csullivan commented on code in PR #11878:
URL: https://github.com/apache/tvm/pull/11878#discussion_r935772265
##########
tests/python/relay/test_conv2d_nchw_texture.py:
##########
@@ -435,3 +435,558 @@ def test_conv2d_vgg16_winograd_4d():
graph = build_run_compare(mod, params1, {"data": input_shape}, dtype,
target)
matches = re.findall("winograd", graph)
assert len(matches) > 0
+
+
[email protected]_opencl
+def test_residual_block():
+ target = "opencl --device=adreno"
+ dtype = "float16"
+
+ input_shape = (1, 32, 40, 40)
+ filter_shape1 = (32, 32, 2, 2)
+ filter_shape2 = (32, 32, 1, 1)
+ filter_shape3 = (32, 32, 2, 2)
+ bias_shape1 = (1, 32, 1, 1)
+ A = relay.var("data", shape=input_shape, dtype=dtype)
+ W1 = relay.var("weight1", shape=filter_shape1, dtype=dtype)
+ B1 = relay.var("bias1", shape=bias_shape1, dtype=dtype)
+ W2 = relay.var("weight2", shape=filter_shape2, dtype=dtype)
+ W3 = relay.var("weight3", shape=filter_shape3, dtype=dtype)
+
+ conv1 = relay.nn.conv2d(
+ A,
+ W1,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ padding=[0, 0, 0, 0],
+ strides=[2, 2],
+ out_dtype=dtype,
+ channels=32,
+ kernel_size=(2, 2),
+ )
+ D = relay.op.add(conv1, B1)
+ D = relay.op.nn.relu(D)
+
+ conv2 = relay.nn.conv2d(
+ D,
+ W2,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ padding=[0, 0, 0, 0],
+ strides=[1, 1],
+ out_dtype=dtype,
+ channels=32,
+ kernel_size=(1, 1),
+ )
+ D = relay.op.add(conv2, D)
+ D = D * relay.const(0.15, "float16")
+ D = relay.op.nn.relu(D)
+
+ conv3 = relay.nn.conv2d(
+ D,
+ W3,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ padding=[0, 0, 0, 0],
+ strides=[2, 2],
+ out_dtype=dtype,
+ channels=32,
+ kernel_size=(2, 2),
+ )
+ D = relay.op.nn.relu(conv3)
+
+ mod = relay.Function([A, W1, B1, W2, W3], D)
+ np.random.seed(0)
+ initializer = relay.testing.init.Xavier()
+ filter_data1 = np.zeros(filter_shape1).astype(dtype)
+ bias_data1 = np.zeros(bias_shape1).astype(dtype)
+ initializer("weight", filter_data1)
+ initializer("bias", bias_data1)
+ filter_data2 = np.zeros(filter_shape2).astype(dtype)
+ initializer("weight", filter_data2)
+ filter_data3 = np.zeros(filter_shape3).astype(dtype)
+ initializer("weight", filter_data3)
+ params1 = {
+ "weight1": tvm.nd.array(filter_data1),
+ "bias1": tvm.nd.array(bias_data1),
+ "weight2": tvm.nd.array(filter_data2),
+ "weight3": tvm.nd.array(filter_data3),
+ }
+
+ static_memory_scope = [
+ "",
+ "global",
+ "global.texture-weight",
+ "global.texture-weight",
+ "global.texture",
+ "global.texture-weight",
+ "global",
+ "global.texture",
+ "global.texture-weight",
+ "",
Review Comment:
Thanks.
>these scopes are applied to any type of the tensor - data/weights/bias.
dividing by buckets are defined only based by values in shape
This is something I hope we can change down the line with the use of
Buffer.axis_separators and `sch.transform_layout`
>To compare the mapped values, we have to build such mapping basing on json.
That is doable but requires more efforts.
If you have extra cycles for adding these ergonomics in a follow up PR, this
would make the unit testing more accessible for other developers to author.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
