tvm-rfcs [RFC][BYOC] Marvell ML/AI Accelerator Integration

Integrate Marvell’s ML/AI accelerator with TVM BYOC framework in order to bring the TVM ecosystem to Marvell customers.

We have also posted a pre-RFC at https://discuss.tvm.apache.org/t/pre-rfc-byoc-marvell-ml-ai-accelerator-integration/11691. Plus, we have up-streamed our POC code changes in: PR-9730 (https://github.com/apache/tvm/pull/9730). We have resolved a Mrvl.cmake issue but we are now waiting for tips from the TVM community in order to make the PR's Jenkins task_rust.sh to pass.

Note1: we have not spend much time on driver/runtime integration and therefore can be missing changes for rust cargo. We are trying to catch up here. Note2: we do run TVM-Jenkinsfile-like build & tests locally but we have skipped the task_rust.sh script during our locally run.

Dec 17 '21 21:12 ccjoechou

Hello,

How can we request a reviewer to review our RFC? Also, we have followed the RFC template and listed several unresolved questions, which need help from reviewer and/or TVM community to resolve? The first few issues are related TVM Jenkins build's rust/cargo failure. I.e., we like to add two more build config flags (use_mrvl and use_mrvl_runtime) in tvm/rust/ setups but need help to update the cargo.io's tvm-build package -- BTW tvm-build seems to be owned by OctoML GitHub. Also, there are other tvm/tests/scripts/task_rush.sh errors. Can a reviewer provide additional information or pointers explain how we should debug the task_rush.sh run to find and resolve issues due to new changes?

Thanks,

Joe

Jan 12 '22 16:01 ccjoechou

@areusch @jroesch Is there some aging process to follow in the TVM Open Source so issues get looked at and advanced without getting stale for weeks? As there are timelines that get affected by such delays.

Thanks

Jan 18 '22 18:01 ssdurako

@areusch - Thanks for replying. Please see my comments below regarding your questions.

Question: device planning, which I think maybe you're doing outside the typical TVM flow? * If I understand your question correctly: Yes and no. * We are using TVM relay flow to generate JSON meta files and IR sub-graphs of the network model * We like to use TVM code-gen and runtime flow to generate binary to run inference for “llvm-part” of the network model * But we also like to use our build-from-outside-TVM-flow, Marvell accelerator backend/code-gen component to generate binary for “Marvell-part” of the network model (to be run on Marvell accelerator) i. Not right now, but in a future RFC, we can and like to provide APIs and library files so that we can embed Marvell backend/code-gen component into libtvm.so and within the typical TVM flow

 *   Not right now, but in a future RFC, when Marvell driver APIs and TVM-Marvell runtime & driver hookups are ready, we like to use the typical TVM flow (with Marvell modifications) to run “Marvell-part” computes of the network on Marvell HW accelerator directly and llvm-part of computes

Question: executor, which I think you may have re-implemented here. * I believe that we implemented specializations of the current executor code in order to generate Marvell JSON meta files. * It is possible that others may have also implemented “parts of” similar specializations in the last 6 months – if this is the case (and we can use them), we like to know how we can merge codebase
Question: to provide code links into your PoC if that would help me understand--I can do some targeted reading * As listed in the RFC, our POC changes have been up-streamed to the TVM GitHub’s PR-9730. If you like to, you should also be able to git clone from https://github.com/ccjoechou/tvm.git and checkout & use the “byoc-mrvl” branch.
Question: it would also be great to spell out a plan for tests here--it seems like it might be possible to checkin your compiler/simulator into our CI, but could you be more explicit about your plans there? * We have added infrastructure code and a test_mrvl suite to run the POC TVM-BYOC-Marvell flow * Currently, there is a code-gen test, which can be run to use a pre-trained a ssd-resnet50 model - please see tvm/tests/python/contrib/test_mrvl/ test_mrvl_codegen.py and its test_ssd_resnet50_aot_json_codegen function * Should also be able to run regular docker steps below to exercise the BYOC-Marvell flow to compile a ssd-resnet50 network to generate JSON meta files for Marvell accelerator:

