[GitHub] [incubator-tvm] jcf94 commented on a change in pull request #6686: [AutoSchedule] Support multiple cache read and fix bugs
jcf94 commented on a change in pull request #6686:
URL: https://github.com/apache/incubator-tvm/pull/6686#discussion_r505237035
##
File path: src/auto_scheduler/compute_dag.cc
##
@@ -970,8 +1005,21 @@ void ComputeDAG::RewriteLayout(const Array&
transform_steps) {
} // end for placeholder
}// end for stage
p_dag->access_analyzer = AccessAnalyzer(p_dag->tensors);
- p_dag->ops = p_dag->access_analyzer->ops_topo_order;
+
+ Array out_ops;
+ for (const auto& op : p_dag->access_analyzer->ops_topo_order) {
+if (p_dag->access_analyzer.IsOutput(op)) {
+ out_ops.push_back(op);
+}
+ }
+
+ p_dag->ops.clear();
+ te::Schedule sch = te::create_schedule(out_ops);
+ for (auto stage : sch->stages) {
+p_dag->ops.push_back(stage->op);
+ }
p_dag->flop_ct = FlopEstimator().EstimateFlop(p_dag->ops);
+ p_dag->init_state = State(p_dag->ops);
Review comment:
We can delete Line 987 since it's added here.
Anyway, this does not matter. I'm doing some updating on layout_write, and
have also modified some code in this part, I'll refine the code after this PR's
merge. :)
##
File path: src/te/schedule/schedule_dataflow_rewrite.cc
##
@@ -138,6 +138,15 @@ Tensor Schedule::cache_read(const Tensor& tensor, const
std::string& scope,
}
os << "." << scope;
+ // when a schedule has multiple cache_read on the same tensor,
+ // we make sure their op names are unique. e.g., w.shared, w.shared.d,
w.shared.d.d
+ for (auto pair : (*this)->stage_map) {
+auto stage = pair.second;
+if (stage->op->name == os.str()) {
+ os << ".d";
Review comment:
Can we add a global map here and mark these name as "w.shared.0",
"w.shared.1"?
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[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6686: [AutoSchedule] Support multiple cache read and fix bugs
comaniac commented on a change in pull request #6686:
URL: https://github.com/apache/incubator-tvm/pull/6686#discussion_r505272389
##
File path: src/te/schedule/schedule_dataflow_rewrite.cc
##
@@ -138,6 +138,15 @@ Tensor Schedule::cache_read(const Tensor& tensor, const
std::string& scope,
}
os << "." << scope;
+ // when a schedule has multiple cache_read on the same tensor,
+ // we make sure their op names are unique. e.g., w.shared, w.shared.d,
w.shared.d.d
+ for (auto pair : (*this)->stage_map) {
+auto stage = pair.second;
+if (stage->op->name == os.str()) {
+ os << ".d";
Review comment:
I was thinking about this too, but in this way we need to maintain
another set in Schedule. Consider this case doesn't happen frequently now, the
current solution should be sufficient. We can definitely use the approach you
suggested in the future after naming conflict becomes much common.
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[GitHub] [incubator-tvm] i24361 opened a new pull request #6688: [TUTORIAL][FIX] Fix VTA autotuning from tutorial fails with one P…
i24361 opened a new pull request #6688: URL: https://github.com/apache/incubator-tvm/pull/6688 [TUTORIAL][FIX] Fix VTA autotuning from tutorial fails with one PYNQ, but succeeds with two PYNQs https://discuss.tvm.apache.org/t/vta-workaround-for-autotuning-with-one-pynq-z1-board/8091 Autotuning can do without `remote`. I use this way to tune Resnet18 on PYNQ Z1 and Faster-RCNN on PYNQ ZCU104. Sometimes we only have one board available and this workaround works. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] liangfu commented on a change in pull request #6603: Add µTVM Zephyr support + QEMU regression test
liangfu commented on a change in pull request #6603:
URL: https://github.com/apache/incubator-tvm/pull/6603#discussion_r505293470
##
File path: tests/python/unittest/test_micro_artifact.py
##
@@ -0,0 +1,137 @@
+# 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.
+
+"""Unit tests for the artifact module."""
+
+import json
+import os
+import shutil
+
+from tvm.contrib import util
+from tvm.micro import artifact
+
+
+FILE_LIST = ["label1", "label2", "label12", "unlabelled"]
+
+
+TEST_METADATA = {"foo": "bar"}
+
+
+TEST_LABELS = {"label1": ["label1", "label12"], "label2": ["label2",
"label12"]}
+
+
+def build_artifact(artifact_path, immobile=False):
+os.mkdir(artifact_path)
+
+for f in FILE_LIST:
+with open(os.path.join(artifact_path, f), "w") as lib_f:
+lib_f.write(f"{f}\n")
+
+sub_dir = os.path.join(artifact_path, "sub_dir")
+os.mkdir(sub_dir)
+os.symlink("label1", os.path.join(artifact_path, "rel_symlink"))
+os.symlink("label2", os.path.join(artifact_path, "abs_symlink"), "label2")
+os.symlink(
+os.path.join(artifact_path, "sub_dir"), os.path.join(artifact_path,
"abs_dir_symlink")
+)
+
+art = artifact.Artifact(artifact_path, TEST_LABELS, TEST_METADATA,
immobile=immobile)
+
+return art
+
+
+def test_basic_functionality():
+temp_dir = util.tempdir()
+artifact_path = temp_dir.relpath("foo")
+art = build_artifact(artifact_path)
+
+assert art.abspath("bar") == os.path.join(artifact_path, "bar")
+
+for label, paths in TEST_LABELS.items():
+assert art.label(label) == paths
+assert art.label_abspath(label) == [os.path.join(artifact_path, p) for
p in paths]
+
+
+def test_archive():
+temp_dir = util.tempdir()
+art = build_artifact(temp_dir.relpath("foo"))
+
+# Create archive
+archive_path = art.archive(temp_dir.temp_dir)
+assert archive_path == temp_dir.relpath("foo.tar")
+
+# Inspect created archive
+unpack_dir = temp_dir.relpath("unpack")
+os.mkdir(unpack_dir)
+shutil.unpack_archive(archive_path, unpack_dir)
+
+for path in FILE_LIST:
