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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/04/27 18:02:48 UTC
[GitHub] [tvm] mehrdadh commented on a diff in pull request #11138: [Hexagon] Add test for depthwise conv2d schedule
mehrdadh commented on code in PR #11138:
URL: https://github.com/apache/tvm/pull/11138#discussion_r860102317
##########
tests/python/contrib/test_hexagon/topi/test_depthwise_conv2d.py:
##########
@@ -0,0 +1,296 @@
+# 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 sys
+
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+import tvm.topi.testing
+
+from tvm import te, topi
+from tvm.topi.utils import get_const_tuple
+from tvm.topi.nn.utils import get_pad_tuple
+from ..conftest import requires_hexagon_toolchain
+
+
+random_seed = tvm.testing.parameter(0)
+
+in_dtype, out_dtype = tvm.testing.parameters(
+ ("float32", "float32"),
+)
+
+
+@tvm.testing.fixture
+def input_shape(layout, batch, in_channel, in_size, filter_shape):
+ if layout == "NCHW":
+ return (batch, in_channel, in_size, in_size)
+ elif layout == "NHWC":
+ return (batch, in_size, in_size, in_channel)
+ elif layout == "NCHWc":
+ oc_block = filter_shape[-1]
+ ic_block = next(bn for bn in range(oc_block, 0, -1) if in_channel % bn == 0)
+ return (batch, in_channel // ic_block, in_size, in_size, ic_block)
+
+
+@tvm.testing.fixture
+def filter_shape(layout, in_channel, channel_multiplier, kernel):
+ filter_channel = in_channel
+ if layout == "NCHW":
+ return (filter_channel, channel_multiplier, kernel, kernel)
+ elif layout == "NHWC":
+ return (kernel, kernel, filter_channel, channel_multiplier)
+ elif layout == "NCHWc":
+ out_channel = in_channel * channel_multiplier
+ # For testing the functionality, we choose an arbitrary block
+ # size that can divide out_channel, regardless of the
+ # performance.
+ oc_block = next(bn for bn in range(16, 0, -1) if out_channel % bn == 0)
+ return (out_channel // oc_block, 1, kernel, kernel, 1, oc_block)
+
+
+@tvm.testing.fixture
+def scale_shape(layout, in_channel, channel_multiplier, filter_shape):
+ out_channel = in_channel * channel_multiplier
+
+ if layout in ("NCHW", "NHWC"):
+ return (out_channel,)
+
+ if layout == "NCHWc":
+ oc_block = filter_shape[-1]
+ return (out_channel // oc_block, oc_block)
+
+ raise ValueError("Unknown layout {}".format(layout))
+
+
+@tvm.testing.fixture
+def shift_shape(scale_shape):
+ return scale_shape
+
+
+@tvm.testing.fixture(cache_return_value=True)
+def ref_data(
+ random_seed,
+ in_dtype,
+ out_dtype,
+ layout,
+ input_shape,
+ filter_shape,
+ dilation,
+ stride,
+ padding,
+ scale_shape,
+ shift_shape,
+ use_scale_shift,
+ apply_relu,
+):
+ np.random.seed(random_seed)
+
+ print(input_shape)
+
+ # scipy.signal.convolve2d does not support float16 data types, and
+ # the python fallback is too slow for general use. Computing
+ # ref_data in float32 will have fewer rounding errors than the TVM
+ # float16 compute, but those vary based on schedule anyways.
+ conv_dtype = "float32" if in_dtype == "float16" else in_dtype
+
+ input_np = np.random.uniform(size=input_shape).astype(in_dtype)
+ filter_np = np.random.uniform(size=filter_shape).astype(in_dtype)
+ scale_np = np.random.uniform(size=scale_shape).astype(out_dtype)
+ shift_np = np.random.uniform(size=shift_shape).astype(out_dtype)
+ if layout == "NCHW":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nchw
+ dilation = (1, 1, dilation, dilation)
+ reshape = (1, -1, 1, 1)
+ elif layout == "NHWC":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nhwc
+ dilation = (dilation, dilation, 1, 1)
+ reshape = (1, 1, 1, -1)
+ elif layout == "NCHWc":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nchwc
+ dilation = (1, 1, dilation, dilation, 1, 1)
+ reshape = (1, scale_shape[0], 1, 1, scale_shape[1])
+
+ dilated_filter_np = tvm.topi.testing.dilate_python(filter_np, dilation)
+ output_np = np_depthwise_conv2d(
+ input_np.astype(conv_dtype), dilated_filter_np.astype(conv_dtype), stride, padding
+ ).astype(out_dtype)
+
+ if use_scale_shift:
+ output_np = output_np * scale_np.reshape(reshape) + shift_np.reshape(reshape)
+ if apply_relu:
+ output_np = np.maximum(output_np, 0)
+
+ return (
+ input_np,
+ filter_np,
+ scale_np,
+ shift_np,
+ output_np,
+ )
+
+
+class BaseDepthwiseConv2D:
+ """Provides the test_conv2d test function, to be used by other test classes.
