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Commit 39c8bc2a by Sergey Mironov Committed by Tianqi Chen
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[TOPI] Specify non-zero absolute tolerance in tests (#1925)

parent be9784cc
Showing with 275 additions and 261 deletions
apps/extension/tests/test_ext.py
...@@ -22,7 +22,7 @@ def test_ext_dev(): ...@@ -22,7 +22,7 @@ def test_ext_dev():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), a.asnumpy() + 1) tvm.testing.assert_allclose(b.asnumpy(), a.asnumpy() + 1)
check_llvm() check_llvm()
......
docs/deploy/aocl_fpga.md
...@@ -52,7 +52,7 @@ b = tvm.nd.array(np.random.uniform(size=n).astype("float32"), ctx) ...@@ -52,7 +52,7 @@ b = tvm.nd.array(np.random.uniform(size=n).astype("float32"), ctx)
c = tvm.nd.array(np.zeros(n, dtype="float32"), ctx) c = tvm.nd.array(np.zeros(n, dtype="float32"), ctx)
fadd(a, b, c) fadd(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy()) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
``` ```
Setup Setup
......
docs/deploy/aws_fpga.md
...@@ -55,7 +55,7 @@ b = tvm.nd.array(np.random.uniform(size=n).astype("float32"), ctx) ...@@ -55,7 +55,7 @@ b = tvm.nd.array(np.random.uniform(size=n).astype("float32"), ctx)
c = tvm.nd.array(np.zeros(n, dtype="float32"), ctx) c = tvm.nd.array(np.zeros(n, dtype="float32"), ctx)
fadd(a, b, c) fadd(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy()) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
``` ```
Setup Setup
......
nnvm/python/nnvm/testing/check_computation.py
...@@ -281,10 +281,10 @@ def check_function(symbol, forward=None, backward=None, grad_input_vars=None, ...@@ -281,10 +281,10 @@ def check_function(symbol, forward=None, backward=None, grad_input_vars=None,
Additional parameters for `check_numerical_grads`. Additional parameters for `check_numerical_grads`.
atol : float, optional atol : float, optional
Absolute tolerance for `np.testing.assert_allclose`. NOT used for numerical gradients. Absolute tolerance for `tvm.testing.assert_allclose`. NOT used for numerical gradients.
rtol : float, optional rtol : float, optional
Relative tolerance for `np.testing.assert_allclose`. NOT used for numerical gradients. Relative tolerance for `tvm.testing.assert_allclose`. NOT used for numerical gradients.
quiet : bool, optional quiet : bool, optional
Don't dump additional information to stdout on failure. Don't dump additional information to stdout on failure.
...@@ -466,7 +466,7 @@ def check_function(symbol, forward=None, backward=None, grad_input_vars=None, ...@@ -466,7 +466,7 @@ def check_function(symbol, forward=None, backward=None, grad_input_vars=None,
.format(len(numpy_res), out_len)) .format(len(numpy_res), out_len))
for i in range(out_len): for i in range(out_len):
np.testing.assert_allclose(nnvm_res[i], numpy_res[i], atol=atol, rtol=rtol) tvm.testing.assert_allclose(nnvm_res[i], numpy_res[i], atol=atol, rtol=rtol)
if backward is not None: if backward is not None:
nothing_was_done = False nothing_was_done = False
...@@ -495,7 +495,7 @@ def check_function(symbol, forward=None, backward=None, grad_input_vars=None, ...@@ -495,7 +495,7 @@ def check_function(symbol, forward=None, backward=None, grad_input_vars=None,
.format(set(grad_var_names) - set(numpy_grads))) .format(set(grad_var_names) - set(numpy_grads)))
for x_name in numpy_grads: for x_name in numpy_grads:
np.testing.assert_allclose(nnvm_grads[x_name], numpy_grads[x_name], tvm.testing.assert_allclose(nnvm_grads[x_name], numpy_grads[x_name],
atol=atol, rtol=rtol) atol=atol, rtol=rtol)
if numerical_grads: if numerical_grads:
......
nnvm/tests/python/compiler/test_build.py
...@@ -27,7 +27,7 @@ def test_compile(): ...@@ -27,7 +27,7 @@ def test_compile():
# get outputs # get outputs
out = tvm.nd.empty(shape, dtype) out = tvm.nd.empty(shape, dtype)
get_output(0, out) get_output(0, out)
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy())) out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy()))
graph, lib, _ = nnvm.compiler.build(z, "llvm", shape_dict) graph, lib, _ = nnvm.compiler.build(z, "llvm", shape_dict)
...@@ -49,7 +49,7 @@ def test_run(): ...@@ -49,7 +49,7 @@ def test_run():
nx = tvm.nd.array(np.random.uniform(size=shape).astype(dtype)) nx = tvm.nd.array(np.random.uniform(size=shape).astype(dtype))
ny = tvm.nd.array(np.random.uniform(size=shape).astype(dtype)) ny = tvm.nd.array(np.random.uniform(size=shape).astype(dtype))
res = _run_graph(z, {"x": nx, "y": ny}) res = _run_graph(z, {"x": nx, "y": ny})
np.testing.assert_allclose( tvm.testing.assert_allclose(
res[0].asnumpy(), np.exp(nx.asnumpy() + ny.asnumpy())) res[0].asnumpy(), np.exp(nx.asnumpy() + ny.asnumpy()))
...@@ -73,7 +73,7 @@ def test_precompute_prune(): ...@@ -73,7 +73,7 @@ def test_precompute_prune():
m["load_params"](nnvm.compiler.save_param_dict(params)) m["load_params"](nnvm.compiler.save_param_dict(params))
m.run() m.run()
out = m.get_output(0, out=res) out = m.get_output(0, out=res)
np.testing.assert_allclose( tvm.testing.assert_allclose(
res.asnumpy(), nx.asnumpy() + 1 + ny.asnumpy() + na.asnumpy()) res.asnumpy(), nx.asnumpy() + 1 + ny.asnumpy() + na.asnumpy())
...@@ -92,7 +92,7 @@ def test_dtypes(): ...@@ -92,7 +92,7 @@ def test_dtypes():
m.run(x=data) m.run(x=data)
data = (data > 0) * data data = (data > 0) * data
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
np.testing.assert_allclose(out.asnumpy(), data, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), data, atol=1e-5, rtol=1e-5)
def test_ndarray_output(): def test_ndarray_output():
x = sym.Variable("x") x = sym.Variable("x")
...@@ -110,7 +110,7 @@ def test_ndarray_output(): ...@@ -110,7 +110,7 @@ def test_ndarray_output():
m.set_input("y", ny) m.set_input("y", ny)
m.run() m.run()
out = m.get_output(0) out = m.get_output(0)
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), nx.asnumpy() + ny.asnumpy()) out.asnumpy(), nx.asnumpy() + ny.asnumpy())
def test_ndarray_input(): def test_ndarray_input():
...@@ -131,12 +131,12 @@ def test_ndarray_input(): ...@@ -131,12 +131,12 @@ def test_ndarray_input():
in_y = tvm.nd.empty(shape, dtype) in_y = tvm.nd.empty(shape, dtype)
m.get_input("x", in_x) m.get_input("x", in_x)
m.get_input("y", in_y) m.get_input("y", in_y)
np.testing.assert_allclose(nx.asnumpy(), in_x.asnumpy()) tvm.testing.assert_allclose(nx.asnumpy(), in_x.asnumpy())
np.testing.assert_allclose(ny.asnumpy(), in_y.asnumpy()) tvm.testing.assert_allclose(ny.asnumpy(), in_y.asnumpy())
in_nx = m.get_input("x") in_nx = m.get_input("x")
in_ny = m.get_input("y") in_ny = m.get_input("y")
np.testing.assert_allclose(nx.asnumpy(), in_nx.asnumpy()) tvm.testing.assert_allclose(nx.asnumpy(), in_nx.asnumpy())
np.testing.assert_allclose(ny.asnumpy(), in_ny.asnumpy()) tvm.testing.assert_allclose(ny.asnumpy(), in_ny.asnumpy())
def test_num_outputs(): def test_num_outputs():
x = sym.Variable('x') x = sym.Variable('x')
......
nnvm/tests/python/compiler/test_compiler_cache.py
...@@ -19,7 +19,7 @@ def test_compile_cache(): ...@@ -19,7 +19,7 @@ def test_compile_cache():
m.run(x=na, y=nb) m.run(x=na, y=nb)
# get outputs # get outputs
out = m.get_output(0, tvm.nd.empty(shape, dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy())) out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy()))
engine = nnvm.compiler.engine engine = nnvm.compiler.engine
......
nnvm/tests/python/compiler/test_fold_axis.py
"""Unittest cases for fold_axis""" """Unittest cases for fold_axis"""
import tvm
import nnvm import nnvm
import nnvm.testing.resnet import nnvm.testing.resnet
import numpy as np import numpy as np
...@@ -147,7 +148,7 @@ def test_fold_resnet(): ...@@ -147,7 +148,7 @@ def test_fold_resnet():
x = run_prune(graph, params, 0) x = run_prune(graph, params, 0)
y = run_prune(graph, params, 3) y = run_prune(graph, params, 3)
np.testing.assert_allclose(y[0].asnumpy(), x[0].asnumpy()) tvm.testing.assert_allclose(y[0].asnumpy(), x[0].asnumpy())
if __name__ == "__main__": if __name__ == "__main__":
......
nnvm/tests/python/compiler/test_nhwc_layout.py
...@@ -50,7 +50,7 @@ def test_nhwc(): ...@@ -50,7 +50,7 @@ def test_nhwc():
oshape_nhwc = (1, 224, 224, out_channel) oshape_nhwc = (1, 224, 224, out_channel)
nchw_output = build_and_run(nchw_sym, nchw_params, data, oshape) nchw_output = build_and_run(nchw_sym, nchw_params, data, oshape)
nhwc_output = build_and_run(nhwc_sym, nhwc_params, data.transpose(0, 2, 3, 1), oshape_nhwc) nhwc_output = build_and_run(nhwc_sym, nhwc_params, data.transpose(0, 2, 3, 1), oshape_nhwc)
np.testing.assert_allclose(nchw_output, nhwc_output.transpose(0, 3, 1, 2), rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(nchw_output, nhwc_output.transpose(0, 3, 1, 2), rtol=1e-5, atol=1e-5)
if __name__ == "__main__": if __name__ == "__main__":
......
nnvm/tests/python/compiler/test_op_fusion.py
...@@ -22,7 +22,7 @@ def test_ewise_injective(): ...@@ -22,7 +22,7 @@ def test_ewise_injective():
x_np = np.random.uniform(size=dshape).astype(dtype) x_np = np.random.uniform(size=dshape).astype(dtype)
m.run(x=x_np) m.run(x=x_np)
out = m.get_output(0, tvm.nd.empty((10, 6))) out = m.get_output(0, tvm.nd.empty((10, 6)))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), x_np.reshape(out.shape) * 2 + 1, out.asnumpy(), x_np.reshape(out.shape) * 2 + 1,
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -54,7 +54,7 @@ def test_conv_ewise_injective(): ...@@ -54,7 +54,7 @@ def test_conv_ewise_injective():
data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME') data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME')
c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1) + 1 c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1) + 1
c_np = c_np.reshape(c_np.shape[0], np.prod(c_np.shape[1:])) + 1 c_np = c_np.reshape(c_np.shape[0], np.prod(c_np.shape[1:])) + 1
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_injective_reduce_injective(): def test_injective_reduce_injective():
...@@ -74,7 +74,7 @@ def test_injective_reduce_injective(): ...@@ -74,7 +74,7 @@ def test_injective_reduce_injective():
c_np = np.sum(data.reshape(32, 18 * 18) + 1, axis=1) c_np = np.sum(data.reshape(32, 18 * 18) + 1, axis=1)
# get output # get output
out = m.get_output(0, tvm.nd.empty(c_np.shape, dtype)) out = m.get_output(0, tvm.nd.empty(c_np.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_injective_conv2d(): def test_injective_conv2d():
...@@ -107,7 +107,7 @@ def test_injective_conv2d(): ...@@ -107,7 +107,7 @@ def test_injective_conv2d():
data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME') data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME')
weight = np.mean(data.asnumpy(), axis=(2, 3)) weight = np.mean(data.asnumpy(), axis=(2, 3))
c_np = weight[:, :, np.newaxis, np.newaxis] * data.asnumpy() + residual c_np = weight[:, :, np.newaxis, np.newaxis] * data.asnumpy() + residual
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_concatenate_conv2d(): def test_concatenate_conv2d():
...@@ -140,7 +140,7 @@ def test_concatenate_conv2d(): ...@@ -140,7 +140,7 @@ def test_concatenate_conv2d():
conv = topi.testing.conv2d_nchw_python( conv = topi.testing.conv2d_nchw_python(
concat, kernel.asnumpy(), (1,1), 'SAME') concat, kernel.asnumpy(), (1,1), 'SAME')
ref = concat + conv ref = concat + conv
np.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5)
def test_residual_block_layout_transform(): def test_residual_block_layout_transform():
...@@ -178,7 +178,7 @@ def test_residual_block_layout_transform(): ...@@ -178,7 +178,7 @@ def test_residual_block_layout_transform():
conv2 = topi.testing.conv2d_nchw_python( conv2 = topi.testing.conv2d_nchw_python(
conv1, kernel2.asnumpy(), (1,1), 'SAME') conv1, kernel2.asnumpy(), (1,1), 'SAME')
ref = np.maximum(conv1 + conv2, 0) ref = np.maximum(conv1 + conv2, 0)
np.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5)
def build_and_run(sym, params, data, out_shape, target, ctx, opt_level=2): def build_and_run(sym, params, data, out_shape, target, ctx, opt_level=2):
...@@ -218,7 +218,7 @@ def test_fuse_conv2d_elu(): ...@@ -218,7 +218,7 @@ def test_fuse_conv2d_elu():
_, params2 = utils.create_workload(sym2, 1, dshape[1:], seed=0) _, params2 = utils.create_workload(sym2, 1, dshape[1:], seed=0)
output1, g1 = build_and_run(sym1, params1, data, oshape, target, ctx, opt_level=2) output1, g1 = build_and_run(sym1, params1, data, oshape, target, ctx, opt_level=2)
output2, g2 = build_and_run(sym2, params2, data, oshape, target, ctx, opt_level=0) output2, g2 = build_and_run(sym2, params2, data, oshape, target, ctx, opt_level=0)
np.testing.assert_allclose(output1, output2, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(output1, output2, rtol=1e-5, atol=1e-5)
# data, conv weight, bias, batch norm gamma, batch norm beta, conv op # data, conv weight, bias, batch norm gamma, batch norm beta, conv op
assert g1.index.num_nodes == 6 assert g1.index.num_nodes == 6
......
nnvm/tests/python/compiler/test_optimizer.py
...@@ -27,7 +27,7 @@ def helper(symbol, inputs, params, update_func, run_times, target, ctx, dtype="f ...@@ -27,7 +27,7 @@ def helper(symbol, inputs, params, update_func, run_times, target, ctx, dtype="f
m.run() m.run()
y_np = update_func(**np_inputs) y_np = update_func(**np_inputs)
out = m.get_output(0, tvm.nd.empty(y_np.shape, dtype)) out = m.get_output(0, tvm.nd.empty(y_np.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
def test_sgd(): def test_sgd():
......
nnvm/tests/python/compiler/test_param_dict.py
...@@ -68,7 +68,7 @@ def test_bigendian_rpc_param(): ...@@ -68,7 +68,7 @@ def test_bigendian_rpc_param():
m.load_params(nnvm.compiler.save_param_dict(params)) m.load_params(nnvm.compiler.save_param_dict(params))
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype, ctx=ctx)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype, ctx=ctx))
np.testing.assert_allclose(a + 1, out.asnumpy()) tvm.testing.assert_allclose(a + 1, out.asnumpy())
print("Test RPC connection to PowerPC...") print("Test RPC connection to PowerPC...")
remote = rpc.connect(host, port) remote = rpc.connect(host, port)
......
nnvm/tests/python/compiler/test_rpc_exec.py
...@@ -43,7 +43,7 @@ def test_rpc_executor(): ...@@ -43,7 +43,7 @@ def test_rpc_executor():
# get outputs # get outputs
out = tvm.nd.empty(shape, dtype, ctx) out = tvm.nd.empty(shape, dtype, ctx)
get_output(0, out) get_output(0, out)
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy())) out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy()))
server.terminate() server.terminate()
......
nnvm/tests/python/compiler/test_top_assign.py
...@@ -27,11 +27,11 @@ def test_update(): ...@@ -27,11 +27,11 @@ def test_update():
m.set_input("w", data) m.set_input("w", data)
m.run() m.run()
out = m.get_input("w2", tvm.nd.empty(dshape, dtype)) out = m.get_input("w2", tvm.nd.empty(dshape, dtype))
np.testing.assert_allclose(out.asnumpy(), data.asnumpy() + 2, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), data.asnumpy() + 2, rtol=1e-5)
m.run() m.run()
out = m.get_input("w2", tvm.nd.empty(dshape, dtype)) out = m.get_input("w2", tvm.nd.empty(dshape, dtype))
np.testing.assert_allclose(out.asnumpy(), data.asnumpy() + 3, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), data.asnumpy() + 3, rtol=1e-5)
for target, ctx in ctx_list(): for target, ctx in ctx_list():
check(target, ctx) check(target, ctx)
......