+with open(os.path.join(unpack_dir, "foo", path)) as f:
+assert f.read() == f"{path}\n"
+
+with open(os.path.join(unpack_dir, "foo", "metadata.json")) as metadata_f:
+metadata = json.load(metadata_f)
+
+assert metadata["version"] == 2
+assert metadata["labelled_files"] == TEST_LABELS
+assert metadata["metadata"] == TEST_METADATA
+
+# Unarchive and verify basic functionality
+unarchive_base_dir = temp_dir.relpath("unarchive")
+unarch = artifact.Artifact.unarchive(archive_path, unarchive_base_dir)
+
+assert unarch.metadata == TEST_METADATA
+assert unarch.labelled_files == TEST_LABELS
+for f in FILE_LIST:
+assert os.path.exists(os.path.join(unarchive_base_dir, f))
+
+
+def test_metadata_only():
+temp_dir = util.tempdir()
+base_dir = temp_dir.relpath("foo")
+art = build_artifact(base_dir)
+
+artifact_path = art.archive(temp_dir.relpath("foo.artifact"),
metadata_only=True)
+unarch_base_dir = temp_dir.relpath("bar")
+unarch = artifact.Artifact.unarchive(artifact_path, unarch_base_dir)
+assert unarch.base_dir == base_dir
+
+for p in unarch.label_abspath("label1") + unarch.label_abspath("label2"):
+assert os.path.exists(p)
+
+os.unlink(art.abspath("label1"))
+with open(art.abspath("label2"), "w+") as f:
+f.write("changed line\n")
+
+try:
+artifact.Artifact.unarchive(artifact_path,
os.path.join(temp_dir.temp_dir, "bar2"))
+assert False, "unarchive should raise error"
+except artifact.ArchiveModifiedError as err:
+assert str(err) == (
+"Files in metadata-only archive have been modified:\n"
+" * label1: original file not found\n"
+" * label2: sha256 mismatch: expected "
+"6aa3c5668c8794c791400e19ecd7123949ded1616eafb0395acdd2d896354e83,
got "
+"ed87db21670a81819d65eccde87c5ae0243b2b61783bf77e9b27993be9a3eca0"
Review comment:
Just curious, why do we need to hard code these hash here?
This is an autom
[incubator-tvm] branch main updated (9564925 -> c7ff885)
This is an automated email from the ASF dual-hosted git repository.
liangfu pushed a change to branch main
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git.
from 9564925 [Relay][Frontend][Onnx] Allow A to B broadcasting of
batch_matmul and reverse strided slice (#6681)
add c7ff885 Add µTVM Zephyr support + QEMU regression test (#6603)
No new revisions were added by this update.
Summary of changes:
include/tvm/runtime/crt/error_codes.h | 1 +
include/tvm/runtime/crt/utvm_rpc_server.h | 24 +-
python/tvm/exec/rpc_server.py | 69 ---
python/tvm/micro/__init__.py | 2 +-
python/tvm/micro/artifact.py | 108 +++-
.../micro_kernel => micro/contrib}/__init__.py | 0
python/tvm/micro/contrib/base.py | 67 +++
python/tvm/micro/contrib/zephyr.py | 621 +
python/tvm/micro/debugger.py | 25 +-
python/tvm/micro/micro_binary.py | 15 +-
python/tvm/micro/micro_library.py | 13 +-
python/tvm/micro/session.py| 50 +-
python/tvm/micro/transport.py | 238
.../graph_tuner => micro/transport}/__init__.py| 15 +-
python/tvm/micro/transport/base.py | 299 ++
python/tvm/micro/transport/debug.py| 63 +++
python/tvm/micro/transport/file_descriptor.py | 105
python/tvm/micro/transport/subprocess.py | 67 +++
python/tvm/micro/transport/wakeup.py | 74 +++
src/runtime/crt/host/main.cc | 19 +-
src/runtime/crt/utvm_rpc_server/rpc_server.cc | 50 +-
src/runtime/micro/micro_session.cc | 136 -
tests/lint/check_file_type.py | 3 +
tests/micro/qemu/.gitignore| 2 +
tests/micro/qemu/test_zephyr.py| 143 +
tests/micro/qemu/zephyr-runtime/.gitignore | 3 +
tests/micro/qemu/zephyr-runtime/CMakeLists.txt | 27 +
.../micro/qemu/zephyr-runtime/crt/crt_config.h | 22 +-
.../qemu/zephyr-runtime/prj.conf} | 21 +-
.../zephyr-runtime/qemu-hack/qemu-system-i386} | 26 +-
.../micro/qemu/zephyr-runtime/sample.yaml | 12 +-
tests/micro/qemu/zephyr-runtime/src/main.c | 238
tests/python/unittest/test_crt.py | 3 +-
tests/python/unittest/test_micro_artifact.py | 137 +
tests/scripts/task_python_microtvm.sh | 9 +
35 files changed, 2244 insertions(+), 463 deletions(-)
copy python/tvm/{topi/arm_cpu/cortex_m7/micro_kernel =>
micro/contrib}/__init__.py (100%)
create mode 100644 python/tvm/micro/contrib/base.py
create mode 100644 python/tvm/micro/contrib/zephyr.py
delete mode 100644 python/tvm/micro/transport.py
copy python/tvm/{autotvm/graph_tuner => micro/transport}/__init__.py (69%)
create mode 100644 python/tvm/micro/transport/base.py
create mode 100644 python/tvm/micro/transport/debug.py
create mode 100644 python/tvm/micro/transport/file_descriptor.py
create mode 100644 python/tvm/micro/transport/subprocess.py
create mode 100644 python/tvm/micro/transport/wakeup.py
create mode 100644 tests/micro/qemu/.gitignore
create mode 100644 tests/micro/qemu/test_zephyr.py
create mode 100644 tests/micro/qemu/zephyr-runtime/.gitignore
create mode 100644 tests/micro/qemu/zephyr-runtime/CMakeLists.txt
copy src/runtime/crt/crt_config-template.h =>
tests/micro/qemu/zephyr-runtime/crt/crt_config.h (77%)
copy tests/{scripts/task_python_ethosn_tests.sh =>
micro/qemu/zephyr-runtime/prj.conf} (74%)
mode change 100755 => 100644
copy tests/{lint/cppdocs.sh =>
micro/qemu/zephyr-runtime/qemu-hack/qemu-system-i386} (64%)
copy conda/conda_build_config.yaml =>
tests/micro/qemu/zephyr-runtime/sample.yaml (88%)
create mode 100644 tests/micro/qemu/zephyr-runtime/src/main.c
create mode 100644 tests/python/unittest/test_micro_artifact.py
[GitHub] [incubator-tvm] liangfu merged pull request #6603: Add µTVM Zephyr support + QEMU regression test
liangfu merged pull request #6603: URL: https://github.com/apache/incubator-tvm/pull/6603 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] liangfu commented on pull request #6603: Add µTVM Zephyr support + QEMU regression test
