[GitHub] [tvm] masahi commented on pull request #7023: Save PyTorch frontend state in object
masahi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738629079 Maybe I'm missing something, I'm just wondering why `full_like` at https://github.com/apache/tvm/blob/464396706ec075fbada0f04629211e1ae7276234/python/tvm/relay/frontend/pytorch.py#L610 is nonstatic while `linspace` just below is static? By "all" I just meant all op converter, I don't see why we cannot make some of op converter static? 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] [tvm] t-vi edited a comment on pull request #7023: Save PyTorch frontend state in object
t-vi edited a comment on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738625073 For 1. I can make all methods nonstatic. I wouldn't know how to reasonably make all methods static. Part of this is that I don't agree with the singleton. With convert map moved into the state and late the graph, it's not a singleton anymore. 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] [tvm] t-vi commented on pull request #7023: Save PyTorch frontend state in object
t-vi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738625073 For 1. I can make all methods nonstatic. I wouldn't know how to reasonably make all methods static. 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] [tvm] masahi edited a comment on pull request #7023: Save PyTorch frontend state in object
masahi edited a comment on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738619723 1. To avoid confusion when we add a new converter, I think we should make everything staticmethod or regular one. Since this class is supposed to be a singleton, staticmethod makes sense to me. 2. Yes, the arguments of each op converter are supposed to be `inputs, input_types`. If you add another arg like `name, inputs, input_types` I'd say it is already not consistent anyway. So I prefer lifting `name` arg to a wrapper function and returning a new impl function. I think being able to remove all the `functools.partial(...)` is a big plus. 3. Ok we can do that later. 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] [tvm] masahi commented on pull request #7023: Save PyTorch frontend state in object
masahi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738619723 1. To avoid confusion when we add a new converter, I think we should make everything staticmethod or regular one. Since this class is supposed to be a singleton, staticmethod makes sense to me. 2. Yes, the arguments of each op converter are supposed to be `inputs, input_types`. If you add another arg like `name, inputs, input_types` I'd say it is already not consistent. So I prefer lifting `name` arg to a wrapper function and returning a new impl function. I think being able to remove all the `functools.partial(...)` is a big plus. 3. Ok we can do that later. 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] [tvm] t-vi edited a comment on pull request #7023: Save PyTorch frontend state in object
t-vi edited a comment on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738607046 Thank you for looking at this. 1. I used static methods to not have unused `self`. I'm not terribly attached to it, if you like the consistency of everything being a regular method. 2. This is again a consistency thing. Right now all methods apply the operation. Moving some back to returning impl_ has us doing two different things (though I'm not opposed). One alternative I'd consider is to actually register a method for all ops we handle (`convert_op_aten_mm`) and build the conversion dictionary on the fly. For the time being I'll move the things taking extra arguments back to the impl scheme as you suggest. 3. Yes, I thought I'd do the conversion in pieces. If you prefer I can move these now, too. I will tentatively add them to this PR. 4. Good point about `_`, in particular as leading `_` in classes is special in Python... 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] [tvm] t-vi commented on pull request #7023: Save PyTorch frontend state in object
t-vi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738607046 Thank you for looking at this. 1. I used static methods to not have unused `self`. I'm not terribly attached to it, if you like the consistency of everything being a regular method. 2. This is again a consistency thing. Right now all methods apply the operation. Moving some back to returning impl_ has us doing two different things (though I'm not opposed). One alternative I'd consider is to actually register a method for all ops we handle (`convert_op_aten_mm`) and build the conversion dictionary on the fly. For the time being I'll move the things taking extra arguments back to the impl scheme as you suggest. 3. Yes, I thought I'd do the conversion in pieces. If you prefer I can move these now, too. 4. Good point about `_`, in particular as leading `_` in classes is special in Python... 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] [tvm-vta] dsteger commented on pull request #19: config: small spelling fix
dsteger commented on pull request #19: URL: https://github.com/apache/tvm-vta/pull/19#issuecomment-738583141 @tqchen Do you mind taking a look at this? 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] [tvm-vta] dsteger commented on pull request #17: vta: Update VTA to use load_pad_2d in compute
dsteger commented on pull request #17: URL: https://github.com/apache/tvm-vta/pull/17#issuecomment-738582984 @tqchen Do you mind taking a look at this? 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] [tvm-vta] dsteger opened a new pull request #19: config: small spelling fix
dsteger opened a new pull request #19: URL: https://github.com/apache/tvm-vta/pull/19 Small spelling correction to "offet" = "offset". 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] [tvm] Meteorix closed pull request #7032: Legalize tensorcore
Meteorix closed pull request #7032: URL: https://github.com/apache/tvm/pull/7032 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] [tvm] jroesch commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535842427
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,128 +25,179 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
-
-
-def test_inline():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.c))
-orig = run_opt_pass(orig, transform.DeadCodeElimination(True))
-tvm.ir.assert_structural_equal(Function(free_vars(orig), orig),
Function([e.d], e.d))
-
-
-def test_chain_unused_let():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.e))
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.e], e.e))
-
-
-def use_f(func):
-f = relay.Var("f")
-n = relay.Var("n", e.int32)
-data = relay.Var("data", e.float32)
-funcbody = relay.If(
-equal(n, relay.const(0)), data, relay.Call(f, [subtract(n,
relay.const(1)), log(data)])
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
+assert tvm.ir.structural_equal(after_module, optimize_module)
+
+
+def test_dead_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%z
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+%z
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
+
+
+def test_one_live_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+let %y = 2;
+%x + %x
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%x + %x
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
+
+
+def test_nested_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %a = %b;
+let %c = %d;
+%c
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %c = %d;
+%c
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
Review comment:
I'll fix it in follow up.
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[GitHub] [tvm] jroesch merged pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch merged pull request #7029: URL: https://github.com/apache/tvm/pull/7029 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
[tvm] branch main updated (f4c6517 -> 91c905d)
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/tvm.git. from f4c6517 [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi (#7018) add 91c905d [Relay][Pass] Clean up DCE tests in preparation for refactoring. (#7029) No new revisions were added by this update. Summary of changes: .../relay/test_pass_dead_code_elimination.py | 267 - 1 file changed, 159 insertions(+), 108 deletions(-)
[GitHub] [tvm] zhiics commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
zhiics commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738563250 Thanks @kevinthesun @mbrookhart @icemelon9 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] [tvm] zhiics merged pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
zhiics merged pull request #7018: URL: https://github.com/apache/tvm/pull/7018 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
[tvm] branch main updated (e6c1baf -> f4c6517)
This is an automated email from the ASF dual-hosted git repository. zhic pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/tvm.git. from e6c1baf [AutoScheduler] Misc update to hardware parameter and task scheduler (#7020) add f4c6517 [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi (#7018) No new revisions were added by this update. Summary of changes: include/tvm/topi/detail/constant_utils.h | 15 + include/tvm/topi/nn.h| 2 +- include/tvm/topi/transform.h | 43 +++- python/tvm/topi/cuda/sort.py | 20 ++- src/relay/op/tensor/transform.cc | 19 --- tests/python/relay/dyn/test_dynamic_op_level6.py | 4 +-- tests/python/relay/test_any.py | 10 ++ 7 files changed, 80 insertions(+), 33 deletions(-)
[GitHub] [tvm] jwfromm commented on a change in pull request #7031: [Relay][Frontend][Onnx] Add support for Size op in Onnx frontend.
jwfromm commented on a change in pull request #7031:
URL: https://github.com/apache/tvm/pull/7031#discussion_r535810837
##
File path: tests/python/frontend/onnx/test_forward.py
##
@@ -3888,6 +3888,36 @@ def test_if():
tvm.testing.assert_allclose(correct_out[i], tvm_out[i],
rtol=1e-05, atol=1e-05)
+@tvm.testing.uses_gpu
+def test_size():
+def verify_size(indata):
+node = helper.make_node(
+"Size",
+inputs=["X"],
+outputs=["Y"],
+)
+
+graph = helper.make_graph(
+[node],
+"size_test",
+inputs=[helper.make_tensor_value_info("X", TensorProto.INT64,
list(indata.shape))],
+outputs=[helper.make_tensor_value_info("Y", TensorProto.INT64,
[])],
+)
+
+model = helper.make_model(graph, producer_name="size_test")
+
+for target, _ in tvm.testing.enabled_targets():
+verify_with_ort_with_inputs(
+model, [indata], targets=[target], dtype="int64", use_vm=True,
opset=11
Review comment:
Whoops, fixed now. Thanks for pointing that out!