./docker/bash.sh --name tvm_mrvl tlcpack/ci-cpu:v0.79 ./tests/scripts/task_config_build_cpu.sh
./docker/bash.sh --name tvm_mrvl tlcpack/ci-cpu:v0.79 ./tests/scripts/task_build.sh build -j10
./docker/bash.sh --name tvm_mrvl tlcpack/ci-cpu:v0.79 ./tests/scripts/task_ci_setup.sh
./docker/bash.sh --name tvm_mrvl tlcpack/ci-cpu:v0.79 ./tests/scripts/task_python_integration.sh

 *   For the last task_python_integration.sh suite, can edit the file in order to skip steps to run other test suites but focus on running only tests/python/contrib/test_mrvl:

sudo pip3 install gluoncv run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-contrib tests/python/contrib/test_mrvl

Pleas also see my comments in-line below. Let me raise a difference here:

The TVM partition’s sub-graph seems to represent a relay function, which can include multiple frontend operators captured by utilizing the relay merge-composite patten
The Marvell sub-graph is a connected graph of multiple relay merge-composite functions – I did not know how to include a Figure in the RFC file before (now I do). But if you look at the listed pre-RFC link, we did include figures at end of the corresponding pre-RFC on the discuss forum – please check the end of pre-RFC and its figure to see whether they can help explaining the definition of Marvell sub-graphs here. https://discuss.tvm.apache.org/t/pre-rfc-byoc-marvell-ml-ai-accelerator-integration/11691].

Thanks again and please let us know, if you like to discuss more.

Joe

From: Andrew Reusch @.> Sent: Tuesday, January 18, 2022 12:48 PM To: apache/tvm-rfcs @.> Cc: Joe Chou @.>; Mention @.> Subject: [EXT] Re: [apache/tvm-rfcs] [RFC][BYOC] Marvell ML/AI Accelerator Integration (PR #48)

External Email

@areusch requested changes on this pull request.

@ssdurakohttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_ssdurako&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=d2Phi5OhJYC30chQlDSTXGdBUHLcKOytTeAstHx3XVU&e= @ccjoechouhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_ccjoechou&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=WZdEJI-EoLFqhJJcO9YZeCcSYSSC-s48_LgSB5F5K1c&e= apologies for the long delay! i think we missed this one since it was mailed during TVMCon and also just before we all took off for the holidays. I'll try to be a bit better about reviewing this.

Overall I have some understanding of your approach with this RFC. I'd like to further discuss some of the rationale behind:

device planning, which I think maybe you're doing outside the typical TVM flow?
executor, which I think you may have re-implemented here.

I'm a bit low on bandwidth to read your full PoC PR. would you mind clarifying the RFC as a starting point (or feel free to provide code links into your PoC if that would help me understand--I can do some targeted reading, I'm just fairly busy for a full read-through right now)

it would also be great to spell out a plan for tests here--it seems like it might be possible to checkin your compiler/simulator into our CI, but could you be more explicit about your plans there?

also cc @comaniachttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_comaniac&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=5likf9V3g5a6XmvwPAr-ais9kJ9tL1Oe3UCcdIReMIQ&e= @mbs-octomlhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_mbs-2Doctoml&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=e3YsFOFCwaZu1YZYwMB9BPZ3m6b0Ne-2byTRo1cYgvQ&e= @Mousiushttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_Mousius&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=mqoZGl7ZzAvBheq3qBAww4oxaItbLwRtTPxO_jkI7w4&e= @junrushao1994https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_junrushao1994&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=MuKxC3uDBMZAx2b6eewBCqm_vk3zVN29bCKxGNYf8a0&e= for further comments on BYOC, device planning, and support for custom executors

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787106344&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=R7fm2C2KsgY7__XRfP-OLUzHCTDIV2vV74gcKzdXtns&e=:

 conv2d + add + batch_norm + tuple.getitem(0) + relu

- For the first Marvell-BYOC revision, at most one for-accelerator Mrvl subgraph and at most one for-TVM-target

 non-Mrvl subgraph (let's call this sub-graph B) can be identified; plus, the for-accelerator Mrvl subgraph can

 only use input tensor(s) of given pre-trained network as its subgraph’s input tensors