liangfu commented on pull request #6603: URL: https://github.com/apache/incubator-tvm/pull/6603#issuecomment-708971948 Thanks @areusch @tqchen . This is now merged. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm-vta] liangfu edited a comment on pull request #9: [Hardware][OpenCL] Intelfocl support
liangfu edited a comment on pull request #9: URL: https://github.com/apache/incubator-tvm-vta/pull/9#issuecomment-706899033 ci tests failed on tsim seems to be unrelated, @zhanghaohit do you mind retrigger ci to see if it succeeds. (It was [successful previously](https://ci.tlcpack.ai/blue/organizations/jenkins/tvm-vta/detail/PR-12/1/pipeline).) 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm-vta] liangfu edited a comment on pull request #9: [Hardware][OpenCL] Intelfocl support
liangfu edited a comment on pull request #9: URL: https://github.com/apache/incubator-tvm-vta/pull/9#issuecomment-706899033 ci tests failed on tsim seems to be unrelated, @zhanghaohit do you mind retrigger ci to see if it passes? (It was [successful previously](https://ci.tlcpack.ai/blue/organizations/jenkins/tvm-vta/detail/PR-12/1/pipeline).) 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[incubator-tvm] branch ci-docker-staging updated: Completely disable ResNet
This is an automated email from the ASF dual-hosted git repository. jroesch pushed a commit to branch ci-docker-staging in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git The following commit(s) were added to refs/heads/ci-docker-staging by this push: new 5691c3c Completely disable ResNet 5691c3c is described below commit 5691c3c0e00eb6d0e4e4290cfb3e6b52a3ab106e Author: Jared Roesch AuthorDate: Thu Oct 15 01:47:08 2020 -0700 Completely disable ResNet --- rust/Cargo.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 28312a5..9935ce7 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -23,7 +23,6 @@ members = [ "tvm", "tvm/tests/basics", "tvm/tests/callback", - "tvm/examples/resnet", "tvm-graph-rt", "tvm-graph-rt/tests/test_tvm_basic", "tvm-graph-rt/tests/test_tvm_dso",
[GitHub] [incubator-tvm] ANSHUMAN87 commented on pull request #6685: [Relay][Frontend] SparseTensorDenseMatMul support for Tensorflow
ANSHUMAN87 commented on pull request #6685: URL: https://github.com/apache/incubator-tvm/pull/6685#issuecomment-709001815 Looks like Tensorflow version in CI is older, may be we need to bump it!!! 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] thilinikb commented on issue #4272: [VTA] Tutorial on how to deploy and execute model on device without RPC
thilinikb commented on issue #4272: URL: https://github.com/apache/incubator-tvm/issues/4272#issuecomment-709274690 @tmoreau89 @flip1995 Could you please point me to this document if it is available? 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tqchen merged pull request #6687: int32 pooling with int64 shapes
tqchen merged pull request #6687: URL: https://github.com/apache/incubator-tvm/pull/6687 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[incubator-tvm] branch main updated (c7ff885 -> b121278)
This is an automated email from the ASF dual-hosted git repository. tqchen pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from c7ff885 Add µTVM Zephyr support + QEMU regression test (#6603) add b121278 int32 pooling with int64 shapes (#6687) No new revisions were added by this update. Summary of changes: include/tvm/topi/nn/pooling.h | 42 ++- tests/python/relay/test_op_grad_level2.py | 75 +++--- tests/python/relay/test_op_level10.py | 22 tests/python/relay/test_op_level2.py | 87 +-- 4 files changed, 133 insertions(+), 93 deletions(-)
[GitHub] [incubator-tvm] tqchen commented on issue #6417: [FLAKY] relay/test_op_level1.py::test_binary_op failing intermittently
tqchen commented on issue #6417: URL: https://github.com/apache/incubator-tvm/issues/6417#issuecomment-709314955 ping @leandron :) 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tqchen opened a new issue #6689: [TEST][FLAKY] frontend/pytorch/test_forward.py::test_forward_nms
tqchen opened a new issue #6689: URL: https://github.com/apache/incubator-tvm/issues/6689 https://ci.tlcpack.ai/job/tvm/job/main/19/execution/node/380/log/ Could due to too close ties, perhaps we should construct nms test-cases differently that have no clear ties 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tqchen commented on issue #6689: [TEST][FLAKY] frontend/pytorch/test_forward.py::test_forward_nms
tqchen commented on issue #6689: URL: https://github.com/apache/incubator-tvm/issues/6689#issuecomment-709316506 cc @masahi @Laurawly 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] flip1995 commented on issue #4272: [VTA] Tutorial on how to deploy and execute model on device without RPC
flip1995 commented on issue #4272: URL: https://github.com/apache/incubator-tvm/issues/4272#issuecomment-709335331 Oh damn, I totally forgot about it. Basically you have to add those two lines to your python script, that runs your model on the PYNQ: ```python dll_path = "/home/xilinx/tvm/build/libvta.so" ctypes.CDLL(dll_path, ctypes.RTLD_GLOBAL) ``` before the `graph_runtime.create(..)` command. I hope this still works. I haven't used TVM/VTA for a long time now. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] flip1995 commented on issue #4272: [VTA] Tutorial on how to deploy and execute model on device without RPC
flip1995 commented on issue #4272:
URL: https://github.com/apache/incubator-tvm/issues/4272#issuecomment-709342481
Or to be more precise, you have to load your model like this:
```python
import ctypes
import tvm
from tvm.contrib import graph_runtime as runtime
libvta_path = "/home/xilinx/tvm/build/libvta.so"
ctypes.CDLL(libvta_path, ctypes.RTLD_GLOBAL)
# load compiled model
with open("graph.json", "r") as graph_file:
graph = graph_file.read()
with open("params.params", "rb") as params_file:
params = bytearray(params_file.read())
lib = tvm.module.load("./lib.tar")
ctx = tvm.ext_dev(0)
module = runtime.create(graph, lib, ctx)
module.load_params(params)
```
After that `module.run(**)` should work
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[GitHub] [incubator-tvm] masahi commented on issue #6689: [TEST][FLAKY] frontend/pytorch/test_forward.py::test_forward_nms
masahi commented on issue #6689: URL: https://github.com/apache/incubator-tvm/issues/6689#issuecomment-709392455 cc @yongwww 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] comaniac commented on pull request #6686: [AutoSchedule] Support multiple cache read and fix bugs
comaniac commented on pull request #6686: URL: https://github.com/apache/incubator-tvm/pull/6686#issuecomment-709412464 The failed case in CI seems related to #6417 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] anijain2305 commented on pull request #6670: [TFLite] Fix detection of crop in convert_batch_to_space_nd
anijain2305 commented on pull request #6670: URL: https://github.com/apache/incubator-tvm/pull/6670#issuecomment-709502035 Saw this issue a couple of months ago. This looks good. Thanks Trevor! 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] anijain2305 commented on pull request #6670: [TFLite] Fix detection of crop in convert_batch_to_space_nd
anijain2305 commented on pull request #6670: URL: https://github.com/apache/incubator-tvm/pull/6670#issuecomment-709502703 @siju-samuel Can you PTAL 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] trevor-m commented on pull request #6143: [Relay] support i64 indices
trevor-m commented on pull request #6143: URL: https://github.com/apache/incubator-tvm/pull/6143#issuecomment-709515877 FYI, I found that Keras MobileNetV2 model experiences a heavy perf regression with i64 indices enabled. With ON: 66.55801918395055 FPS With OFF: 435.48951121558594 FPS This is on an AWS m5.12xlarge instance. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] trevor-m edited a comment on pull request #6143: [Relay] support i64 indices
trevor-m edited a comment on pull request #6143: URL: https://github.com/apache/incubator-tvm/pull/6143#issuecomment-709515877 @hzfan FYI, I found that Keras MobileNetV2 model experiences a heavy perf regression with i64 indices enabled. With ON: 66.55801918395055 FPS With OFF: 435.48951121558594 FPS This is on an AWS m5.12xlarge instance. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] jroesch opened a new pull request #6690: [Docker] Update CI CPU and GPU images based on new Docker build files.
jroesch opened a new pull request #6690: URL: https://github.com/apache/incubator-tvm/pull/6690 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] jroesch commented on pull request #6690: [Docker] Update CI CPU and GPU images based on new Docker build files.
jroesch commented on pull request #6690: URL: https://github.com/apache/incubator-tvm/pull/6690#issuecomment-709535200 cc @tmoreau89 @tqchen @u99127 this should include all the newest changes. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6686: [AutoSchedule] Support multiple cache read and fix bugs
comaniac commented on a change in pull request #6686:
URL: https://github.com/apache/incubator-tvm/pull/6686#discussion_r505784167
##
File path: src/te/schedule/schedule_dataflow_rewrite.cc
##
@@ -138,6 +138,15 @@ Tensor Schedule::cache_read(const Tensor& tensor, const
std::string& scope,
}
os << "." << scope;
+ // when a schedule has multiple cache_read on the same tensor,
+ // we make sure their op names are unique. e.g., w.shared, w.shared.d,
w.shared.d.d
+ for (auto pair : (*this)->stage_map) {
+auto stage = pair.second;
+if (stage->op->name == os.str()) {
+ os << ".d";
Review comment:
I changed the naming to `w.shared`, `w_d.shared`, because we use
`StrEndsWith(op->name, ".shared")` in thread binding.
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[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6686: [AutoSchedule] Support multiple cache read and fix bugs
comaniac commented on a change in pull request #6686:
URL: https://github.com/apache/incubator-tvm/pull/6686#discussion_r505784167
##
File path: src/te/schedule/schedule_dataflow_rewrite.cc
##
@@ -138,6 +138,15 @@ Tensor Schedule::cache_read(const Tensor& tensor, const
std::string& scope,
}
os << "." << scope;
+ // when a schedule has multiple cache_read on the same tensor,
+ // we make sure their op names are unique. e.g., w.shared, w.shared.d,
w.shared.d.d
+ for (auto pair : (*this)->stage_map) {
+auto stage = pair.second;
+if (stage->op->name == os.str()) {
+ os << ".d";
Review comment:
I changed the naming to `w.shared`, `w.d.shared`, because we use
`StrEndsWith(op->name, ".shared")` in thread binding.
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[GitHub] [incubator-tvm] trevor-m opened a new issue #6691: [Performance] Large performance regression with int64 indices INDEX_DEFAULT_I64=ON (PR #6143)
trevor-m opened a new issue #6691:
URL: https://github.com/apache/incubator-tvm/issues/6691
I've started noticing a large performance regression affecting Keras
MobileNetV2 caused by `INDEX_DEFAULT_I64=ON` (PR #6143). This is on an AWS
m5.12xlarge instance.