nnvm/tests/python/compiler/test_top_level2.py
...@@ -22,7 +22,7 @@ def test_conv2d(): ...@@ -22,7 +22,7 @@ def test_conv2d():
c_np = topi.testing.conv2d_nchw_python( c_np = topi.testing.conv2d_nchw_python(
data.asnumpy(), kernel.asnumpy(), 1, padding) data.asnumpy(), kernel.asnumpy(), 1, padding)
c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1) c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
x = sym.Variable("x") x = sym.Variable("x")
y = sym.conv2d(x, channels=10, kernel_size=(3,3), y = sym.conv2d(x, channels=10, kernel_size=(3,3),
...@@ -71,7 +71,7 @@ def test_mixed_precision(): ...@@ -71,7 +71,7 @@ def test_mixed_precision():
c_np = topi.testing.conv2d_nchw_python( c_np = topi.testing.conv2d_nchw_python(
data.asnumpy().astype(out_dtype), data.asnumpy().astype(out_dtype),
kernel.asnumpy().astype(out_dtype), 1, 1) kernel.asnumpy().astype(out_dtype), 1, 1)
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_dilated_conv2d(): def test_dilated_conv2d():
...@@ -97,7 +97,7 @@ def test_dilated_conv2d(): ...@@ -97,7 +97,7 @@ def test_dilated_conv2d():
c_np = topi.testing.conv2d_nchw_python( c_np = topi.testing.conv2d_nchw_python(
data.asnumpy(), dkernel_np, 1, 1) data.asnumpy(), dkernel_np, 1, 1)
c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1) c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_grouped_conv2d_nchw(): def test_grouped_conv2d_nchw():
...@@ -120,7 +120,7 @@ def test_grouped_conv2d_nchw(): ...@@ -120,7 +120,7 @@ def test_grouped_conv2d_nchw():
c_np = topi.testing.depthwise_conv2d_python_nchw( c_np = topi.testing.depthwise_conv2d_python_nchw(
data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME') data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME')
c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1) c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_grouped_conv2d_nhwc(): def test_grouped_conv2d_nhwc():
x = sym.Variable("x") x = sym.Variable("x")
...@@ -142,7 +142,7 @@ def test_grouped_conv2d_nhwc(): ...@@ -142,7 +142,7 @@ def test_grouped_conv2d_nhwc():
c_np = topi.testing.depthwise_conv2d_python_nhwc( c_np = topi.testing.depthwise_conv2d_python_nhwc(
data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME') data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME')
c_np = c_np + bias.asnumpy().reshape(1, 1, kshape[2]) c_np = c_np + bias.asnumpy().reshape(1, 1, kshape[2])
np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
def test_conv2d_transpose(): def test_conv2d_transpose():
...@@ -167,7 +167,7 @@ def test_conv2d_transpose(): ...@@ -167,7 +167,7 @@ def test_conv2d_transpose():
c_np = c_np + bias.asnumpy().reshape(kshape[1], 1, 1) c_np = c_np + bias.asnumpy().reshape(kshape[1], 1, 1)
d_np = np.zeros(shape=oshape) d_np = np.zeros(shape=oshape)
d_np[:,:,0:c_np.shape[2],0:c_np.shape[3]] = c_np d_np[:,:,0:c_np.shape[2],0:c_np.shape[3]] = c_np
np.testing.assert_allclose(out.asnumpy(), d_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), d_np, rtol=1e-5)
def test_max_pool2d(): def test_max_pool2d():
...@@ -185,7 +185,7 @@ def test_max_pool2d(): ...@@ -185,7 +185,7 @@ def test_max_pool2d():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = np.max(data.asnumpy().reshape(1,3,14,2,14,2), axis=(3,5)) b_np = np.max(data.asnumpy().reshape(1,3,14,2,14,2), axis=(3,5))
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
def test_avg_pool2d(): def test_avg_pool2d():
...@@ -202,7 +202,7 @@ def test_avg_pool2d(): ...@@ -202,7 +202,7 @@ def test_avg_pool2d():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = np.mean(data.asnumpy().reshape(1,3,14,2,14,2), axis=(3,5)) b_np = np.mean(data.asnumpy().reshape(1,3,14,2,14,2), axis=(3,5))
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
def test_avg_pool2d_no_count_pad(): def test_avg_pool2d_no_count_pad():
...@@ -237,7 +237,7 @@ def test_avg_pool2d_no_count_pad(): ...@@ -237,7 +237,7 @@ def test_avg_pool2d_no_count_pad():
data = tvm.nd.array(a_np) data = tvm.nd.array(a_np)
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty((n, oc, oh, ow), dtype)) out = m.get_output(0, tvm.nd.empty((n, oc, oh, ow), dtype))
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
def test_global_max_pool2d(): def test_global_max_pool2d():
...@@ -254,7 +254,7 @@ def test_global_max_pool2d(): ...@@ -254,7 +254,7 @@ def test_global_max_pool2d():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = np.max(data.asnumpy(), axis=(2,3), keepdims=True) b_np = np.max(data.asnumpy(), axis=(2,3), keepdims=True)
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
def test_global_avg_pool2d(): def test_global_avg_pool2d():
...@@ -271,7 +271,7 @@ def test_global_avg_pool2d(): ...@@ -271,7 +271,7 @@ def test_global_avg_pool2d():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = np.mean(data.asnumpy(), axis=(2,3), keepdims=True) b_np = np.mean(data.asnumpy(), axis=(2,3), keepdims=True)
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
def test_upsampling_nearest_neighbor(): def test_upsampling_nearest_neighbor():
...@@ -290,7 +290,7 @@ def test_upsampling_nearest_neighbor(): ...@@ -290,7 +290,7 @@ def test_upsampling_nearest_neighbor():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = topi.testing.upsampling_python(a_np, scale, "NCHW") b_np = topi.testing.upsampling_python(a_np, scale, "NCHW")
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
def test_upsampling_bilinear(): def test_upsampling_bilinear():
x = sym.Variable("x") x = sym.Variable("x")
...@@ -309,7 +309,7 @@ def test_upsampling_bilinear(): ...@@ -309,7 +309,7 @@ def test_upsampling_bilinear():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = topi.testing.bilinear_resize_python(a_np, (32*scale, 32*scale), "NCHW") b_np = topi.testing.bilinear_resize_python(a_np, (32*scale, 32*scale), "NCHW")
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
def test_resize_bilinear(): def test_resize_bilinear():
x = sym.Variable("x") x = sym.Variable("x")
...@@ -327,7 +327,7 @@ def test_resize_bilinear(): ...@@ -327,7 +327,7 @@ def test_resize_bilinear():
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype))
b_np = topi.testing.bilinear_resize_python(a_np, (60, 60), "NHWC") b_np = topi.testing.bilinear_resize_python(a_np, (60, 60), "NHWC")
np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
if __name__ == "__main__": if __name__ == "__main__":
test_mixed_precision() test_mixed_precision()
......
nnvm/tests/python/compiler/test_top_level4.py
...@@ -24,7 +24,7 @@ def verify_transpose(dshape, axes): ...@@ -24,7 +24,7 @@ def verify_transpose(dshape, axes):
m.run(x=data) m.run(x=data)
out_np = np.transpose(data.asnumpy(), axes=axes) + 1 out_np = np.transpose(data.asnumpy(), axes=axes) + 1
out = m.get_output(0, tvm.nd.empty(out_np.shape)) out = m.get_output(0, tvm.nd.empty(out_np.shape))
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def verify_reduce_explicit(dshape, data, result, fsym, oshape=None, otype='float32', **kwargs): def verify_reduce_explicit(dshape, data, result, fsym, oshape=None, otype='float32', **kwargs):
""" Verify reduce operations by comparign its result with `result` """ """ Verify reduce operations by comparign its result with `result` """
...@@ -43,7 +43,7 @@ def verify_reduce_explicit(dshape, data, result, fsym, oshape=None, otype='float ...@@ -43,7 +43,7 @@ def verify_reduce_explicit(dshape, data, result, fsym, oshape=None, otype='float
out = m.get_output(0, tvm.nd.empty(oshape, dtype=otype)) out = m.get_output(0, tvm.nd.empty(oshape, dtype=otype))
if isinstance(result, np.ndarray): if isinstance(result, np.ndarray):
np.testing.assert_equal(out.asnumpy().shape, result.shape) np.testing.assert_equal(out.asnumpy().shape, result.shape)
np.testing.assert_allclose(out.asnumpy(), result, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), result, atol=1e-5, rtol=1e-5)
else: else:
tvm_out = out.asnumpy() tvm_out = out.asnumpy()
assert abs(result - tvm_out) <= (1e-5 + 1e-5 * abs(tvm_out)) assert abs(result - tvm_out) <= (1e-5 + 1e-5 * abs(tvm_out))
...@@ -68,7 +68,7 @@ def verify_collapse(dshape, target_shape, fnp): ...@@ -68,7 +68,7 @@ def verify_collapse(dshape, target_shape, fnp):
m.run(x=data) m.run(x=data)
out = m.get_output(0, tvm.nd.empty(target_shape)) out = m.get_output(0, tvm.nd.empty(target_shape))
out_np = fnp(data) out_np = fnp(data)
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def test_transpose(): def test_transpose():
...@@ -149,7 +149,7 @@ def verify_flip(ishape, axis): ...@@ -149,7 +149,7 @@ def verify_flip(ishape, axis):
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run(x=x_np) m.run(x=x_np)
out = m.get_output(0, tvm.nd.empty(res.shape)) out = m.get_output(0, tvm.nd.empty(res.shape))
np.testing.assert_allclose(out.asnumpy(), res, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), res, atol=1e-5, rtol=1e-5)
def test_flip(): def test_flip():
...@@ -174,7 +174,7 @@ def verify_reshape(dshape, oshape): ...@@ -174,7 +174,7 @@ def verify_reshape(dshape, oshape):
m.run(x=data) m.run(x=data)
out_np = data.asnumpy().reshape(oshape) + 1 out_np = data.asnumpy().reshape(oshape) + 1
out = m.get_output(0, tvm.nd.empty(out_np.shape)) out = m.get_output(0, tvm.nd.empty(out_np.shape))
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def test_reshape(): def test_reshape():
...@@ -435,7 +435,7 @@ def test_full(): ...@@ -435,7 +435,7 @@ def test_full():
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype)) m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), out.asnumpy(),
np.full(shape, fill_value=value, dtype=dtype), np.full(shape, fill_value=value, dtype=dtype),
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -445,7 +445,7 @@ def test_full(): ...@@ -445,7 +445,7 @@ def test_full():
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype)) m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), out.asnumpy(),
np.full(shape, fill_value=1, dtype=dtype), np.full(shape, fill_value=1, dtype=dtype),
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -455,7 +455,7 @@ def test_full(): ...@@ -455,7 +455,7 @@ def test_full():
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype)) m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), out.asnumpy(),
np.full(shape, fill_value=0, dtype=dtype), np.full(shape, fill_value=0, dtype=dtype),
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -465,7 +465,7 @@ def test_full(): ...@@ -465,7 +465,7 @@ def test_full():
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), out.asnumpy(),
np.full(shape, fill_value=value, dtype=dtype), np.full(shape, fill_value=value, dtype=dtype),
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -475,7 +475,7 @@ def test_full(): ...@@ -475,7 +475,7 @@ def test_full():
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), out.asnumpy(),
np.full(shape, fill_value=1, dtype=dtype), np.full(shape, fill_value=1, dtype=dtype),
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -485,7 +485,7 @@ def test_full(): ...@@ -485,7 +485,7 @@ def test_full():
m = graph_runtime.create(graph, lib, ctx) m = graph_runtime.create(graph, lib, ctx)
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype)) out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose( tvm.testing.assert_allclose(
out.asnumpy(), out.asnumpy(),
np.full(shape, fill_value=0, dtype=dtype), np.full(shape, fill_value=0, dtype=dtype),
atol=1e-5, rtol=1e-5) atol=1e-5, rtol=1e-5)
...@@ -534,7 +534,7 @@ def verify_multibox_prior(dshape, sizes=(1,), ratios=(1,), steps=(-1, -1), ...@@ -534,7 +534,7 @@ def verify_multibox_prior(dshape, sizes=(1,), ratios=(1,), steps=(-1, -1),
m.set_input("data", np.random.uniform(size=dshape).astype(dtype)) m.set_input("data", np.random.uniform(size=dshape).astype(dtype))
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(np_out.shape, dtype)) out = m.get_output(0, tvm.nd.empty(np_out.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5)
def test_multibox_prior(): def test_multibox_prior():
verify_multibox_prior((1, 3, 50, 50)) verify_multibox_prior((1, 3, 50, 50))
...@@ -571,7 +571,7 @@ def test_multibox_transform_loc(): ...@@ -571,7 +571,7 @@ def test_multibox_transform_loc():
m.set_input(**{"cls_prob": np_cls_prob.astype(dtype), "loc_preds": np_loc_preds.astype(dtype), "anchors": np_anchors.astype(dtype)}) m.set_input(**{"cls_prob": np_cls_prob.astype(dtype), "loc_preds": np_loc_preds.astype(dtype), "anchors": np_anchors.astype(dtype)})
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(expected_np_out.shape, dtype)) out = m.get_output(0, tvm.nd.empty(expected_np_out.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), expected_np_out, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), expected_np_out, atol=1e-5, rtol=1e-5)
def test_nms(): def test_nms():
dshape = (1, 5, 6) dshape = (1, 5, 6)
...@@ -599,7 +599,7 @@ def test_nms(): ...@@ -599,7 +599,7 @@ def test_nms():
m.set_input(**{"data": np_data, "valid_count": np_valid_count}) m.set_input(**{"data": np_data, "valid_count": np_valid_count})
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(np_result.shape, "float32")) out = m.get_output(0, tvm.nd.empty(np_result.shape, "float32"))
np.testing.assert_allclose(out.asnumpy(), np_result, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_result, atol=1e-5, rtol=1e-5)
def np_slice_like(np_data, np_shape_like, axis=[]): def np_slice_like(np_data, np_shape_like, axis=[]):
begin_idx = [0 for _ in np_data.shape] begin_idx = [0 for _ in np_data.shape]
...@@ -634,7 +634,7 @@ def verify_slice_like(np_data, np_shape_like, axis=[]): ...@@ -634,7 +634,7 @@ def verify_slice_like(np_data, np_shape_like, axis=[]):
m.set_input(**{"data1": np_data, "data2": np_shape_like}) m.set_input(**{"data1": np_data, "data2": np_shape_like})
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(np_result.shape, dtype)) out = m.get_output(0, tvm.nd.empty(np_result.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), np_result, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_result, atol=1e-5, rtol=1e-5)
def test_slice_like(): def test_slice_like():
np_data = np.random.uniform(size=(3, 4, 5)) np_data = np.random.uniform(size=(3, 4, 5))
...@@ -673,7 +673,7 @@ def verify_where(condition, x, y): ...@@ -673,7 +673,7 @@ def verify_where(condition, x, y):
m.set_input(**{"condition": condition, "x": x, "y": y}) m.set_input(**{"condition": condition, "x": x, "y": y})
m.run() m.run()
out = m.get_output(0, tvm.nd.empty(x.shape, dtype)) out = m.get_output(0, tvm.nd.empty(x.shape, dtype))
np.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_out, atol=1e-5, rtol=1e-5)
def test_where(): def test_where():
shape = (13, 8, 224, 224, 6) shape = (13, 8, 224, 224, 6)
......