+
+ Test parameter sets are split out into different classes for
+ readability (e.g. used for mobilenet), and for restrictions
+ (e.g. implemented only for llvm).
+ """
+
+ use_scale_shift = tvm.testing.parameter(False, ids=["no_scale_shift"])
Review Comment:
can you move these two to `TestDepthwiseConv2D_MobilenetWorkloads` to make the test more readable? Similar to this line: https://github.com/apache/tvm/pull/11138/files#diff-f7a72e6fc41da6c6de2cfe56dd2dbf40ea4765422fe03de9a8e210014b3946cfR288
##########
tests/python/contrib/test_hexagon/topi/test_depthwise_conv2d.py:
##########
@@ -0,0 +1,296 @@
+# 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 sys
+
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+import tvm.topi.testing
+
+from tvm import te, topi
+from tvm.topi.utils import get_const_tuple
+from tvm.topi.nn.utils import get_pad_tuple
+from ..conftest import requires_hexagon_toolchain
+
+
+random_seed = tvm.testing.parameter(0)
+
+in_dtype, out_dtype = tvm.testing.parameters(
+ ("float32", "float32"),
+)
+
+
+@tvm.testing.fixture
+def input_shape(layout, batch, in_channel, in_size, filter_shape):
+ if layout == "NCHW":
+ return (batch, in_channel, in_size, in_size)
+ elif layout == "NHWC":
+ return (batch, in_size, in_size, in_channel)
+ elif layout == "NCHWc":
+ oc_block = filter_shape[-1]
+ ic_block = next(bn for bn in range(oc_block, 0, -1) if in_channel % bn == 0)
+ return (batch, in_channel // ic_block, in_size, in_size, ic_block)
+
+
+@tvm.testing.fixture
+def filter_shape(layout, in_channel, channel_multiplier, kernel):
+ filter_channel = in_channel
+ if layout == "NCHW":
+ return (filter_channel, channel_multiplier, kernel, kernel)
+ elif layout == "NHWC":
+ return (kernel, kernel, filter_channel, channel_multiplier)
+ elif layout == "NCHWc":
+ out_channel = in_channel * channel_multiplier
+ # For testing the functionality, we choose an arbitrary block
+ # size that can divide out_channel, regardless of the
+ # performance.
+ oc_block = next(bn for bn in range(16, 0, -1) if out_channel % bn == 0)
+ return (out_channel // oc_block, 1, kernel, kernel, 1, oc_block)
+
+
+@tvm.testing.fixture
+def scale_shape(layout, in_channel, channel_multiplier, filter_shape):
+ out_channel = in_channel * channel_multiplier
+
+ if layout in ("NCHW", "NHWC"):
+ return (out_channel,)
+
+ if layout == "NCHWc":
+ oc_block = filter_shape[-1]
+ return (out_channel // oc_block, oc_block)
+
+ raise ValueError("Unknown layout {}".format(layout))
+
+
+@tvm.testing.fixture
+def shift_shape(scale_shape):
+ return scale_shape
+
+
+@tvm.testing.fixture(cache_return_value=True)
+def ref_data(
+ random_seed,
+ in_dtype,
+ out_dtype,
+ layout,
+ input_shape,
+ filter_shape,
+ dilation,
+ stride,
+ padding,
+ scale_shape,
+ shift_shape,
+ use_scale_shift,
+ apply_relu,
+):
+ np.random.seed(random_seed)
+
+ print(input_shape)
+
+ # scipy.signal.convolve2d does not support float16 data types, and
+ # the python fallback is too slow for general use. Computing
+ # ref_data in float32 will have fewer rounding errors than the TVM
+ # float16 compute, but those vary based on schedule anyways.
