use_device = None
if len(device_stack) > 0:
use_device = device_stack[-1]
eprint("Will use device", use_device)
# Ensure we have a placeholder for every initial value.
with tf.Graph().as_default():
with tf.device(use_device):
for local_name in initial_local_names:
initial_value_ctx.get_local(local_name)
# Don't let cached placeholders from init_exprs infect our graph.
proxied_placeholders = OrderedDict()
cond_ctx = initial_value_ctx.subcontext()
cond_meta_graph_def, cond_cleanup_funcs, cond_retval_name = _sf_while_inner(
use_device, type(visitor), cond_ctx, [cond_expr])
# Don't let cached placeholders from cond_exprs infect our graph.
proxied_placeholders = OrderedDict()
body_ctx = initial_value_ctx.subcontext()
body_meta_graph_def, body_cleanup_funcs, _ = _sf_while_inner(
use_device, type(visitor), body_ctx, body_exprs)
# HACK(adamb) Don't actually import any nodes that are only proxies.
# This should probably be done automatically by the TF import
# logic, but empirically this is not the case.
_while_prune(cond_meta_graph_def, proxy_cruft)
_while_fix_colocations(cond_meta_graph_def, proxy_cruft)
_while_prune(body_meta_graph_def, proxy_cruft)
_while_fix_colocations(body_meta_graph_def, proxy_cruft)
body_retval_dict = dict(body_retvals)
body_retval_names = []
next_value_ixs = []
loop_vars = [
g.get_tensor_by_name(v_name)
for v_name in proxied_placeholder_names.keys()
]
ix = -1
for t in loop_vars:
ix += 1
# if it's in initial_tensor_list, then look up its init_local_name
# if we have a retval for this init_local_name, then use the inner_retval
# otherwise pass through.
if t.name in local_name_by_tensor_name:
local_name = local_name_by_tensor_name[t.name]
if local_name in body_retval_dict:
# eprint("while next vals", ix, t.get_shape(), t.name, local_name, body_retval_dict[local_name])
body_retval_names.append("%s:0" % body_retval_dict[local_name])
next_value_ixs.append(ix)
else:
# eprint("while next vals skipped", ix, local_name)
pass
else:
# eprint("while next vals t.name", ix, t.name)
pass
# eprint("while initial_local_names", initial_local_names)
# eprint("while initial_tensor_list", initial_tensor_list)
# eprint("while proxied_placeholder_names", proxied_placeholder_names)
# eprint("while local_name_by_tensor_name", local_name_by_tensor_name)
def cond(*a):
# We use a variable_scope because name_scope has a strange
# only-sometimes-present trailing / that messes with everything.
cond_import_scope = '%s_cond' % while_loop_name
_while_fix_context_scope(cond_meta_graph_def, cond_import_scope)
return _sf_while_embed(
cond_import_scope, dict(zip(proxied_placeholder_names.values(),
a)), [cond_retval_name],
cond_meta_graph_def, cond_cleanup_funcs)[0]
def body(*a):
body_input_map = dict(zip(proxied_placeholder_names.values(), a))
# eprint("while body", body_input_map)
# We use a variable_scope because name_scope has a strange
# only-sometimes-present trailing / that messes with everything.
body_import_scope = '%s_body' % while_loop_name
_while_fix_context_scope(body_meta_graph_def, body_import_scope)
next_values = _sf_while_embed(body_import_scope, body_input_map,
body_retval_names, body_meta_graph_def,
body_cleanup_funcs)
body_results = list(a)
for ix, val in zip(next_value_ixs, next_values):
val.set_shape(a[ix].get_shape())
# eprint('while shape', ix, a[ix], a[ix].get_shape(), val, val.get_shape())
# val.set_shape(val.get_shape())
body_results[ix] = val
# eprint('while body_results', body_results)
return body_results
# If we're referencing variables, we need to alert listeners.
for v in loop_vars:
visitor._visit_result(v)
results = None
results = tf.while_loop(
cond=cond,
body=body,
loop_vars=loop_vars,
parallel_iterations=1,
back_prop=False,
name=while_loop_name.split("/")[-1],
)
if type(results) != list:
results = [results]
r = {}
for k_name, v in zip(proxied_placeholder_names.keys(), results):
if k_name in local_name_by_tensor_name:
r[local_name_by_tensor_name[k_name]] = v
return RetvalBag(r)
def apply_attrs(self, ctx, function, attrs): return unwrap_bag(function).apply_attrs(self, attrs)
def list(self, ctx, *entries): return [unwrap_bag(e) for e in entries]