INDEX_DEFAULT_I64 | Frames per second
| -
ON | 66.56
OFF | 435.49
I profiled the ops and found the slowdown comes from the
## Profile with `INDEX_DEFAULT_I64=OFF` (fast)
```
Node NameOps
Time(us) Time(%) Shape Inputs
Outputs
----
--- - --
---
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_7
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_7 64.7043.571(1, 9,
56, 56, 16)3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_6
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_6 53.3622.945(1, 2,
112, 112, 16) 3 1
fused_nn_pad_3 fused_nn_pad_3
50.5822.791(1, 6, 113, 113, 16) 1 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_5
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_5 47.8742.642(1, 6,
56, 56, 16)3 1
fused_nn_contrib_conv2d_NCHWc_add_clip_6
fused_nn_contrib_conv2d_NCHWc_add_clip_646.8282.584(1, 6,
112, 112, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_8
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_8 42.3642.338(1, 12,
28, 28, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_91
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_9 39.5542.183(1, 36,
14, 14, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_81
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_8 39.4182.175(1, 12,
28, 28, 16) 3 1
fused_nn_contrib_conv2d_NCHWc_add_add_4
fused_nn_contrib_conv2d_NCHWc_add_add_4 38.8712.145(1, 2,
56, 56, 12)4 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_9
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_9 37.9262.093(1, 36,
14, 14, 16) 3 1
fused_nn_contrib_conv2d_NCHWc_add_clip_5
fused_nn_contrib_conv2d_NCHWc_add_clip_537.4072.064(1, 9,
56, 56, 16)3 1
fused_nn_contrib_conv2d_NCHWc_add_clip_51
fused_nn_contrib_conv2d_NCHWc_add_clip_535.3491.951(1, 9,
56, 56, 16)3 1
fused_nn_contrib_conv2d_NCHWc_add_clip
fused_nn_contrib_conv2d_NCHWc_add_clip 34.6921.915(1, 80,
7, 7, 16) 3 1
fused_nn_contrib_conv2d_NCHWc_add_6
fused_nn_contrib_conv2d_NCHWc_add_6 34.0521.879(1, 1,
112, 112, 16) 3 1
fused_nn_contrib_conv2d_NCHWc_add
fused_nn_contrib_conv2d_NCHWc_add 33.58 1.853(1, 20,
7, 7, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_21
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_2 33.2981.838(1, 24,
14, 14, 16) 3 1
fused_nn_pad_2 fused_nn_pad_2
33.2011.832(1, 9, 57, 57, 16)1 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_22
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_2 33.0571.824(1, 24,
14, 14, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_2
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_2 33.0271.823(1, 24,
14, 14, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_23
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip_2 32.7871.809(1, 24,
14, 14, 16) 3 1
fused_nn_contrib_conv2d_NCHWc_add_5
fused_nn_contrib_conv2d_NCHWc_add_5 32.3321.784(1, 2,
56, 56, 12)3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip32.1561.775(1, 60,
7, 7, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip31.68 1.748(1, 60,
7, 7, 16) 3 1
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip2
fused_nn_contrib_depthwise_conv2d_NCHWc_add_clip30.8321.701(1, 60,
7, 7, 16) 3 1
fused_nn_contrib_conv2d_NCHWc_add_clip_7
fused_nn_contrib_conv2d_NCHWc_add_clip_7
[GitHub] [incubator-tvm] trevor-m commented on issue #6691: [Performance] Large performance regression with int64 indices INDEX_DEFAULT_I64=ON (PR #6143)
trevor-m commented on issue #6691: URL: https://github.com/apache/incubator-tvm/issues/6691#issuecomment-709569953 FYI @kevinthesun @hzfan @zhiics @tqchen 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] rkimball opened a new pull request #6692: Refactor diagnostic to avoid circular dependencies
rkimball opened a new pull request #6692: URL: https://github.com/apache/incubator-tvm/pull/6692 There were circular dependencies in object.h which made use of ICHECK in Object problematic. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tqchen commented on issue #6691: [Performance] Performance regression with int64 indices INDEX_DEFAULT_I64=ON (PR #6143)
tqchen commented on issue #6691: URL: https://github.com/apache/incubator-tvm/issues/6691#issuecomment-709583351 Thanks @trevor-m cc @hzfan Given that those are constant shapes, we should expect NarrowDataTypes to narrow dtypes to i32, it would be great to look into the IR those kernels and see why the NarrowDataType does not produce the right optimization 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] jroesch commented on a change in pull request #6692: Refactor diagnostic to avoid circular dependencies
jroesch commented on a change in pull request #6692:
URL: https://github.com/apache/incubator-tvm/pull/6692#discussion_r505858000
##
File path: include/tvm/ir/diagnostic_context.h
##
@@ -0,0 +1,222 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file diagnostic.h
+ * \brief A new diagnostic interface for TVM error reporting.
+ *
+ * A prototype of the new diagnostic reporting interface for TVM.
+ *
+ * Eventually we hope to promote this file to the top-level and
+ * replace the existing errors.h.
+ */
+
+#ifndef TVM_IR_DIAGNOSTIC_CONTEXT_H_
+#define TVM_IR_DIAGNOSTIC_CONTEXT_H_
+
+#include
+#include
+
+#include
+#include
+
+namespace tvm {
+
+using tvm::parser::SourceMap;
+using tvm::runtime::TypedPackedFunc;
+
+extern const char* kTVM_INTERNAL_ERROR_MESSAGE;
+
+class DiagnosticBuilder;
+
+/*! \brief A compiler diagnostic. */
+class Diagnostic;
+
+/*! \brief A compiler diagnostic message. */
+class DiagnosticNode : public Object {
+ public:
+ /*! \brief The level. */
Review comment:
We should improve this comment.