+* Do code-gen step for each for-accelerator Mrvl subgraph:

- Marvell-BYOC-specific attributes are introduced for each composite-merged/fused Call node so that a Nodes-JSON

 file and a Constants-JSON file are produced for the Mrvl subgraph

+STEP (2) Run Mrvl-ML/AI Backend Compiler to generate model binary for each Mrvl subgraph

+* The Mrvl-ML/AI backend compiler will be distributed as an executable in the OCTEON SDK; and it can be used to read

in Nodes-JSON and Constants-JSON files of each Mrvl subgraph as input meta-data in order to generate final instructions,
in model binary file

+* Note: Mrvl-ML/AI backend compiler, which does accelerator-specific optimization and code generation, is not included

what's the test plan for this RFC? Would it be possible to add the Marvell backend compiler and simulator to our ci images and run against it in CI? [ccjoechou writes: for this BYOC-Marvell RFC, the POC PR codebase only contains code to generate JSON meta files. We have up-streamed our test_mrvl test suite but only contains JSON codegen. In our next RFC, we will provide runtime & driver hookups. We are working on a Marvell backend package with Marvell backend code-gen and Marvell software simulator, which mimics a cycle-approximate Marvell HW accelerator. This package can become available later for external usage. Currently, we are having problems run TVM rust/cargo and can’t find useful document to debug issues – plus, tvm-build is owned by OctoML (not GitHub TVM, right?)]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787107277&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=dTFGJaoaEmF7zEr2T3eNCq4rM8GuJUGIcBCKJwUjp7I&e=:

+STEP (2) Run Mrvl-ML/AI Backend Compiler to generate model binary for each Mrvl subgraph

+* The Mrvl-ML/AI backend compiler will be distributed as an executable in the OCTEON SDK; and it can be used to read

in Nodes-JSON and Constants-JSON files of each Mrvl subgraph as input meta-data in order to generate final instructions,
in model binary file

+* Note: Mrvl-ML/AI backend compiler, which does accelerator-specific optimization and code generation, is not included

to upstream

+STEP (3a) or (3b) Run inference on the software Simulator or on the Mrvl ML/AI HW accelerator for the Mrvl subgraph

+* The Mrvl Software Simulator of the Mrvl ML/AI HW accelerator will be distributed as an executable in a Mrvl-ML/AI tar

ball; and it can be used to read in input file(s) and the model binary to run inference for the Mrvl subgraph

+* Note: Mrvl ML/AI accelerator can run inference in either float16 mode or int8 quantization mode. For this RFC, we will

focus only on float16 inference run

just checking if this was the end of the sentence here [ccjoechou writes: yes]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787114611&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=ENAAXEINGM7EwToNOLBW3SyS_1du7HQF34XpbsKcVwc&e=:

     /* en_id=599 */) /* ty=Tensor[(256, 1568), float32] */;

 %11 = nn.dense(%9, %10, units=None, out_dtype="float32", /* en_id=600 */) /* ty=Tensor[(1, 256), float32] */;

 %12 = add(%11, meta[relay.Constant][5] /* ty=Tensor[(256), float32] */,

     /* en_id=601 */) /* ty=Tensor[(1, 256), float32] */;

 %13 = nn.relu(%12, /* en_id=602 */) /* ty=Tensor[(1, 256), float32] */;

 %14 = transpose(meta[relay.Constant][6] /* ty=Tensor[(256, 10), float32] */, axes=[1, 0],

     /* en_id=675 */) /* ty=Tensor[(10, 256), float32] */;

 %15 = nn.dense(%13, %14, units=None, out_dtype="float32", /* en_id=676 */) /* ty=Tensor[(1, 10), float32] */;

 add(%15, meta[relay.Constant][7] /* ty=Tensor[(10), float32] */, /* en_id=677 */) /* ty=Tensor[(1, 10), float32] */

+}

+```

+* We can get to the following one Mrvl subgraph by applying the default strategy.

- in the mrvl.py file: the compute_two_subgraphs() function of the class MrvlIRGraphUtils is used

 to create mod_mrvl_subgraph and mod_non_mrvl_subgraph for

could you clarify this sentence?