+ conv_dtype = "float32" if in_dtype == "float16" else in_dtype
+
+ input_np = np.random.uniform(size=input_shape).astype(in_dtype)
+ filter_np = np.random.uniform(size=filter_shape).astype(in_dtype)
+ scale_np = np.random.uniform(size=scale_shape).astype(out_dtype)
+ shift_np = np.random.uniform(size=shift_shape).astype(out_dtype)
+ if layout == "NCHW":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nchw
+ dilation = (1, 1, dilation, dilation)
+ reshape = (1, -1, 1, 1)
+ elif layout == "NHWC":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nhwc
+ dilation = (dilation, dilation, 1, 1)
+ reshape = (1, 1, 1, -1)
+ elif layout == "NCHWc":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nchwc
+ dilation = (1, 1, dilation, dilation, 1, 1)
+ reshape = (1, scale_shape[0], 1, 1, scale_shape[1])
+
+ dilated_filter_np = tvm.topi.testing.dilate_python(filter_np, dilation)
+ output_np = np_depthwise_conv2d(
+ input_np.astype(conv_dtype), dilated_filter_np.astype(conv_dtype), stride, padding
+ ).astype(out_dtype)
+
+ if use_scale_shift:
+ output_np = output_np * scale_np.reshape(reshape) + shift_np.reshape(reshape)
+ if apply_relu:
+ output_np = np.maximum(output_np, 0)
+
+ return (
+ input_np,
+ filter_np,
+ scale_np,
+ shift_np,
+ output_np,
+ )
+
+
+class BaseDepthwiseConv2D:
+ """Provides the test_conv2d test function, to be used by other test classes.
+
+ Test parameter sets are split out into different classes for
+ readability (e.g. used for mobilenet), and for restrictions
+ (e.g. implemented only for llvm).
+ """
+
+ use_scale_shift = tvm.testing.parameter(False, ids=["no_scale_shift"])
+ apply_relu = tvm.testing.parameter(False, ids=["no_relu"])
+
+ @requires_hexagon_toolchain
+ def test_conv2d(
+ self,
+ hexagon_session,
+ in_dtype,
+ out_dtype,
+ layout,
+ input_shape,
+ filter_shape,
+ scale_shape,
+ shift_shape,
+ use_scale_shift,
+ apply_relu,
+ batch,
+ in_channel,
+ channel_multiplier,
+ kernel,
+ stride,
+ padding,
+ dilation,
+ ref_data,
+ ):
+ target_hexagon = tvm.target.hexagon("v68")
+
+ # Transform the padding argument from 'str' to 'tuple' to
+ # match the "workload" tuple in TopHub. Which padding_args to
+ # use for each layout chosen to reproduce previous behavior.
+ if dilation == 1:
+ padding_args = get_pad_tuple(padding, (kernel, kernel))
+ padding_args_i = [0, 1, 2, 3] if layout == "NCHW" else [0, 1]
+ padding_args = [padding_args[i] for i in padding_args_i]
+ else:
+ padding_args = padding
+
+ # placeholder
+ Input = te.placeholder(input_shape, name="Input", dtype=in_dtype)
+ Filter = te.placeholder(filter_shape, name="Filter", dtype=in_dtype)
+ Scale = te.placeholder(scale_shape, name="Scale", dtype=out_dtype)
+ Shift = te.placeholder(shift_shape, name="Shift", dtype=out_dtype)
+
+ if layout == "NCHW":
+ topi_scale_shift = topi.nn.scale_shift_nchw
+ fcompute_args = (Input, Filter, stride, padding_args, dilation, out_dtype)
+
+ elif layout == "NHWC":
+ topi_scale_shift = topi.nn.scale_shift_nhwc
+ fcompute_args = (Input, Filter, stride, padding_args, dilation, out_dtype)
+
+ elif layout == "NCHWc":
+ topi_scale_shift = topi.nn.scale_shift_nchwc
+ in_layout = "NCHW{}c".format(input_shape[-1])
+ out_layout = "NCHW{}c".format(filter_shape[-1])
+ fcompute_args = (
+ Input,
+ Filter,
+ stride,
+ padding,
+ dilation,
+ in_layout,
+ out_layout,
+ out_dtype,
