from .adt import Constructor, TypeData, Clause, Match
from .adt import PatternConstructor, PatternVar, PatternWildcard
from . import op, transform
-
+from .analysis import free_vars
def get_tensor_array_shape(expr, dtype, prelude):
"""Get the static shape of a tensor array if it has fixed rank shape.
has dynamic shape.
"""
mod = prelude.mod
- mod["main"] = Function([], expr)
+ mod["main"] = Function(free_vars(expr), expr)
mod = transform.InferType()(mod)
checked_type = mod["main"].body.checked_type
assert isinstance(checked_type, TypeCall), "Input must be a tensor array."
// Type check the item before we add it to the module.
auto fv = relay::FreeVars(func);
auto ftv = relay::FreeTypeVars(func, mod);
- if (fv.size() != 0) {
- LOG(WARNING) << "There are free variables: " << fv << " in function: " << AsText(func, false)
- << std::endl;
- }
- if (ftv.size() != 0) {
- LOG(WARNING) << "There are free type variables: " << ftv
- << " in function: " << AsText(func, false) << std::endl;
- }
- func = relay::Function(concat(func->params, fv), func->body, func->ret_type,
- concat(func->type_params, ftv), func->attrs);
+ CHECK_EQ(fv.size(), 0) << "There are free variables: " << fv
+ << " in function: " << AsText(func, false);
+ CHECK_EQ(ftv.size(), 0) << "There are free type variables: " << fv
+ << " in function: " << AsText(func, false);
// Type check the item before we add it to the module.
relay::Function checked_func = InferType(func, mod, var);
return checked_func;
tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
-#######################################################################
-# Main
-# ----
if __name__ == '__main__':
- # Transforms
- test_forward_slice()
- test_forward_transpose()
- test_forward_reshape()
- test_forward_depthtospace()
- test_forward_spacetodepth()
- test_forward_squeeze()
- test_forward_pack()
- test_forward_size()
- test_forward_broadcast_to()
- test_forward_fill()
- test_forward_crop()
- test_forward_resize()
- test_forward_crop_and_resize()
- test_forward_pad()
- test_forward_unpack()
- test_forward_gather()
- test_forward_gather_nd()
- test_forward_stridedslice()
- test_forward_split()
- test_forward_unstack()
- test_forward_tile()
- test_forward_top_k_v2()
- test_forward_clip_by_value()
- test_forward_maximum()
- test_forward_minimum()
- test_forward_range()
- test_forward_right_shift()
- test_forward_left_shift()
- test_forward_truncatemod()
- test_forward_one_hot()
- test_forward_atan2()
- test_forward_nms()
-
- # Activations
- test_forward_sigmoid()
- test_forward_relu()
- test_forward_leaky_relu()
- test_forward_elu()
- test_forward_selu()
- test_forward_tanh()
-
- # Tensor
- test_forward_round()
- test_forward_reverse_v2()
- test_forward_pow_exp()
- test_forward_sign()
- test_forward_negative()
- test_forward_divide()
- test_forward_abs()
- test_forward_softplus()
- test_forward_sqrt()
- test_forward_rsqrt()
- test_forward_expand_dims()
- test_forward_square()
- test_forward_softmax()
- test_forward_log_softmax()
- test_forward_bias_add()
- test_forward_zeros_like()
- test_forward_squared_difference()
- test_forward_add_n()
- test_forward_floormod()
- test_forward_isfinite()
- test_forward_isinf()
- test_forward_unravel_index()
- test_forward_unary()
-
- # Reductions
- test_forward_argminmax()
- test_forward_reduce()
- test_forward_mean()
-
- # TensorArray
- test_tensor_array_write_read()
- test_tensor_array_concat()
- test_tensor_array_scatter()
- test_tensor_array_gather()
- test_tensor_array_size()
- test_tensor_array_split()
- test_tensor_array_stack()
- test_tensor_array_unstack()