[ccjoechou writes: did not know how to include a Figure in the RFC file – but I did include figures at end of the corresponding pre-RFC on the discuss forum – please check the end of pre-RFC and its figure to see whether they can help explaining the definition of Marvell sub-graphs here. https://discuss.tvm.apache.org/t/pre-rfc-byoc-marvell-ml-ai-accelerator-integration/11691]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787115633&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=NMd2x1139DrgTciUNgZ8CD9PiwmazbEys_2ZMegmtEU&e=:

 %13 = nn.relu(%12, /* en_id=602 */) /* ty=Tensor[(1, 256), float32] */;

 %14 = transpose(meta[relay.Constant][6] /* ty=Tensor[(256, 10), float32] */, axes=[1, 0],

     /* en_id=675 */) /* ty=Tensor[(10, 256), float32] */;

 %15 = nn.dense(%13, %14, units=None, out_dtype="float32", /* en_id=676 */) /* ty=Tensor[(1, 10), float32] */;

 add(%15, meta[relay.Constant][7] /* ty=Tensor[(10), float32] */, /* en_id=677 */) /* ty=Tensor[(1, 10), float32] */

+}

+```

+* We can get to the following one Mrvl subgraph by applying the default strategy.

- in the mrvl.py file: the compute_two_subgraphs() function of the class MrvlIRGraphUtils is used

 to create mod_mrvl_subgraph and mod_non_mrvl_subgraph for

+```

def @main(%permute_input: Tensor[(1, 1, 28, 28), float32]) -> Tensor[(1, 10), float32] {

 %0 = @tvmgen_mrvl_main_0(%permute_input, /* en_id=4136 */) /* ty=Tensor[(1, 28, 28, 1), float32] */;

above, the RFC discusses having exactly one Marvell and non-Marvell subcgraph, but here I see 8 different function calls. do you mean that there are two targets, and you partition the graph into 8 subgraphs, but each subgraph is assigned to one or the other target? (reading further, I can see this is not the case, but it would help with reader comprehension to clarify this example)

[ccjoechou writes: We are talking about different definitions of “(sub-)graphs” here. In the TVM partition pass, TVM’s graph or sub-graph is a merge-composite IR function, which can contain a pre-define pattern of original frontend operators. In BYOC-Marvell RFC’s definition, a sub-graph is a connected graph of Marvell-merge-composite functions. For instance, a tvmgen_mrvl_main_4 (see below in original email), it is a TVM-partition sub-graph, which is a Marvell merge-composite function containing frontend operators: conv, add, batchnorm, tuple-get-item, relu. But a Marvell sub-graph contains, in the given test case, several Marvell merge-composite functions.]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787117464&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=9V5lRxYmfESbgL3yM5Ilb_xMOPZt-l7-zSzW5591Rko&e=:

 %3 = @tvmgen_mrvl_main_3(%2, /* en_id=4139 */) /* ty=Tensor[(1, 14, 14, 32), float32] */;

 %4 = @tvmgen_mrvl_main_4(%3, /* en_id=4140 */) /* ty=Tensor[(1, 7, 7, 32), float32] */;

 %5 = @tvmgen_mrvl_main_5(%4, /* en_id=4141 */) /* ty=Tensor[(1, 1568), float32] */;

 %6 = @tvmgen_mrvl_main_6(%5, /* en_id=4142 */) /* ty=Tensor[(1, 256), float32] */;

 @tvmgen_mrvl_main_7(%6, /* en_id=4143 */) /* ty=Tensor[(1, 10), float32] */

}

+```

+* In the above Mrvl subgraph, it is formed by "not-yet optimized Marvell (backend) layers". For example,

tvmgen_mrvl_main_0 to tvmgen_mrvl_main_7 are composited/fused Marvell layers.
- In the mrvl.mrvl_pattern_table() function, fusing patterns have been defined in order to composite

 original IR nodes into Marvell backend layers.

- For example, the following 3 IR call nodes (nn.conv2d + nn.bias_add + nn.relu) in the original IR graph

 are composited into one Marvell layer: tvmgen_mrvl_main_1, conceptually speaking.