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[GitHub] [incubator-tvm] masahi commented on pull request #6602: [Torch, Quantization] Necessary workaround to prepare for 1.6 update
masahi commented on pull request #6602: URL: https://github.com/apache/incubator-tvm/pull/6602#issuecomment-709602965 @siju-samuel @anijain2305 can you merge this? It is a prereq for upgrading our CI to the latest pytorch version 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tkonolige opened a new pull request #6693: [FIX,MICROTVM] Skip microtvm tests if microtvm is not built
tkonolige opened a new pull request #6693: URL: https://github.com/apache/incubator-tvm/pull/6693 This is a simple PR that adds `@tvm.testing.requires_micro` for microtvm tests. Previously, if microtvm was not built, no tests would be run. @areusch 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tqchen commented on pull request #6692: Refactor diagnostic to avoid circular dependencies
tqchen commented on pull request #6692: URL: https://github.com/apache/incubator-tvm/pull/6692#issuecomment-709642068 In this particular case, ICHECK perhaps should not end up in ir/diagnostic, but instead in `support/logging.h`, moving the marco defs there would be the simplest solution 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tqchen edited a comment on pull request #6692: Refactor diagnostic to avoid circular dependencies
tqchen edited a comment on pull request #6692: URL: https://github.com/apache/incubator-tvm/pull/6692#issuecomment-709642068 In this particular case, ICHECK perhaps should not end up in ir/diagnostic, but instead in `support/logging.h`, moving the marco defs there would be the simplest solution. It would also avoid the dep from runtime to ir 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] jroesch merged pull request #6690: [Docker] Update CI CPU and GPU images based on new Docker build files.
jroesch merged pull request #6690: URL: https://github.com/apache/incubator-tvm/pull/6690 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[incubator-tvm] branch main updated (b121278 -> 3e8ba2a)
This is an automated email from the ASF dual-hosted git repository. jroesch pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from b121278 int32 pooling with int64 shapes (#6687) add 3e8ba2a [Docker] Update CI CPU and GPU images based on new Docker build files. (#6690) No new revisions were added by this update. Summary of changes: Jenkinsfile | 4 ++-- docker/Dockerfile.ci_cpu | 4 docker/Dockerfile.ci_gpu | 7 +++ docker/install/ubuntu_install_darknet.sh | 7 ++- docker/install/ubuntu_install_dgl.sh | 0 docker/install/ubuntu_install_sphinx.sh | 2 +- rust/Cargo.toml | 1 - 7 files changed, 16 insertions(+), 9 deletions(-) mode change 100644 => 100755 docker/install/ubuntu_install_dgl.sh
[GitHub] [incubator-tvm] jroesch opened a new pull request #6694: [Docker] Fix tutorial broken by Docker build
jroesch opened a new pull request #6694: URL: https://github.com/apache/incubator-tvm/pull/6694 cc @tqchen @tmoreau89 this change is required for CI to pass on the new images. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[incubator-tvm] branch main updated (3e8ba2a -> 8243145)
This is an automated email from the ASF dual-hosted git repository. tqchen pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 3e8ba2a [Docker] Update CI CPU and GPU images based on new Docker build files. (#6690) add 8243145 [FIX,MICROTVM] Skip microtvm tests if microtvm is not built (#6693) No new revisions were added by this update. Summary of changes: python/tvm/testing.py | 17 + tests/python/unittest/test_crt.py | 14 +- 2 files changed, 30 insertions(+), 1 deletion(-)
[GitHub] [incubator-tvm] tqchen merged pull request #6693: [FIX,MICROTVM] Skip microtvm tests if microtvm is not built
tqchen merged pull request #6693: URL: https://github.com/apache/incubator-tvm/pull/6693 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] jroesch commented on pull request #6685: [Relay][Frontend] SparseTensorDenseMatMul support for Tensorflow
jroesch commented on pull request #6685: URL: https://github.com/apache/incubator-tvm/pull/6685#issuecomment-709673851 cc @tkonolige 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] siju-samuel commented on a change in pull request #6685: [Relay][Frontend] SparseTensorDenseMatMul support for Tensorflow
siju-samuel commented on a change in pull request #6685: URL: https://github.com/apache/incubator-tvm/pull/6685#discussion_r505991645 ## File path: python/tvm/relay/frontend/tensorflow.py ## @@ -890,6 +890,44 @@ def _impl(inputs, attr, params, mod): return _impl +def _sparse_tensor_dense_matmul(): +# Sparse utility from Numpy +from scipy import sparse Review comment: use `from scipy.sparse import csr_matrix` 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] siju-samuel commented on a change in pull request #6685: [Relay][Frontend] SparseTensorDenseMatMul support for Tensorflow
siju-samuel commented on a change in pull request #6685:
URL: https://github.com/apache/incubator-tvm/pull/6685#discussion_r505991920
##
File path: python/tvm/relay/frontend/tensorflow.py
##
@@ -890,6 +890,44 @@ def _impl(inputs, attr, params, mod):
return _impl
+def _sparse_tensor_dense_matmul():
+# Sparse utility from Numpy
Review comment:
Numpy > Scipy
##
File path: python/tvm/relay/frontend/tensorflow.py
##
@@ -890,6 +890,44 @@ def _impl(inputs, attr, params, mod):
return _impl
+def _sparse_tensor_dense_matmul():
+# Sparse utility from Numpy
+from scipy import sparse
+
+def _impl(inputs, attr, params, mod):
+assert len(inputs) == 4, "There should be 4 input tensors"
+
+indices_tensor = _infer_value(inputs[0], params, mod).asnumpy()
+values_tensor = _infer_value(inputs[1], params, mod).asnumpy()
+dense_shape_tensor = _infer_value(inputs[2], params, mod).asnumpy()
+
+data = inputs[3]
+
+rows = [x[0] for x in indices_tensor]
+cols = [x[1] for x in indices_tensor]
+
+# Create Numpy sparse Tensor(CSR)
+weight_sp = sparse.csr_matrix(
+(values_tensor, (rows, cols)),
shape=tuple(dense_shape_tensor.tolist())
+)
+weight_sp = sparse.csr_matrix(weight_sp.transpose())
+
+weight_data = _expr.const(weight_sp.data, weight_sp.data.dtype)
+weight_indptrs = _expr.const(weight_sp.indptr, weight_sp.indptr.dtype)
+weight_indices = _expr.const(weight_sp.indices,
weight_sp.indices.dtype)
+
+ret = _op.nn.sparse_dense(data, [weight_data, weight_indices,
weight_indptrs])
+
+# If both are true means First input was dense and second was sparse
+# TODO: Support other adjoint option too
+if attr.get("adjoint_a") and attr.get("adjoint_b"):
Review comment:
return not supported error for other adjoint options
##
File path: tests/python/frontend/tensorflow/test_forward.py
##
@@ -1750,6 +1750,64 @@ def test_forward_batch_matmul():