-
- # General
- test_forward_multi_input()
- test_forward_multi_output()
- test_forward_variable()
- test_placeholder()
-
- # NN
- test_forward_convolution()
- test_forward_convolution3d()
- test_forward_convolution3d_transpose()
- test_forward_pooling()
- test_forward_concat_v2()
- test_forward_lrn()
- test_forward_l2_normalize()
- test_forward_space_to_batch_nd()
- test_forward_batch_to_space_nd()
- test_forward_dilation()
-
- # End to End
- test_forward_inception_v3()
- test_forward_inception_v1()
- test_forward_mobilenet()
- test_forward_resnetv2()
- test_forward_ssd()
- test_forward_placeholder()
- test_forward_ptb()
-
- # RNN
- test_forward_lstm()
-
- # Elementwise
- test_forward_ceil()
- test_forward_floor()
-
- # Relational ops
- test_forward_rel_ops()
- test_forward_logical()
- test_forward_where()
- test_forward_matmul()
- test_forward_batch_matmul()
-
- # Internal misc. ops
- test_read_variable_op()
-
- # Sharing params case using Mean ops
- test_sharing_node()
-
- # StatefulPartitionedCall
- test_forward_spop()
-
- # Test dynamic input shape
- test_forward_dynamic_input_shape()
-
- test_forward_dynmaic_rnn_lstmblockcell()
+ pytest.main([__file__])
"""Test that type checker correcly computes types
for expressions.
"""
+import pytest
import tvm
from tvm import te
from tvm import relay
from tvm.relay import op, transform, analysis
from tvm.relay import Any
-
def run_infer_type(expr, mod=None):
if not mod:
mod = tvm.IRModule.from_expr(expr)
f = relay.Var('f', choice_t)
true_branch = relay.Var('True', relay.TensorType([Any(), 1], dtype='float32'))
false_branch = relay.Var('False', relay.TensorType([Any(), Any()], dtype='float32'))
- top = relay.Function([true_branch, false_branch], relay.If(f(), true_branch, false_branch))
+ top = relay.Function([f, true_branch, false_branch], relay.If(f(), true_branch, false_branch))
ft = run_infer_type(top)
tvm.ir.assert_structural_equal(ft.ret_type, relay.TensorType([Any(), 1], dtype='float32'))
if __name__ == "__main__":
- test_free_expr()
- test_dual_op()
- test_single_op()
- test_recursion()
- test_monomorphic_let()
- test_decl()
- test_recursion()
- test_tuple()
- test_incomplete_call()
- test_type_args()
- test_global_var_recursion()
- test_equal()
- test_ref()
- test_constructor_type()
- test_constructor_call()
- test_adt_match()
- test_let_polymorphism()
- test_if()
+ pytest.main([__file__])
# under the License.
# pylint: disable=invalid-name, missing-docstring, no-else-return
"""Unit tests for the Relay VM serialization and deserialization."""
+import pytest
import numpy as np
import tvm
newshape_var = relay.var('newshape', shape=(2,), dtype='int64')
args.append(np.array((1, -1), dtype='int64'))
- main = relay.reshape(relu_x, newshape=newshape_var)
+ main = relay.Function([x, newshape_var], relay.reshape(relu_x, newshape=newshape_var))
res = get_serialized_output(main, *args).asnumpy()
tvm.testing.assert_allclose(res.flatten(), data.flatten())
if __name__ == "__main__":
- test_serializer()
- test_save_load()
- test_const()
- test_if()
- test_loop()
- test_tuple()
- test_adt_list()
- test_adt_compose()
- test_closure()
- test_synthetic()
- test_mobilenet()
- test_vm_shape_of()
+ pytest.main([__file__])
z = relay.add(y, c)
z1 = relay.add(y, c)
z2 = relay.add(z, z1)
- return relay.Function([x], z2)
+ return relay.Function([x, weight], z2)
###############################################################################
# Let us register layout alteration for a conv2d op so that we can apply the
projects / platform / upstream / tvm.git / commitdiff