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[GitHub] [tvm] Meteorix opened a new pull request #7032: Legalize tensorcore
Meteorix opened a new pull request #7032: URL: https://github.com/apache/tvm/pull/7032 Thanks for contributing to TVM! Please refer to guideline https://tvm.apache.org/docs/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread. 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] [tvm] merrymercy edited a comment on pull request #7009: [TFLite] Bugfix - ensure pad calcalution to be in int32
merrymercy edited a comment on pull request #7009: URL: https://github.com/apache/tvm/pull/7009#issuecomment-738542246 I also found the same problem and sent #7030 to fix it. Your modification of `AsText` is nice. 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] [tvm] merrymercy edited a comment on pull request #7009: [TFLite] Bugfix - ensure pad calcalution to be in int32
merrymercy edited a comment on pull request #7009: URL: https://github.com/apache/tvm/pull/7009#issuecomment-738542246 I also found the same problem and sent #7030 to solve it. Your modification of `AsText` is nice. 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] [tvm] merrymercy edited a comment on pull request #7009: [TFLite] Bugfix - ensure pad calcalution to be in int32
merrymercy edited a comment on pull request #7009: URL: https://github.com/apache/tvm/pull/7009#issuecomment-738542246 #7030 solves the same problem. The modification of `AsText` is nice. 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] [tvm] merrymercy commented on pull request #7009: [TFLite] Bugfix - ensure pad calcalution to be in int32
merrymercy commented on pull request #7009: URL: https://github.com/apache/tvm/pull/7009#issuecomment-738542246 #7030 solves the same problem. But the modification of `AsText` is nice. 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] [tvm] FrozenGene commented on pull request #7030: [Frontend] Prevent tflite frontend from producing int64 shape/parameters
FrozenGene commented on pull request #7030: URL: https://github.com/apache/tvm/pull/7030#issuecomment-738540812 Seems this pr should resolve https://github.com/apache/tvm/pull/7009's goal 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] [tvm] masahi commented on a change in pull request #7031: [Relay][Frontend][Onnx] Add support for Size op in Onnx frontend.
masahi commented on a change in pull request #7031:
URL: https://github.com/apache/tvm/pull/7031#discussion_r535806394
##
File path: tests/python/frontend/onnx/test_forward.py
##
@@ -3888,6 +3888,36 @@ def test_if():
tvm.testing.assert_allclose(correct_out[i], tvm_out[i],
rtol=1e-05, atol=1e-05)
+@tvm.testing.uses_gpu
+def test_size():
+def verify_size(indata):
+node = helper.make_node(
+"Size",
+inputs=["X"],
+outputs=["Y"],
+)
+
+graph = helper.make_graph(
+[node],
+"size_test",
+inputs=[helper.make_tensor_value_info("X", TensorProto.INT64,
list(indata.shape))],
+outputs=[helper.make_tensor_value_info("Y", TensorProto.INT64,
[])],
+)
+
+model = helper.make_model(graph, producer_name="size_test")
+
+for target, _ in tvm.testing.enabled_targets():
+verify_with_ort_with_inputs(
+model, [indata], targets=[target], dtype="int64", use_vm=True,
opset=11
Review comment:
Note that you don't have to explicitly pass `targets` if you test
against all targets. If `targets` is None, `verify_with_ort_with_inputs` will
test on all targets.
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[GitHub] [tvm] jwfromm commented on pull request #7031: [Relay][Frontend][Onnx] Add support for Size op in Onnx frontend.
jwfromm commented on pull request #7031: URL: https://github.com/apache/tvm/pull/7031#issuecomment-738531822 @masahi @mbrookhart can you guys take a look at this PR? 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] [tvm] jwfromm opened a new pull request #7031: [Relay][Frontend][Onnx] Add support for Size op in Onnx frontend.
jwfromm opened a new pull request #7031: URL: https://github.com/apache/tvm/pull/7031 This little PR adds support for the Size operator in the Onnx frontend. It addresses one concern brought up in [this thread](https://discuss.tvm.apache.org/t/tvm-support-for-conv1d-operator/8569). 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] [tvm] merrymercy opened a new pull request #7030: [Frontend] Prevent tflite frontend from producing int64 shape
merrymercy opened a new pull request #7030: URL: https://github.com/apache/tvm/pull/7030 I found some shapes and parameters (padding) are converted to int64 from tflite frontend. It will result in a placeholder shape be `[1, (int64) 112, (int64) 112, 64]`. Some of them are int32, the others are int32. This fails some optimization in the auto-scheduler. This is because `ShapeAsNumpy` returns `np.int32`, but it is automatically upcasted to `np.int64` when doing computation. We convert all `np.int32` to python's int to prevent this auto upcast. 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] [tvm] yongwww commented on issue #4102: [Relay] Support for Tensorflow 2.0
yongwww commented on issue #4102: URL: https://github.com/apache/tvm/issues/4102#issuecomment-738507269 We have looked into the models like image classification, object detection, segmentation, etc. Seems we need to enable the support for TensorList, control-flow, function, and some missing operations. We would like to work on this project from scratch. As we discussed above, we might create a new tf frontend for tf2.x (and I believe the parser for tf1.x will be deprecated in the future as tf community stops the support for 1.x). Please comment here or ping me/us if anyone is interested in working with us on this. I synced with siva @srkreddy1238 in the past few weeks, we will keep him and the community updated if we work together on this. 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] [tvm] altanh commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
altanh commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535747377
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,128 +25,179 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
-
-
-def test_inline():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.c))
-orig = run_opt_pass(orig, transform.DeadCodeElimination(True))
-tvm.ir.assert_structural_equal(Function(free_vars(orig), orig),
Function([e.d], e.d))
-
-
-def test_chain_unused_let():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.e))
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.e], e.e))
-
-
-def use_f(func):
-f = relay.Var("f")
-n = relay.Var("n", e.int32)
-data = relay.Var("data", e.float32)
-funcbody = relay.If(
-equal(n, relay.const(0)), data, relay.Call(f, [subtract(n,
relay.const(1)), log(data)])
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
+assert tvm.ir.structural_equal(after_module, optimize_module)
+
+
+def test_dead_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%z
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+%z
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
+
+
+def test_one_live_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+let %y = 2;
+%x + %x
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%x + %x
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
+
+
+def test_nested_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %a = %b;
+let %c = %d;
+%c
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %c = %d;
+%c
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
Review comment:
the reason why the old test had a different result is because the pass
was constructed as `DeadCodeElimination(True)`, whereas the default is `False`
(for the parameter called `inline_once`)
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[GitHub] [tvm] jroesch commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535742798
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, add(e.three, e.two))
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+(let %a = 1; 3) + 2
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+3 + 2
+}
+"""
-def test_tuple_get_item():
-tt = relay.TupleType([e.float32, e.float32])
-t = relay.Var("t", tt)
-a = relay.Var("a")
-g = relay.TupleGetItem(t, 0)
-dced = run_opt_pass(g, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
-orig = relay.TupleGetItem(relay.Let(a, e.one, t), 0)
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-@pytest.mark.timeout(timeout=10, method="thread")
-def test_complexity():
Review comment:
No these are not good tests, they don't really assert anything except
that this thing isn't like crazy slow and slows CI down by running passes
against large models is why CI performance is really bad.