+ )
+
+ with tvm.target.Target(target_hexagon):
+ # Declare, build schedule
+ if layout == "NCHW":
+ fcompute = topi.nn.depthwise_conv2d_nchw
+ fschedule = topi.hexagon.schedule_depthwise_conv2d_nchw
+ elif layout == "NHWC":
+ fcompute = topi.nn.depthwise_conv2d_nhwc
+ fschedule = topi.hexagon.schedule_depthwise_conv2d_nhwc
+ C = fcompute(*fcompute_args)
+ if use_scale_shift:
+ C = topi_scale_shift(C, Scale, Shift)
+ if apply_relu:
+ C = topi.nn.relu(C)
+
+ s = fschedule([C])
+
+ # Build and run
+ f = tvm.build(
+ s,
+ [Input, Filter, Scale, Shift, C],
+ tvm.target.Target(target_hexagon, host=target_hexagon),
+ )
+ mod = hexagon_session.load_module(f)
+
+ input_np, filter_np, scale_np, shift_np, output_np = ref_data
+
+ dev = hexagon_session.device
+ input_tvm = tvm.nd.array(input_np, dev)
+ filter_tvm = tvm.nd.array(filter_np, dev)
+ scale_tvm = tvm.nd.array(scale_np, dev)
+ shift_tvm = tvm.nd.array(shift_np, dev)
+ output_tvm = tvm.nd.array(
+ np.zeros(shape=get_const_tuple(C.shape), dtype=C.dtype),
+ dev,
+ )
+
+ mod(input_tvm, filter_tvm, scale_tvm, shift_tvm, output_tvm)
+
+ tol = {"rtol": 1e-4, "atol": 1e-5}
+ tvm.testing.assert_allclose(output_np, output_tvm.numpy(), **tol)
+
+
+class TestDepthwiseConv2D_MobilenetWorkloads(BaseDepthwiseConv2D):
+ """Extra tests to verify functionality for workloads used by mobilenet."""
+
+ layout = tvm.testing.parameter("NCHW", "NHWC")
+
+ batch = tvm.testing.parameter(1)
+ channel_multiplier = tvm.testing.parameter(1)
+ kernel = tvm.testing.parameter(3)
+ padding = tvm.testing.parameter("SAME")
+ dilation = tvm.testing.parameter(1)
+
+ in_channel, in_size, stride = tvm.testing.parameters(
+ (32, 112, 1),
+ (64, 112, 2),
+ (128, 56, 1),
+ (128, 56, 2),
+ (256, 28, 1),
+ )
+
+
+class TestDepthwiseConv2D_More(BaseDepthwiseConv2D):
Review Comment:
can you rename this to `TestDepthwiseConv2D`?
##########
tests/python/contrib/test_hexagon/topi/test_depthwise_conv2d.py:
##########
@@ -0,0 +1,296 @@
+# 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 sys
+
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+import tvm.topi.testing
+
+from tvm import te, topi
+from tvm.topi.utils import get_const_tuple
+from tvm.topi.nn.utils import get_pad_tuple
+from ..conftest import requires_hexagon_toolchain
+
+
+random_seed = tvm.testing.parameter(0)
+
+in_dtype, out_dtype = tvm.testing.parameters(
+ ("float32", "float32"),
+)
+
+
+@tvm.testing.fixture
+def input_shape(layout, batch, in_channel, in_size, filter_shape):
+ if layout == "NCHW":
+ return (batch, in_channel, in_size, in_size)
+ elif layout == "NHWC":
+ return (batch, in_size, in_size, in_channel)
+ elif layout == "NCHWc":
+ oc_block = filter_shape[-1]
+ ic_block = next(bn for bn in range(oc_block, 0, -1) if in_channel % bn == 0)
+ return (batch, in_channel // ic_block, in_size, in_size, ic_block)
+
+
+@tvm.testing.fixture
+def filter_shape(layout, in_channel, channel_multiplier, kernel):
+ filter_channel = in_channel
+ if layout == "NCHW":
+ return (filter_channel, channel_multiplier, kernel, kernel)
+ elif layout == "NHWC":
+ return (kernel, kernel, filter_channel, channel_multiplier)
+ elif layout == "NCHWc":
+ out_channel = in_channel * channel_multiplier
+ # For testing the functionality, we choose an arbitrary block
+ # size that can divide out_channel, regardless of the
+ # performance.
