[TOPI] [Hexagon] Batch flatten slice op initial version - #11522

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kparzysz-quic merged 12 commits into
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abhikran-quic:batch_flatten_slice
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[TOPI] [Hexagon] Batch flatten slice op initial version#11522
kparzysz-quic merged 12 commits into
apache:mainfrom
abhikran-quic:batch_flatten_slice

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@abhikran-quicabhikran-quic commented Jun 1, 2022•
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This patch adds the initial python implementation batch flatten slice op for hexagon.

Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.

cc @Lunderberg@cconvey@mehrdadh

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abhikran-quic commented Jun 2, 2022

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HI @Lunderberg , @cconvey : Could you please review this PR ?

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Overall looks good, just some general comments and nitpicks.

Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py Outdated
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py Outdated
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg for your review. I have fixed the comments.

Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.

tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor)

Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py Outdated
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py Outdated
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CI is passing now.

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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?

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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.

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abhikran-quicforce-pushed the batch_flatten_slice branch 2 times, most recently from 199bd85 to e24bbd6CompareJune 21, 2022 07:11
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Hi @Lunderberg@cconvey@mehrdadh,
Can you please review this PR ? It's ready to be merged from my side.

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Can you please review this PR ? It's ready to be merged from my side.

Reviewing now.

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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.

Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py Outdated
@LunderbergLunderberg mentioned this pull request Jun 23, 2022
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Comment threadpython/tvm/topi/hexagon/utils.py Outdated
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py Outdated
Comment threadtests/python/contrib/test_hexagon/infrastructure.py Outdated
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.

Comment threadpython/tvm/topi/hexagon/utils.py Outdated
Comment threadtests/python/contrib/test_hexagon/infrastructure.py Outdated
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py Outdated
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py Outdated
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!

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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.

Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

batch_flatten_func = te.create_prim_func([inp, out])
sch = tir.Schedule(batch_flatten_func, debug_mask="all")
compute = sch.get_block("compute")

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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.

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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.

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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below

@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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abhikran-quic commented Jun 27, 2022

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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.

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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.

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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.

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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.

@mehrdadh : I have resolved merge conflicts. Could you please review this ?

@kparzysz-quic
kparzysz-quic merged commit 915c23b into apache:mainJun 30, 2022
@abhikran-quic
abhikran-quic deleted the batch_flatten_slice branch June 30, 2022 14:38
blackkker pushed a commit to blackkker/tvm that referenced this pull request Jul 7, 2022
@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
masahi pushed a commit to masahi/tvm that referenced this pull request Jul 15, 2022
@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
@AndrewZhaoLuoAndrewZhaoLuo mentioned this pull request Oct 4, 2022
Closed
mikeseven pushed a commit to mikeseven/tvm that referenced this pull request Sep 27, 2023
@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
Merged
kparzysz-quic merged 12 commits into
apache:mainfrom 
abhikran-quic:batch_flatten_slice
Jun 30, 2022
Merged
[TOPI] [Hexagon] Batch flatten slice op initial version#11522
kparzysz-quic merged 12 commits into
apache:mainfrom 
abhikran-quic:batch_flatten_slice
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@abhikran-quicabhikran-quic
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
cc @Lunderberg@cconvey@mehrdadh
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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CI is passing now.
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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
@abhikran-quic
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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
Comment threadpython/tvm/topi/hexagon/utils.py
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Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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👍
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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
@kparzysz-quic
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merged commit 915c23b
into
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deleted the

batch_flatten_slice

branch
June 30, 2022 14:38
blackkker pushed a commit
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Jul 7, 2022
@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
masahi pushed a commit
to masahi/tvm
that referenced
this pull request
Jul 15, 2022
@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
@AndrewZhaoLuoAndrewZhaoLuo
mentioned this pull request
Oct 4, 2022