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[GitHub] [tvm] altanh commented on pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
altanh commented on pull request #7029: URL: https://github.com/apache/tvm/pull/7029#issuecomment-738467164 sorry for the duplicated comments, didn't see the other reviews 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] [tvm] altanh commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
altanh commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535742377
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
+
+
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
+assert tvm.ir.structural_equal(after_module, optimize_module)
+
+
+def test_dead_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%z
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+%z
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_inline():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.c))
-orig = run_opt_pass(orig, transform.DeadCodeElimination(True))
-tvm.ir.assert_structural_equal(Function(free_vars(orig), orig),
Function([e.d], e.d))
+def test_one_live_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+let %y = 2;
+%x + %x
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%x + %x
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_chain_unused_let():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.e))
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.e], e.e))
+def test_nested_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %a = %b;
+let %c = %d;
+%c
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %c = %d;
Review comment:
not sure if something changed
This is an automated message
[GitHub] [tvm] altanh commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
altanh commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535742349
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
+
+
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
+assert tvm.ir.structural_equal(after_module, optimize_module)
+
+
+def test_dead_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%z
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+%z
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_inline():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.c))
-orig = run_opt_pass(orig, transform.DeadCodeElimination(True))
-tvm.ir.assert_structural_equal(Function(free_vars(orig), orig),
Function([e.d], e.d))
+def test_one_live_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+let %y = 2;
+%x + %x
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%x + %x
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_chain_unused_let():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.e))
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.e], e.e))
+def test_nested_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %a = %b;
+let %c = %d;
+%c
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %c = %d;
Review comment:
but yeah I think the old DCE pass behavior would indeed collapse it to
just `%d`
[GitHub] [tvm] altanh commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
altanh commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535741743
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
+
+
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
+assert tvm.ir.structural_equal(after_module, optimize_module)
+
+
+def test_dead_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%z
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+%z
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_inline():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.c))
-orig = run_opt_pass(orig, transform.DeadCodeElimination(True))
-tvm.ir.assert_structural_equal(Function(free_vars(orig), orig),
Function([e.d], e.d))
+def test_one_live_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+let %y = 2;
+%x + %x
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%x + %x
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_chain_unused_let():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.e))
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.e], e.e))
+def test_nested_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %a = %b;
+let %c = %d;
+%c
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %c = %d;
Review comment:
that might need PE?
This is an automated message from the
[GitHub] [tvm] jroesch commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535741593
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
+
+
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
+assert tvm.ir.structural_equal(after_module, optimize_module)
+
+
+def test_dead_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%z
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+%z
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_inline():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.c))
-orig = run_opt_pass(orig, transform.DeadCodeElimination(True))
-tvm.ir.assert_structural_equal(Function(free_vars(orig), orig),
Function([e.d], e.d))
+def test_one_live_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+let %y = 2;
+%x + %x
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%z: int) {
+let %x = 1;
+%x + %x
+}
+"""
+optimize_and_check(before_program, after_program,
transform.DeadCodeElimination())
-def test_chain_unused_let():
-orig = relay.Let(e.a, e.b, relay.Let(e.c, e.d, e.e))
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.e], e.e))
+def test_nested_let():
+before_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %a = %b;
+let %c = %d;
+%c
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main(%d: int, %b: int) {
+let %c = %d;
Review comment:
It looks like current liveness keeps the binding around unless the
constructed IR is slightly different.
[GitHub] [tvm] mbrookhart commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
mbrookhart commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535741224
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, add(e.three, e.two))
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+(let %a = 1; 3) + 2
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+3 + 2
+}
+"""
-def test_tuple_get_item():
-tt = relay.TupleType([e.float32, e.float32])
-t = relay.Var("t", tt)
-a = relay.Var("a")
-g = relay.TupleGetItem(t, 0)
-dced = run_opt_pass(g, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
-orig = relay.TupleGetItem(relay.Let(a, e.one, t), 0)
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-@pytest.mark.timeout(timeout=10, method="thread")
Review comment:
Agreed on this one.
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[GitHub] [tvm] altanh commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
altanh commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535739423
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, add(e.three, e.two))
+def test_dead_recursion():
Review comment:
樂 duplicated, maybe you meant `test_op_let`
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
[GitHub] [tvm] jroesch commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535741004
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, add(e.three, e.two))
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+(let %a = 1; 3) + 2
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+3 + 2
+}
+"""
-def test_tuple_get_item():
-tt = relay.TupleType([e.float32, e.float32])
-t = relay.Var("t", tt)
-a = relay.Var("a")
-g = relay.TupleGetItem(t, 0)
-dced = run_opt_pass(g, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
-orig = relay.TupleGetItem(relay.Let(a, e.one, t), 0)
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-@pytest.mark.timeout(timeout=10, method="thread")
Review comment:
To elaborate people need to write unit tests for this not just keep
running large integration tests.
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[GitHub] [tvm] jroesch commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535740180
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, add(e.three, e.two))
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+(let %a = 1; 3) + 2
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+3 + 2
+}
+"""
-def test_tuple_get_item():
-tt = relay.TupleType([e.float32, e.float32])
-t = relay.Var("t", tt)
-a = relay.Var("a")
-g = relay.TupleGetItem(t, 0)
-dced = run_opt_pass(g, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
-orig = relay.TupleGetItem(relay.Let(a, e.one, t), 0)
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-@pytest.mark.timeout(timeout=10, method="thread")
Review comment:
No integration tests are bad, this is why CI takes like 10hours.
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[GitHub] [tvm] mbrookhart commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
mbrookhart commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535738565
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
+
+
+def optimize_source(source, passes):
+if not isinstance(passes, list):
+passes = [passes]
+
+optimize = tvm.transform.Sequential(passes)
+module = tvm.parser.parse(source)
+return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+optimize_module = optimize_source(before_source, passes)
+after_module = tvm.parser.parse(after_source)
+print(optimize_module)
+print(after_module)
Review comment:
Remove Prints
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(orig), orig),
Function([e.z], e.z))
-
-
-def test_used_let():
-orig = relay.Let(e.c, e.one, e.c + e.c)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-expected = relay.Let(e.c, e.one, e.c + e.c)
-assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2,
[GitHub] [tvm] MarisaKirisame commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
MarisaKirisame commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535737856
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -91,62 +138,107 @@ def use_f(func):
return relay.Let(f, value, func(f))
-# make sure we dont infinite loop
-def test_recursion():
+def test_live_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
+"""
+