+ oc_block = next(bn for bn in range(16, 0, -1) if out_channel % bn == 0)
+ return (out_channel // oc_block, 1, kernel, kernel, 1, oc_block)
+
+
+@tvm.testing.fixture
+def scale_shape(layout, in_channel, channel_multiplier, filter_shape):
+ out_channel = in_channel * channel_multiplier
+
+ if layout in ("NCHW", "NHWC"):
+ return (out_channel,)
+
+ if layout == "NCHWc":
+ oc_block = filter_shape[-1]
+ return (out_channel // oc_block, oc_block)
+
+ raise ValueError("Unknown layout {}".format(layout))
+
+
+@tvm.testing.fixture
+def shift_shape(scale_shape):
+ return scale_shape
+
+
+@tvm.testing.fixture(cache_return_value=True)
+def ref_data(
+ random_seed,
+ in_dtype,
+ out_dtype,
+ layout,
+ input_shape,
+ filter_shape,
+ dilation,
+ stride,
+ padding,
+ scale_shape,
+ shift_shape,
+ use_scale_shift,
+ apply_relu,
+):
+ np.random.seed(random_seed)
+
+ print(input_shape)
+
+ # scipy.signal.convolve2d does not support float16 data types, and
+ # the python fallback is too slow for general use. Computing
+ # ref_data in float32 will have fewer rounding errors than the TVM
+ # float16 compute, but those vary based on schedule anyways.
+ conv_dtype = "float32" if in_dtype == "float16" else in_dtype
+
+ input_np = np.random.uniform(size=input_shape).astype(in_dtype)
+ filter_np = np.random.uniform(size=filter_shape).astype(in_dtype)
+ scale_np = np.random.uniform(size=scale_shape).astype(out_dtype)
+ shift_np = np.random.uniform(size=shift_shape).astype(out_dtype)
+ if layout == "NCHW":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nchw
+ dilation = (1, 1, dilation, dilation)
+ reshape = (1, -1, 1, 1)
+ elif layout == "NHWC":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nhwc
+ dilation = (dilation, dilation, 1, 1)
+ reshape = (1, 1, 1, -1)
+ elif layout == "NCHWc":
+ np_depthwise_conv2d = tvm.topi.testing.depthwise_conv2d_python_nchwc
+ dilation = (1, 1, dilation, dilation, 1, 1)
+ reshape = (1, scale_shape[0], 1, 1, scale_shape[1])
+
+ dilated_filter_np = tvm.topi.testing.dilate_python(filter_np, dilation)
+ output_np = np_depthwise_conv2d(
+ input_np.astype(conv_dtype), dilated_filter_np.astype(conv_dtype), stride, padding
+ ).astype(out_dtype)
+
+ if use_scale_shift:
+ output_np = output_np * scale_np.reshape(reshape) + shift_np.reshape(reshape)
+ if apply_relu:
+ output_np = np.maximum(output_np, 0)
+
+ return (
+ input_np,
+ filter_np,
+ scale_np,
+ shift_np,
+ output_np,
+ )
+
+
+class BaseDepthwiseConv2D:
+ """Provides the test_conv2d test function, to be used by other test classes.
+
+ Test parameter sets are split out into different classes for
+ readability (e.g. used for mobilenet), and for restrictions
+ (e.g. implemented only for llvm).
+ """
+
+ use_scale_shift = tvm.testing.parameter(False, ids=["no_scale_shift"])
+ apply_relu = tvm.testing.parameter(False, ids=["no_relu"])
+
+ @requires_hexagon_toolchain
+ def test_conv2d(
+ self,
+ hexagon_session,
+ in_dtype,
+ out_dtype,
+ layout,
+ input_shape,
+ filter_shape,
+ scale_shape,
+ shift_shape,
+ use_scale_shift,
+ apply_relu,
+ batch,
+ in_channel,
+ channel_multiplier,
+ kernel,
+ stride,
+ padding,
+ dilation,
+ ref_data,
+ ):
+ target_hexagon = tvm.target.hexagon("v68")
+
+ # Transform the padding argument from 'str' to 'tuple' to
+ # match the "workload" tuple in TopHub. Which padding_args to
+ # use for each layout chosen to reproduce previous behavior.