+```

```
 # from original IR graphs
```

this process looks rather similar to the device planning pass used in tvm.relay.build. are they the same? if not, could you motivate why you don't want to reuse that one? [ccjoechou: sorry I am not sure what you meant by “device planning pass”? We have been following what others did in tvm/python/tvm/relay/op/contrib by utilizing relay passes (for example, ConvertLayout, MergeComposite, AnnotateTarget, and etc.). Please note that in this RFC, we only want to generate JSON meta files and we are not ready to propose/up-stream our runtime & driver hookups yet).]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787129775&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=mipKEVzGwI-kUn_2n_Pb71FU6tS4AsBlfYeJzyoNlJg&e=:

 %0 = @tvmgen_mrvl_main_0(%permute_input, /* en_id=4136 */) /* ty=Tensor[(1, 28, 28, 1), float32] */;

 %1 = @tvmgen_mrvl_main_1(%0, /* en_id=4137 */) /* ty=Tensor[(1, 28, 28, 64), float32] */;

 %2 = @tvmgen_mrvl_main_2(%1, /* en_id=4138 */) /* ty=Tensor[(1, 14, 14, 64), float32] */;

 %3 = @tvmgen_mrvl_main_3(%2, /* en_id=4139 */) /* ty=Tensor[(1, 14, 14, 32), float32] */;

 %4 = @tvmgen_mrvl_main_4(%3, /* en_id=4140 */) /* ty=Tensor[(1, 7, 7, 32), float32] */;

 def @tvmgen_mrvl_main_0(%mrvl_0_i0: Tensor[(1, 1, 28, 28), float32], Inline=1, Compiler="mrvl",

     global_symbol="tvmgen_mrvl_main_0", Primitive=1) -> Tensor[(1, 28, 28, 1), float32] {

   layout_transform(%mrvl_0_i0, src_layout="NCHW", dst_layout="NHWC",

       /* en_id=3334 */) /* ty=Tensor[(1, 28, 28, 1), float32] */

```
 }
```

+```

+* Currently, in order for the following Marvell classes/functions to identify a Mrvl subgraphs and a non-Mrvl

subgraph from the layout-converted, composited/fused IR graph, we are utilizing the unique en_id attribute

could you motivate the naming of en_id a bit? i recognize this is a common thing, but it might be nice to choose a slightly more specific name [ccjoechou writes: en_id as ExprNode ID. It is an extra field, which has been defined in the include/./tvm/ir/expr.h file for the RelayExprNode or just ExprNode class.]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787130884&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=Ew6gvJZix-z7H4QsP5VlpdhjQ4fJ-0-iezoY2L2FyAk&e=:

```
       mod = seq(mod)
```

```
   return mod
```
mod_new = tvm.IRModule(mod_mrvl.functions, mod_mrvl.type_definitions)
mod_new["main"] = MrvlSubgraphToRevert(mrvl_layers_in_mrvl_subgraph, mod_mrvl).visit(mod_mrvl["main"])
mod_new = relay.transform.RemoveUnusedFunctions()(mod_new)
mod_new = relay.transform.InferType()(mod_new)
mod_new = run_opt_pass(mod_new, relay.transform.DefuseOps())
mod_new = run_opt_pass(mod_new, relay.transform.ConvertLayout({"nn.conv2d": ["NCHW", "OIHW"], "nn.max_pool2d": ["NCHW"]}))
mod_new = run_opt_pass(mod_new, relay.transform.SimplifyExpr())
mod_new = run_opt_pass(mod_new, relay.transform._ffi_api.DropNoopTranspose())
mod_new = run_opt_pass(mod_new, relay.transform.InferType())
return mod_new

+```

+* Marvell-specific graph executor codegen, We have defined call backs and extension functions in the following files:

could you motivate this further? it's hard to understand why you need to output your own JSON format without some explanation here. [ccjoechou writes: the above code block is not to output our own JSON format; instead, it is to “revert” a sub-graph, which went over Marvell passes (e.g., ConvertLayout, MergeComposite, AnnotateTarget, and etc), back to its original, say, llvm-IR graph. Hence, we are: defuse-ops (opposing to Merge-Composite), reverted ConvertLayout, and etc. Motivation for reverting back to this llvm-part subgraph is to allow this llvm-part subgraph to go through TVM llvm-flow to generate runtime binary.]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787131656&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=rToD4fZ9EIg8eIkLKttKsDNIv0v45-SouZi0No4Qt5g&e=:

+```

+* the need to link between pre-trained model and final Marvell backend layer - for instance, through tvm_custom

- We did not include prototype code in PR-9730 but intend to provide our sample changes in another RFC and PR.