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+%f(2, 1)
+}
"""
-Program:
- let f(n: i32, data: f32) -> f32 = {
- if (n == 0) {
- return data;
- } else {
- return f(n - 1, log(data));
- }
- }
- f(2, 1);
+
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
+
+
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+let %f = fn (%n: int, %data: int) -> int {
+if (%n == 0) {
+%data
+} else {
+%f(%n - 1, log(%data))
+}
+};
+()
+}
"""
-orig = use_f(lambda f: relay.Call(f, [relay.const(2),
relay.const(1.0)]))
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-orig = run_opt_pass(orig, transform.InferType())
-tvm.ir.assert_structural_equal(dced, orig)
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+()
+}
+"""
-def test_recursion_dead():
-x = relay.Let(e.a, e.one, e.three)
-dced_f = lambda f: x
-dced = run_opt_pass(use_f(dced_f), transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, e.three)
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-def test_op_let():
-dced = run_opt_pass(add(relay.Let(e.a, e.one, e.three), e.two),
transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(dced, add(e.three, e.two))
+def test_dead_recursion():
+before_program = """
+#[version = "0.0.5"]
+def @main() {
+(let %a = 1; 3) + 2
+}
+"""
+after_program = """
+#[version = "0.0.5"]
+def @main() {
+3 + 2
+}
+"""
-def test_tuple_get_item():
-tt = relay.TupleType([e.float32, e.float32])
-t = relay.Var("t", tt)
-a = relay.Var("a")
-g = relay.TupleGetItem(t, 0)
-dced = run_opt_pass(g, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
-orig = relay.TupleGetItem(relay.Let(a, e.one, t), 0)
-dced = run_opt_pass(orig, transform.DeadCodeElimination())
-assert tvm.ir.structural_equal(Function(free_vars(dced), dced),
Function(free_vars(g), g))
+optimize_and_check(
+before_program, after_program, [transform.DeadCodeElimination(),
transform.InferType()]
+)
-@pytest.mark.timeout(timeout=10, method="thread")
-def test_complexity():
-g = inception_v3.get_net(1, 1000, (3, 299, 299), "float32")
-run_opt_pass(g, transform.DeadCodeElimination())
+def test_dead_recursion():
Review comment:
wrong name
##
File path: tests/python/relay/test_pass_dead_code_elimination.py
##
@@ -25,59 +25,106 @@
import pytest
-class env:
-def __init__(self):
-self.shape = tvm.runtime.convert([1, 2, 3])
-self.tt = relay.TensorType(self.shape, "float32")
-self.int32 = relay.TensorType([], "int32")
-self.float32 = relay.TensorType([], "float32")
-self.one = relay.const(1.0)
-self.two = relay.const(2.0)
-self.three = relay.const(3.0)
-self.a = relay.Var("a", self.float32)
-self.b = relay.Var("b", self.float32)
-self.c = relay.Var("c", self.float32)
-self.d = relay.Var("d", self.float32)
-self.e = relay.Var("e", self.float32)
-self.x = relay.Var("x", self.int32)
-self.y = relay.Var("y", self.int32)
-self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
-assert isinstance(opt_pass, tvm.transform.Pass)
-mod = tvm.IRModule.from_expr(expr)
-mod = opt_pass(mod)
-entry = mod["main"]
-return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
-orig = relay.Let(e.x, e.y, e.z)
-orig = run_opt_pass(orig, transform.DeadCodeElimination())
-
[GitHub] [tvm] jroesch opened a new pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
jroesch opened a new pull request #7029: URL: https://github.com/apache/tvm/pull/7029 This PR just cleans up DCE tests. cc @MarisaKirisame @mbrookhart 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] [tvm] merrymercy commented on pull request #7028: [AutoScheduler] Refactor task interface for tuning single operators
merrymercy commented on pull request #7028: URL: https://github.com/apache/tvm/pull/7028#issuecomment-738441762 @FrozenGene @jcf94 @comaniac Please take another 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
[tvm] branch main updated (b06b64d -> e6c1baf)
This is an automated email from the ASF dual-hosted git repository. lmzheng pushed a change to branch main in repository https://gitbox.apache.org/repos/asf/tvm.git. from b06b64d [CI] Hotfix CI (see #7010) (#7025) add e6c1baf [AutoScheduler] Misc update to hardware parameter and task scheduler (#7020) No new revisions were added by this update. Summary of changes: docs/conf.py | 1 + include/tvm/auto_scheduler/search_task.h | 26 ++- python/tvm/auto_scheduler/__init__.py | 7 ++- python/tvm/auto_scheduler/auto_schedule.py | 32 +- python/tvm/auto_scheduler/relay_integration.py | 12 + python/tvm/auto_scheduler/search_policy.py | 2 +- python/tvm/auto_scheduler/task_scheduler.py| 8 +++- python/tvm/relay/op/strategy/cuda.py | 9 ++-- python/tvm/relay/op/strategy/x86.py| 17 ++-- src/auto_scheduler/search_task.cc | 51 -- .../unittest/test_auto_scheduler_compute_dag.py| 2 +- .../python/unittest/test_auto_scheduler_feature.py | 4 +- 12 files changed, 121 insertions(+), 50 deletions(-)
[GitHub] [tvm] merrymercy merged pull request #7020: [AutoScheduler] Misc update to hardware parameter and task scheduler
merrymercy merged pull request #7020: URL: https://github.com/apache/tvm/pull/7020 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] [tvm] altanh commented on issue #7010: [TEST][FLAKY] test_op_grad_level2.py::test_conv2d_grad.py
altanh commented on issue #7010: URL: https://github.com/apache/tvm/issues/7010#issuecomment-738383860 We should keep this issue but rename to dependency libomp conflict I think (or open a new one), since it might arise in the future 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] [tvm] mbrookhart commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
mbrookhart commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738375832 :/ OddEvenTransportSort should be stable, but something looks very wrong about the threading in this kernel. I'll see if I can edit to to solve these problems at some point in the near-ish future. If somehow this sort isn't stable, that would easily explain flakiness in argwhere/argsort. 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] [tvm] masahi edited a comment on pull request #7023: Save PyTorch frontend state in object
masahi edited a comment on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738344230 Thanks for working on this. I have three questions: 1. What is the distinction between op converters method with `@staticmethod` annotation and the ones without it (the ones which take `self` as argument)? 2. Can we remove `functools.partial` stuff? So rather than having `def _unary(name, inputs, input_types):` etc, can we have something like `def _unary(name): return lambda inputs, input_types: ...` 3. Do you intend to move functions such as `convert_operators`, `convert_block` etc into the class later? These are the functions that currently require passing around "global" variables such as `outputs, convert_map, prelude, default_dtype`. cc @siju-samuel We will be doing big refactoring of pytorch frontend. 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] [tvm] masahi commented on pull request #7023: Save PyTorch frontend state in object
masahi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738362461 Also, now that we are encapsulating each converter inside a class, I think it is ok to remove underscore `_` in each converter method, if you prefer (replace `def _` -> `def `, `self._` -> `self.`). I'm fine either way. 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
[tvm] branch main updated (c1f7820 -> b06b64d)
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/tvm.git. from c1f7820 [RPC] Prefer IPv4 between IPv4 and IPv6 (#7013) add b06b64d [CI] Hotfix CI (see #7010) (#7025) No new revisions were added by this update. Summary of changes: python/tvm/relay/testing/__init__.py | 12 ++-- tests/python/relay/test_op_grad_level2.py | 51 +-- 2 files changed, 11 insertions(+), 52 deletions(-)
[GitHub] [tvm] tqchen merged pull request #7025: [CI] Hotfix CI (see #7010)
tqchen merged pull request #7025: URL: https://github.com/apache/tvm/pull/7025 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] [tvm] zhiics commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
zhiics commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738355992 @mbrookhart yeah, argwhere is flaky on large inputs if sort is used 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] [tvm] mbrookhart commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
mbrookhart commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738347327 Yeah, the perf of the kernel isn't great, and I see some thread definition issues that will cause issues with dynamic shapes. Do we have a flaky test we can include? I don't think it's important for this PR, but it might be interesting to tackle later. 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] [tvm] comaniac commented on a change in pull request #7028: [AutoScheduler] Refactor task interface for tuning single operators