Closed

mikeseven pushed a commit
to mikeseven/tvm
that referenced
this pull request
Sep 27, 2023
@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
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* Update tests to use util functions
* Update __init__.py
* Fix review comments
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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
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[TOPI] [Hexagon] Batch flatten slice op initial version#11522
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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CI is passing now.
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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
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Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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👍
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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
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[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
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[TOPI] [Hexagon] Batch flatten slice op initial version#11522
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
cc @Lunderberg@cconvey@mehrdadh
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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CI is passing now.
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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
Comment threadpython/tvm/topi/hexagon/utils.py
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Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
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merged commit 915c23b
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@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
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abhikran-quic:batch_flatten_slice
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[TOPI] [Hexagon] Batch flatten slice op initial version#11522
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
cc @Lunderberg@cconvey@mehrdadh
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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CI is passing now.
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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
Comment threadpython/tvm/topi/hexagon/utils.py
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Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
@kparzysz-quic
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merged commit 915c23b
into
apache:mainJun 30, 2022
@abhikran-quic
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deleted the

batch_flatten_slice

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June 30, 2022 14:38
blackkker pushed a commit
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Jul 7, 2022
@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
masahi pushed a commit
to masahi/tvm
that referenced
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Jul 15, 2022
@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
@AndrewZhaoLuoAndrewZhaoLuo
mentioned this pull request
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@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
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kparzysz-quic merged 12 commits into
apache:mainfrom 
abhikran-quic:batch_flatten_slice
Jun 30, 2022
Merged
[TOPI] [Hexagon] Batch flatten slice op initial version#11522
kparzysz-quic merged 12 commits into
apache:mainfrom 
abhikran-quic:batch_flatten_slice
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
cc @Lunderberg@cconvey@mehrdadh
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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CI is passing now.
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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
Comment threadpython/tvm/topi/hexagon/utils.py
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Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
@jverma-quicjverma-quicJun 24, 2022

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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
@abhikran-quicabhikran-quicJun 24, 2022

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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
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merged commit 915c23b
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@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
masahi pushed a commit
to masahi/tvm
that referenced
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Jul 15, 2022
@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
@AndrewZhaoLuoAndrewZhaoLuo
mentioned this pull request
Oct 4, 2022

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mikeseven pushed a commit
to mikeseven/tvm
that referenced
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Sep 27, 2023
@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
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kparzysz-quic merged 12 commits into
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abhikran-quic:batch_flatten_slice
Jun 30, 2022
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[TOPI] [Hexagon] Batch flatten slice op initial version#11522
kparzysz-quic merged 12 commits into
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abhikran-quic:batch_flatten_slice
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@abhikran-quicabhikran-quic
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
cc @Lunderberg@cconvey@mehrdadh
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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CI is passing now.
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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
Comment threadpython/tvm/topi/hexagon/utils.py
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Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/topi/test_batch_flatten.py
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Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
@jverma-quicjverma-quicJun 24, 2022

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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
@abhikran-quicabhikran-quicJun 24, 2022

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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
@kparzysz-quic
kparzysz-quic
merged commit 915c23b
into
apache:mainJun 30, 2022
@abhikran-quic
abhikran-quic
deleted the

batch_flatten_slice

branch
June 30, 2022 14:38
blackkker pushed a commit
to blackkker/tvm
that referenced
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Jul 7, 2022
@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
masahi pushed a commit
to masahi/tvm
that referenced
this pull request
Jul 15, 2022
@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
@AndrewZhaoLuoAndrewZhaoLuo
mentioned this pull request
Oct 4, 2022

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mikeseven pushed a commit
to mikeseven/tvm
that referenced
this pull request
Sep 27, 2023
@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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[TOPI] [Hexagon] Batch flatten slice op initial version - #11522
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kparzysz-quic merged 12 commits into
apache:mainfrom 
abhikran-quic:batch_flatten_slice
Jun 30, 2022
Merged
[TOPI] [Hexagon] Batch flatten slice op initial version#11522
kparzysz-quic merged 12 commits into
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abhikran-quic:batch_flatten_slice
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This patch adds the initial python implementation batch flatten slice op for hexagon.
Slice ops are basically ops that make certain assumptions about the input and output dimensions and are expected to be called after the original op has been sliced according to those dimensions at the graph level.
cc @Lunderberg@cconvey@mehrdadh
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HI @Lunderberg , @cconvey : Could you please review this PR ?
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Overall looks good, just some general comments and nitpicks.
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
Comment threadtests/python/contrib/test_hexagon/infrastructure.py
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Thank you @Lunderberg for your review. I have fixed the comments.
Right now CI is failing because of v69 support not being available in LLVM. Hopefully #11539 should address this and then the tests will pass in CI.
tests/python/contrib/test_hexagon/test_batch_flatten.py::TestBatchFlatten::test_batch_flatten[float16-input_shape3-n11c-1024c-1d-input_axis_sep0-nc-1d-output_axis_sep0] 'hexagonv69' is not a recognized processor for this target (ignoring processor) 
Comment threadtests/python/contrib/test_hexagon/test_batch_flatten.py
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Jun 16, 2022