+# Drawbacks

+[drawbacks]: #drawbacks

+* We haven't identified any major not do items. Several other designs are by choices - that is we understand that

there are benefits for doing or benefits for not-doing.

+# Rationale and alternatives

+[rationale-and-alternatives]: #rationale-and-alternatives

+* We follow the TVM BYOC framework to enable BYOC Marvell flow without impacting any TVM core features.

it seems like there has been some impact to the GraphExecutor, and I think one point of confusion here is whether it was necessary to do that or whether you could have handled the additional runtime complexity inside a Marvell-specific runtime.Module. could you explain a bit further here?

[ccjoechou writes: I do not see GraphExecutor term in above. Please provide an example or point us to a TVM file so we can understand your comment a bit more. Thanks.]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787132217&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=JRaYKexNQLso0O88polwIXQyGMTNHhdyLbV1bKRfSrM&e=:

bypassing the tvm/Jenkinsfile's tests/scripts/task_rust.sh step. We will need help to re-enable the step.

+* We like to duplicate the Jenkins environment in order to run tvm/Jenkinsfile as is, but, we ran into many issues.

Currently, we have a tvm-like Jenksinsfile environment to only run a subset of test suites using a modified
Jenkinsfile.

+* We have identified a need to allow a call-back function to be registered when generating Mrvl-BYOC-specific

Nodes-JSON file. We are trying to follow TVM Python/CPP-CB style as much as possible. But, since our callback
function tvm/src/relay/backend/contrib/mrvl/graph_executor_codegen_mrvl.cc::GetExternalJSON() function is using
non-simple argument types, we need help from TVM community to provide suggestions/guidelines in order to make
new CB code better to meet TVM community requirements here.

+* For one Mrvl-BYOC relay transformation pass, we have identified a need to inject a (global) expr node ID for the

RelayExprNode class and its derived classes: Tuple and CallNode, so that during the transformation pass, we can
uniquely identify each Tuple or CallNode object. Again, we need help from TVM community to provide
suggestions/guidelines here in order to know whether this is one of the best ways to achieve the Mrvl-BYOC need.

i think it would help to spell out why you guys need to be able to identify each expression here.

[ccjoechou writes: yes but not just us but a data-scientist customer who is using TVM flow may like to know, for example, the linkages between the runtime performance #s and their corresponding user frontend model’s operators (e.g., each expression).]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787132545&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=dIcIPdSahtIX9piD7O2f_UhKljEWi-K6XvL8MI3svgY&e=:

RelayExprNode class and its derived classes: Tuple and CallNode, so that during the transformation pass, we can

uniquely identify each Tuple or CallNode object. Again, we need help from TVM community to provide
suggestions/guidelines here in order to know whether this is one of the best ways to achieve the Mrvl-BYOC need.

+* We also identified a need to maintain linkages between (operator-)information described in the original, given

pre-trained network model and the code-gen JSON files so that the compiler backend will be able to report user-level
(e.g., meaningful-to-user) messages regarding the given pre-trained network. For instance, in the
tvm/python/tvm/relay/frontend/onnx.py and common.py files, we can see user-level information being captured using
“tvm_custom” related code as in original onnx.py file for the given pre-trained network; but, in common.py, the code
later drops the linkage, via attrs.pop(“tvm_custom”), and does not pass the linkage onto the initial relay IR graph.
We have a draft solution to maintain linkages between the given pre-trained network model and its relay IR graph
(using expr node ID and tvm custom ID, plus, a few utility functions), but would like to know whether the TVM
community has any better or work-in-progress resolution.

+* When using TVM RPC code to exercise and run inference on a remote-hosted Mrvl ML/AI HW accelerator for the Mrvl

subgraph, we ran into one minor issue and have made local TVM RPC enhancement so that, when a TVM RPC client sends

could you explain the nature of the problem that requires the client to know the absolute path?