comaniac commented on a change in pull request #7028: URL: https://github.com/apache/tvm/pull/7028#discussion_r535668480 ## File path: python/tvm/auto_scheduler/search_task.py ## @@ -42,18 +153,124 @@ class SearchTask(Object): The target host device of this search task. hardware_params : Optional[HardwareParams] Hardware parameters used in this search task. + +Examples + +.. code-block:: python + + # We support two ways to create a search task + + # Way 1: create a task by a workload generation function. + # The `workload_func` is a function decorated by @auto_scheduler.register_workload + task = SearchTask(func=workload_func, args=args, target=target) + + # Way 2: create a task by a workload_key. + # The `workload_key` is a string, which can be either a hash key or a json-serialized + # tuple(func, args). + task = SearchTask(workload_key=workload_key, target=target) """ -def __init__(self, dag, workload_key, target, target_host=None, hardware_params=None): -self.dag = dag +def __init__( +self, +func=None, +args=None, +compute_dag=None, +workload_key=None, +target=None, +target_host=None, +hardware_params=None, +): +assert ( +func is not None or workload_key is not None +), "Either a workload generation function or a workload key should be provided" + +if func is not None: +workload_key = make_workload_key(func, args) +if compute_dag is None: +compute_dag = ComputeDAG(workload_key) + +assert target is not None, "Must specify a target." +if isinstance(target, str): +target = Target(target) +if isinstance(target_host, str): +target_host = Target(target_host) + +self.dag = compute_dag self.workload_key = workload_key self.target = target self.target_host = target_host self.hardware_params = hardware_params self.__init_handle_by_constructor__( -_ffi_api.SearchTask, dag, workload_key, target, target_host, hardware_params +_ffi_api.SearchTask, compute_dag, workload_key, target, target_host, hardware_params ) +def tune(self, tuning_options, search_policy=None): +"""Run auto scheduling search for a task + +Parameters +-- +tuning_options : Optional[TuningOptions] +Tuning and measurement options. +search_policy : Optional[SearchPolicy] +The search policy to be used for schedule search. +""" +if search_policy is None: +cost_model = XGBModel() +search_policy = SketchPolicy(self, cost_model) + +_ffi_api.AutoSchedule(search_policy, tuning_options) + +def apply_best(self, log_file, layout_rewrite_option=None): +"""Apply the history best from a log file and return the schedule. + +Parameters +-- +log_file : str + The name of the log file +layout_rewrite_option : Optional[LayoutRewriteOption] + The layout rewrite option + +Returns +--- +A `te.Schedule` and the a list of `te.Tensor` to be used in `tvm.lower` or `tvm.build`. +""" +inp, res = load_best_record(log_file, self.workload_key) + +if layout_rewrite_option is None: +layout_rewrite_option = LayoutRewriteOption.NO_REWRITE +if self.target.kind.name == "llvm": +layout_rewrite_option = LayoutRewriteOption.INSERT_TRANSFORM_STAGE +sch, args = self.compute_dag.apply_steps_from_state(inp.state, layout_rewrite_option) +return sch, args + +def print_best(self, log_file, print_mode="schedule"): +"""Print the best schedule as python schedule API code or CUDA source code. + +Parameters +-- +log_file : str + The name of the log file +print_mode: str + if "schedule", print the best schedule as python schedule API code. + if "cude", print the best schedule as CUDA source code. Review comment: - s/cude/cuda - This looks inconsistent to "schedule". Maybe just name it "code" and throw RuntimeError if the target is not CUDA? ## File path: tutorials/auto_scheduler/tune_matmul_x86.py ## @@ -147,22 +156,10 @@ def matmul_add(N, L, M, dtype): # file "matmul.json". The measurement records can be used to re-apply search results, # resume the search, and perform other analyses. -## -# Here is an example where we load the best schedule from a file, -# print the equivalent python schedule API, and build the binary again. - -# Load the measuremnt record for the best schedule -inp, res =
[GitHub] [tvm] masahi commented on pull request #7023: Save PyTorch frontend state in object
masahi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738346443 Right now CI is having an issue, please retrigger after https://github.com/apache/tvm/pull/7025 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] [tvm] masahi edited a comment on pull request #7023: Save PyTorch frontend state in object
masahi edited a comment on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738344230 Thanks for working on this. I have two questions: 1. What is the distinction between op converters method with `@staticmethod` annotation and the ones without it (the ones which take `self` as argument)? 2. Can we remove `functools.partial` stuff? So rather than having `def _unary(name, inputs, input_types):` etc, can we have something like `def _unary(name): return lambda inputs, input_types: ...` cc @siju-samuel We will be doing big refactoring of pytorch frontend. 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] [tvm] masahi edited a comment on pull request #7023: Save PyTorch frontend state in object
masahi edited a comment on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738344230 Ok I have two questions: 1. What is the distinction between op converters method with `@staticmethod` annotation and the ones without it (the ones which take `self` as argument)? 2. Can we remove `functools.partial` stuff? So rather than having `def _unary(name, inputs, input_types):` etc, can we have something like `def _unary(name): return lambda inputs, input_types: ...` cc @siju-samuel We will be doing big refactoring of pytorch frontend. 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] [tvm] masahi commented on pull request #7023: Save PyTorch frontend state in object
masahi commented on pull request #7023: URL: https://github.com/apache/tvm/pull/7023#issuecomment-738344230 Ok I have two questions: 1. What is the distinction between op converters method with `@staticmethod` annotation and the ones without it (the ones which take `self` as argument)? 2. Can we remove `functools.partial` stuff? So rather than having `def _unary(name, inputs, input_types):` etc, `def _unary(name): return lambda inputs, input_types: ...` Would that be possible? cc @siju-samuel We will be doing big refactoring of pytorch frontend. 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] [tvm] kevinthesun closed pull request #7024: [CI] Ugrade CI cmake for GPU
kevinthesun closed pull request #7024: URL: https://github.com/apache/tvm/pull/7024 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] [tvm] kevinthesun commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
kevinthesun commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738342059 AFAIK cuda sort has several issues: 1. Performance is bad for large workloads. 2. Can't handle dynamic data shape well. 3. Can generate flaky result. There is no clear path to a solution to these problems. For now the best way is to let user turn on Thrust, when they want to compile sort related op on nvidia gpu. 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] [tvm] kevinthesun commented on a change in pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
kevinthesun commented on a change in pull request #7018:
URL: https://github.com/apache/tvm/pull/7018#discussion_r535663839
##
File path: python/tvm/topi/cuda/sort.py
##
@@ -561,10 +561,11 @@ def topk_thrust(data, k=1, axis=-1, ret_type="both",
is_ascend=False, dtype="int
tag="topk_gpu",
)
-if k > 0:
+if not isinstance(k, int) or k > 0:
beg = [0] * ndim
-end = data.shape[:-1] + [k]
-out = [strided_slice(o, beg, end) for o in out]
+end = data.shape[:-1] + [k if isinstance(k, int) else
tvm.te.size_var("dim")]
+strides = [1] * ndim
+out = [strided_slice(o, beg, end, strides) for o in out]
Review comment:
I modified cuda topk so that topk in dyn can pass. However, topk in test
any in which data has dynamic shape can't pass without Thrust. I disable that
test for now.
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[GitHub] [tvm] merrymercy opened a new pull request #7028: [AutoScheduler] Refactor task interface
merrymercy opened a new pull request #7028: URL: https://github.com/apache/tvm/pull/7028 - Refactor the task and tuning interface for tuning a single operator - Use `InsertTransformStage` as the default layout rewrite option for CPU target. - **NOTE: This PR breaks APIs** ### Before ``` task = auto_scheduler.create_task(matmul, (1024, 1024), "llvm") sch, args = auto_scheduler.auto_schedule(task) ``` ### After ``` task = auto_scheduler.SearchTask(func=matmul, args=(1024, 1024), target="llvm") task.tune(tune_option) sch, args = task.apply_best(log_file) ``` ### Rationality: 1. clean separated API for tuning and applying best 2. Choose a style similar to `tvm.target.Target`, deprecate the `create_XXX` style. 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] [tvm] mbrookhart commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
mbrookhart commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738328610 I'm not really sure what's wrong with the tir sort, do we have a regression test/issue we could track? 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] [tvm] zhiics commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
zhiics commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738327979 I think without thrust, we then have to fix sort. We can probably disable the test for now and come back to work on sorting and then enable the test. This would at least unblock downstream users to run models through thrust. @mbrookhart @icemelon9 @kevinthesun how do you think? 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] [tvm] tqchen merged pull request #7013: Change default hostname in rpc_tracker
tqchen merged pull request #7013: URL: https://github.com/apache/tvm/pull/7013 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
[tvm] branch main updated (8daa97e -> c1f7820)
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/tvm.git. from 8daa97e [Diagnostics] Add environment variable for controlling top-level printing and fix issue with pretty printing/parsing roundtrip. (#6874) add c1f7820 [RPC] Prefer IPv4 between IPv4 and IPv6 (#7013) No new revisions were added by this update. Summary of changes: python/tvm/rpc/base.py | 3 +++ 1 file changed, 3 insertions(+)
[GitHub] [tvm] mbrookhart commented on a change in pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
mbrookhart commented on a change in pull request #7018:
URL: https://github.com/apache/tvm/pull/7018#discussion_r535596794
##
File path: python/tvm/topi/cuda/sort.py
##
@@ -561,10 +561,11 @@ def topk_thrust(data, k=1, axis=-1, ret_type="both",
is_ascend=False, dtype="int
tag="topk_gpu",
)
-if k > 0:
+if not isinstance(k, int) or k > 0:
beg = [0] * ndim
-end = data.shape[:-1] + [k]
-out = [strided_slice(o, beg, end) for o in out]
+end = data.shape[:-1] + [k if isinstance(k, int) else
tvm.te.size_var("dim")]
+strides = [1] * ndim
+out = [strided_slice(o, beg, end, strides) for o in out]
Review comment:
@kevinthesun, why don't we just repeat this change in the tir topk
above? that would fix the unit test, I think.