nnvm/tests/python/frontend/coreml/test_forward.py
...@@ -109,7 +109,7 @@ def verify_AddLayerParams(input_dim, alpha=2): ...@@ -109,7 +109,7 @@ def verify_AddLayerParams(input_dim, alpha=2):
['input1', 'input2'], ['input1', 'input2'],
b_np.shape, b_np.shape,
dtype) dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_AddLayerParams(): def test_forward_AddLayerParams():
verify_AddLayerParams((1, 2, 2), 0) verify_AddLayerParams((1, 2, 2), 0)
...@@ -139,7 +139,7 @@ def verify_MultiplyLayerParams(input_dim, alpha): ...@@ -139,7 +139,7 @@ def verify_MultiplyLayerParams(input_dim, alpha):
['input1', 'input2'], ['input1', 'input2'],
b_np.shape, b_np.shape,
dtype) dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_MultiplyLayerParams(): def test_forward_MultiplyLayerParams():
verify_MultiplyLayerParams((1, 2, 2), 0) verify_MultiplyLayerParams((1, 2, 2), 0)
...@@ -168,7 +168,7 @@ def verify_ConcatLayerParams(input1_dim, input2_dim): ...@@ -168,7 +168,7 @@ def verify_ConcatLayerParams(input1_dim, input2_dim):
['input1', 'input2'], ['input1', 'input2'],
b_np.shape, b_np.shape,
dtype) dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_ConcatLayerParams(): def test_forward_ConcatLayerParams():
verify_ConcatLayerParams((1, 1, 2, 2), (1, 2, 2, 2)) verify_ConcatLayerParams((1, 1, 2, 2), (1, 2, 2, 2))
...@@ -198,7 +198,7 @@ def verify_UpsampleLayerParams(input_dim, scale, mode): ...@@ -198,7 +198,7 @@ def verify_UpsampleLayerParams(input_dim, scale, mode):
model = cm.models.MLModel(builder.spec) model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list(): for target, ctx in ctx_list():
out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype) out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_UpsampleLayerParams(): def test_forward_UpsampleLayerParams():
verify_UpsampleLayerParams((1, 16, 32, 32), 2, 'NN') verify_UpsampleLayerParams((1, 16, 32, 32), 2, 'NN')
...@@ -218,7 +218,7 @@ def verify_l2_normalize(input_dim, eps): ...@@ -218,7 +218,7 @@ def verify_l2_normalize(input_dim, eps):
model = cm.models.MLModel(builder.spec) model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list(): for target, ctx in ctx_list():
out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype) out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_l2_normalize(): def test_forward_l2_normalize():
verify_l2_normalize((1, 3, 20, 20), 0.001) verify_l2_normalize((1, 3, 20, 20), 0.001)
...@@ -243,7 +243,7 @@ def verify_lrn(input_dim, size, bias, alpha, beta): ...@@ -243,7 +243,7 @@ def verify_lrn(input_dim, size, bias, alpha, beta):
model = cm.models.MLModel(builder.spec) model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list(): for target, ctx in ctx_list():
out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype) out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_lrn(): def test_forward_lrn():
verify_lrn((1, 3, 10, 20), 3, 1.0, 1.0, 0.5) verify_lrn((1, 3, 10, 20), 3, 1.0, 1.0, 0.5)
...@@ -271,7 +271,7 @@ def verify_average(input_dim1, input_dim2, axis=0): ...@@ -271,7 +271,7 @@ def verify_average(input_dim1, input_dim2, axis=0):
['input1', 'input2'], ['input1', 'input2'],
b_np.shape, b_np.shape,
dtype) dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_average(): def test_forward_average():
verify_average((1, 3, 20, 20), (1, 3, 20, 20)) verify_average((1, 3, 20, 20), (1, 3, 20, 20))
...@@ -303,7 +303,7 @@ def verify_max(input_dim): ...@@ -303,7 +303,7 @@ def verify_max(input_dim):
['input1', 'input2', 'input3'], ['input1', 'input2', 'input3'],
b_np.shape, b_np.shape,
dtype) dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_max(): def test_forward_max():
verify_max((1, 3, 20, 20)) verify_max((1, 3, 20, 20))
...@@ -334,7 +334,7 @@ def verify_min(input_dim): ...@@ -334,7 +334,7 @@ def verify_min(input_dim):
['input1', 'input2', 'input3'], ['input1', 'input2', 'input3'],
b_np.shape, b_np.shape,
dtype) dtype)
np.testing.assert_allclose(out, b_np, rtol=1e-5) tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def test_forward_min(): def test_forward_min():
verify_min((1, 3, 20, 20)) verify_min((1, 3, 20, 20))
......
nnvm/tests/python/frontend/darknet/test_forward.py
...@@ -139,7 +139,7 @@ def test_forward(net, build_dtype='float32'): ...@@ -139,7 +139,7 @@ def test_forward(net, build_dtype='float32'):
tvm_out = _get_tvm_output(net, data, build_dtype) tvm_out = _get_tvm_output(net, data, build_dtype)
for tvm_outs, darknet_out in zip(tvm_out, darknet_output): for tvm_outs, darknet_out in zip(tvm_out, darknet_output):
np.testing.assert_allclose(darknet_out, tvm_outs, rtol=1e-3, atol=1e-3) tvm.testing.assert_allclose(darknet_out, tvm_outs, rtol=1e-3, atol=1e-3)
def test_rnn_forward(net): def test_rnn_forward(net):
'''Test network with given input data on both darknet and tvm''' '''Test network with given input data on both darknet and tvm'''
...@@ -158,7 +158,7 @@ def test_rnn_forward(net): ...@@ -158,7 +158,7 @@ def test_rnn_forward(net):
last_layer = net.layers[net.n-1] last_layer = net.layers[net.n-1]
darknet_outshape = (last_layer.batch, last_layer.outputs) darknet_outshape = (last_layer.batch, last_layer.outputs)
darknet_out = darknet_out.reshape(darknet_outshape) darknet_out = darknet_out.reshape(darknet_outshape)
np.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-4, atol=1e-4) tvm.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-4, atol=1e-4)
def test_forward_extraction(): def test_forward_extraction():
'''test extraction model''' '''test extraction model'''
......
nnvm/tests/python/frontend/keras/test_forward.py
...@@ -52,7 +52,7 @@ def verify_keras_frontend(keras_model, need_transpose=True): ...@@ -52,7 +52,7 @@ def verify_keras_frontend(keras_model, need_transpose=True):
for kout, tout in zip(keras_out, tvm_out): for kout, tout in zip(keras_out, tvm_out):
if need_transpose: if need_transpose:
tout = to_channels_last(tout) tout = to_channels_last(tout)
np.testing.assert_allclose(kout, tout, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(kout, tout, rtol=1e-5, atol=1e-5)
def test_forward_elemwise_add(): def test_forward_elemwise_add():
r = [] r = []
......
nnvm/tests/python/frontend/mxnet/test_forward.py
...@@ -62,13 +62,13 @@ def verify_mxnet_frontend_impl(mx_symbol, data_shape=(1, 3, 224, 224), out_shape ...@@ -62,13 +62,13 @@ def verify_mxnet_frontend_impl(mx_symbol, data_shape=(1, 3, 224, 224), out_shape
gluon_out, gluon_sym = get_gluon_output(name, x) gluon_out, gluon_sym = get_gluon_output(name, x)
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(gluon_sym, x, None, None, target, ctx, dtype) tvm_out = get_tvm_output(gluon_sym, x, None, None, target, ctx, dtype)
np.testing.assert_allclose(gluon_out, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(gluon_out, tvm_out, rtol=1e-5, atol=1e-5)
else: else:
mx_out, args, auxs = get_mxnet_output(mx_symbol, x, dtype) mx_out, args, auxs = get_mxnet_output(mx_symbol, x, dtype)
assert "data" not in args assert "data" not in args
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(mx_symbol, x, args, auxs, target, ctx, dtype) tvm_out = get_tvm_output(mx_symbol, x, args, auxs, target, ctx, dtype)
np.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_mlp(): def test_forward_mlp():
mlp = model_zoo.mx_mlp mlp = model_zoo.mx_mlp
......
nnvm/tests/python/frontend/onnx/test_forward.py
...@@ -70,7 +70,7 @@ def verify_onnx_forward_impl(graph_file, data_shape, out_shape): ...@@ -70,7 +70,7 @@ def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
c2_out = get_caffe2_output(model, x, dtype) c2_out = get_caffe2_output(model, x, dtype)
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype) tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype)
np.testing.assert_allclose(c2_out, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(c2_out, tvm_out, rtol=1e-5, atol=1e-5)
def verify_super_resolution_example(): def verify_super_resolution_example():
verify_onnx_forward_impl(super_resolution, (1, 1, 224, 224), (1, 1, 672, 672)) verify_onnx_forward_impl(super_resolution, (1, 1, 224, 224), (1, 1, 672, 672))
...@@ -112,7 +112,7 @@ def test_reshape(): ...@@ -112,7 +112,7 @@ def test_reshape():
x = np.random.uniform(size=in_shape).astype('int32') x = np.random.uniform(size=in_shape).astype('int32')
tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32') tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
np.testing.assert_allclose(ref_shape, tvm_out.shape) tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
def test_reshape_like(): def test_reshape_like():
in_shape = (4, 3, 3, 4) in_shape = (4, 3, 3, 4)
...@@ -142,7 +142,7 @@ def test_reshape_like(): ...@@ -142,7 +142,7 @@ def test_reshape_like():
x = np.random.uniform(size=in_shape).astype('float32') x = np.random.uniform(size=in_shape).astype('float32')
tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32') tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
np.testing.assert_allclose(ref_shape, tvm_out.shape) tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
def _test_power_iteration(x_shape, y_shape): def _test_power_iteration(x_shape, y_shape):
if isinstance(y_shape, int): if isinstance(y_shape, int):
...@@ -168,7 +168,7 @@ def _test_power_iteration(x_shape, y_shape): ...@@ -168,7 +168,7 @@ def _test_power_iteration(x_shape, y_shape):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [x, y], target, ctx, np_res.shape) tvm_out = get_tvm_output(model, [x, y], target, ctx, np_res.shape)
np.testing.assert_allclose(np_res, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(np_res, tvm_out, rtol=1e-5, atol=1e-5)
def test_power(): def test_power():
_test_power_iteration((1, 3), (1)) _test_power_iteration((1, 3), (1))
...@@ -193,7 +193,7 @@ def test_squeeze(): ...@@ -193,7 +193,7 @@ def test_squeeze():
x = np.random.uniform(size=in_shape).astype('float32') x = np.random.uniform(size=in_shape).astype('float32')
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, 'float32') tvm_out = get_tvm_output(model, x, target, ctx, out_shape, 'float32')
np.testing.assert_allclose(out_shape, tvm_out.shape) tvm.testing.assert_allclose(out_shape, tvm_out.shape)
def test_unsqueeze(): def test_unsqueeze():
in_shape = (3, 3) in_shape = (3, 3)
...@@ -214,7 +214,7 @@ def test_unsqueeze(): ...@@ -214,7 +214,7 @@ def test_unsqueeze():
x = np.random.uniform(size=in_shape).astype('float32') x = np.random.uniform(size=in_shape).astype('float32')
tvm_out = get_tvm_output(model, x, target, ctx, out_shape, 'float32') tvm_out = get_tvm_output(model, x, target, ctx, out_shape, 'float32')
np.testing.assert_allclose(out_shape, tvm_out.shape) tvm.testing.assert_allclose(out_shape, tvm_out.shape)
def verify_gather(in_shape, indices, axis, dtype): def verify_gather(in_shape, indices, axis, dtype):
x = np.random.uniform(size=in_shape).astype(dtype) x = np.random.uniform(size=in_shape).astype(dtype)
...@@ -235,7 +235,7 @@ def verify_gather(in_shape, indices, axis, dtype): ...@@ -235,7 +235,7 @@ def verify_gather(in_shape, indices, axis, dtype):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [x, indices], target, ctx, out_np.shape) tvm_out = get_tvm_output(model, [x, indices], target, ctx, out_np.shape)
np.testing.assert_allclose(out_np, tvm_out) tvm.testing.assert_allclose(out_np, tvm_out)
def test_gather(): def test_gather():
verify_gather((4,), [1], 0, 'int32') verify_gather((4,), [1], 0, 'int32')
...@@ -263,7 +263,7 @@ def _test_slice_iteration(indata, outdata, starts, ends, axes=None): ...@@ -263,7 +263,7 @@ def _test_slice_iteration(indata, outdata, starts, ends, axes=None):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, 'float32') tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, 'float32')
np.testing.assert_allclose(outdata, tvm_out) tvm.testing.assert_allclose(outdata, tvm_out)
def test_slice(): def test_slice():
x = np.random.randn(20, 10, 5).astype(np.float32) x = np.random.randn(20, 10, 5).astype(np.float32)
...@@ -290,7 +290,7 @@ def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs): ...@@ -290,7 +290,7 @@ def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, dtype) tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, dtype)
np.testing.assert_allclose(outdata, tvm_out) tvm.testing.assert_allclose(outdata, tvm_out)
def test_floor(): def test_floor():
_test_onnx_op_elementwise((2, 4, 5, 6), np.floor, {}, 'float32', 'Floor', {}) _test_onnx_op_elementwise((2, 4, 5, 6), np.floor, {}, 'float32', 'Floor', {})
...@@ -329,7 +329,7 @@ def test_matmul(): ...@@ -329,7 +329,7 @@ def test_matmul():
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_array, b_array], target, ctx, out_np.shape) tvm_out = get_tvm_output(model, [a_array, b_array], target, ctx, out_np.shape)
np.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
def verify_lrn(shape, nsize, dtype, alpha=None, beta=None, bias=None): def verify_lrn(shape, nsize, dtype, alpha=None, beta=None, bias=None):
in_array = np.random.uniform(size=shape).astype(dtype) in_array = np.random.uniform(size=shape).astype(dtype)
...@@ -376,7 +376,7 @@ def verify_lrn(shape, nsize, dtype, alpha=None, beta=None, bias=None): ...@@ -376,7 +376,7 @@ def verify_lrn(shape, nsize, dtype, alpha=None, beta=None, bias=None):
# get outputs # get outputs
tvm_out = m.get_output(0, tvm.nd.empty(shape, dtype)) tvm_out = m.get_output(0, tvm.nd.empty(shape, dtype))
py_out = _get_python_lrn() py_out = _get_python_lrn()
np.testing.assert_allclose(py_out, tvm_out.asnumpy(), rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(py_out, tvm_out.asnumpy(), rtol=1e-5, atol=1e-5)
def test_lrn(): def test_lrn():
verify_lrn((5, 5, 5, 5), 3, 'float32') verify_lrn((5, 5, 5, 5), 3, 'float32')
...@@ -400,7 +400,7 @@ def _test_upsample_nearest(): ...@@ -400,7 +400,7 @@ def _test_upsample_nearest():
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, 'float32') tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, 'float32')
np.testing.assert_allclose(out_array, tvm_out) tvm.testing.assert_allclose(out_array, tvm_out)
def _test_upsample_bilinear(): def _test_upsample_bilinear():
scale = 2 scale = 2
...@@ -420,7 +420,7 @@ def _test_upsample_bilinear(): ...@@ -420,7 +420,7 @@ def _test_upsample_bilinear():
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, 'float32') tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, 'float32')
np.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)
def test_upsample(): def test_upsample():
_test_upsample_nearest() _test_upsample_nearest()
...@@ -447,7 +447,7 @@ def _test_softmax(inshape, axis): ...@@ -447,7 +447,7 @@ def _test_softmax(inshape, axis):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, indata, target, ctx, outshape, 'float32') tvm_out = get_tvm_output(model, indata, target, ctx, outshape, 'float32')
np.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
def test_softmax(): def test_softmax():
_test_softmax((1, 10), None) _test_softmax((1, 10), None)
...@@ -479,7 +479,7 @@ def verify_min(input_dim): ...@@ -479,7 +479,7 @@ def verify_min(input_dim):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape) tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_min(): def test_forward_min():
verify_min((1, 3, 20, 20)) verify_min((1, 3, 20, 20))
...@@ -511,7 +511,7 @@ def verify_max(input_dim): ...@@ -511,7 +511,7 @@ def verify_max(input_dim):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape) tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_max(): def test_forward_max():
verify_max((1, 3, 20, 20)) verify_max((1, 3, 20, 20))
...@@ -543,7 +543,7 @@ def verify_mean(input_dim): ...@@ -543,7 +543,7 @@ def verify_mean(input_dim):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape) tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_mean(): def test_forward_mean():
verify_mean((1, 3, 20, 20)) verify_mean((1, 3, 20, 20))
...@@ -569,7 +569,7 @@ def verify_hardsigmoid(input_dim, alpha, beta): ...@@ -569,7 +569,7 @@ def verify_hardsigmoid(input_dim, alpha, beta):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape) tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape)
np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_hardsigmoid(): def test_forward_hardsigmoid():
verify_hardsigmoid((1, 3, 20, 20), 0.5, 0.6) verify_hardsigmoid((1, 3, 20, 20), 0.5, 0.6)
...@@ -618,7 +618,7 @@ def verify_argmin(input_dim, axis=None, keepdims=None): ...@@ -618,7 +618,7 @@ def verify_argmin(input_dim, axis=None, keepdims=None):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype) tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def verify_argmax(input_dim, axis=None, keepdims=None): def verify_argmax(input_dim, axis=None, keepdims=None):
def _argmax_numpy(data, axis=0, keepdims=True): def _argmax_numpy(data, axis=0, keepdims=True):
...@@ -665,7 +665,7 @@ def verify_argmax(input_dim, axis=None, keepdims=None): ...@@ -665,7 +665,7 @@ def verify_argmax(input_dim, axis=None, keepdims=None):
for target, ctx in ctx_list(): for target, ctx in ctx_list():
tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype) tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_arg_min_max(): def test_forward_arg_min_max():
'''Verify argmin and argmax''' '''Verify argmin and argmax'''
...@@ -705,7 +705,7 @@ def verify_constantfill(is_shape, input_dim, out_dim, value, dtype, **kwargs): ...@@ -705,7 +705,7 @@ def verify_constantfill(is_shape, input_dim, out_dim, value, dtype, **kwargs):
else: else:
tvm_out = get_tvm_output(model, [input_a], target, ctx, out.shape) tvm_out = get_tvm_output(model, [input_a], target, ctx, out.shape)
np.testing.assert_allclose(out, tvm_out, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(out, tvm_out, rtol=1e-5, atol=1e-5)
def test_constantfill(): def test_constantfill():
verify_constantfill(True, (2, 3, 4, 5), (2, 3, 4, 5), 10, 'float32') verify_constantfill(True, (2, 3, 4, 5), (2, 3, 4, 5), 10, 'float32')
......