[ccjoechou writes: First, the TVM RPC server choses path for any uploaded file under tmp randomly (which can be good to reduce possible security problem). But, in our use case, we like to have the TVM RPC client to send a “runtime” command to the RPC server side to pre-process the just uploaded file before the file can be consumed autonomously by the RPC server using pre-defined script. We can’t find a way or via a TVM example, which shows how this can be done.]

In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.mdhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23discussion-5Fr787133542&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=9sG9W_t_KkSawUyjosauynkwCGnYieI8qZo2TOrwXQU&e=:

pre-trained network model and the code-gen JSON files so that the compiler backend will be able to report user-level

(e.g., meaningful-to-user) messages regarding the given pre-trained network. For instance, in the
tvm/python/tvm/relay/frontend/onnx.py and common.py files, we can see user-level information being captured using
“tvm_custom” related code as in original onnx.py file for the given pre-trained network; but, in common.py, the code
later drops the linkage, via attrs.pop(“tvm_custom”), and does not pass the linkage onto the initial relay IR graph.
We have a draft solution to maintain linkages between the given pre-trained network model and its relay IR graph
(using expr node ID and tvm custom ID, plus, a few utility functions), but would like to know whether the TVM
community has any better or work-in-progress resolution.

+* When using TVM RPC code to exercise and run inference on a remote-hosted Mrvl ML/AI HW accelerator for the Mrvl

subgraph, we ran into one minor issue and have made local TVM RPC enhancement so that, when a TVM RPC client sends
a file to the remote server, the TVM RPC client can know where the remote server saves the file on the remote machine.
Since this is not directly related to this Mrvl-BYOC PR, we will find time to contribute this enhance back in another
TVM PR soon.

+* In order for us to generate the constants-JSON file, we must “NOT” remove external params, which were stored in

why is this? params passed in MetadataModule are meant for consumption only by the runtime.Module which defines them. it seems like perhaps you need to consume them at the executor level. could you explain that?

[ccjoechou writes: We are using relay to generate JSON meta files representing the given network model in a way our backend code can process directly (e.g., only the marvell-part sub-graph(s). If we have include 100% of our backend code in the TVM codebase, then, we do not need to dump constants in JSON mega file; but, due to our backend code is built outside the typical TVM flow and can do other compile-time optimization including manipulating constants, we need constants JSON.]

— Reply to this email directly, view it on GitHubhttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_tvm-2Drfcs_pull_48-23pullrequestreview-2D855912667&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=L2xG3uKvJ4yJ_ixp1fyRD1bRTpZgR-yWZtSVJPsEHLo&e=, or unsubscribehttps://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AM636PACHANYNAATB4KA7B3UWXGYFANCNFSM5KJYF53A&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=NB9WVT4nlWSlIeHit8jurEkvilrrj22R7iAhBtdylbI&e=. Triage notifications on the go with GitHub Mobile for iOShttps://urldefense.proofpoint.com/v2/url?u=https-3A__apps.apple.com_app_apple-2Dstore_id1477376905-3Fct-3Dnotification-2Demail-26mt-3D8-26pt-3D524675&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=-StSSo5XOXXHK4bMwp_B8eLiRGQ0DXvLD_42vrZOwyQ&e= or Androidhttps://urldefense.proofpoint.com/v2/url?u=https-3A__play.google.com_store_apps_details-3Fid-3Dcom.github.android-26referrer-3Dutm-5Fcampaign-253Dnotification-2Demail-2526utm-5Fmedium-253Demail-2526utm-5Fsource-253Dgithub&d=DwMFaQ&c=nKjWec2b6R0mOyPaz7xtfQ&r=84zI-iH2xz28Q_xujSGbjYaq38CgcRgl_vGzCtF6TwQ&m=VNqzldKdQt96drtn_9Nfv7pyo4q1twvxtd6wMIR7FYI4cpvEKxaaVtBsanbms18J&s=HJ1qy1TGjh0KDSnnPWyF-MoI-UYA3X9I_er2DAq4ICs&e=. You are receiving this because you were mentioned.Message ID: @.***>

