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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/05/11 21:32:59 UTC
[GitHub] [tvm] mehrdadh opened a new pull request, #11279: [Hexagon] Add USMP tests
mehrdadh opened a new pull request, #11279:
URL: https://github.com/apache/tvm/pull/11279
This PR adds USMP tests for hexagon using `LLVM` and `C` codegen.
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[GitHub] [tvm] csullivan commented on a diff in pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
csullivan commented on code in PR #11279:
URL: https://github.com/apache/tvm/pull/11279#discussion_r870777584
##########
tests/python/contrib/test_hexagon/test_usmp.py:
##########
@@ -0,0 +1,110 @@
+# 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 pytest
+import numpy as np
+
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+from tvm.contrib.hexagon.session import Session
+from tvm.testing.usmp import check_for_no_tvm_backendallocworkspace_calls
+
+from .conftest import requires_hexagon_toolchain
+
+
+@requires_hexagon_toolchain
+def test_conv2d(hexagon_session: Session, aot_host_target, aot_target):
+ dtype = "float32"
+ input_shape = (1, 8, 8, 3)
+ w1_shape = (5, 5, 3, 1)
+ w2_shape = (5, 5, 1, 3)
+ data = relay.var("data", relay.TensorType(input_shape, dtype))
+ weight1 = relay.var("weight1", relay.TensorType(w1_shape, dtype))
+ weight2 = relay.var("weight2", relay.TensorType(w2_shape, dtype))
+ y1 = relay.nn.conv2d(
+ data,
+ weight1,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ y2 = relay.nn.conv2d(
+ y1,
+ weight2,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ f = relay.Function([data, weight1, weight2], y2)
+ relay_mod = tvm.IRModule.from_expr(f)
+ relay_mod = relay.transform.InferType()(relay_mod)
+
+ weight1_data = np.random.rand(w1_shape[0], w1_shape[1], w1_shape[2], w1_shape[3]).astype(
+ dtype=dtype
+ )
+ weight2_data = np.random.rand(w2_shape[0], w2_shape[1], w2_shape[2], w2_shape[3]).astype(
+ dtype=dtype
+ )
+ input_data = np.random.rand(
+ input_shape[0], input_shape[1], input_shape[2], input_shape[3]
+ ).astype(dtype=dtype)
+
+ params = {"weight1": weight1_data, "weight2": weight2_data}
+ inputs = {"data": input_data}
+
+ with tvm.transform.PassContext(opt_level=3, config={"tir.usmp.enable": True}):
Review Comment:
Can we also verify in this test that without this pass we do detect TVMBackendAllocWorkspace and then with this pass we detect that they no longer are present?
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[GitHub] [tvm] csullivan commented on a diff in pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
csullivan commented on code in PR #11279:
URL: https://github.com/apache/tvm/pull/11279#discussion_r871773235
##########
tests/python/contrib/test_hexagon/test_usmp.py:
##########
@@ -0,0 +1,112 @@
+# 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 pytest
+import numpy as np
+
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+from tvm.contrib.hexagon.session import Session
+from tvm.testing.usmp import is_tvm_backendallocworkspace_calls
+
+from .conftest import requires_hexagon_toolchain
+
+usmp_enabled = tvm.testing.parameter(False, True)
+
+
+@requires_hexagon_toolchain
+def test_conv2d(hexagon_session: Session, aot_host_target, aot_target, usmp_enabled):
+ dtype = "float32"
+ input_shape = (1, 8, 8, 3)
+ w1_shape = (5, 5, 3, 1)
+ w2_shape = (5, 5, 1, 3)
+ data = relay.var("data", relay.TensorType(input_shape, dtype))
+ weight1 = relay.var("weight1", relay.TensorType(w1_shape, dtype))