nnvm/tests/python/frontend/tensorflow/test_forward.py
...@@ -120,7 +120,7 @@ def compare_tf_with_tvm(in_data, in_name, out_name, init_global_variables=False, ...@@ -120,7 +120,7 @@ def compare_tf_with_tvm(in_data, in_name, out_name, init_global_variables=False,
continue continue
tvm_output = run_tvm_graph(final_graph_def, in_data, in_node, target=device) tvm_output = run_tvm_graph(final_graph_def, in_data, in_node, target=device)
np.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5) tvm.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5)
sess.close() sess.close()
...@@ -580,7 +580,7 @@ def _test_lstm_cell(batch_size, num_hidden, num_layers, forget_bias, dtype): ...@@ -580,7 +580,7 @@ def _test_lstm_cell(batch_size, num_hidden, num_layers, forget_bias, dtype):
out_state_c = np.reshape(out_state_tup[0], (batch_size, num_hidden)) out_state_c = np.reshape(out_state_tup[0], (batch_size, num_hidden))
out_state_h = np.reshape(out_state_tup[1], (batch_size, num_hidden)) out_state_h = np.reshape(out_state_tup[1], (batch_size, num_hidden))
tvm_out = [out, out_state_c, out_state_h] tvm_out = [out, out_state_c, out_state_h]
np.testing.assert_allclose(tf_out, tvm_out, rtol=1e-3, atol=1e-3) tvm.testing.assert_allclose(tf_out, tvm_out, rtol=1e-3, atol=1e-3)
def test_forward_lstm(): def test_forward_lstm():
'''test LSTM block cell''' '''test LSTM block cell'''
...@@ -653,7 +653,7 @@ def test_forward_inception_v3(): ...@@ -653,7 +653,7 @@ def test_forward_inception_v3():
with tf.Session() as sess: with tf.Session() as sess:
tf_output = run_tf_graph(sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0') tf_output = run_tf_graph(sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0')
tvm_output = run_tvm_graph(graph_def, data, 'input') tvm_output = run_tvm_graph(graph_def, data, 'input')
np.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5)
####################################################################### #######################################################################
# Inception V1 # Inception V1
...@@ -689,7 +689,7 @@ def test_forward_inception_v1(): ...@@ -689,7 +689,7 @@ def test_forward_inception_v1():
with tf.Session() as sess: with tf.Session() as sess:
tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0') tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents') tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents')
np.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5)
####################################################################### #######################################################################
# Mobilenet # Mobilenet
...@@ -707,7 +707,7 @@ def test_forward_mobilenet(): ...@@ -707,7 +707,7 @@ def test_forward_mobilenet():
with tf.Session() as sess: with tf.Session() as sess:
tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0') tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0')
tvm_output = run_tvm_graph(graph_def, data, 'input') tvm_output = run_tvm_graph(graph_def, data, 'input')
np.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
####################################################################### #######################################################################
# ResnetV2 # ResnetV2
...@@ -726,7 +726,7 @@ def test_forward_resnetv2(): ...@@ -726,7 +726,7 @@ def test_forward_resnetv2():
with tf.Session() as sess: with tf.Session() as sess:
tf_output = run_tf_graph(sess, data, 'input_tensor:0', out_node + ':0') tf_output = run_tf_graph(sess, data, 'input_tensor:0', out_node + ':0')
tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', tf_output.shape, 'float32') tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', tf_output.shape, 'float32')
np.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
####################################################################### #######################################################################
# PTB # PTB
...@@ -834,7 +834,7 @@ def test_forward_ptb(): ...@@ -834,7 +834,7 @@ def test_forward_ptb():
in_state, cnt_sample) in_state, cnt_sample)
tf_sample_str = _pretty_print(tf_samples, False, id_to_word) tf_sample_str = _pretty_print(tf_samples, False, id_to_word)
inpt = tvm_sample_str inpt = tvm_sample_str
np.testing.assert_allclose(tf_samples, tvm_samples, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(tf_samples, tvm_samples, rtol=1e-5, atol=1e-5)
assert(tvm_sample_str == tf_sample_str) assert(tvm_sample_str == tf_sample_str)
####################################################################### #######################################################################
......
python/tvm/__init__.py
...@@ -17,6 +17,7 @@ from . import ir_builder ...@@ -17,6 +17,7 @@ from . import ir_builder
from . import target from . import target
from . import generic from . import generic
from . import hybrid from . import hybrid
from . import testing
from . import ndarray as nd from . import ndarray as nd
from .ndarray import context, cpu, gpu, opencl, cl, vulkan, metal, mtl from .ndarray import context, cpu, gpu, opencl, cl, vulkan, metal, mtl
......
python/tvm/testing.py 0 → 100644
""" TVM testing utilities """
import numpy as np
def assert_allclose(actual, desired, rtol=1e-7, atol=1e-7):
""" Version of np.testing.assert_allclose with `atol` and `rtol` fields set
in reasonable defaults.
Arguments `actual` and `desired` are not interchangable, since the function
compares the `abs(actual-desired)` with `atol+rtol*abs(desired)`. Since we
often allow `desired` to be close to zero, we generally want non-zero `atol`.
"""
np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol, verbose=True)
tests/python/contrib/test_cblas.py
...@@ -27,7 +27,7 @@ def test_matmul_add(): ...@@ -27,7 +27,7 @@ def test_matmul_add():
d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx) d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx)
bb = 10.0 bb = 10.0
f(a, b, d, bb) f(a, b, d, bb)
np.testing.assert_allclose( tvm.testing.assert_allclose(
d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()) + bb, rtol=1e-5) d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()) + bb, rtol=1e-5)
verify() verify()
......
tests/python/contrib/test_cublas.py
...@@ -24,7 +24,7 @@ def test_matmul_add(): ...@@ -24,7 +24,7 @@ def test_matmul_add():
b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5) c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5)
verify() verify()
......
tests/python/contrib/test_dlpack.py
...@@ -34,7 +34,7 @@ def test(): ...@@ -34,7 +34,7 @@ def test():
f_pytorch = to_pytorch_func(f) f_pytorch = to_pytorch_func(f)
zz2 = torch.empty(137,137) zz2 = torch.empty(137,137)
f_pytorch(xx, yy, zz2) f_pytorch(xx, yy, zz2)
np.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-6) tvm.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-6)
except ImportError: except ImportError:
pass pass
......
tests/python/contrib/test_miopen.py
...@@ -56,7 +56,7 @@ def test_conv2d(): ...@@ -56,7 +56,7 @@ def test_conv2d():
y_ref = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), ctx) y_ref = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), ctx)
f_ref(x, w, y_ref) f_ref(x, w, y_ref)
print("Max abs diff:", np.max(np.abs(y.asnumpy() - y_ref.asnumpy()))) print("Max abs diff:", np.max(np.abs(y.asnumpy() - y_ref.asnumpy())))
np.testing.assert_allclose(y.asnumpy(), y_ref.asnumpy(), atol=1e-3) tvm.testing.assert_allclose(y.asnumpy(), y_ref.asnumpy(), atol=1e-3)
verify() verify()
......
tests/python/contrib/test_mps.py
...@@ -41,7 +41,7 @@ def test_matmul(): ...@@ -41,7 +41,7 @@ def test_matmul():
b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()) + 1, rtol=1e-5) c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()) + 1, rtol=1e-5)
verify(A, B, D, s) verify(A, B, D, s)
......
tests/python/contrib/test_mxnet_bridge.py
...@@ -40,7 +40,7 @@ def mxnet_check(): ...@@ -40,7 +40,7 @@ def mxnet_check():
mxf(xx, yy, zz, 10.0) mxf(xx, yy, zz, 10.0)
np.testing.assert_allclose( tvm.testing.assert_allclose(
zz.asnumpy(), (xx.asnumpy() + yy.asnumpy()) * 10) zz.asnumpy(), (xx.asnumpy() + yy.asnumpy()) * 10)
......
tests/python/contrib/test_nnpack.py
...@@ -28,7 +28,7 @@ def test_fully_connected_output(): ...@@ -28,7 +28,7 @@ def test_fully_connected_output():
d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx) d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx)
bb = 10.0 bb = 10.0
f(a, b, d, bb) f(a, b, d, bb)
np.testing.assert_allclose( tvm.testing.assert_allclose(
d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5) d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
verify() verify()
...@@ -58,7 +58,7 @@ def test_fully_connected_inference(): ...@@ -58,7 +58,7 @@ def test_fully_connected_inference():
d = tvm.nd.array(np.zeros((m, ), dtype=D.dtype), ctx) d = tvm.nd.array(np.zeros((m, ), dtype=D.dtype), ctx)
bb = 10.0 bb = 10.0
f(a, b, d, bb) f(a, b, d, bb)
np.testing.assert_allclose( tvm.testing.assert_allclose(
d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5) d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
verify() verify()
...@@ -142,7 +142,7 @@ def test_convolution_inference(): ...@@ -142,7 +142,7 @@ def test_convolution_inference():
td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx) td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
f(ta, tb, tc, td) f(ta, tb, tc, td)
nd = np_conv(np.reshape(na, (1, IC, IH, IW)), nb, PAD, STRIDE) nd = np_conv(np.reshape(na, (1, IC, IH, IW)), nb, PAD, STRIDE)
np.testing.assert_allclose( tvm.testing.assert_allclose(
td.asnumpy(), nd.reshape(IC, IH, IW), rtol=1e-5) td.asnumpy(), nd.reshape(IC, IH, IW), rtol=1e-5)
verify() verify()
...@@ -187,7 +187,7 @@ def test_convolution_output(): ...@@ -187,7 +187,7 @@ def test_convolution_output():
td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx) td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
f(ta, tb, tc, td) f(ta, tb, tc, td)
nd = np_conv(na, nb, PAD) nd = np_conv(na, nb, PAD)
np.testing.assert_allclose( tvm.testing.assert_allclose(
td.asnumpy(), nd, rtol=1e-5) td.asnumpy(), nd, rtol=1e-5)
verify() verify()
......
tests/python/contrib/test_rocblas.py
...@@ -24,7 +24,7 @@ def test_matmul_add(): ...@@ -24,7 +24,7 @@ def test_matmul_add():
b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5) c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5)
verify() verify()
......
tests/python/contrib/test_sort.py
...@@ -28,7 +28,7 @@ def test_sort(): ...@@ -28,7 +28,7 @@ def test_sort():
b = tvm.nd.array(np.array(sort_num_input).astype(sort_num.dtype), ctx) b = tvm.nd.array(np.array(sort_num_input).astype(sort_num.dtype), ctx)
c = tvm.nd.array(np.zeros(a.shape, dtype=out.dtype), ctx) c = tvm.nd.array(np.zeros(a.shape, dtype=out.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose(c.asnumpy(), np.array(sorted_index).astype(out.dtype), rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), np.array(sorted_index).astype(out.dtype), rtol=1e-5)
def test_sort_np(): def test_sort_np():
dshape = (1, 2, 3, 4, 5, 6) dshape = (1, 2, 3, 4, 5, 6)
...@@ -55,7 +55,7 @@ def test_sort_np(): ...@@ -55,7 +55,7 @@ def test_sort_np():
b = tvm.nd.array(np.array(sort_num_input).astype(sort_num.dtype), ctx) b = tvm.nd.array(np.array(sort_num_input).astype(sort_num.dtype), ctx)
c = tvm.nd.array(np.zeros(a.shape, dtype=out.dtype), ctx) c = tvm.nd.array(np.zeros(a.shape, dtype=out.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose(c.asnumpy(), np_out, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), np_out, rtol=1e-5)
if __name__ == "__main__": if __name__ == "__main__":
test_sort() test_sort()
......
tests/python/contrib/test_sparse.py
...@@ -27,7 +27,7 @@ def test_static_tensor(): ...@@ -27,7 +27,7 @@ def test_static_tensor():
c.indices = a.indices c.indices = a.indices
c.indptr = a.indptr c.indptr = a.indptr
f(a.data, c.data) f(a.data, c.data)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2., rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2., rtol=1e-5)
def test_dynamic_tensor(): def test_dynamic_tensor():
dtype = 'float32' dtype = 'float32'
...@@ -53,7 +53,7 @@ def test_dynamic_tensor(): ...@@ -53,7 +53,7 @@ def test_dynamic_tensor():
c.indices = a.indices c.indices = a.indices
c.indptr = a.indptr c.indptr = a.indptr
f(a.data.shape[0], a.data, c.data) f(a.data.shape[0], a.data, c.data)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2., rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2., rtol=1e-5)
def test_sparse_array_tuple(): def test_sparse_array_tuple():
dtype, itype = 'float32', 'int32' dtype, itype = 'float32', 'int32'
...@@ -91,7 +91,7 @@ def test_sparse_array_tuple(): ...@@ -91,7 +91,7 @@ def test_sparse_array_tuple():
c.indices = a.indices c.indices = a.indices
c.indptr = a.indptr c.indptr = a.indptr
f(a.data.shape[0], a.data, c.data) f(a.data.shape[0], a.data, c.data)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2., rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2., rtol=1e-5)
if __name__ == "__main__": if __name__ == "__main__":
test_static_tensor() test_static_tensor()
......
tests/python/integration/test_dot.py
...@@ -46,7 +46,7 @@ def test_dot(): ...@@ -46,7 +46,7 @@ def test_dot():
b = tvm.nd.array(np.random.uniform(size=(nn,)).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=(nn,)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((1,), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((1,), dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-4) c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-4)
verify("llvm") verify("llvm")
......
tests/python/integration/test_ewise.py
...@@ -31,7 +31,7 @@ def test_exp(): ...@@ -31,7 +31,7 @@ def test_exp():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
fexp(a, b) fexp(a, b)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), np.exp(a.asnumpy()), rtol=1e-5) b.asnumpy(), np.exp(a.asnumpy()), rtol=1e-5)
check_device("opencl -device=intel_graphics") check_device("opencl -device=intel_graphics")
...@@ -75,7 +75,7 @@ def test_multiple_cache_write(): ...@@ -75,7 +75,7 @@ def test_multiple_cache_write():
a1 = tvm.nd.array(np.random.uniform(size=n).astype(A1.dtype), ctx) a1 = tvm.nd.array(np.random.uniform(size=n).astype(A1.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
func(a0, a1, c) func(a0, a1, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a0.asnumpy() + a1.asnumpy() + (a0.asnumpy() * a1.asnumpy()), c.asnumpy(), a0.asnumpy() + a1.asnumpy() + (a0.asnumpy() * a1.asnumpy()),
rtol=1e-5) rtol=1e-5)
...@@ -106,7 +106,7 @@ def test_log_pow_llvm(): ...@@ -106,7 +106,7 @@ def test_log_pow_llvm():
ftimer = flog.time_evaluator(flog.entry_name, ctx, number=1, repeat=repeat) ftimer = flog.time_evaluator(flog.entry_name, ctx, number=1, repeat=repeat)
res = ftimer(a, b) res = ftimer(a, b)
assert(len(res.results) == repeat) assert(len(res.results) == repeat)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), np.power(np.log(a.asnumpy()), 2.0), rtol=1e-5) b.asnumpy(), np.power(np.log(a.asnumpy()), 2.0), rtol=1e-5)
...@@ -136,7 +136,7 @@ def test_popcount(): ...@@ -136,7 +136,7 @@ def test_popcount():
a = tvm.nd.array(np.random.randint(low=0, high=1000, size=n, dtype=A.dtype), ctx) a = tvm.nd.array(np.random.randint(low=0, high=1000, size=n, dtype=A.dtype), ctx)
b = tvm.nd.array(np.zeros(shape=n, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(shape=n, dtype=B.dtype), ctx)
func(a, b) func(a, b)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), list(map(lambda x: bin(x).count('1'), a.asnumpy())), rtol=1e-5) b.asnumpy(), list(map(lambda x: bin(x).count('1'), a.asnumpy())), rtol=1e-5)
check_device("llvm") check_device("llvm")
...@@ -186,7 +186,7 @@ def test_add(): ...@@ -186,7 +186,7 @@ def test_add():
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
ftimer = fadd.time_evaluator(fadd.entry_name, ctx, number=1) ftimer = fadd.time_evaluator(fadd.entry_name, ctx, number=1)
tcost = ftimer(a, b, c).mean tcost = ftimer(a, b, c).mean
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy(), rtol=1e-6) c.asnumpy(), a.asnumpy() + b.asnumpy(), rtol=1e-6)
check_device("opencl") check_device("opencl")
...@@ -233,7 +233,7 @@ def try_warp_memory(): ...@@ -233,7 +233,7 @@ def try_warp_memory():
a = tvm.nd.array((np.random.uniform(size=m) * 256).astype(A.dtype), ctx) a = tvm.nd.array((np.random.uniform(size=m) * 256).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(m, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(m, dtype=B.dtype), ctx)
f(a, b) f(a, b)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), a.asnumpy() + 3, rtol=1e-6) b.asnumpy(), a.asnumpy() + 3, rtol=1e-6)
check_device("cuda") check_device("cuda")
......