_test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6),
"float32", False, True)
+###
+# SparseTensorDenseMatMul
+# --
+
+
+def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype,
flip=False):
+""" One iteration of sparse_dense_matmul """
+
+# TODO: Support adjoint options too
+for adjoint_a in [False]:
+for adjoint_b in [False]:
+with tf.Graph().as_default():
+A_sp = tf.sparse.SparseTensor(
+indices=[[0, 0], [1, 2]], values=[4.0, 8.0],
dense_shape=A_shape
+)
+B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
+
+if flip:
+result = tf.sparse.sparse_dense_matmul(
+B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+)
+else:
+result = tf.sparse.sparse_dense_matmul(
+A_sp, B, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+)
+
+B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+
+# TODO: There is an issue in cuda scheduling for csr, work in
progress
+compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
Review comment:
Need a followup pr to solve the cuda scheduling issue for csr
##
File path: tests/python/frontend/tensorflow/test_forward.py
##
@@ -1750,6 +1750,64 @@ def test_forward_batch_matmul():
_test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6),
"float32", False, True)
+###
+# SparseTensorDenseMatMul
+# --
+
+
+def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype,
flip=False):
+""" One iteration of sparse_dense_matmul """
+
+# TODO: Support adjoint options too
+for adjoint_a in [False]:
+for adjoint_b in [False]:
+with tf.Graph().as_default():
+A_sp = tf.sparse.SparseTensor(
+indices=[[0, 0], [1, 2]], values=[4.0, 8.0],
dense_shape=A_shape
+)
+B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
+
+if flip:
+result = tf.sparse.sparse_dense_matmul(
+B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+)
+else:
+result = tf.sparse.sparse_dense_matmul(
+A_sp, B, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+)
+
+B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+
+# TODO: There is an issue in cuda scheduling for csr,
[GitHub] [incubator-tvm] siju-samuel commented on a change in pull request #6602: [Torch, Quantization] Necessary workaround to prepare for 1.6 update
siju-samuel commented on a change in pull request #6602: URL: https://github.com/apache/incubator-tvm/pull/6602#discussion_r505998308 ## File path: python/tvm/relay/frontend/qnn_torch.py ## @@ -26,6 +26,14 @@ from tvm.relay import op as _op from tvm.relay.frontend.common import infer_shape +from packaging import version Review comment: move inside `_is_newer_than_1_5` ## File path: python/tvm/relay/frontend/qnn_torch.py ## @@ -46,59 +54,95 @@ def __init__(self, weight, bias, scale, zero_point, param_key): self.zero_point = _expr.const(zero_point, dtype="int32") -def _unpack_quant_params(param_name, packed_params, unpack_func): -# Torch stores quantized params in a custom packed format, -# need to unpack and retrieve them as numpy arrays -qweight, bias = unpack_func(packed_params) -weight_np = qweight.dequantize().numpy() +class ConvPackedParam(QNNParam): +"""A placeholder for quantized conv2d op attributs Review comment: attributs > attributes ## File path: python/tvm/relay/frontend/qnn_torch.py ## @@ -458,24 +513,40 @@ def _impl(inputs, _): # inputs[7]: output_zero_point # inputs[8]: input_scale (added manually by frontend) # inputs[9]: input_zero_point (added manually by frontend) -weight = inputs[1][0] -weight_scale = inputs[1][1] -weight_zero_point = inputs[1][2] - -output_scale = _expr.const(inputs[6]) -output_zero_point = _expr.const(inputs[7]) +conv_params = inputs[1] +weight = conv_params[0] +weight_scale = conv_params[1] +weight_zero_point = conv_params[2] +bias = conv_params[3] + +if len(conv_params) > 4: +# Torch 1.6 or newer case +strides = conv_params[4] +padding = conv_params[5] +dilation = conv_params[6] +groups = conv_params[7] + +output_scale = _expr.const(inputs[2]) +output_zero_point = _expr.const(inputs[3]) + +assert len(inputs) == 6, "Input quant params not found in op inputs" + +# These are manually added by add_input_quant_params_to_op_inputs above +# In torch, they are retrieved from QTensor data structure at runt Review comment: runtime? 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] siju-samuel merged pull request #6670: [TFLite] Fix detection of crop in convert_batch_to_space_nd
siju-samuel merged pull request #6670: URL: https://github.com/apache/incubator-tvm/pull/6670 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[incubator-tvm] branch main updated (8243145 -> 08b08b1)
This is an automated email from the ASF dual-hosted git repository. sijusamuel pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 8243145 [FIX,MICROTVM] Skip microtvm tests if microtvm is not built (#6693) add 08b08b1 [TFLite] Fix detection of crop in convert_batch_to_space_nd (#6670) No new revisions were added by this update. Summary of changes: python/tvm/relay/frontend/tflite.py | 2 +- tests/python/frontend/tflite/test_forward.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-)
[GitHub] [incubator-tvm] siju-samuel commented on pull request #6670: [TFLite] Fix detection of crop in convert_batch_to_space_nd
siju-samuel commented on pull request #6670: URL: https://github.com/apache/incubator-tvm/pull/6670#issuecomment-709695528 Thanks @trevor-m @anijain2305. This PR is merged. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] lixiaoquan opened a new pull request #6695: [Relay] Change some passes to mix mode
lixiaoquan opened a new pull request #6695: URL: https://github.com/apache/incubator-tvm/pull/6695 @mbrookhart Could you please take a look? 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] Beya2019 commented on pull request #6516: [RELAY][OP] roi_pool operator alter layout
Beya2019 commented on pull request #6516: URL: https://github.com/apache/incubator-tvm/pull/6516#issuecomment-709700238 Hi @yzhliu, can you help to have a look at this submit? Thanks very much. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] masahi commented on pull request #6602: [Torch, Quantization] Necessary workaround to prepare for 1.6 update
masahi commented on pull request #6602: URL: https://github.com/apache/incubator-tvm/pull/6602#issuecomment-709702663 thanks @siju-samuel I added `pytorch_utils.py` and moved the version check function there 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-tvm] tmoreau89 merged pull request #6694: [Docker] Fix tutorial broken by Docker build
tmoreau89 merged pull request #6694: URL: https://github.com/apache/incubator-tvm/pull/6694 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[incubator-tvm] branch main updated: Fix tutorial broken by Docker build (#6694)
This is an automated email from the ASF dual-hosted git repository.