+ weight2 = relay.var("weight2", relay.TensorType(w2_shape, dtype))
+ y1 = relay.nn.conv2d(
+ data,
+ weight1,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ y2 = relay.nn.conv2d(
+ y1,
+ weight2,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ f = relay.Function([data, weight1, weight2], y2)
+ relay_mod = tvm.IRModule.from_expr(f)
+ relay_mod = relay.transform.InferType()(relay_mod)
+
+ weight1_data = np.random.rand(w1_shape[0], w1_shape[1], w1_shape[2], w1_shape[3]).astype(
+ dtype=dtype
+ )
+ weight2_data = np.random.rand(w2_shape[0], w2_shape[1], w2_shape[2], w2_shape[3]).astype(
+ dtype=dtype
+ )
+ input_data = np.random.rand(
+ input_shape[0], input_shape[1], input_shape[2], input_shape[3]
+ ).astype(dtype=dtype)
+
+ params = {"weight1": weight1_data, "weight2": weight2_data}
+ inputs = {"data": input_data}
+
+ with tvm.transform.PassContext(opt_level=3, config={"tir.usmp.enable": usmp_enabled}):
+ lowered = tvm.relay.build(
+ relay_mod,
+ params=params,
+ target=tvm.target.Target(aot_target, host=aot_host_target),
+ runtime=Runtime("cpp"),
+ executor=Executor("aot", {"unpacked-api": False, "interface-api": "packed"}),
+ )
+
+ assert is_tvm_backendallocworkspace_calls(lowered.lib) == usmp_enabled
Review Comment:
`is_tvm_backendallocworkspace_calls(lowered.lib)` returns True if there are backendalloc workspace calls yes?
So if `usmp_enabled = True`, then `is_tvm_backendallocworkspace_calls(lowered.lib)` should return False.
But that's the opposite of what's written, what did I miss?
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[GitHub] [tvm] csullivan commented on a diff in pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
csullivan commented on code in PR #11279:
URL: https://github.com/apache/tvm/pull/11279#discussion_r870778381
##########
tests/python/contrib/test_hexagon/test_usmp.py:
##########
@@ -0,0 +1,110 @@
+# 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 pytest
+import numpy as np
+
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+from tvm.contrib.hexagon.session import Session
+from tvm.testing.usmp import check_for_no_tvm_backendallocworkspace_calls
+
+from .conftest import requires_hexagon_toolchain
+
+
+@requires_hexagon_toolchain
+def test_conv2d(hexagon_session: Session, aot_host_target, aot_target):
+ dtype = "float32"
+ input_shape = (1, 8, 8, 3)
+ w1_shape = (5, 5, 3, 1)
+ w2_shape = (5, 5, 1, 3)
+ data = relay.var("data", relay.TensorType(input_shape, dtype))
+ weight1 = relay.var("weight1", relay.TensorType(w1_shape, dtype))
+ weight2 = relay.var("weight2", relay.TensorType(w2_shape, dtype))
+ y1 = relay.nn.conv2d(
+ data,
+ weight1,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ y2 = relay.nn.conv2d(
+ y1,
+ weight2,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ f = relay.Function([data, weight1, weight2], y2)
+ relay_mod = tvm.IRModule.from_expr(f)
+ relay_mod = relay.transform.InferType()(relay_mod)
+
+ weight1_data = np.random.rand(w1_shape[0], w1_shape[1], w1_shape[2], w1_shape[3]).astype(
+ dtype=dtype
+ )
+ weight2_data = np.random.rand(w2_shape[0], w2_shape[1], w2_shape[2], w2_shape[3]).astype(
+ dtype=dtype
+ )
+ input_data = np.random.rand(
+ input_shape[0], input_shape[1], input_shape[2], input_shape[3]
+ ).astype(dtype=dtype)
+
+ params = {"weight1": weight1_data, "weight2": weight2_data}
+ inputs = {"data": input_data}
+
+ with tvm.transform.PassContext(opt_level=3, config={"tir.usmp.enable": True}):
Review Comment:
I suspect we should because of the padding in conv2d as long as it isn't inlined, but worth checking just in case.