+ if dilation == 1:
+ padding_args = get_pad_tuple(padding, (kernel, kernel))
+ padding_args_i = [0, 1, 2, 3] if layout == "NCHW" else [0, 1]
+ padding_args = [padding_args[i] for i in padding_args_i]
+ else:
+ padding_args = padding
+
+ # placeholder
+ Input = te.placeholder(input_shape, name="Input", dtype=in_dtype)
+ Filter = te.placeholder(filter_shape, name="Filter", dtype=in_dtype)
+ Scale = te.placeholder(scale_shape, name="Scale", dtype=out_dtype)
+ Shift = te.placeholder(shift_shape, name="Shift", dtype=out_dtype)
+
+ if layout == "NCHW":
+ topi_scale_shift = topi.nn.scale_shift_nchw
+ fcompute_args = (Input, Filter, stride, padding_args, dilation, out_dtype)
+
+ elif layout == "NHWC":
+ topi_scale_shift = topi.nn.scale_shift_nhwc
+ fcompute_args = (Input, Filter, stride, padding_args, dilation, out_dtype)
+
+ elif layout == "NCHWc":
+ topi_scale_shift = topi.nn.scale_shift_nchwc
+ in_layout = "NCHW{}c".format(input_shape[-1])
+ out_layout = "NCHW{}c".format(filter_shape[-1])
+ fcompute_args = (
+ Input,
+ Filter,
+ stride,
+ padding,
+ dilation,
+ in_layout,
+ out_layout,
+ out_dtype,
+ )
+
+ with tvm.target.Target(target_hexagon):
+ # Declare, build schedule
+ if layout == "NCHW":
+ fcompute = topi.nn.depthwise_conv2d_nchw
+ fschedule = topi.hexagon.schedule_depthwise_conv2d_nchw
+ elif layout == "NHWC":
+ fcompute = topi.nn.depthwise_conv2d_nhwc
+ fschedule = topi.hexagon.schedule_depthwise_conv2d_nhwc
+ C = fcompute(*fcompute_args)
+ if use_scale_shift:
+ C = topi_scale_shift(C, Scale, Shift)
+ if apply_relu:
+ C = topi.nn.relu(C)
+
+ s = fschedule([C])
+
+ # Build and run
+ f = tvm.build(
+ s,
+ [Input, Filter, Scale, Shift, C],
+ tvm.target.Target(target_hexagon, host=target_hexagon),
+ )
+ mod = hexagon_session.load_module(f)
+
+ input_np, filter_np, scale_np, shift_np, output_np = ref_data
+
+ dev = hexagon_session.device
+ input_tvm = tvm.nd.array(input_np, dev)
+ filter_tvm = tvm.nd.array(filter_np, dev)
+ scale_tvm = tvm.nd.array(scale_np, dev)
+ shift_tvm = tvm.nd.array(shift_np, dev)
+ output_tvm = tvm.nd.array(
+ np.zeros(shape=get_const_tuple(C.shape), dtype=C.dtype),
+ dev,
+ )
+
+ mod(input_tvm, filter_tvm, scale_tvm, shift_tvm, output_tvm)
+
+ tol = {"rtol": 1e-4, "atol": 1e-5}
+ tvm.testing.assert_allclose(output_np, output_tvm.numpy(), **tol)
+
+
+class TestDepthwiseConv2D_MobilenetWorkloads(BaseDepthwiseConv2D):
+ """Extra tests to verify functionality for workloads used by mobilenet."""
+
+ layout = tvm.testing.parameter("NCHW", "NHWC")
+
+ batch = tvm.testing.parameter(1)
+ channel_multiplier = tvm.testing.parameter(1)
+ kernel = tvm.testing.parameter(3)
+ padding = tvm.testing.parameter("SAME")
+ dilation = tvm.testing.parameter(1)
+
+ in_channel, in_size, stride = tvm.testing.parameters(
+ (32, 112, 1),
+ (64, 112, 2),
+ (128, 56, 1),
+ (128, 56, 2),
+ (256, 28, 1),
+ )
+
+
+class TestDepthwiseConv2D_More(BaseDepthwiseConv2D):
+
+ layout = tvm.testing.parameter("NCHW", "NHWC")
+ use_scale_shift = tvm.testing.parameter(True, False, ids=["with_scale_shift", "no_scale_shift"])
+ apply_relu = tvm.testing.parameter(True, False, ids=["with_relu", "no_relu"])
+
+ (batch, in_channel, in_size, channel_multiplier, kernel, stride) = tvm.testing.parameters(
+ (1, 64, 32, 1, 3, 1),
+ (1, 128, 64, 2, 5, 2),
+ )
+ padding = tvm.testing.parameter("VALID")
+ dilation = tvm.testing.parameter(1)
Review Comment:
Please add this line to the end of the file so we don't forget to add test for NCHWc format:
`TODO(hexagon-team): add TestDepthwiseConv2D_NCHWc test.`
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