tests/python/integration/test_ewise_fpga.py
...@@ -37,7 +37,7 @@ def test_exp(): ...@@ -37,7 +37,7 @@ def test_exp():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
fexp(a, b) fexp(a, b)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), np.exp(a.asnumpy()), rtol=1e-5) b.asnumpy(), np.exp(a.asnumpy()), rtol=1e-5)
check_device("sdaccel") check_device("sdaccel")
...@@ -78,7 +78,7 @@ def test_multi_kernel(): ...@@ -78,7 +78,7 @@ def test_multi_kernel():
c = tvm.nd.array(np.random.uniform(size=n).astype(C.dtype), ctx) c = tvm.nd.array(np.random.uniform(size=n).astype(C.dtype), ctx)
d = tvm.nd.array(np.random.uniform(size=n).astype(D.dtype), ctx) d = tvm.nd.array(np.random.uniform(size=n).astype(D.dtype), ctx)
fadd(a, b, c, d) fadd(a, b, c, d)
np.testing.assert_allclose( tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() * 2 + b.asnumpy(), rtol=1e-5) d.asnumpy(), a.asnumpy() * 2 + b.asnumpy(), rtol=1e-5)
check_device("sdaccel") check_device("sdaccel")
......
tests/python/integration/test_gemm.py
...@@ -85,7 +85,7 @@ def test_gemm(): ...@@ -85,7 +85,7 @@ def test_gemm():
ftimer = f.time_evaluator(f.entry_name, ctx, number=1) ftimer = f.time_evaluator(f.entry_name, ctx, number=1)
tcost = ftimer(a, b, c).mean tcost = ftimer(a, b, c).mean
print("%s: exec=%g sec/op" % (ctx, tcost)) print("%s: exec=%g sec/op" % (ctx, tcost))
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), np.dot(a_np, b_np.T), rtol=1e-5) c.asnumpy(), np.dot(a_np, b_np.T), rtol=1e-5)
check_device("vulkan") check_device("vulkan")
......
tests/python/integration/test_reduce.py
...@@ -42,7 +42,7 @@ def test_reduce_prims(): ...@@ -42,7 +42,7 @@ def test_reduce_prims():
npy[:2] = 0 npy[:2] = 0
res = np_reducer(x.asnumpy(), axis=1) res = np_reducer(x.asnumpy(), axis=1)
res[:2] = 0 res[:2] = 0
np.testing.assert_allclose(npy, res, rtol=1e-4) tvm.testing.assert_allclose(npy, res, rtol=1e-4)
check_device("metal") check_device("metal")
check_device("vulkan") check_device("vulkan")
...@@ -78,7 +78,7 @@ def test_rfactor(): ...@@ -78,7 +78,7 @@ def test_rfactor():
b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx)
fsum(a, b) fsum(a, b)
res = np.sum(a.asnumpy(), axis=0) res = np.sum(a.asnumpy(), axis=0)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), res, rtol=1e-4) b.asnumpy(), res, rtol=1e-4)
check_target() check_target()
...@@ -108,7 +108,7 @@ def test_rfactor_factor_axis(): ...@@ -108,7 +108,7 @@ def test_rfactor_factor_axis():
b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx)
fsum(a, b) fsum(a, b)
res = np.sum(a.asnumpy(), axis=0) res = np.sum(a.asnumpy(), axis=0)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), res, rtol=1e-4) b.asnumpy(), res, rtol=1e-4)
check_target() check_target()
...@@ -155,7 +155,7 @@ def test_rfactor_threads(): ...@@ -155,7 +155,7 @@ def test_rfactor_threads():
fsum(a, b) fsum(a, b)
res = np.sum(a.asnumpy(), axis=1) res = np.sum(a.asnumpy(), axis=1)
res[:2] = 0 res[:2] = 0
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), res, rtol=1e-4) b.asnumpy(), res, rtol=1e-4)
check_target("vulkan") check_target("vulkan")
...@@ -206,7 +206,7 @@ def test_rfactor_elemwise_threads(): ...@@ -206,7 +206,7 @@ def test_rfactor_elemwise_threads():
b = tvm.nd.array(np.zeros(m, dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(m, dtype=B.dtype), ctx)
fsum(a, b) fsum(a, b)
res = np.sum(a.asnumpy(), axis=1) + 2 res = np.sum(a.asnumpy(), axis=1) + 2
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), res, rtol=1e-4) b.asnumpy(), res, rtol=1e-4)
check_target("vulkan") check_target("vulkan")
...@@ -256,7 +256,7 @@ def test_argmax(): ...@@ -256,7 +256,7 @@ def test_argmax():
nd_res0 = tvm.nd.array(np.zeros(mm, dtype='int32'), ctx) nd_res0 = tvm.nd.array(np.zeros(mm, dtype='int32'), ctx)
nd_res1 = tvm.nd.array(np.zeros(mm, dtype='float32'), ctx) nd_res1 = tvm.nd.array(np.zeros(mm, dtype='float32'), ctx)
fargmax(nd_idx, nd_val, nd_res0, nd_res1) fargmax(nd_idx, nd_val, nd_res0, nd_res1)
np.testing.assert_allclose(np_res, nd_res0.asnumpy()) tvm.testing.assert_allclose(np_res, nd_res0.asnumpy())
check_target() check_target()
...@@ -316,7 +316,7 @@ def test_rfactor_argmax(): ...@@ -316,7 +316,7 @@ def test_rfactor_argmax():
nd_res0 = tvm.nd.array(np.zeros(mm, dtype='int32'), ctx) nd_res0 = tvm.nd.array(np.zeros(mm, dtype='int32'), ctx)
nd_res1 = tvm.nd.array(np.zeros(mm, dtype='float32'), ctx) nd_res1 = tvm.nd.array(np.zeros(mm, dtype='float32'), ctx)
fargmax(nd_idx, nd_val, nd_res0, nd_res1) fargmax(nd_idx, nd_val, nd_res0, nd_res1)
np.testing.assert_allclose(np_res, nd_res0.asnumpy()) tvm.testing.assert_allclose(np_res, nd_res0.asnumpy())
check_target("cuda") check_target("cuda")
check_target("vulkan") check_target("vulkan")
......
tests/python/integration/test_scan.py
...@@ -38,7 +38,7 @@ def test_scan(): ...@@ -38,7 +38,7 @@ def test_scan():
a = tvm.nd.array(a_np, ctx) a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros((m, n), dtype=res.dtype), ctx) b = tvm.nd.array(np.zeros((m, n), dtype=res.dtype), ctx)
fscan(a, b) fscan(a, b)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), np.cumsum(a_np, axis=0)) b.asnumpy(), np.cumsum(a_np, axis=0))
check_device("vulkan") check_device("vulkan")
......
tests/python/unittest/test_codegen_cross_llvm.py
...@@ -67,7 +67,7 @@ def test_llvm_add_pipeline(): ...@@ -67,7 +67,7 @@ def test_llvm_add_pipeline():
b = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
farm(a, b, c) farm(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy()) c.asnumpy(), a.asnumpy() + b.asnumpy())
print("Verification finish on remote..") print("Verification finish on remote..")
......
tests/python/unittest/test_codegen_cuda.py
...@@ -27,7 +27,7 @@ def test_cuda_vectorize_add(): ...@@ -27,7 +27,7 @@ def test_cuda_vectorize_add():
np.random.uniform(size=(n, lanes))) np.random.uniform(size=(n, lanes)))
c = tvm.nd.empty((n,), B.dtype, ctx) c = tvm.nd.empty((n,), B.dtype, ctx)
fun(a, c) fun(a, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + 1) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + 1)
check_cuda("float32", 64, 2) check_cuda("float32", 64, 2)
check_cuda("float16", 64, 2) check_cuda("float16", 64, 2)
...@@ -62,7 +62,7 @@ def test_cuda_multiply_add(): ...@@ -62,7 +62,7 @@ def test_cuda_multiply_add():
c = tvm.nd.empty((n,), C.dtype, ctx).copyfrom(np_c) c = tvm.nd.empty((n,), C.dtype, ctx).copyfrom(np_c)
d = tvm.nd.empty((n,), D.dtype, ctx) d = tvm.nd.empty((n,), D.dtype, ctx)
fun(a, b, c, d) fun(a, b, c, d)
np.testing.assert_allclose(d.asnumpy(), np_d) tvm.testing.assert_allclose(d.asnumpy(), np_d)
check_cuda("int8", 64, 4) check_cuda("int8", 64, 4)
def test_cuda_vectorize_load(): def test_cuda_vectorize_load():
...@@ -83,7 +83,7 @@ def test_cuda_vectorize_load(): ...@@ -83,7 +83,7 @@ def test_cuda_vectorize_load():
a = tvm.nd.empty((n,), A.dtype, ctx).copyfrom(np_a) a = tvm.nd.empty((n,), A.dtype, ctx).copyfrom(np_a)
b = tvm.nd.empty((n,), B.dtype, ctx) b = tvm.nd.empty((n,), B.dtype, ctx)
fun(a,b) fun(a,b)
np.testing.assert_allclose(a.asnumpy(), b.asnumpy()) tvm.testing.assert_allclose(a.asnumpy(), b.asnumpy())
check_cuda("int8", 64, 8) check_cuda("int8", 64, 8)
check_cuda("int8", 64, 16) check_cuda("int8", 64, 16)
......
tests/python/unittest/test_codegen_device.py
...@@ -51,7 +51,7 @@ def test_add_pipeline(): ...@@ -51,7 +51,7 @@ def test_add_pipeline():
b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx) d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx)
f(a, b, d) f(a, b, d)
np.testing.assert_allclose( tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() + b.asnumpy() + 1) d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
def check_module_save(device, host="stackvm"): def check_module_save(device, host="stackvm"):
...@@ -75,7 +75,7 @@ def test_add_pipeline(): ...@@ -75,7 +75,7 @@ def test_add_pipeline():
b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=()).astype(Bb.dtype), ctx)
d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx) d = tvm.nd.array(np.zeros(n, dtype=Db.dtype), ctx)
f(a, b, d) f(a, b, d)
np.testing.assert_allclose( tvm.testing.assert_allclose(
d.asnumpy(), a.asnumpy() + b.asnumpy() + 1) d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
check_target("cuda", host="stackvm") check_target("cuda", host="stackvm")
......
tests/python/unittest/test_codegen_extern.py
...@@ -46,7 +46,7 @@ def test_add_pipeline(): ...@@ -46,7 +46,7 @@ def test_add_pipeline():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + 1) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + 1)
check_target("llvm") check_target("llvm")
check_target("opencl") check_target("opencl")
...@@ -80,7 +80,7 @@ def test_pack_buffer_simple(): ...@@ -80,7 +80,7 @@ def test_pack_buffer_simple():
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy()) c.asnumpy(), a.asnumpy())
check_target("stackvm") check_target("stackvm")
check_target("llvm") check_target("llvm")
...@@ -112,12 +112,12 @@ def test_pack_buffer_intermediate(): ...@@ -112,12 +112,12 @@ def test_pack_buffer_intermediate():
@tvm.register_func @tvm.register_func
def my_extern_array_func2(aa, bb): def my_extern_array_func2(aa, bb):
assert aa.shape == a.shape assert aa.shape == a.shape
np.testing.assert_allclose( tvm.testing.assert_allclose(
aa.asnumpy(), a.asnumpy() + 1) aa.asnumpy(), a.asnumpy() + 1)
aa.copyto(bb) aa.copyto(bb)
f(a, c) f(a, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + 1) c.asnumpy(), a.asnumpy() + 1)
check_target("llvm") check_target("llvm")
......
tests/python/unittest/test_codegen_llvm.py
...@@ -52,7 +52,7 @@ def test_llvm_import(): ...@@ -52,7 +52,7 @@ def test_llvm_import():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
f(a, b) f(a, b)
np.testing.assert_allclose( tvm.testing.assert_allclose(
b.asnumpy(), a.asnumpy() + 1.0) b.asnumpy(), a.asnumpy() + 1.0)
check_llvm(use_file=True) check_llvm(use_file=True)
check_llvm(use_file=False) check_llvm(use_file=False)
...@@ -106,7 +106,7 @@ def test_llvm_add_pipeline(): ...@@ -106,7 +106,7 @@ def test_llvm_add_pipeline():
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy()) c.asnumpy(), a.asnumpy() + b.asnumpy())
with tvm.build_config(offset_factor=4): with tvm.build_config(offset_factor=4):
...@@ -138,7 +138,7 @@ def test_llvm_persist_parallel(): ...@@ -138,7 +138,7 @@ def test_llvm_persist_parallel():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose(c.asnumpy(), tvm.testing.assert_allclose(c.asnumpy(),
np.sqrt(a.asnumpy() + 1) * 2 + 2, np.sqrt(a.asnumpy() + 1) * 2 + 2,
rtol=1e-5) rtol=1e-5)
...@@ -164,7 +164,7 @@ def test_llvm_flip_pipeline(): ...@@ -164,7 +164,7 @@ def test_llvm_flip_pipeline():
a = tvm.nd.array(np.random.uniform(size=(n + base)).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=(n + base)).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy()[::-1][:n]) c.asnumpy(), a.asnumpy()[::-1][:n])
check_llvm(4, 0) check_llvm(4, 0)
check_llvm(128, 8) check_llvm(128, 8)
...@@ -195,7 +195,7 @@ def test_llvm_vadd_pipeline(): ...@@ -195,7 +195,7 @@ def test_llvm_vadd_pipeline():
np.random.uniform(size=(n, lanes))) np.random.uniform(size=(n, lanes)))
c = tvm.nd.empty((n,), C.dtype, ctx) c = tvm.nd.empty((n,), C.dtype, ctx)
f(a, c) f(a, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + 1) c.asnumpy(), a.asnumpy() + 1)
check_llvm(64, 2) check_llvm(64, 2)
check_llvm(512, 2) check_llvm(512, 2)
...@@ -220,7 +220,7 @@ def test_llvm_madd_pipeline(): ...@@ -220,7 +220,7 @@ def test_llvm_madd_pipeline():
a = tvm.nd.array(np.random.uniform(size=(n + base, stride)).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=(n + base, stride)).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros((n, stride), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((n, stride), dtype=C.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy()[base:] + 1) c.asnumpy(), a.asnumpy()[base:] + 1)
check_llvm(64, 0, 2) check_llvm(64, 0, 2)
check_llvm(4, 0, 1) check_llvm(4, 0, 1)
...@@ -247,7 +247,7 @@ def test_llvm_temp_space(): ...@@ -247,7 +247,7 @@ def test_llvm_temp_space():
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx) a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + 1 + 1) c.asnumpy(), a.asnumpy() + 1 + 1)
check_llvm() check_llvm()
...@@ -277,10 +277,10 @@ def test_multiple_func(): ...@@ -277,10 +277,10 @@ def test_multiple_func():
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
fadd1(a, b, c) fadd1(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy()) c.asnumpy(), a.asnumpy() + b.asnumpy())
fadd2(a, b, c) fadd2(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy()) c.asnumpy(), a.asnumpy() + b.asnumpy())
check_llvm() check_llvm()
...@@ -302,7 +302,7 @@ def test_llvm_select(): ...@@ -302,7 +302,7 @@ def test_llvm_select():
f(a, c) f(a, c)
c_np = a.asnumpy() c_np = a.asnumpy()
c_np[:offset] = 0 c_np[:offset] = 0
np.testing.assert_allclose(c.asnumpy(), c_np) tvm.testing.assert_allclose(c.asnumpy(), c_np)
check_llvm(64, 8) check_llvm(64, 8)
...@@ -321,7 +321,7 @@ def test_llvm_bool(): ...@@ -321,7 +321,7 @@ def test_llvm_bool():
c = tvm.nd.empty((n,), C.dtype, ctx) c = tvm.nd.empty((n,), C.dtype, ctx)
f(a, c) f(a, c)
c_np = a.asnumpy() == 1 c_np = a.asnumpy() == 1
np.testing.assert_allclose(c.asnumpy(), c_np) tvm.testing.assert_allclose(c.asnumpy(), c_np)
check_llvm(64) check_llvm(64)
...@@ -345,7 +345,7 @@ def test_rank_zero(): ...@@ -345,7 +345,7 @@ def test_rank_zero():
d = tvm.nd.empty((), D.dtype, ctx) d = tvm.nd.empty((), D.dtype, ctx)
f(a, sc, d) f(a, sc, d)
d_np = np.sum(a.asnumpy()) * sc.asnumpy() + 1 d_np = np.sum(a.asnumpy()) * sc.asnumpy() + 1
np.testing.assert_allclose(d.asnumpy(), d_np) tvm.testing.assert_allclose(d.asnumpy(), d_np)
check_llvm(64) check_llvm(64)
......