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[GitHub] [tvm] mehrdadh commented on a diff in pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on code in PR #11279:
URL: https://github.com/apache/tvm/pull/11279#discussion_r870783178
##########
tests/python/contrib/test_hexagon/test_usmp.py:
##########
@@ -0,0 +1,110 @@
+# 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 pytest
+import numpy as np
+
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+from tvm.contrib.hexagon.session import Session
+from tvm.testing.usmp import check_for_no_tvm_backendallocworkspace_calls
+
+from .conftest import requires_hexagon_toolchain
+
+
+@requires_hexagon_toolchain
+def test_conv2d(hexagon_session: Session, aot_host_target, aot_target):
+ dtype = "float32"
+ input_shape = (1, 8, 8, 3)
+ w1_shape = (5, 5, 3, 1)
+ w2_shape = (5, 5, 1, 3)
+ data = relay.var("data", relay.TensorType(input_shape, dtype))
+ weight1 = relay.var("weight1", relay.TensorType(w1_shape, dtype))
+ weight2 = relay.var("weight2", relay.TensorType(w2_shape, dtype))
+ y1 = relay.nn.conv2d(
+ data,
+ weight1,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ y2 = relay.nn.conv2d(
+ y1,
+ weight2,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ f = relay.Function([data, weight1, weight2], y2)
+ relay_mod = tvm.IRModule.from_expr(f)
+ relay_mod = relay.transform.InferType()(relay_mod)
+
+ weight1_data = np.random.rand(w1_shape[0], w1_shape[1], w1_shape[2], w1_shape[3]).astype(
+ dtype=dtype
+ )
+ weight2_data = np.random.rand(w2_shape[0], w2_shape[1], w2_shape[2], w2_shape[3]).astype(
+ dtype=dtype
+ )
+ input_data = np.random.rand(
+ input_shape[0], input_shape[1], input_shape[2], input_shape[3]
+ ).astype(dtype=dtype)
+
+ params = {"weight1": weight1_data, "weight2": weight2_data}
+ inputs = {"data": input_data}
+
+ with tvm.transform.PassContext(opt_level=3, config={"tir.usmp.enable": True}):
Review Comment:
yes, will add that.
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[GitHub] [tvm] mehrdadh commented on a diff in pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on code in PR #11279:
URL: https://github.com/apache/tvm/pull/11279#discussion_r871800670
##########
tests/python/contrib/test_hexagon/test_usmp.py:
##########
@@ -0,0 +1,112 @@
+# 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 pytest
+import numpy as np
+
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+from tvm.contrib.hexagon.session import Session
+from tvm.testing.usmp import is_tvm_backendallocworkspace_calls
+
+from .conftest import requires_hexagon_toolchain
+
+usmp_enabled = tvm.testing.parameter(False, True)
+
+
+@requires_hexagon_toolchain
+def test_conv2d(hexagon_session: Session, aot_host_target, aot_target, usmp_enabled):
+ dtype = "float32"
+ input_shape = (1, 8, 8, 3)
+ w1_shape = (5, 5, 3, 1)
+ w2_shape = (5, 5, 1, 3)
+ data = relay.var("data", relay.TensorType(input_shape, dtype))
+ weight1 = relay.var("weight1", relay.TensorType(w1_shape, dtype))
+ weight2 = relay.var("weight2", relay.TensorType(w2_shape, dtype))
+ y1 = relay.nn.conv2d(
+ data,
+ weight1,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ y2 = relay.nn.conv2d(
+ y1,
+ weight2,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ f = relay.Function([data, weight1, weight2], y2)
+ relay_mod = tvm.IRModule.from_expr(f)
+ relay_mod = relay.transform.InferType()(relay_mod)
+
+ weight1_data = np.random.rand(w1_shape[0], w1_shape[1], w1_shape[2], w1_shape[3]).astype(
+ dtype=dtype
+ )
+ weight2_data = np.random.rand(w2_shape[0], w2_shape[1], w2_shape[2], w2_shape[3]).astype(
+ dtype=dtype
+ )
+ input_data = np.random.rand(
+ input_shape[0], input_shape[1], input_shape[2], input_shape[3]
+ ).astype(dtype=dtype)
+
+ params = {"weight1": weight1_data, "weight2": weight2_data}
+ inputs = {"data": input_data}
+
+ with tvm.transform.PassContext(opt_level=3, config={"tir.usmp.enable": usmp_enabled}):
+ lowered = tvm.relay.build(
+ relay_mod,
+ params=params,
+ target=tvm.target.Target(aot_target, host=aot_host_target),
+ runtime=Runtime("cpp"),
+ executor=Executor("aot", {"unpacked-api": False, "interface-api": "packed"}),
+ )
+
+ assert is_tvm_backendallocworkspace_calls(lowered.lib) == usmp_enabled
Review Comment:
You are absolutely right, thanks for catching this!
