srkreddy1238 commented on code in PR #15786:
URL: https://github.com/apache/tvm/pull/15786#discussion_r1382318827
##########
python/tvm/relay/op/strategy/adreno.py:
##########
@@ -215,6 +215,58 @@ def
conv2d_winograd_without_weight_transform_strategy_adreno(attrs, inputs, out_
return strategy
+@conv2d_transpose_strategy.register("adreno")
+def conv2d_transpose_strategy_adreno(attrs, inputs, out_type, target):
+ """conv2d_transpose adreno strategy"""
+ strategy = _op.OpStrategy()
+ _, kernel = inputs
+ dilation = attrs.get_int_tuple("dilation")
+ groups = attrs.groups
+ data_layout = attrs.data_layout
+ kernel_layout = attrs.kernel_layout
+ assert dilation == (1, 1), "not support dilate now"
+
+ if (groups == 1) and (
+ (data_layout == "NCHW" and kernel_layout == "IOHW")
+ or (data_layout == "NCHW4c" and kernel_layout == "IOHW4o")
+ or (data_layout == "NCHW" and kernel_layout == "IOHW4o")
+ ):
+ if len(kernel.shape) == 4:
+ oc, _, _, _ = get_const_tuple(kernel.shape)
+ else:
+ oc, _, _, _, _ = get_const_tuple(kernel.shape)
+ # We cannot use textures for case than number of channels is less than
4.
+ # So, we use compute functions from cuda.
+ if len(kernel.shape) == 4 and oc < 4:
Review Comment:
Probably can add a case for oc < 4.
##########
tests/python/relay/opencl_texture/test_conv2d_transpose_nchw_texture.py:
##########
@@ -0,0 +1,315 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import re
+import tvm
+import numpy as np
+from tvm import relay
+from tvm.relay import testing
+from tvm.contrib import utils
+from utils.adreno_utils import gpu_preprocess, build_run_compare,
build_run_compare_vm
+import pytest
+
+
+executor_type = tvm.testing.parameter("ge", "vm")
+dtype = tvm.testing.parameter("float32")
+
+
[email protected]_opencl
[email protected]_targets("opencl -device=adreno")
+def test_conv2d_transpose_adreno(remote, target, executor_type, dtype):
+ # Conv2d transpose test cases lists
+ trials = [
+ [4, 4, (1, 1), (2, 2), (1, 1), 64, (256, 100, 100), (False, False)],
+ [4, 4, (0, 0), (2, 2), (1, 1), 256, (32, 64, 64), (False, False)],
+ [3, 3, (0, 0), (2, 2), (1, 1), 64, (256, 100, 100), (True, True)],
+ [4, 4, (1, 1), (1, 1), (1, 1), 512, (16, 100, 100), (False, False)],
+ [5, 5, (2, 2), (2, 2), (1, 1), 4, (16, 100, 100), (True, False)],
+ [7, 7, (3, 3), (2, 2), (1, 1), 8, (4, 100, 100), (False, True)],
+ ]
+ ge_texture_scopes = [
+ ["", "global.texture", "global.texture-weight", "", ""],
+ ["", "global.texture", "global.texture-weight", "", ""],
+ ["", "global.texture", "global.texture-weight",
"global.texture-weight", "", ""],
+ ["", "global.texture", "global.texture-weight", "", ""],
+ ["", "global.texture", "global.texture-weight",
"global.texture-weight", "", ""],
+ ["", "global.texture", "global.texture-nhwc", "", ""],
+ ]
+ vm_texture_scopes = [
+ """
+ VM VirtualDevice[0]: device type 1, id 0 and mem_scope
+ VM VirtualDevice[1]: device type 4, id 0 and mem_scope
+ VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
+ VM VirtualDevice[3]: device type 4, id 0 and mem_scope
global.texture-weight
+ """,
+ """
+ VM VirtualDevice[0]: device type 1, id 0 and mem_scope
+ VM VirtualDevice[1]: device type 4, id 0 and mem_scope
+ VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
+ VM VirtualDevice[3]: device type 4, id 0 and mem_scope
global.texture-weight
+ """,
+ """
+ VM VirtualDevice[0]: device type 1, id 0 and mem_scope
+ VM VirtualDevice[1]: device type 4, id 0 and mem_scope
+ VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
+ VM VirtualDevice[3]: device type 4, id 0 and mem_scope
global.texture-weight
+ VM VirtualDevice[4]: device type 4, id 0 and mem_scope
global.texture-weight
+ """,
+ """
+ VM VirtualDevice[0]: device type 1, id 0 and mem_scope
+ VM VirtualDevice[1]: device type 4, id 0 and mem_scope
+ VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
+ VM VirtualDevice[3]: device type 4, id 0 and mem_scope
global.texture-weight
+ """,
+ """
+ VM VirtualDevice[0]: device type 1, id 0 and mem_scope
+ VM VirtualDevice[1]: device type 4, id 0 and mem_scope
+ VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
+ VM VirtualDevice[3]: device type 4, id 0 and mem_scope
global.texture-weight
+ VM VirtualDevice[4]: device type 4, id 0 and mem_scope
global.texture-weight
+ """,
+ """
+ VM VirtualDevice[0]: device type 1, id 0 and mem_scope
+ VM VirtualDevice[1]: device type 4, id 0 and mem_scope
+ VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
+ VM VirtualDevice[3]: device type 4, id 0 and mem_scope
global.texture-nhwc
+ """,
+ ]
+
+ for i, (
+ kernel_h,
+ kernel_w,
+ pad,
+ stride,
+ dilation,
+ out_channels,
+ shape,
+ composite,
+ ) in enumerate(trials):
+ shape = (1, *shape)
+ has_bias = composite[0]
+ has_activation = composite[1]
+ input_shape = shape
+ filter_shape = (shape[1], out_channels, kernel_w, kernel_h)
+ x = relay.var("data", shape=input_shape, dtype=dtype)
+ w = relay.var("weight", shape=filter_shape, dtype=dtype)
+ inputs = [x, w]
+ y = relay.nn.conv2d_transpose(
+ x,
+ w,
+ channels=out_channels,
+ kernel_size=(kernel_w, kernel_h),
+ strides=stride,
+ padding=pad,
+ kernel_layout="IOHW",
+ data_layout="NCHW",
+ dilation=dilation,
+ )
+
+ np.random.seed(0)
+ initializer = relay.testing.init.Xavier()
+ filter_data = np.zeros(filter_shape).astype(dtype)
+ initializer("weight", filter_data)
+ params1 = {
+ "weight": tvm.nd.array(filter_data),
+ }
+
+ if has_bias:
+ b = relay.var("bias", shape=(out_channels,), dtype=dtype)
+ y = relay.nn.bias_add(y, b, axis=1)
+ inputs.append(b)
+ bias_data = np.zeros((out_channels,)).astype(dtype)
+ initializer("bias", bias_data)
+ params1["bias"] = tvm.nd.array(bias_data)
+ if has_activation:
+ y = relay.nn.relu(y)
+
+ mod = relay.Function(inputs, y)
+ if executor_type == "ge":
+ build_run_compare(
+ remote,
+ mod,
+ params1,
+ {"data": input_shape},
+ {"data": dtype},
+ target,
+ ge_texture_scopes[i],
+ gpu_preprocess,
+ )
+ else:
+ build_run_compare_vm(
+ remote,
+ mod,
+ params1,
+ {"data": input_shape},
+ {"data": dtype},
+ target,
+ vm_texture_scopes[i],
+ gpu_preprocess,
Review Comment:
Can we add few cases without explicit preprocessing ?
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