moreau pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/main by this push:
new 4c4d3dc Fix tutorial broken by Docker build (#6694)
4c4d3dc is described below
commit 4c4d3dc2d8723a5b78d34f1bb0540ef66ee185a3
Author: Jared Roesch
AuthorDate: Thu Oct 15 22:27:11 2020 -0700
Fix tutorial broken by Docker build (#6694)
---
tutorials/frontend/build_gcn.py | 6 +-
1 file changed, 5 insertions(+), 1 deletion(-)
diff --git a/tutorials/frontend/build_gcn.py b/tutorials/frontend/build_gcn.py
index 5c571ef..b832d18 100644
--- a/tutorials/frontend/build_gcn.py
+++ b/tutorials/frontend/build_gcn.py
@@ -242,7 +242,9 @@ import networkx as nx
def prepare_params(g, data):
params = {}
-params["infeats"] = data.features.astype("float32") # Only support
float32 as feature for now
+params["infeats"] = data.features.numpy().astype(
+"float32"
+) # Only support float32 as feature for now
# Generate adjacency matrix
adjacency = nx.to_scipy_sparse_matrix(g)
@@ -350,5 +352,7 @@ test_mask = data.test_mask
acc = evaluate(data, logits_tvm)
print("Test accuracy of TVM results: {:.2%}".format(acc))
+import tvm.testing
+
# Verify the results with the DGL model
tvm.testing.assert_allclose(logits_torch, logits_tvm, atol=1e-3)
[incubator-tvm] branch main updated (08b08b1 -> 4c4d3dc)
This is an automated email from the ASF dual-hosted git repository. moreau pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 08b08b1 [TFLite] Fix detection of crop in convert_batch_to_space_nd (#6670) add 4c4d3dc Fix tutorial broken by Docker build (#6694) No new revisions were added by this update. Summary of changes: tutorials/frontend/build_gcn.py | 6 +- 1 file changed, 5 insertions(+), 1 deletion(-)
[incubator-tvm] branch main updated (08b08b1 -> 4c4d3dc)
This is an automated email from the ASF dual-hosted git repository. moreau pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 08b08b1 [TFLite] Fix detection of crop in convert_batch_to_space_nd (#6670) add 4c4d3dc Fix tutorial broken by Docker build (#6694) No new revisions were added by this update. Summary of changes: tutorials/frontend/build_gcn.py | 6 +- 1 file changed, 5 insertions(+), 1 deletion(-)
[incubator-tvm] branch main updated (08b08b1 -> 4c4d3dc)
This is an automated email from the ASF dual-hosted git repository. moreau pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 08b08b1 [TFLite] Fix detection of crop in convert_batch_to_space_nd (#6670) add 4c4d3dc Fix tutorial broken by Docker build (#6694) No new revisions were added by this update. Summary of changes: tutorials/frontend/build_gcn.py | 6 +- 1 file changed, 5 insertions(+), 1 deletion(-)
[GitHub] [incubator-tvm] sxjscience opened a new pull request #6696: [Frontent][Relay][WIP] Fix MXNet frontend to support BERT in GluonNLP V1
sxjscience opened a new pull request #6696:
URL: https://github.com/apache/incubator-tvm/pull/6696
Fix the MXNet 2.0 integration in relay. Tested the BERT and ALBERT model in
the new GluonNLP v1 and has passed the test. Will later on add unittests in
GluonNLP to ensure that most backbones can be run with the graph runtime.
```python
import mxnet as mx
import numpy as np
import gluonnlp
from gluonnlp.models import get_backbone
import numpy.testing as npt
mx.npx.set_np()
model_cls, cfg, tokenizer, backbone_param_path, _ =
get_backbone('google_albert_base_v2')
model = model_cls.from_cfg(cfg)
model.load_parameters(backbone_param_path)
model.hybridize()
batch_size = 1
seq_length = 128
token_ids = mx.np.random.randint(0, cfg.MODEL.vocab_size, (batch_size,
seq_length), dtype=np.int32)
token_types = mx.np.random.randint(0, 2, (batch_size, seq_length),
dtype=np.int32)
valid_length = mx.np.random.randint(seq_length // 2, seq_length,
(batch_size,), dtype=np.int32)
mx_out = model(token_ids, token_types, valid_length)
import tvm
from tvm import relay
import tvm.contrib.graph_runtime as runtime
shape_dict = {
'data0': (batch_size, seq_length),
'data1': (batch_size, seq_length),
'data2': (batch_size,)
}
dtype_dict = {
'data0': 'int32',
'data1': 'int32',
'data2': 'int32'
}
sym = model._cached_graph[1]
params = {}
for k, v in model.collect_params().items():
params[v._var_name] = tvm.nd.array(v.data().asnumpy())
mod, params = relay.frontend.from_mxnet(sym, shape=shape_dict,
dtype=dtype_dict, arg_params=params)
print(mod)
# G4
target = "cuda -model=t4"
with relay.build_config(opt_level=3, required_pass=["FastMath"]):
graph, lib, cparams = relay.build(mod, target, params=params)
ctx = tvm.gpu()
rt = runtime.create(graph, lib, ctx)
rt.set_input(**cparams)
rt.set_input(data0=token_ids, data1=token_types, data2=valid_length)
rt.run()
for i in range(rt.get_num_outputs()):
out = rt.get_output(i)
print(out.asnumpy())# verify the correctness
npt.assert_allclose(out.asnumpy(), mx_out[i].asnumpy(), rtol=1e-3,
atol=1e-2)
```
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