tests/python/unittest/test_hybrid_script.py
...@@ -38,7 +38,7 @@ def run_and_check(func, args, outs, var_dict={}, target='llvm'): ...@@ -38,7 +38,7 @@ def run_and_check(func, args, outs, var_dict={}, target='llvm'):
module(*nd_args) module(*nd_args)
for nd, np in to_check: for nd, np in to_check:
numpy.testing.assert_allclose(nd.asnumpy(), np, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(nd.asnumpy(), np, rtol=1e-5, atol=1e-5)
@script @script
...@@ -257,7 +257,7 @@ def test_math_intrin(): ...@@ -257,7 +257,7 @@ def test_math_intrin():
tvm_a = tvm.ndarray.array(a) tvm_a = tvm.ndarray.array(a)
func(tvm_a) func(tvm_a)
intrin_real(a) intrin_real(a)
numpy.testing.assert_allclose(a, tvm_a.asnumpy(), rtol=1e-5) tvm.testing.assert_allclose(a, tvm_a.asnumpy(), rtol=1e-5)
@script @script
def intrin_int(a): def intrin_int(a):
......
tests/python/unittest/test_ir_builder.py
...@@ -84,7 +84,7 @@ def test_cpu(): ...@@ -84,7 +84,7 @@ def test_cpu():
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
fadd(a, b, c) fadd(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy()) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
check_target("llvm") check_target("llvm")
def test_gpu(): def test_gpu():
...@@ -125,7 +125,7 @@ def test_gpu(): ...@@ -125,7 +125,7 @@ def test_gpu():
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx) b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
fadd(a, b, c) fadd(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy()) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
check_target("opencl") check_target("opencl")
check_target("cuda") check_target("cuda")
......
tests/python/unittest/test_lang_tensor_overload_op.py
...@@ -66,7 +66,7 @@ def test_combination(): ...@@ -66,7 +66,7 @@ def test_combination():
c = tvm.nd.array(np.random.uniform(size=(n, m)).astype(C.dtype), ctx) c = tvm.nd.array(np.random.uniform(size=(n, m)).astype(C.dtype), ctx)
d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx) d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx)
foo(x, a, b, c, d) foo(x, a, b, c, d)
np.testing.assert_allclose(d.asnumpy(), k + a.asnumpy() - b.asnumpy() * c.asnumpy() / x) tvm.testing.assert_allclose(d.asnumpy(), k + a.asnumpy() - b.asnumpy() * c.asnumpy() / x)
def verify_tensor_scalar_bop(shape, typ="add"): def verify_tensor_scalar_bop(shape, typ="add"):
...@@ -111,7 +111,7 @@ def verify_tensor_scalar_bop(shape, typ="add"): ...@@ -111,7 +111,7 @@ def verify_tensor_scalar_bop(shape, typ="add"):
a_nd = tvm.nd.array(a_npy, ctx) a_nd = tvm.nd.array(a_npy, ctx)
b_nd = tvm.nd.array(np.empty(b_npy.shape).astype(B.dtype), ctx) b_nd = tvm.nd.array(np.empty(b_npy.shape).astype(B.dtype), ctx)
foo(a_nd, b_nd, k_, *shape) foo(a_nd, b_nd, k_, *shape)
np.testing.assert_allclose(b_nd.asnumpy(), b_npy, rtol=1e-5) tvm.testing.assert_allclose(b_nd.asnumpy(), b_npy, rtol=1e-5)
for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan']: for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
check_device(device) check_device(device)
...@@ -160,7 +160,7 @@ def verify_broadcast_bop(lhs_shape, rhs_shape, typ="add"): ...@@ -160,7 +160,7 @@ def verify_broadcast_bop(lhs_shape, rhs_shape, typ="add"):
out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(B.dtype), ctx) out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(B.dtype), ctx)
for _ in range(1): for _ in range(1):
foo(lhs_nd, rhs_nd, out_nd) foo(lhs_nd, rhs_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4)
for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan']: for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
check_device(device) check_device(device)
...@@ -213,7 +213,7 @@ def verify_conv2d_scalar_bop(batch, in_size, in_channel, num_filter, kernel, str ...@@ -213,7 +213,7 @@ def verify_conv2d_scalar_bop(batch, in_size, in_channel, num_filter, kernel, str
b_nd = tvm.nd.array(np.empty(b_npy.shape).astype(B.dtype), ctx) b_nd = tvm.nd.array(np.empty(b_npy.shape).astype(B.dtype), ctx)
c_nd = tvm.nd.array(np.empty(c_npy.shape).astype(C.dtype), ctx) c_nd = tvm.nd.array(np.empty(c_npy.shape).astype(C.dtype), ctx)
foo(a_nd, w_nd, b_nd, c_nd) foo(a_nd, w_nd, b_nd, c_nd)
np.testing.assert_allclose(c_nd.asnumpy(), c_npy, rtol=1E-4, atol=1E-4) tvm.testing.assert_allclose(c_nd.asnumpy(), c_npy, rtol=1E-4, atol=1E-4)
for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan']: for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
check_device(device) check_device(device)
......
tests/python/unittest/test_runtime_ndarray.py
...@@ -53,7 +53,7 @@ def test_fp16_conversion(): ...@@ -53,7 +53,7 @@ def test_fp16_conversion():
expected = x_tvm.asnumpy().astype(dst) expected = x_tvm.asnumpy().astype(dst)
real = y_tvm.asnumpy() real = y_tvm.asnumpy()
np.testing.assert_allclose(expected, real) tvm.testing.assert_allclose(expected, real)
if __name__ == "__main__": if __name__ == "__main__":
test_nd_create() test_nd_create()
......
tests/python/unittest/test_runtime_rpc.py
...@@ -31,7 +31,7 @@ def test_bigendian_rpc(): ...@@ -31,7 +31,7 @@ def test_bigendian_rpc():
remote.upload(path_dso) remote.upload(path_dso)
f = remote.load_module("dev_lib.o") f = remote.load_module("dev_lib.o")
f(a, b) f(a, b)
np.testing.assert_allclose(a.asnumpy() + 1, b.asnumpy()) tvm.testing.assert_allclose(a.asnumpy() + 1, b.asnumpy())
print("Test RPC connection to PowerPC...") print("Test RPC connection to PowerPC...")
remote = rpc.connect(host, port) remote = rpc.connect(host, port)
......
tests/verilog/integration/test_codegen_verilog.py
...@@ -60,7 +60,7 @@ def test_add_pipeline(): ...@@ -60,7 +60,7 @@ def test_add_pipeline():
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
print("Check correctness...") print("Check correctness...")
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy()) c.asnumpy(), a.asnumpy() + b.asnumpy())
check_target("verilog") check_target("verilog")
......
tests/webgl/test_local_gemm.py
...@@ -35,7 +35,7 @@ def test_local_gemm(): ...@@ -35,7 +35,7 @@ def test_local_gemm():
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose(c.asnumpy(), np.dot(a_np, b_np.T)) tvm.testing.assert_allclose(c.asnumpy(), np.dot(a_np, b_np.T))
if __name__ == "__main__": if __name__ == "__main__":
test_local_gemm() test_local_gemm()
tests/webgl/test_local_multi_stage.py
...@@ -24,7 +24,7 @@ def test_local_multi_stage(): ...@@ -24,7 +24,7 @@ def test_local_multi_stage():
c = tvm.nd.array(np.random.uniform(size=(n,)).astype(B.dtype), ctx) c = tvm.nd.array(np.random.uniform(size=(n,)).astype(B.dtype), ctx)
f(a, c) f(a, c)
np.testing.assert_allclose(c.asnumpy(), (a.asnumpy() + 1) * 2) tvm.testing.assert_allclose(c.asnumpy(), (a.asnumpy() + 1) * 2)
if __name__ == "__main__": if __name__ == "__main__":
test_local_multi_stage() test_local_multi_stage()
tests/webgl/test_local_save_load.py
...@@ -30,7 +30,7 @@ def test_local_save_load(): ...@@ -30,7 +30,7 @@ def test_local_save_load():
f.export_library(path_so) f.export_library(path_so)
f1 = tvm.module.load(path_so) f1 = tvm.module.load(path_so)
f1(a, b, c) f1(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy()) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
if __name__ == "__main__": if __name__ == "__main__":
test_local_save_load() test_local_save_load()
tests/webgl/test_local_topi_conv2d_nchw.py
...@@ -49,8 +49,8 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p ...@@ -49,8 +49,8 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p
func2 = tvm.build(s2, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding)) func2 = tvm.build(s2, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding))
func1(a, w, b) func1(a, w, b)
func2(a, w, c) func2(a, w, c)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in ['opengl']: for device in ['opengl']:
check_device(device) check_device(device)
......
tests/webgl/test_local_topi_dense.py
...@@ -45,7 +45,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True): ...@@ -45,7 +45,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True):
d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx) d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B, C, D], device, name="dense") f = tvm.build(s, [A, B, C, D], device, name="dense")
f(a, b, c, d) f(a, b, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5)
for device in ['opengl']: for device in ['opengl']:
check_device(device) check_device(device)
......
tests/webgl/test_local_topi_pooling.py
...@@ -60,7 +60,7 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode): ...@@ -60,7 +60,7 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['opengl']: for device in ['opengl']:
check_device(device) check_device(device)
...@@ -98,7 +98,7 @@ def verify_global_pool(n, c, h, w, pool_type): ...@@ -98,7 +98,7 @@ def verify_global_pool(n, c, h, w, pool_type):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['opengl']: for device in ['opengl']:
check_device(device) check_device(device)
......
tests/webgl/test_local_topi_softmax.py
...@@ -32,7 +32,7 @@ def verify_softmax(m, n): ...@@ -32,7 +32,7 @@ def verify_softmax(m, n):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
foo = tvm.build(s, [A, B], device, name="softmax") foo = tvm.build(s, [A, B], device, name="softmax")
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ["opengl"]: for device in ["opengl"]:
check_device(device) check_device(device)
...@@ -63,7 +63,7 @@ def verify_log_softmax(m, n): ...@@ -63,7 +63,7 @@ def verify_log_softmax(m, n):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
foo = tvm.build(s, [A, B], device, name="log_softmax") foo = tvm.build(s, [A, B], device, name="log_softmax")
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ["opengl"]: for device in ["opengl"]:
check_device(device) check_device(device)
......
tests/webgl/test_remote_save_load.py
...@@ -73,7 +73,7 @@ def try_remote_save_load(): ...@@ -73,7 +73,7 @@ def try_remote_save_load():
b = tvm.nd.array(np.zeros(16, dtype=A.dtype), ctx) b = tvm.nd.array(np.zeros(16, dtype=A.dtype), ctx)
c = tvm.nd.array(np.zeros(16, dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros(16, dtype=C.dtype), ctx)
fhost(a, b, c) fhost(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy()) tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
if __name__ == "__main__": if __name__ == "__main__":
try_remote_save_load() try_remote_save_load()
topi/recipe/broadcast/test_broadcast_map.py
...@@ -48,7 +48,7 @@ def test_broadcast_to(in_shape, out_shape): ...@@ -48,7 +48,7 @@ def test_broadcast_to(in_shape, out_shape):
out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), tvm.gpu()) out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), tvm.gpu())
for _ in range(2): for _ in range(2):
fcuda(data_nd, out_nd) fcuda(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
def test_broadcast_binary_op(lhs_shape, rhs_shape, typ="add"): def test_broadcast_binary_op(lhs_shape, rhs_shape, typ="add"):
...@@ -95,7 +95,7 @@ def test_broadcast_binary_op(lhs_shape, rhs_shape, typ="add"): ...@@ -95,7 +95,7 @@ def test_broadcast_binary_op(lhs_shape, rhs_shape, typ="add"):
out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(B.dtype), tvm.gpu()) out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(B.dtype), tvm.gpu())
for _ in range(2): for _ in range(2):
fcuda(lhs_nd, rhs_nd, out_nd) fcuda(lhs_nd, rhs_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
if __name__ == "__main__": if __name__ == "__main__":
......
topi/recipe/conv/depthwise_conv2d_test.py
...@@ -106,9 +106,9 @@ def test_depthwise_conv2d_nchw(): ...@@ -106,9 +106,9 @@ def test_depthwise_conv2d_nchw():
for c in range(in_channel * channel_multiplier): for c in range(in_channel * channel_multiplier):
scale_shift_scipy[:,c,:,:] = depthwise_conv2d_scipy[:,c,:,:] * scale_np[c] + shift_np[c] scale_shift_scipy[:,c,:,:] = depthwise_conv2d_scipy[:,c,:,:] * scale_np[c] + shift_np[c]
relu_scipy = np.maximum(scale_shift_scipy, 0) relu_scipy = np.maximum(scale_shift_scipy, 0)
np.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5)
np.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5) tvm.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5)
np.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5)
print("success") print("success")
for device in ['cuda', 'opencl', 'rocm']: for device in ['cuda', 'opencl', 'rocm']:
...@@ -195,9 +195,9 @@ def test_depthwise_conv2d_nhwc(): ...@@ -195,9 +195,9 @@ def test_depthwise_conv2d_nhwc():
for c in range(in_channel * channel_multiplier): for c in range(in_channel * channel_multiplier):
scale_shift_scipy[:,:,:,c] = depthwise_conv2d_scipy[:,:,:,c] * scale_np[c] + shift_np[c] scale_shift_scipy[:,:,:,c] = depthwise_conv2d_scipy[:,:,:,c] * scale_np[c] + shift_np[c]
relu_scipy = np.maximum(scale_shift_scipy, 0) relu_scipy = np.maximum(scale_shift_scipy, 0)
np.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5)
np.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5) tvm.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5)
np.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5)
print("success") print("success")
for device in ['cuda', 'opencl', 'rocm']: for device in ['cuda', 'opencl', 'rocm']:
......
topi/recipe/conv/test_conv2d_hwcn_map.py
...@@ -64,10 +64,10 @@ def test_conv2d_hwcn_map(): ...@@ -64,10 +64,10 @@ def test_conv2d_hwcn_map():
unroll_explicit=device == 'rocm'): unroll_explicit=device == 'rocm'):
func1 = tvm.build(s1, [A, W, B], device) func1 = tvm.build(s1, [A, W, B], device)
func1(a, w, b) func1(a, w, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
func2 = tvm.build(s2, [A, W, C], device) func2 = tvm.build(s2, [A, W, C], device)
func2(a, w, c) func2(a, w, c)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in ['cuda', 'opencl', 'rocm']: for device in ['cuda', 'opencl', 'rocm']:
check_device(device) check_device(device)
......
topi/recipe/gemm/cuda_gemm_square.py
...@@ -118,7 +118,7 @@ def test_gemm(): ...@@ -118,7 +118,7 @@ def test_gemm():
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx) c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
for i in range(2): for i in range(2):
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), np.dot(b_np.T, a_np), rtol=1e-5) c.asnumpy(), np.dot(b_np.T, a_np), rtol=1e-5)
num_flops = 2 * nn * nn * nn num_flops = 2 * nn * nn * nn
......
topi/recipe/gemm/gemm_int8.py
...@@ -140,7 +140,7 @@ if __name__ == '__main__': ...@@ -140,7 +140,7 @@ if __name__ == '__main__':
c = tvm.nd.array(np.zeros((n, m), dtype='int32'), ctx) c = tvm.nd.array(np.zeros((n, m), dtype='int32'), ctx)
f(a, b, c) f(a, b, c)
np.testing.assert_allclose( tvm.testing.assert_allclose(
c.asnumpy(), c.asnumpy(),
np.dot( np.dot(
a_np.astype('int32'), a_np.astype('int32'),
......
topi/recipe/reduce/test_reduce_map.py
...@@ -67,7 +67,7 @@ def test_reduce_map(in_shape, axis, keepdims, type="sum", test_id=0): ...@@ -67,7 +67,7 @@ def test_reduce_map(in_shape, axis, keepdims, type="sum", test_id=0):
for _ in range(2): for _ in range(2):
fcuda(data_tvm, out_tvm) fcuda(data_tvm, out_tvm)
np.testing.assert_allclose(out_tvm.asnumpy(), out_npy, 4E-4, 4E-4) tvm.testing.assert_allclose(out_tvm.asnumpy(), out_npy, rtol=4e-4, atol=4e-4)
if __name__ == "__main__": if __name__ == "__main__":
test_reduce_map(in_shape=(128, 24, 128, 24), test_reduce_map(in_shape=(128, 24, 128, 24),
......
topi/recipe/rnn/matexp.py
...@@ -145,7 +145,7 @@ def rnn_matexp(): ...@@ -145,7 +145,7 @@ def rnn_matexp():
for j in range(n_num_hidden): for j in range(n_num_hidden):
if abs(res_cmp[i,0,j] - res_gpu[i,0,j]) > 1e-5: if abs(res_cmp[i,0,j] - res_gpu[i,0,j]) > 1e-5:
print("%d, %d: %g vs %g" % (i,j, res_cmp[i,0,j], res_gpu[i,0,j])) print("%d, %d: %g vs %g" % (i,j, res_cmp[i,0,j], res_gpu[i,0,j]))
np.testing.assert_allclose(res_gpu, res_cmp, rtol=1e-3) tvm.testing.assert_allclose(res_gpu, res_cmp, rtol=1e-3)
check_device("cuda") check_device("cuda")
if __name__ == "__main__": if __name__ == "__main__":
......