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[GitHub] [tvm] kevinthesun edited a comment on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
kevinthesun edited a comment on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738287828 I think we can raise an exception when compiling dynamic topk but Thrust is not enabled. Building with Thrust usually needs extra effort since it requires cmake >=3.13. User can enable it when necessary. For tvm cuda sorting, I'm not sure whether it covers some cases which Thrust doesn't. Maybe we can keep it a while. 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] [tvm] kevinthesun commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
kevinthesun commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738287828 I think we can raise an exception when compiling dynamic topk but Thrust is not enable. Building with Thrust usually needs extra effort since it requires cmake >=3.13. User can enable it when necessary. For tvm cuda sorting, I'm not sure whether it covers some cases which Thrust doesn't. Maybe we can keep it a while. 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] [tvm] samskalicky opened a new pull request #7027: [GraphRuntime] remove print from GetInputIndex
samskalicky opened a new pull request #7027: URL: https://github.com/apache/tvm/pull/7027 Remove print statement from `GetInputIndex` API in GraphRuntime. The function already returns -1 when the input isnt found so the print statement is unnecessary and degrades performance at 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] [tvm] trevor-m commented on pull request #7026: [BYOC][TRT] Support batch norm for all ranks <=5, and all axes
trevor-m commented on pull request #7026: URL: https://github.com/apache/tvm/pull/7026#issuecomment-738275838 All tests in test_tensorrt.py passed locally 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] [tvm] tqchen commented on pull request #7024: [CI] Ugrade CI cmake for GPU
tqchen commented on pull request #7024: URL: https://github.com/apache/tvm/pull/7024#issuecomment-738272541 Unfortunately we cannot simply upgrade the CMake version as we want to be able to keep backward compact on LTS we want to support, see also https://discuss.tvm.apache.org/t/update-cmake-version/8553 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] [tvm] anijain2305 commented on a change in pull request #7026: [BYOC][TRT] Support batch norm for all ranks <=5, and all axes
anijain2305 commented on a change in pull request #7026:
URL: https://github.com/apache/tvm/pull/7026#discussion_r535541583
##
File path: python/tvm/relay/op/contrib/tensorrt.py
##
@@ -341,6 +341,11 @@ def batch_norm_annotate_fn(expr): # pylint:
disable=unused-variable
if any([x.checked_type.dtype != "float32" for x in args]):
logger.info("Only float32 inputs are supported for TensorRT.")
return False
+if len(args[0].checked_type.shape) == 5 and get_tensorrt_version() < (6,
0, 1):
+logger.info("nn.batch_norm: TensorRT 6.0.1 or higher is required for
rank 5 inputs.")
Review comment:
MIssing `return False`?
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[GitHub] [tvm] trevor-m opened a new pull request #7026: [BYOC][TRT] Support batch norm for all ranks <=5, and all axes
trevor-m opened a new pull request #7026: URL: https://github.com/apache/tvm/pull/7026 Previous batch norm only supported rank 4 inputs with axis 1 or 3. Now we support input ranks and axes 1-5. 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] [tvm] echuraev removed a comment on pull request #7013: Change default hostname in rpc_tracker
echuraev removed a comment on pull request #7013: URL: https://github.com/apache/tvm/pull/7013#issuecomment-738243984 CI is green. @tqchen, could you please take a look on this PR once again? 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] [tvm] mbrookhart commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
mbrookhart commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738243991 I don't love making thrust a necessary component unless we automatically enable it when we turn on cuda? If we don't support the tir-based sort, should we remove it from the codebase? 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] [tvm] echuraev commented on pull request #7013: Change default hostname in rpc_tracker
echuraev commented on pull request #7013: URL: https://github.com/apache/tvm/pull/7013#issuecomment-738243984 CI is green. @tqchen, could you please take a look on this PR once again? 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] [tvm] kevinthesun commented on pull request #7018: [Topi] Fix GPU Dynamic Topk by Improving Dynamic Strided Slice in Topi
kevinthesun commented on pull request #7018: URL: https://github.com/apache/tvm/pull/7018#issuecomment-738240723 @mbrookhart Generally we need thrust for this dynamic sorting ops. nvptx will have issue to compile them. @icemelon9 We need to enable thrust for ci gpu. https://github.com/apache/tvm/pull/7024 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] [tvm] zhiics commented on pull request #7024: [CI] Update CI to use Thrust for GPU
zhiics commented on pull request #7024: URL: https://github.com/apache/tvm/pull/7024#issuecomment-738236603 We can just upgrade cmake first and leave turning on thrust to the other pr. @tqchen 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] [tvm] altanh commented on issue #7010: [TEST][FLAKY] test_op_grad_level2.py::test_conv2d_grad.py
altanh commented on issue #7010: URL: https://github.com/apache/tvm/issues/7010#issuecomment-738218571 @tkonolige found that `pytest-xdist` package supports passing `--forked` argument to `pytest`. This seems to fix the problem for running contrib tests. 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] [tvm] merrymercy commented on a change in pull request #7020: [AutoScheduler] Misc update to hardware parameter and task scheduler
merrymercy commented on a change in pull request #7020: URL: https://github.com/apache/tvm/pull/7020#discussion_r535492994 ## File path: python/tvm/auto_scheduler/relay_integration.py ## @@ -342,3 +343,14 @@ def rewrite_compute_body(compute_tensor, new_layout): num = op_node.num_outputs outputs = tuple(op_node.output(i) for i in range(num)) return outputs[0] if num == 1 else outputs + + +def is_auto_scheduler_enabled(): +"""Return whether the auto-scheduler is enabled Review comment: ```suggestion """Return whether the auto-scheduler is enabled. ``` 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] [tvm] mbrookhart commented on pull request #6874: [Diagnostics] Add environment variable for controlling top-level printing and fix issue with pretty printing/parsing roundtrip.
mbrookhart commented on pull request #6874: URL: https://github.com/apache/tvm/pull/6874#issuecomment-738205638 Thanks @jroesch @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] [tvm] mbrookhart merged pull request #6874: [Diagnostics] Add environment variable for controlling top-level printing and fix issue with pretty printing/parsing roundtrip.
mbrookhart merged pull request #6874: URL: https://github.com/apache/tvm/pull/6874 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
[tvm] branch main updated: [Diagnostics] Add environment variable for controlling top-level printing and fix issue with pretty printing/parsing roundtrip. (#6874)
This is an automated email from the ASF dual-hosted git repository.
mbrookhart pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new 8daa97e [Diagnostics] Add environment variable for controlling
top-level printing and fix issue with pretty printing/parsing roundtrip. (#6874)
8daa97e is described below
commit 8daa97ec87118ecdf38453ca878655cb08fba329
Author: Jared Roesch
AuthorDate: Thu Dec 3 10:32:37 2020 -0800
[Diagnostics] Add environment variable for controlling top-level printing
and fix issue with pretty printing/parsing roundtrip. (#6874)
* Update Parser in order to handle the NMS code
* Add support for displaying traces optionally
* WIP
* Fix
* Fix error reporting in parser and clean up __init__.py due to CR
* Format
* Quick fix for If
* Fix format
* Fix lint
---
python/tvm/__init__.py | 21 +++--
src/parser/parser.cc | 91 +---
tests/python/relay/test_ir_parser.py | 14 ++
3 files changed, 95 insertions(+), 31 deletions(-)
diff --git a/python/tvm/__init__.py b/python/tvm/__init__.py
index 569e8f0..c2b4fdb 100644
--- a/python/tvm/__init__.py
+++ b/python/tvm/__init__.py
@@ -68,15 +68,28 @@ from . import support
from .contrib import rocm as _rocm, nvcc as _nvcc, sdaccel as _sdaccel
+def _should_print_backtrace():
+in_pytest = "PYTEST_CURRENT_TEST" in os.environ
+tvm_backtrace = os.environ.get("TVM_BACKTRACE", "0")
+
+try:
+tvm_backtrace = bool(int(tvm_backtrace))
+except ValueError:
+raise ValueError(
+f"invalid value for TVM_BACKTRACE `{tvm_backtrace}`, please set to
0 or 1."