The issue was the semantic of is_tvm_backendallocworkspace_calls, it had the opposite semantic of its name.
##########
tests/python/contrib/test_hexagon/test_usmp.py:
##########
@@ -0,0 +1,112 @@
+# 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 pytest
+import numpy as np
+
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+from tvm.contrib.hexagon.session import Session
+from tvm.testing.usmp import is_tvm_backendallocworkspace_calls
+
+from .conftest import requires_hexagon_toolchain
+
+usmp_enabled = tvm.testing.parameter(False, True)
+
+
+@requires_hexagon_toolchain
+def test_conv2d(hexagon_session: Session, aot_host_target, aot_target, usmp_enabled):
+ dtype = "float32"
+ input_shape = (1, 8, 8, 3)
+ w1_shape = (5, 5, 3, 1)
+ w2_shape = (5, 5, 1, 3)
+ data = relay.var("data", relay.TensorType(input_shape, dtype))
+ weight1 = relay.var("weight1", relay.TensorType(w1_shape, dtype))
+ weight2 = relay.var("weight2", relay.TensorType(w2_shape, dtype))
+ y1 = relay.nn.conv2d(
+ data,
+ weight1,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ y2 = relay.nn.conv2d(
+ y1,
+ weight2,
+ padding=(2, 2),
+ kernel_size=(5, 5),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="float32",
+ )
+ f = relay.Function([data, weight1, weight2], y2)
+ relay_mod = tvm.IRModule.from_expr(f)
+ relay_mod = relay.transform.InferType()(relay_mod)
+
+ weight1_data = np.random.rand(w1_shape[0], w1_shape[1], w1_shape[2], w1_shape[3]).astype(
+ dtype=dtype
+ )
+ weight2_data = np.random.rand(w2_shape[0], w2_shape[1], w2_shape[2], w2_shape[3]).astype(
+ dtype=dtype
+ )
+ input_data = np.random.rand(
+ input_shape[0], input_shape[1], input_shape[2], input_shape[3]
+ ).astype(dtype=dtype)
+
+ params = {"weight1": weight1_data, "weight2": weight2_data}
+ inputs = {"data": input_data}
+
+ with tvm.transform.PassContext(opt_level=3, config={"tir.usmp.enable": usmp_enabled}):
+ lowered = tvm.relay.build(
+ relay_mod,
+ params=params,
+ target=tvm.target.Target(aot_target, host=aot_host_target),
+ runtime=Runtime("cpp"),
+ executor=Executor("aot", {"unpacked-api": False, "interface-api": "packed"}),
+ )
+
+ assert is_tvm_backendallocworkspace_calls(lowered.lib) == usmp_enabled
Review Comment:
addressed here: https://github.com/apache/tvm/pull/11279/commits/53912b5a96d164cc88d72fecb2cc6805e8f6ed32
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[GitHub] [tvm] mehrdadh commented on pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on PR #11279:
URL: https://github.com/apache/tvm/pull/11279#issuecomment-1126234405
@csullivan PTAL, thanks!
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[GitHub] [tvm] csullivan merged pull request #11279: [Hexagon] Add USMP tests
Posted by GitBox <gi...@apache.org>.
csullivan merged PR #11279:
URL: https://github.com/apache/tvm/pull/11279
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