topi/tests/python/test_topi_bitserial_conv2d.py
...@@ -46,7 +46,7 @@ def verify_bitserial_conv2d_nchw(batch, in_size, in_channel, num_filter, kernel, ...@@ -46,7 +46,7 @@ def verify_bitserial_conv2d_nchw(batch, in_size, in_channel, num_filter, kernel,
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
func = tvm.build(s, [A, W, B], "llvm") func = tvm.build(s, [A, W, B], "llvm")
func(a, w, b) func(a, w, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
def verify_bitserial_conv2d_nhwc(batch, in_size, in_channel, num_filter, kernel, stride, padding, def verify_bitserial_conv2d_nhwc(batch, in_size, in_channel, num_filter, kernel, stride, padding,
activation_bits, weight_bits, dorefa): activation_bits, weight_bits, dorefa):
...@@ -85,7 +85,7 @@ def verify_bitserial_conv2d_nhwc(batch, in_size, in_channel, num_filter, kernel, ...@@ -85,7 +85,7 @@ def verify_bitserial_conv2d_nhwc(batch, in_size, in_channel, num_filter, kernel,
func = tvm.build(s, [A, W, B], 'llvm') func = tvm.build(s, [A, W, B], 'llvm')
func(a, w, b) func(a, w, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
def test_bitserial_conv2d(): def test_bitserial_conv2d():
in_size = 56 in_size = 56
......
topi/tests/python/test_topi_bnn.py
...@@ -44,7 +44,7 @@ def verify_binary_dense(batch, in_dim, out_dim): ...@@ -44,7 +44,7 @@ def verify_binary_dense(batch, in_dim, out_dim):
f1(a, bnn_a) f1(a, bnn_a)
f2(b, bnn_b) f2(b, bnn_b)
f3(bnn_a, bnn_b, bnn_c) f3(bnn_a, bnn_b, bnn_c)
np.testing.assert_allclose(bnn_c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(bnn_c.asnumpy(), c_np, rtol=1e-5)
def test_binary_dense(): def test_binary_dense():
verify_binary_dense(1, 4096, 1024) verify_binary_dense(1, 4096, 1024)
......
topi/tests/python/test_topi_broadcast.py
...@@ -23,7 +23,7 @@ def verify_broadcast_to_ele(in_shape, out_shape, fbcast): ...@@ -23,7 +23,7 @@ def verify_broadcast_to_ele(in_shape, out_shape, fbcast):
data_nd = tvm.nd.array(data_npy, ctx) data_nd = tvm.nd.array(data_npy, ctx)
out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), ctx) out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), ctx)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for target in get_all_backend(): for target in get_all_backend():
check_device(target) check_device(target)
...@@ -77,7 +77,7 @@ def verify_broadcast_binary_ele(lhs_shape, rhs_shape, ...@@ -77,7 +77,7 @@ def verify_broadcast_binary_ele(lhs_shape, rhs_shape,
out_npy = fnumpy(lhs_npy, rhs_npy) out_npy = fnumpy(lhs_npy, rhs_npy)
out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(C.dtype), ctx) out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(C.dtype), ctx)
foo(lhs_nd, rhs_nd, out_nd) foo(lhs_nd, rhs_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4)
for target in get_all_backend(): for target in get_all_backend():
check_device(target) check_device(target)
......
topi/tests/python/test_topi_clip.py
...@@ -33,7 +33,7 @@ def verify_clip(N, a_min, a_max, dtype): ...@@ -33,7 +33,7 @@ def verify_clip(N, a_min, a_max, dtype):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B], device, name="clip") f = tvm.build(s, [A, B], device, name="clip")
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
......
topi/tests/python/test_topi_conv2d_hwcn.py
...@@ -47,8 +47,8 @@ def verify_conv2d_hwcn(batch, in_channel, in_size, num_filter, kernel, stride, p ...@@ -47,8 +47,8 @@ def verify_conv2d_hwcn(batch, in_channel, in_size, num_filter, kernel, stride, p
func2 = tvm.build(s2, [A, W, C], device) func2 = tvm.build(s2, [A, W, C], device)
func1(a, w, b) func1(a, w, b)
func2(a, w, c) func2(a, w, c)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']: for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_conv2d_int8.py
...@@ -83,7 +83,7 @@ def verify_conv2d_NCHWc_int8(batch, in_channel, in_size, num_filter, kernel, str ...@@ -83,7 +83,7 @@ def verify_conv2d_NCHWc_int8(batch, in_channel, in_size, num_filter, kernel, str
else: else:
func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
func(a, w, c) func(a, w, c)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in ["cuda"]: for device in ["cuda"]:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_conv2d_nchw.py
...@@ -66,7 +66,7 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p ...@@ -66,7 +66,7 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p
else: else:
func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
func(a, w, c) func(a, w, c)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
with autotvm.tophub.context(device): # load tophub pre-tuned parameters with autotvm.tophub.context(device): # load tophub pre-tuned parameters
......
topi/tests/python/test_topi_conv2d_nhwc.py
...@@ -42,7 +42,7 @@ def verify_conv2d_nhwc(batch, in_channel, in_size, num_filter, kernel, stride, p ...@@ -42,7 +42,7 @@ def verify_conv2d_nhwc(batch, in_channel, in_size, num_filter, kernel, stride, p
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
func = tvm.build(s, [A, W, B], device) func = tvm.build(s, [A, W, B], device)
func(a, w, b) func(a, w, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm']: for device in ['llvm']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_conv2d_transpose_nchw.py
...@@ -48,8 +48,8 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel, ...@@ -48,8 +48,8 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
func2 = tvm.build(s2, [A, W, C], device) func2 = tvm.build(s2, [A, W, C], device)
func1(a, w, b) func1(a, w, b)
func2(a, w, c) func2(a, w, c)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
......
topi/tests/python/test_topi_conv2d_winograd.py
...@@ -65,7 +65,7 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p ...@@ -65,7 +65,7 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p
else: else:
func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
func(a, w, c) func(a, w, c)
np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
for device in ['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']: for device in ['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']:
......
topi/tests/python/test_topi_dense.py
...@@ -44,7 +44,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True): ...@@ -44,7 +44,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True):
d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx) d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B, C, D], device, name="dense") f = tvm.build(s, [A, B, C, D], device, name="dense")
f(a, b, c, d) f(a, b, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
......
topi/tests/python/test_topi_depthwise_conv2d.py
...@@ -97,9 +97,9 @@ def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_mu ...@@ -97,9 +97,9 @@ def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_mu
# launch kernel 3 (depthwise_conv2d + scale_shift + relu) # launch kernel 3 (depthwise_conv2d + scale_shift + relu)
timer_3 = f3.time_evaluator(f3.entry_name, ctx, number=1) timer_3 = f3.time_evaluator(f3.entry_name, ctx, number=1)
tcost_3 = timer_3(input_tvm, filter_tvm, scale_tvm, shift_tvm, relu_tvm).mean tcost_3 = timer_3(input_tvm, filter_tvm, scale_tvm, shift_tvm, relu_tvm).mean
np.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5)
np.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5) tvm.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5)
np.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
with autotvm.tophub.context(device): # load tophub pre-tuned parameters with autotvm.tophub.context(device): # load tophub pre-tuned parameters
...@@ -197,9 +197,9 @@ def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_mu ...@@ -197,9 +197,9 @@ def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_mu
timer_3 = f3.time_evaluator(f3.entry_name, ctx, number=1) timer_3 = f3.time_evaluator(f3.entry_name, ctx, number=1)
tcost_3 = timer_3(input_tvm, filter_tvm, scale_tvm, shift_tvm, relu_tvm).mean tcost_3 = timer_3(input_tvm, filter_tvm, scale_tvm, shift_tvm, relu_tvm).mean
relu_scipy = np.maximum(scale_shift_scipy, 0) relu_scipy = np.maximum(scale_shift_scipy, 0)
np.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5)
np.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5) tvm.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5)
np.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
with autotvm.tophub.context(device): # load tophub pre-tuned parameters with autotvm.tophub.context(device): # load tophub pre-tuned parameters
......
topi/tests/python/test_topi_depthwise_conv2d_back_input.py
...@@ -80,7 +80,7 @@ def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multipli ...@@ -80,7 +80,7 @@ def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multipli
# launch the kernel # launch the kernel
timer = f.time_evaluator(f.entry_name, ctx, number=1) timer = f.time_evaluator(f.entry_name, ctx, number=1)
tcost = timer(filter_tvm, out_grad_tvm, in_grad_tvm).mean tcost = timer(filter_tvm, out_grad_tvm, in_grad_tvm).mean
np.testing.assert_allclose(in_grad_np, in_grad_tvm.asnumpy(), rtol=1e-5) tvm.testing.assert_allclose(in_grad_np, in_grad_tvm.asnumpy(), rtol=1e-5)
check_device("opencl") check_device("opencl")
check_device("cuda") check_device("cuda")
......
topi/tests/python/test_topi_depthwise_conv2d_back_weight.py
...@@ -73,7 +73,7 @@ def verify_depthwise_conv2d_back_weight(batch, in_channel, in_h, channel_multipl ...@@ -73,7 +73,7 @@ def verify_depthwise_conv2d_back_weight(batch, in_channel, in_h, channel_multipl
# launch the kernel # launch the kernel
timer = f.time_evaluator(f.entry_name, ctx, number=1) timer = f.time_evaluator(f.entry_name, ctx, number=1)
tcost = timer(input_tvm, out_grad_tvm, weight_grad_tvm).mean tcost = timer(input_tvm, out_grad_tvm, weight_grad_tvm).mean
np.testing.assert_allclose(weight_grad_np, weight_grad_tvm.asnumpy(), rtol=1e-4) tvm.testing.assert_allclose(weight_grad_np, weight_grad_tvm.asnumpy(), rtol=1e-4)
check_device("opencl") check_device("opencl")
check_device("cuda") check_device("cuda")
......
topi/tests/python/test_topi_dilate.py
...@@ -19,7 +19,7 @@ def test_dilate(): ...@@ -19,7 +19,7 @@ def test_dilate():
output_tvm = tvm.nd.array(np.zeros(shape=output_size).astype(Output.dtype), ctx=ctx) output_tvm = tvm.nd.array(np.zeros(shape=output_size).astype(Output.dtype), ctx=ctx)
f = tvm.build(schedule, [Input, Output], target) f = tvm.build(schedule, [Input, Output], target)
f(input_tvm, output_tvm) f(input_tvm, output_tvm)
np.testing.assert_allclose(output_tvm.asnumpy(), output_np, rtol=1e-5) tvm.testing.assert_allclose(output_tvm.asnumpy(), output_np, rtol=1e-5)
_test_dilate((32,), (2,)) _test_dilate((32,), (2,))
_test_dilate((32,32), (2,2)) _test_dilate((32,32), (2,2))
......
topi/tests/python/test_topi_l2norm.py
...@@ -29,7 +29,7 @@ def verify_l2_normalize(ishape, eps, axis=None): ...@@ -29,7 +29,7 @@ def verify_l2_normalize(ishape, eps, axis=None):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']: for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_lrn.py
...@@ -28,7 +28,7 @@ def verify_lrn(shape, size, axis, bias, alpha, beta): ...@@ -28,7 +28,7 @@ def verify_lrn(shape, size, axis, bias, alpha, beta):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']: for device in ['llvm', 'cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_math.py
...@@ -37,7 +37,7 @@ def test_ewise(): ...@@ -37,7 +37,7 @@ def test_ewise():
a = tvm.nd.array(a_np, ctx) a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros_like(b_np), ctx) b = tvm.nd.array(np.zeros_like(b_np), ctx)
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'llvm', 'nvptx', 'sdaccel', for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'llvm', 'nvptx', 'sdaccel',
'aocl_sw_emu']: 'aocl_sw_emu']:
......
topi/tests/python/test_topi_matmul.py
...@@ -27,7 +27,7 @@ def verify_matmul(sa, sb, transp_a, transp_b): ...@@ -27,7 +27,7 @@ def verify_matmul(sa, sb, transp_a, transp_b):
c1 = np.matmul(np.transpose(a) if transp_a else a, c1 = np.matmul(np.transpose(a) if transp_a else a,
np.transpose(b) if transp_b else b) np.transpose(b) if transp_b else b)
c2 = with_tvm(lambda A,B: topi.matmul(A,B,transp_a,transp_b), a,b) c2 = with_tvm(lambda A,B: topi.matmul(A,B,transp_a,transp_b), a,b)
np.testing.assert_allclose(c1, c2, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(c1, c2, rtol=1e-5, atol=1e-5)
def test_matmul(): def test_matmul():
verify_matmul((1,1),(1,1),False,False) verify_matmul((1,1),(1,1),False,False)
......
topi/tests/python/test_topi_pooling.py
...@@ -64,7 +64,7 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_ ...@@ -64,7 +64,7 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -109,7 +109,7 @@ def verify_global_pool(n, c, h, w, pool_type): ...@@ -109,7 +109,7 @@ def verify_global_pool(n, c, h, w, pool_type):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
......
topi/tests/python/test_topi_reduce.py
...@@ -87,11 +87,11 @@ def verify_reduce_map_ele(in_shape, axis, keepdims, type="sum", dtype="float32") ...@@ -87,11 +87,11 @@ def verify_reduce_map_ele(in_shape, axis, keepdims, type="sum", dtype="float32")
sel_indices = other_indices[0:axis] + (out_tvm_indices,) + other_indices[axis:] sel_indices = other_indices[0:axis] + (out_tvm_indices,) + other_indices[axis:]
out_tvm_val = in_npy_map[sel_indices] out_tvm_val = in_npy_map[sel_indices]
if type == "argmax": if type == "argmax":
np.testing.assert_allclose(out_tvm_val, in_npy_map.max(axis=axis), 1E-3, 1E-3) tvm.testing.assert_allclose(out_tvm_val, in_npy_map.max(axis=axis), 1E-3, 1E-3)
elif type == "argmin": elif type == "argmin":
np.testing.assert_allclose(out_tvm_val, in_npy_map.min(axis=axis), 1E-3, 1E-3) tvm.testing.assert_allclose(out_tvm_val, in_npy_map.min(axis=axis), 1E-3, 1E-3)
else: else:
np.testing.assert_allclose(out_tvm.asnumpy(), out_npy, 1E-3, 1E-3) tvm.testing.assert_allclose(out_tvm.asnumpy(), out_npy, 1E-3, 1E-3)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
......
topi/tests/python/test_topi_region.py
...@@ -37,7 +37,7 @@ def verify_region(batch, in_size, in_channel, n, classes, coords, background, l_ ...@@ -37,7 +37,7 @@ def verify_region(batch, in_size, in_channel, n, classes, coords, background, l_
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
func = tvm.build(s, [A, B], device) func = tvm.build(s, [A, B], device)
func(a, b) func(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm', 'cuda']: for device in ['llvm', 'cuda']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_relu.py
...@@ -27,7 +27,7 @@ def verify_relu(m, n): ...@@ -27,7 +27,7 @@ def verify_relu(m, n):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
foo = tvm.build(s, [A, B], device, name="relu") foo = tvm.build(s, [A, B], device, name="relu")
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -45,7 +45,7 @@ def verify_leaky_relu(m, alpha): ...@@ -45,7 +45,7 @@ def verify_leaky_relu(m, alpha):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
foo = tvm.build(s, [A, B], "llvm", name="leaky_relu") foo = tvm.build(s, [A, B], "llvm", name="leaky_relu")
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
def verify_prelu(x, w, axis, weight_reshape): def verify_prelu(x, w, axis, weight_reshape):
...@@ -68,7 +68,7 @@ def verify_prelu(x, w, axis, weight_reshape): ...@@ -68,7 +68,7 @@ def verify_prelu(x, w, axis, weight_reshape):
foo = tvm.build(s, [X, W, B], "llvm", name="prelu") foo = tvm.build(s, [X, W, B], "llvm", name="prelu")
foo(x_tvm, w_tvm, b) foo(x_tvm, w_tvm, b)
out_np = _prelu_numpy(x_np, w_np) out_np = _prelu_numpy(x_np, w_np)
np.testing.assert_allclose(b.asnumpy(), out_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), out_np, rtol=1e-5)
def test_relu(): def test_relu():
verify_relu(10, 128) verify_relu(10, 128)
......