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CI is passing now.
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Jun 17, 2022

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Hi @Lunderberg@cconvey : Could you please review this PR for any more comments ?
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Jun 17, 2022•

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@abhikran-quic : Apologies for being slow to reply. I'm hoping to review this early next week if that's helpful.
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Jun 22, 2022

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Hi @Lunderberg@cconvey@mehrdadh,

Can you please review this PR ? It's ready to be merged from my side.
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Jun 23, 2022

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Can you please review this PR ? It's ready to be merged from my side.
Reviewing now.
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Taking one more read-through after the latest commits, and found a couple more nitpicky changes.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py
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Thank you @Lunderberg and @jverma-quic for your comments. I've fixed them in the latest commit.
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Thank you for making the changes, and LGTM!
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LGTM! One minor comment, but feel free to ignore it. We can revisit the issue later if necessary.
Comment threadpython/tvm/topi/hexagon/slice_ops/batch_flatten.py

 batch_flatten_func = te.create_prim_func([inp, out])
 sch = tir.Schedule(batch_flatten_func, debug_mask="all")
 compute = sch.get_block("compute")
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I'm a bit suspicious about assuming that there's a block named "compute", as I don't see any promises in the documentation about the name and what it represents. But making assumptions like this seems somewhat idiomatic within TVM, so IMHO it's okay enough.
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I completely agree with you and don't like it either. But, there doesn't seem to be a way to specify a different block name for topi defined ops. Please correct me if I'm wrong.
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Since I'm reusing batch_flatten compute in topi , the block name compute comes up. Sharing the schedule below
@main = primfn(var_A: handle, var_compute: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_1: Pointer(global float16), float16, [1, 1, 1, 2, 1024], [], axis_separators=[4]),
compute: Buffer(compute_1: Pointer(global float16), float16, [1, 2, 1024], [], axis_separators=[2])}
buffer_map = {var_A: A, var_compute: compute} {
block([], "root") {
tir.reads([])
tir.writes([])
for (i0: int32, 0, 1) {
for (i1_0_0: int32, 0, 1) {
for (i1_0_1: int32, 0, 1) {
for (i1_1_0: int32, 0, 2) {
for (i1_1_1_0: int32, 0, 16) {
for (i1_1_1_1: int32, 0, 64) "vectorized" {
block([1, 2048], "compute") as [i, j] {
bind(i, 0)
bind(j, (((i1_1_0*1024) + (i1_1_1_0*64)) + i1_1_1_1))
tir.reads([A[0, 0, 0, (j / 1024), (j % 1024)]])
tir.writes([compute[0, (j / 1024), (j % 1024)]])
compute[0, (j / 1024), (j % 1024)] = A[0, 0, 0, (j / 1024), (j % 1024)]
}
}
}
}
}
}
}
}
}

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👍
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Jun 27, 2022

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@Lunderberg , @mehrdadh , @cconvey : Could you please merge this PR ? I've fixed merge conflicts.
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Jun 28, 2022

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Gentle reminder! Please help in merging this PR. I want to raise another PR that's dependent on this one.
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Jun 29, 2022

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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
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Jun 30, 2022

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@abhikran-quic please resolve the conflict by rebasing with main and push into this branch.
@mehrdadh : I have resolved merge conflicts. Could you please review this ?
@kparzysz-quic
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blackkker pushed a commit
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@abhikran-quic@blackkker
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
3a92c85
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
masahi pushed a commit
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@abhikran-quic@masahi
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
e391dcb
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
@AndrewZhaoLuoAndrewZhaoLuo
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mikeseven pushed a commit
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Sep 27, 2023
@abhikran-quicMikael Sevenier
[TOPI] [Hexagon] Batch flatten slice op initial version (apache#11522)
c5f1e03
* [TOPI] [Hexagon] Batch flatten slice op initial version
* Fix lint errors
* Fix more lint errors
* Fix lint warnings
* Fix review comments
* Update tests to use util functions
* Update __init__.py
* Fix review comments
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