+)
+
+return in_pytest or tvm_backtrace
+
+
def tvm_wrap_excepthook(exception_hook):
"""Wrap given excepthook with TVM additional work."""
def wrapper(exctype, value, trbk):
"""Clean subprocesses when TVM is interrupted."""
-in_pytest = "PYTEST_CURRENT_TEST" in os.environ
-
-if exctype is error.DiagnosticError and not in_pytest:
-pass
+if exctype is error.DiagnosticError and not _should_print_backtrace():
+# TODO(@jroesch): consider moving to C++?
+print("note: run with `TVM_BACKTRACE=1` environment variable to
display a backtrace.")
else:
exception_hook(exctype, value, trbk)
diff --git a/src/parser/parser.cc b/src/parser/parser.cc
index 987a6e2..afcf707 100644
--- a/src/parser/parser.cc
+++ b/src/parser/parser.cc
@@ -605,30 +605,43 @@ class Parser {
return ast;
}
+ struct MetaRef {
+std::string type_key;
+uint64_t node_index;
+Span span;
+MetaRef(std::string type_key, uint64_t node_index, Span span)
+: type_key(type_key), node_index(node_index), span(span) {}
+ };
+
+ MetaRef MetaRefFromToken(const Token& tok) {
+Call ref = Downcast(tok->data);
+auto attrs = ref->attrs.as();
+auto type_key = attrs->node_type_key;
+auto index = attrs->node_index;
+return MetaRef(type_key, index, ref->span);
+ }
+
/*! \brief Parse a meta reference of the form `meta[type_key][node_index]`.
* For example `meta[relay.Constant][0]` references the first constant,
`meta[relay.Constant][1]`
* the second, and so on.
*/
ObjectRef ParseMetaRef() {
-auto meta_ref = Match(TokenType::kMetaReference);
-Call ref = Downcast(meta_ref->data);
-auto attrs = ref->attrs.as();
-auto type_key = attrs->node_type_key;
-auto index = attrs->node_index;
-auto it = this->meta_table.find(type_key);
+auto meta_ref_tok = Match(TokenType::kMetaReference);
+auto meta_ref = MetaRefFromToken(meta_ref_tok);
+auto it = this->meta_table.find(meta_ref.type_key);
if (it != this->meta_table.end()) {
auto nodes = (*it).second;
- if (index < nodes.size()) {
-return nodes[index];
+ if (meta_ref.node_index < nodes.size()) {
+return nodes[meta_ref.node_index];
} else {
-this->diag_ctx.Emit(Diagnostic::Error(meta_ref->span)
-<< "the node index `" << index << "` is out of
bounds for `" << type_key
-<< "`");
+this->diag_ctx.Emit(Diagnostic::Error(meta_ref.span)
+<< "the node index `" << meta_ref.node_index
+<< "` is out of bounds for `" << meta_ref.type_key
<< "`");
return ObjectRef();
}
} else {
- this->diag_ctx.Emit(Diagnostic::Error(meta_ref->span)
- << "no entry in the meta table for `" << type_key <<
"`");
+ this->diag_ctx.Emit(Diagnostic::Error(meta_ref.span)
+ << "no entry in the meta table for `" <<
meta_ref.type_key << "`");
return ObjectRef();
}
}
@@ -922,10 +935,7
[GitHub] [tvm] altanh opened a new pull request #7025: [CI] Hotfix CI (see #7010)
altanh opened a new pull request #7025: URL: https://github.com/apache/tvm/pull/7025 Addresses #7010 partially, hopefully enough for now while we work on a broader solution. Removes PyTorch dependency from conv2d gradient test, this should fix the CI failures. We might want to get this merged ASAP so people can retrigger CI on PRs. cc @tqchen @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] [tvm] kevinthesun opened a new pull request #7024: Update CI to use Thrust for GPU
kevinthesun opened a new pull request #7024: URL: https://github.com/apache/tvm/pull/7024 We also need to update docker image. @tqchen @zhiics 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] [tvm] jwfromm commented on a change in pull request #7006: [frontend][keras] Add support for TimeDistributed
jwfromm commented on a change in pull request #7006:
URL: https://github.com/apache/tvm/pull/7006#discussion_r535456535
##
File path: python/tvm/relay/frontend/keras.py
##
@@ -927,6 +1031,66 @@ def _convert_repeat_vector(inexpr, keras_layer, _):
return out
+def _convert_time_distributed(inexpr, keras_layer, etab, input_shape=None,
data_layout=None):
+# TimeDistributed: split input tensor along the second dimension (assumed
to be time),
+# apply inner layer to each split individually,
+# and then combine the results
+if input_shape is None:
+input_shape = keras_layer.input_shape
+if data_layout is None:
+data_layout = etab.data_layout
+
+assert len(input_shape) >= 2, "Input to TimeDistributed must have at least
two dimensions"
+
+inner_layer = keras_layer.layer
+inner_input_shape = [d for (i, d) in enumerate(input_shape) if i != 1]
+
+# for NDHWC, inner data layout will drop the D
+inner_data_layout = None
+if data_layout == "NDHWC":
+inner_data_layout = "NHWC"
+
+# some code duplication from keras_op_to_relay
+# but it's useful to avoid cluttering the etab
+inner_layer_op_name = type(keras_layer.layer).__name__
+if inner_layer_op_name not in _convert_map:
+raise tvm.error.OpNotImplemented(
+"The inner layer for TimeDistributed {} is not supported for
frontend Keras.".format(
+inner_layer_op_name
+)
+)
+
+conversion_func = lambda expr: _convert_map[inner_layer_op_name](
+expr, inner_layer, etab, input_shape=inner_input_shape,
data_layout=inner_data_layout
Review comment:
Ok that seems reasonable. I think its fine to remove `data_layout` from
etab and use explicit inputs.
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[GitHub] [tvm] altanh commented on issue #7010: [TEST][FLAKY] test_op_grad_level2.py::test_conv2d_grad.py
altanh commented on issue #7010: URL: https://github.com/apache/tvm/issues/7010#issuecomment-738177491 I agree. I think first we should address #7017 to confirm it's the same failure that is happening on CI, and then look into removing the dependencies. If we can't remove the dependency (like in the case of `test_onnx.py` and `test_dlpack.py`), I propose sandboxing based on dependency so that files with conflicting dependencies will always be run on separate pytest processes. If a single file uses two conflicting dependencies, I'm not sure how to proceed- we may need to build dependencies with special libomp configuration on the CI machine (at least we can cache this?) 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] [tvm] merrymercy commented on a change in pull request #7020: [AutoScheduler] Misc update to hardware parameter and task scheduler
merrymercy commented on a change in pull request #7020:
URL: https://github.com/apache/tvm/pull/7020#discussion_r535444853
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -168,15 +175,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
else: # group_conv2d
if layout == "NCHW":
assert kernel_layout == "OIHW"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
Review comment:
```suggestion
logger.warning("group_conv2d is not optimized for x86 with
autotvm.")
```
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -168,15 +175,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
else: # group_conv2d
if layout == "NCHW":
assert kernel_layout == "OIHW"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
strategy.add_implementation(
wrap_compute_conv2d(topi.nn.group_conv2d_nchw,
has_groups=True),
wrap_topi_schedule(topi.generic.schedule_group_conv2d_nchw),
name="group_conv2d_nchw.generic",
)
elif layout == "NHWC":
assert kernel_layout == "HWIO"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
Review comment:
```suggestion
logger.warning("group_conv2d is not optimized for x86 with
autotvm.")
```
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -117,14 +118,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
return conv2d_NCHWc_strategy_cpu(attrs, inputs, out_type, target)
elif layout == "NHWC":
assert kernel_layout == "HWIO"
+if not is_auto_scheduler_enabled():
+logger.warning("conv2d NHWC layout is not optimized for x86 in
autotvm.")
Review comment:
```suggestion
logger.warning("conv2d NHWC layout is not optimized for x86
with autotvm.")