topi/tests/python/test_topi_reorg.py
...@@ -38,7 +38,7 @@ def verify_reorg(batch, in_size, in_channel, stride): ...@@ -38,7 +38,7 @@ def verify_reorg(batch, in_size, in_channel, stride):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
func = tvm.build(s, [A, B], device) func = tvm.build(s, [A, B], device)
func(a, b) func(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm', 'cuda']: for device in ['llvm', 'cuda']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_resize.py
...@@ -38,7 +38,7 @@ def verify_bilinear_scale(batch, in_channel, in_height, in_width, out_height, ou ...@@ -38,7 +38,7 @@ def verify_bilinear_scale(batch, in_channel, in_height, in_width, out_height, ou
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-3, atol=1e-3) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-3, atol=1e-3)
for device in ['llvm', 'cuda', 'vulkan', 'nvptx']: for device in ['llvm', 'cuda', 'vulkan', 'nvptx']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_shortcut.py
...@@ -36,7 +36,7 @@ def verify_shortcut(batch, in_size, in_channel): ...@@ -36,7 +36,7 @@ def verify_shortcut(batch, in_size, in_channel):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
func = tvm.build(s, [A1, A2, B], device) func = tvm.build(s, [A1, A2, B], device)
func(a1, a2, b) func(a1, a2, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm', 'cuda']: for device in ['llvm', 'cuda']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_softmax.py
...@@ -32,7 +32,7 @@ def verify_softmax(m, n, dtype="float32"): ...@@ -32,7 +32,7 @@ def verify_softmax(m, n, dtype="float32"):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
foo = tvm.build(s, [A, B], device, name="softmax") foo = tvm.build(s, [A, B], device, name="softmax")
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']: for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']:
check_device(device) check_device(device)
...@@ -63,7 +63,7 @@ def verify_log_softmax(m, n, dtype="float32"): ...@@ -63,7 +63,7 @@ def verify_log_softmax(m, n, dtype="float32"):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
foo = tvm.build(s, [A, B], device, name="log_softmax") foo = tvm.build(s, [A, B], device, name="log_softmax")
foo(a, b) foo(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
......
topi/tests/python/test_topi_sparse.py
...@@ -47,7 +47,7 @@ def verify_dynamic_csrmv(batch, in_dim, out_dim, use_bias=True): ...@@ -47,7 +47,7 @@ def verify_dynamic_csrmv(batch, in_dim, out_dim, use_bias=True):
assert a.indptr.dtype == A.indptr.dtype assert a.indptr.dtype == A.indptr.dtype
f = tvm.build(s, [nr, A.data, A.indices, A.indptr, B, C, D], device, name="csrmv") f = tvm.build(s, [nr, A.data, A.indices, A.indptr, B, C, D], device, name="csrmv")
f(_nr, a.data, a.indices, a.indptr, b, c, d) f(_nr, a.data, a.indices, a.indptr, b, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-4, atol=1e-4) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-4, atol=1e-4)
for device in ["llvm"]: for device in ["llvm"]:
check_device(device) check_device(device)
...@@ -89,7 +89,7 @@ def verify_dynamic_csrmm(batch, in_dim, out_dim, use_bias=True): ...@@ -89,7 +89,7 @@ def verify_dynamic_csrmm(batch, in_dim, out_dim, use_bias=True):
f = tvm.build(s, [nr, A.data, A.indices, A.indptr, B, C, D], device, name="csrmm") f = tvm.build(s, [nr, A.data, A.indices, A.indptr, B, C, D], device, name="csrmm")
f(_nr, a.data, a.indices, a.indptr, b, c, d) f(_nr, a.data, a.indices, a.indptr, b, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-2, atol=1e-2) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-2, atol=1e-2)
for device in ["llvm"]: for device in ["llvm"]:
check_device(device) check_device(device)
...@@ -127,7 +127,7 @@ def verify_dense_si(batch, in_dim, out_dim, use_bias=True, dtype='float32'): ...@@ -127,7 +127,7 @@ def verify_dense_si(batch, in_dim, out_dim, use_bias=True, dtype='float32'):
d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx) d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx)
f = tvm.build(s, [A.data, A.indices, A.indptr, B, C, D], device, name="dense") f = tvm.build(s, [A.data, A.indices, A.indptr, B, C, D], device, name="dense")
f(a.data, a.indices, a.indptr, b, c, d) f(a.data, a.indices, a.indptr, b, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-4, atol=1e-4) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-4, atol=1e-4)
check_device('llvm') check_device('llvm')
...@@ -164,7 +164,7 @@ def verify_dense_sw(batch, in_dim, out_dim, use_bias=True, dtype='float32'): ...@@ -164,7 +164,7 @@ def verify_dense_sw(batch, in_dim, out_dim, use_bias=True, dtype='float32'):
d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx) d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B.data, B.indices, B.indptr, C, D], device, name="dense") f = tvm.build(s, [A, B.data, B.indices, B.indptr, C, D], device, name="dense")
f(a, b.data, b.indices, b.indptr, c, d) f(a, b.data, b.indices, b.indptr, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-4, atol=1e-4) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-4, atol=1e-4)
check_device('llvm') check_device('llvm')
......
topi/tests/python/test_topi_tensor.py
...@@ -32,7 +32,7 @@ def verify_elemwise_sum(num_args, dtype): ...@@ -32,7 +32,7 @@ def verify_elemwise_sum(num_args, dtype):
tvm_nd = [tvm.nd.array(nd, ctx) for nd in np_nd] + [out] tvm_nd = [tvm.nd.array(nd, ctx) for nd in np_nd] + [out]
f(*tvm_nd) f(*tvm_nd)
np_out = np.sum(np.array(np_nd), axis=0) np_out = np.sum(np.array(np_nd), axis=0)
np.testing.assert_allclose(out.asnumpy(), np_out, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_out, rtol=1e-5)
for device in ["llvm"]: for device in ["llvm"]:
check_device(device) check_device(device)
...@@ -59,11 +59,11 @@ def verify_full(shape, dtype, fill_value): ...@@ -59,11 +59,11 @@ def verify_full(shape, dtype, fill_value):
out = tvm.nd.array(np.zeros(shape, dtype=dtype), ctx) out = tvm.nd.array(np.zeros(shape, dtype=dtype), ctx)
f = tvm.build(s1, [A, B], device, name="full_like") f = tvm.build(s1, [A, B], device, name="full_like")
f(tvm.nd.array(np.zeros(shape, dtype), ctx), out) f(tvm.nd.array(np.zeros(shape, dtype), ctx), out)
np.testing.assert_allclose(out.asnumpy(), np_nd, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_nd, rtol=1e-5)
f = tvm.build(s2, [C], device, name="full") f = tvm.build(s2, [C], device, name="full")
f(out) f(out)
np.testing.assert_allclose(out.asnumpy(), np_nd, rtol=1e-5) tvm.testing.assert_allclose(out.asnumpy(), np_nd, rtol=1e-5)
for device in ["llvm"]: for device in ["llvm"]:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_transform.py
...@@ -22,7 +22,7 @@ def verify_expand_dims(in_shape, out_shape, axis, num_newaxis): ...@@ -22,7 +22,7 @@ def verify_expand_dims(in_shape, out_shape, axis, num_newaxis):
data_nd = tvm.nd.array(data_npy, ctx) data_nd = tvm.nd.array(data_npy, ctx)
out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), ctx) out_nd = tvm.nd.array(np.empty(out_shape).astype(B.dtype), ctx)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -45,7 +45,7 @@ def verify_tranpose(in_shape, axes): ...@@ -45,7 +45,7 @@ def verify_tranpose(in_shape, axes):
data_nd = tvm.nd.array(data_npy, ctx) data_nd = tvm.nd.array(data_npy, ctx)
out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=B.dtype) out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=B.dtype)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -68,7 +68,7 @@ def verify_reshape(src_shape, dst_shape): ...@@ -68,7 +68,7 @@ def verify_reshape(src_shape, dst_shape):
data_nd = tvm.nd.array(data_npy, ctx) data_nd = tvm.nd.array(data_npy, ctx)
out_nd = tvm.nd.empty(dst_shape, ctx=ctx, dtype=B.dtype) out_nd = tvm.nd.empty(dst_shape, ctx=ctx, dtype=B.dtype)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -96,7 +96,7 @@ def verify_squeeze(src_shape, axis): ...@@ -96,7 +96,7 @@ def verify_squeeze(src_shape, axis):
out_nd_shape = out_npy.shape out_nd_shape = out_npy.shape
out_nd = tvm.nd.empty(out_nd_shape, ctx=ctx, dtype=B.dtype) out_nd = tvm.nd.empty(out_nd_shape, ctx=ctx, dtype=B.dtype)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -121,7 +121,7 @@ def verify_concatenate(shapes, axis): ...@@ -121,7 +121,7 @@ def verify_concatenate(shapes, axis):
data_nds = [tvm.nd.array(data_npy, ctx) for data_npy in data_npys] data_nds = [tvm.nd.array(data_npy, ctx) for data_npy in data_npys]
out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=out_tensor.dtype) out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=out_tensor.dtype)
foo(*(data_nds + [out_nd])) foo(*(data_nds + [out_nd]))
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -146,7 +146,7 @@ def verify_split(src_shape, indices_or_sections, axis): ...@@ -146,7 +146,7 @@ def verify_split(src_shape, indices_or_sections, axis):
out_nds = [tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=tensor_l[0].dtype) for out_npy in out_npys] out_nds = [tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=tensor_l[0].dtype) for out_npy in out_npys]
foo(*([data_nd] + out_nds)) foo(*([data_nd] + out_nds))
for out_nd, out_npy in zip(out_nds, out_npys): for out_nd, out_npy in zip(out_nds, out_npys):
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in get_all_backend(): for device in get_all_backend():
check_device(device) check_device(device)
...@@ -181,7 +181,7 @@ def verify_expand_like(in_shape, out_shape, axis): ...@@ -181,7 +181,7 @@ def verify_expand_like(in_shape, out_shape, axis):
tvm_shape_like = tvm.nd.array(np.zeros(out_shape).astype(B.dtype), ctx) tvm_shape_like = tvm.nd.array(np.zeros(out_shape).astype(B.dtype), ctx)
out = tvm.nd.array(np.zeros(out_shape).astype(A.dtype), ctx) out = tvm.nd.array(np.zeros(out_shape).astype(A.dtype), ctx)
f(tvm_input, tvm_shape_like, out) f(tvm_input, tvm_shape_like, out)
np.testing.assert_allclose(out.asnumpy(), input) tvm.testing.assert_allclose(out.asnumpy(), input)
for device in ["llvm"]: for device in ["llvm"]:
check_device(device) check_device(device)
...@@ -204,7 +204,7 @@ def verify_flip(in_shape, axis): ...@@ -204,7 +204,7 @@ def verify_flip(in_shape, axis):
data_nd = tvm.nd.array(x_np, ctx) data_nd = tvm.nd.array(x_np, ctx)
out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=A.dtype) out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=A.dtype)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in ["llvm", "cuda", "opencl", "sdaccel", "aocl_sw_emu"]: for device in ["llvm", "cuda", "opencl", "sdaccel", "aocl_sw_emu"]:
check_device(device) check_device(device)
...@@ -243,7 +243,7 @@ def verify_take(src_shape, indices_src, axis=None): ...@@ -243,7 +243,7 @@ def verify_take(src_shape, indices_src, axis=None):
indices_nd = tvm.nd.array(indices_src, ctx) indices_nd = tvm.nd.array(indices_src, ctx)
out_nd = tvm.nd.empty(out_npys.shape, ctx=ctx, dtype=src_dtype) out_nd = tvm.nd.empty(out_npys.shape, ctx=ctx, dtype=src_dtype)
foo(data_nd, indices_nd, out_nd) foo(data_nd, indices_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npys) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npys)
for device in ["llvm", "opencl", "sdaccel", "aocl_sw_emu"]: for device in ["llvm", "opencl", "sdaccel", "aocl_sw_emu"]:
check_device(device) check_device(device)
...@@ -270,7 +270,7 @@ def verify_strided_slice(in_shape, begin, end, stride=None): ...@@ -270,7 +270,7 @@ def verify_strided_slice(in_shape, begin, end, stride=None):
data_nd = tvm.nd.array(x_np, ctx) data_nd = tvm.nd.array(x_np, ctx)
out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=A.dtype) out_nd = tvm.nd.empty(out_npy.shape, ctx=ctx, dtype=A.dtype)
foo(data_nd, out_nd) foo(data_nd, out_nd)
np.testing.assert_allclose(out_nd.asnumpy(), out_npy) tvm.testing.assert_allclose(out_nd.asnumpy(), out_npy)
for device in ["llvm", "opencl", "sdaccel", "aocl_sw_emu"]: for device in ["llvm", "opencl", "sdaccel", "aocl_sw_emu"]:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_upsampling.py
...@@ -43,7 +43,7 @@ def verify_upsampling(batch, in_channel, in_height, in_width, scale, layout='NCH ...@@ -43,7 +43,7 @@ def verify_upsampling(batch, in_channel, in_height, in_width, scale, layout='NCH
f = tvm.build(s, [A, B], device) f = tvm.build(s, [A, B], device)
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5, atol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
for device in ['llvm', 'cuda', 'vulkan', 'nvptx']: for device in ['llvm', 'cuda', 'vulkan', 'nvptx']:
check_device(device) check_device(device)
......
topi/tests/python/test_topi_vision.py
...@@ -41,7 +41,7 @@ def test_nms(): ...@@ -41,7 +41,7 @@ def test_nms():
tvm_out = tvm.nd.array(np.zeros(dshape, dtype=data.dtype), ctx) tvm_out = tvm.nd.array(np.zeros(dshape, dtype=data.dtype), ctx)
f = tvm.build(s, [data, valid_count, out], device) f = tvm.build(s, [data, valid_count, out], device)
f(tvm_data, tvm_valid_count, tvm_out) f(tvm_data, tvm_valid_count, tvm_out)
np.testing.assert_allclose(tvm_out.asnumpy(), np_result, rtol=1e-4) tvm.testing.assert_allclose(tvm_out.asnumpy(), np_result, rtol=1e-4)
for device in ['llvm', 'opencl']: for device in ['llvm', 'opencl']:
check_device(device) check_device(device)
...@@ -100,7 +100,7 @@ def verify_multibox_prior(dshape, sizes=(1,), ratios=(1,), steps=(-1, -1), offse ...@@ -100,7 +100,7 @@ def verify_multibox_prior(dshape, sizes=(1,), ratios=(1,), steps=(-1, -1), offse
tvm_out = tvm.nd.array(np.zeros(oshape, dtype=dtype), ctx) tvm_out = tvm.nd.array(np.zeros(oshape, dtype=dtype), ctx)
f = tvm.build(s, [data, out], device) f = tvm.build(s, [data, out], device)
f(tvm_input_data, tvm_out) f(tvm_input_data, tvm_out)
np.testing.assert_allclose(tvm_out.asnumpy(), np_out, rtol=1e-3) tvm.testing.assert_allclose(tvm_out.asnumpy(), np_out, rtol=1e-3)
for device in ['llvm', 'opencl']: for device in ['llvm', 'opencl']:
check_device(device) check_device(device)
...@@ -148,7 +148,7 @@ def test_multibox_detection(): ...@@ -148,7 +148,7 @@ def test_multibox_detection():
tvm_out = tvm.nd.array(np.zeros((batch_size, num_anchors, 6)).astype(out.dtype), ctx) tvm_out = tvm.nd.array(np.zeros((batch_size, num_anchors, 6)).astype(out.dtype), ctx)
f = tvm.build(s, [cls_prob, loc_preds, anchors, out], device) f = tvm.build(s, [cls_prob, loc_preds, anchors, out], device)
f(tvm_cls_prob, tvm_loc_preds, tvm_anchors, tvm_out) f(tvm_cls_prob, tvm_loc_preds, tvm_anchors, tvm_out)
np.testing.assert_allclose(tvm_out.asnumpy(), expected_np_out, rtol=1e-4) tvm.testing.assert_allclose(tvm_out.asnumpy(), expected_np_out, rtol=1e-4)
for device in ['llvm', 'opencl']: for device in ['llvm', 'opencl']:
check_device(device) check_device(device)
......
topi/tests/python_cpp/test_topi_bnn.py
...@@ -44,7 +44,7 @@ def verify_binary_dense(batch, in_dim, out_dim): ...@@ -44,7 +44,7 @@ def verify_binary_dense(batch, in_dim, out_dim):
f1(a, bnn_a) f1(a, bnn_a)
f2(b, bnn_b) f2(b, bnn_b)
f3(bnn_a, bnn_b, bnn_c) f3(bnn_a, bnn_b, bnn_c)
np.testing.assert_allclose(bnn_c.asnumpy(), c_np, rtol=1e-5) tvm.testing.assert_allclose(bnn_c.asnumpy(), c_np, rtol=1e-5)
def test_binary_dense(): def test_binary_dense():
verify_binary_dense(1, 4096, 1024) verify_binary_dense(1, 4096, 1024)
......
topi/tests/python_cpp/test_topi_clip.py
...@@ -29,7 +29,7 @@ def verify_clip(N, a_min, a_max, dtype): ...@@ -29,7 +29,7 @@ def verify_clip(N, a_min, a_max, dtype):
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B], device, name="clip") f = tvm.build(s, [A, B], device, name="clip")
f(a, b) f(a, b)
np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
for device in ['llvm']: for device in ['llvm']:
check_device(device) check_device(device)
......
topi/tests/python_cpp/test_topi_dense.py
...@@ -47,7 +47,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True): ...@@ -47,7 +47,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True):
d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx) d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx)
f = tvm.build(s, [A, B, C, D], device, name="dense") f = tvm.build(s, [A, B, C, D], device, name="dense")
f(a, b, c, d) f(a, b, c, d)
np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5) tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5)
for device in ['cuda', 'opencl', 'metal', 'rocm']: for device in ['cuda', 'opencl', 'metal', 'rocm']:
check_device(device) check_device(device)
......
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