def test_recast_medium():
"""Recast a slightly larger graph."""
def before():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
c = relay.nn.conv2d(x, w, padding=(1, 1), out_dtype="float32")
w2 = relay.var("w2", shape=[8, 8, 3, 3])
c2 = relay.nn.conv2d(c, w2, padding=(1, 1), out_dtype="float32")
return relay.Function([x, w, w2], c2)
def expected():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
x_int = relay.cast(x, "int8")
w_int = relay.cast(w, "int8")
c = relay.nn.conv2d(x_int, w_int, padding=(1, 1), out_dtype="int32")
c_float = relay.cast(c, "float32")
w2 = relay.var("w2", shape=[8, 8, 3, 3])
w2_int = relay.cast(w2, "int8")
c_float_int = relay.cast(c_float, "int8")
c2 = relay.nn.conv2d(c_float_int,
w2_int,
padding=(1, 1),
out_dtype="int32")
c2_float = relay.cast(c2, "float32")
return relay.Function([x, w, w2], c2_float)
pre = before()
post = recast(pre, "int8", "int32")
expected = expected()
assert tvm.ir.structural_equal(expected, post)
def test_recast_skip():
"""Recast a graph using skip layers."""
def before():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
c = relay.nn.conv2d(x, w, padding=(1, 1), out_dtype="float32")
w2 = relay.var("w2", shape=[8, 8, 3, 3])
c2 = relay.nn.conv2d(c, w2, padding=(1, 1), out_dtype="float32")
return relay.Function([x, w, w2], c2)
def expected():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
c = relay.nn.conv2d(x, w, padding=(1, 1), out_dtype="float32")
w2 = relay.var("w2", shape=[8, 8, 3, 3])
w2_int = relay.cast(w2, "int8")
c_int = relay.cast(c, "int8")
c2 = relay.nn.conv2d(c_int, w2_int, padding=(1, 1), out_dtype="int32")
c2_float = relay.cast(c2, "float32")
return relay.Function([x, w, w2], c2_float)
pre = before()
post = recast(pre, "int8", "int32", skip_layers=[0])
expected = expected()
assert tvm.ir.structural_equal(expected, post)
def test_recast_relu():
"""Recast a ReLU operator which does not have attributes."""
def before():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
c = relay.nn.conv2d(x, w, padding=(1, 1), out_dtype="float32")
r = relay.nn.relu(c)
return relay.Function([x, w], r)
def expected():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
x_fp16 = relay.cast(x, "float16")
w_fp16 = relay.cast(w, "float16")
c = relay.nn.conv2d(x_fp16,
w_fp16,
padding=(1, 1),
out_dtype="float16")
c_float32 = relay.cast(c, "float32")
c_float16 = relay.cast(c_float32, "float16")
r = relay.nn.relu(c_float16)
r_float32 = relay.cast(r, "float32")
return relay.Function([x, w], r_float32)
pre = before()
post = recast(pre, "float16", "float16", ops=["nn.conv2d", "nn.relu"])
expected = expected()
assert tvm.ir.structural_equal(expected, post)
def get_cpu_reference(mod, params1, input_shape, inputs):
mod_fp32 = recast(mod, "float32", "float32", ops=["nn.conv2d", "add", "nn.relu"])
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(mod_fp32, "llvm", params=params1)
ctx = tvm.cpu()
m = graph_runtime.create(graph, lib, ctx)
if isinstance(input_shape, dict):
for key in input_shape:
m.set_input(key, inputs[-1])
else:
m.set_input("data", inputs[-1])
m.set_input(**params)
m.run()
return [
m.get_output(0).asnumpy(),
]
def test_recast_simple():
"""Recast a single convolution operator."""
def before():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
c = relay.nn.conv2d(x, w, padding=(1, 1), out_dtype="float32")
return relay.Function([x, w], c)
def expected():
x = relay.var("x", shape=[8, 8, 8, 8])
w = relay.var("w", shape=[8, 8, 3, 3])
x_int = relay.cast(x, "int8")
w_int = relay.cast(w, "int8")
c = relay.nn.conv2d(x_int, w_int, padding=(1, 1), out_dtype="int32")
c_float = relay.cast(c, "float32")
return relay.Function([x, w], c_float)
pre = before()
post = recast(pre, "int8", "int32")
expected = expected()
assert tvm.ir.structural_equal(expected, post)
def test_recast_concat():
def before():
x = relay.var("x", shape=[1, 4])
y = relay.var("y", shape=[1, 4])
t = relay.Tuple([x, y])
c = relay.op.concatenate(t, axis=1)
return relay.Function([x, y], c)
def expected():
xv = relay.var("x", shape=[1, 4])
yv = relay.var("y", shape=[1, 4])
x = relay.cast(xv, "float16")
y = relay.cast(yv, "float16")
t = relay.Tuple([x, y])
c = relay.op.concatenate(t, axis=1)
c = relay.cast(c, "float32")
return relay.Function([xv, yv], c)
pre = before()
post = recast(pre, "float16", "float32", ops=["concatenate"])
expected = expected()
assert tvm.ir.structural_equal(expected, post)