```
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -117,14 +118,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
return conv2d_NCHWc_strategy_cpu(attrs, inputs, out_type, target)
elif layout == "NHWC":
assert kernel_layout == "HWIO"
+if not is_auto_scheduler_enabled():
+logger.warning("conv2d NHWC layout is not optimized for x86 in
autotvm.")
strategy.add_implementation(
wrap_compute_conv2d(topi.nn.conv2d_nhwc,
need_auto_scheduler_layout=True),
wrap_topi_schedule(topi.x86.schedule_conv2d_nhwc),
name="conv2d_nhwc.x86",
)
elif layout == "HWCN":
assert kernel_layout == "HWIO"
-logger.warning("conv2d HWCN layout is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("conv2d HWCN layout is not optimized for x86 in
autotvm.")
Review comment:
```suggestion
logger.warning("conv2d HWCN layout is not optimized for x86
with autotvm.")
```
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[GitHub] [tvm] merrymercy commented on a change in pull request #7020: [AutoScheduler] Misc update to hardware parameter and task scheduler
merrymercy commented on a change in pull request #7020:
URL: https://github.com/apache/tvm/pull/7020#discussion_r535444556
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -168,15 +175,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
else: # group_conv2d
if layout == "NCHW":
assert kernel_layout == "OIHW"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
strategy.add_implementation(
wrap_compute_conv2d(topi.nn.group_conv2d_nchw,
has_groups=True),
wrap_topi_schedule(topi.generic.schedule_group_conv2d_nchw),
name="group_conv2d_nchw.generic",
)
elif layout == "NHWC":
assert kernel_layout == "HWIO"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
Review comment:
```suggestion
logger.warning("group_conv2d is not optimized for x86 with
autotvm.")
```
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[GitHub] [tvm] comaniac commented on a change in pull request #7020: [AutoScheduler] Misc update to hardware parameter and task scheduler
comaniac commented on a change in pull request #7020:
URL: https://github.com/apache/tvm/pull/7020#discussion_r535429580
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -168,15 +175,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
else: # group_conv2d
if layout == "NCHW":
assert kernel_layout == "OIHW"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
Review comment:
`with autotvm`?
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -168,15 +175,17 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
else: # group_conv2d
if layout == "NCHW":
assert kernel_layout == "OIHW"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
strategy.add_implementation(
wrap_compute_conv2d(topi.nn.group_conv2d_nchw,
has_groups=True),
wrap_topi_schedule(topi.generic.schedule_group_conv2d_nchw),
name="group_conv2d_nchw.generic",
)
elif layout == "NHWC":
assert kernel_layout == "HWIO"
-logger.warning("group_conv2d is not optimized for x86.")
+if not is_auto_scheduler_enabled():
+logger.warning("group_conv2d is not optimized for x86.")
Review comment:
`with autotvm`?
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[tvm] branch main updated: [µTVM] Fix paths in the reference VM tutorial and add vbguest recommendation (#7015)
This is an automated email from the ASF dual-hosted git repository. tqchen pushed a commit to branch main in repository https://gitbox.apache.org/repos/asf/tvm.git The following commit(s) were added to refs/heads/main by this push: new 42583d6 [µTVM] Fix paths in the reference VM tutorial and add vbguest recommendation (#7015) 42583d6 is described below commit 42583d6a722ef117e9ca83460d6a0182dcf44c89 Author: Andrew Reusch AuthorDate: Thu Dec 3 08:35:46 2020 -0800 [µTVM] Fix paths in the reference VM tutorial and add vbguest recommendation (#7015) * Add recommendation to install vbguest plugin. * Update directories to match checked-in.
[GitHub] [tvm] tqchen merged pull request #7015: [µTVM] Fix paths in the reference VM tutorial and add vbguest recommendation
tqchen merged pull request #7015: URL: https://github.com/apache/tvm/pull/7015 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] [tvm] tqchen edited a comment on issue #7010: [TEST][FLAKY] test_op_grad_level2.py::test_conv2d_grad.py
tqchen edited a comment on issue #7010: URL: https://github.com/apache/tvm/issues/7010#issuecomment-738105336 It would be great to propose a fix, given that the flaky error happens quite frequently. Is this related to the fact that we are using pytorch for gradient testing? Ideally we sould move that to a separate set of test suite. By default, we should use numerical gradient checking that is independent from other frameworks 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] [tvm] tqchen commented on issue #7010: [TEST][FLAKY] test_op_grad_level2.py::test_conv2d_grad.py
tqchen commented on issue #7010: URL: https://github.com/apache/tvm/issues/7010#issuecomment-738105336 It would be great to propose a fix. Is this related to the fact that we are using pytorch for gradient testing? Ideally we sould move that to a separate set of test suite. By default, we should use numerical gradient checking that is independent from other frameworks 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] [tvm] merrymercy merged pull request #7022: [auto_scheduler] Part.1 metal default hardware params
merrymercy merged pull request #7022: URL: https://github.com/apache/tvm/pull/7022 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
[tvm] branch main updated: [auto_scheduler] metal default hardware params (#7022)
This is an automated email from the ASF dual-hosted git repository.
lmzheng pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new 965a67e [auto_scheduler] metal default hardware params (#7022)
965a67e is described below
commit 965a67e7a04612806a390b50e2cca1c0a7744900
Author: Bing Xu
AuthorDate: Thu Dec 3 06:39:06 2020 -0800
[auto_scheduler] metal default hardware params (#7022)
---
src/auto_scheduler/search_task.cc | 11 +++
1 file changed, 11 insertions(+)
diff --git a/src/auto_scheduler/search_task.cc
b/src/auto_scheduler/search_task.cc
index 0b85a03..bd09a70 100755
--- a/src/auto_scheduler/search_task.cc
+++ b/src/auto_scheduler/search_task.cc
@@ -72,6 +72,17 @@ HardwareParams
HardwareParamsNode::GetDefaultHardwareParams(const Target& target
p_hardware_params->max_vthread_extent = p_hardware_params->warp_size / 4;
return hardware_params;
+ } else if (target->kind->device_type == kDLMetal) {
+// Reference:
https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
+// This setting looks working for Metal GPUs later than A10
+auto hardware_params = HardwareParams(-1, 16, 64);
+auto* p_hardware_params = hardware_params.CopyOnWrite();
+p_hardware_params->max_shared_memory_per_block = 32 * 1024;
+p_hardware_params->max_registers_per_block = 4 * 1024;
+p_hardware_params->max_threads_per_block = 1024;
+p_hardware_params->warp_size = 8;
+p_hardware_params->max_vthread_extent = p_hardware_params->warp_size / 4;
+return hardware_params;
} else {
LOG(FATAL) << "No default hardware parameters for target: " << target;
}
[GitHub] [tvm] merrymercy merged pull request #7019: [AutoScheduler] Add a tutorial on auto-scheduling a network for x86 CPU
merrymercy merged pull request #7019: URL: https://github.com/apache/tvm/pull/7019 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
[tvm] branch main updated (22a0877 -> 3afde62)
This is an automated email from the ASF dual-hosted git repository.
lmzheng pushed a change to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git.
from 22a0877 Fix trt Test (#7016)
add 3afde62 [AutoScheduler] Add a tutorial on auto-scheduling a network
for x86 CPU (#7019)
No new revisions were added by this update.
Summary of changes:
docs/dev/convert_layout.rst| 1 +
python/tvm/auto_scheduler/measure.py | 4 +
.../ci_logs/resnet-18-NHWC-B1-cuda.json| 26 +
.../auto_scheduler/ci_logs/resnet-18-NHWC-B1.json | 26 -
.../ci_logs/resnet-50-NHWC-B1-llvm.json| 31 ++
tutorials/auto_scheduler/tune_network_cuda.py | 21 ++--
.../{tune_network_cuda.py => tune_network_x86.py} | 108 ++---
7 files changed, 127 insertions(+), 90 deletions(-)
create mode 100644 tutorials/auto_scheduler/ci_logs/resnet-18-NHWC-B1-cuda.json
delete mode 100644 tutorials/auto_scheduler/ci_logs/resnet-18-NHWC-B1.json
create mode 100644 tutorials/auto_scheduler/ci_logs/resnet-50-NHWC-B1-llvm.json
copy tutorials/auto_scheduler/{tune_network_cuda.py => tune_network_x86.py}
(76%)