Jan 19 '22 00:01 ccjoechou

@areusch: Thanks again for your latest responses. Don't worry about timing (we all have our real jobs to do too). I am going to update the RFC #48 doc based on many of your feedback in the next few days -- including to add figures by following the example you gave and to add descriptions to clarify many good questions of reviewers. Once that is done, we will reply to your latest responses individually. For changing the en_id name to exprnode_id, we will update our POC-up-streamed TVM PR accordingly first. Btw, in our RFC flow, we mentioned that we need to call the relay defused-op pass convert merge-composition-ed Relay IR back to w/o-composition Relay IR. We found a bug in the Relay Defuses-op pass and had up-streamed a fix and a test case to TVM (see TVM PR-10069 for details).

Jan 26 '22 18:01 ccjoechou

@areusch: Forgot to answer your first question. Yes, for now, we like to generate external JSON files, only for the accelerator sub graph(s), in the BuildResult step so that we can pass them to our not-(yet-)in-TVM-general-flow, external-accelerator-specific compiler backend program to do further AOT for-inference-run optimization in order to generate more optimized ISA code to run inference for the accelerator sub graph(s). For the llvm sub graphs (let me use llvm here to distinguish them from those for-accelerator sub graphs), we will and do follow the TVM general flow to generate the .so lib files. When the Model Library format and flow become stable, plus, they can be specialized to include extra external accelerator and memory allocations & communications, we definitely like to see how we can advance the BYOC-Marvell flow to use them (with llvm .so lib files together).

Jan 26 '22 21:01 ccjoechou

@ccjoechou great thanks! i'll take another look here when you're done with the updates.

Feb 01 '22 05:02 areusch

Hi @areusch: I have updated RFC-#48 based on most of your feedback. New RFC md file and 4 new figures have been uploaded. Please take a look again (in a view document mode I can see new figures being displayed inside RFC). We are taking couple of your TIR-related advices and will start reviewing RFC-0010 and TVM TIR files. For our TVM PR-9730 POC code, I have renamed en_id to exprnode_id for our changes and update that PR soon.

Feb 02 '22 02:02 ccjoechou

@ccjoechou hey I think you may have had a bad merge--I see a bunch of unrelated RFCs listed as changed underneath "Files changed." Could you take a look and rebase/re-merge?

Feb 08 '22 20:02 areusch

@ccjoechou hey I think you may have had a bad merge--I see a bunch of unrelated RFCs listed as changed underneath "Files changed." Could you take a look and rebase/re-merge?

Let me check.

Feb 08 '22 21:02 ccjoechou

@areusch: You are correct that I must did something wrong the last time and now I lost the linkage between my GitHub forked byoc-mrvl branch and this tvm-rfc PR-#48. Therefore, my changes done a week ago are still staying on my personal GitHub forked byoc-mrvl branch and did not get push to the tvm-rfc PR-#48.

Any suggestion for me to do? I can see my byoc-mrvl branch is good to be merged automatically with the tvm-rfc PR-#48 but I do not see any bottom to click to make it happen.

Feb 08 '22 21:02 ccjoechou

@areusch: Hello, I believe I have cleaned up the PR's commits now (reset a few commits and re-add changes). Sorry about that. Can you take a look again (my latest changes are with this commit: 8a7fd01)? Thanks.

Feb 08 '22 22:02 ccjoechou

@ccjoechou sorry for the delay--i've gotten pretty busy with something and will hopefully have some bandwidth towards the end of the week.

cc @jroesch @mbs-octoml in case they have cycles

Feb 15 '22 23:02 areusch

@areusch: No worries. I saw lots TVM emails coming from you & others working on other also important stuffs. We will wait for your feedback.

Feb 16 '22 00:02 ccjoechou

@areusch: Yes, using one or two zoom sessions to go over some questions of yours and some questions of ours will definitely be very, very helpful. This way we can sync up quicker. I am open tomorrow (2/25) as well as Monday (2/28) in the afternoons from 1:30 pm to 6 pm Pacific time zone. Will you be available? My company email address is: [email protected]. Please feel free to schedule a zoom meeting using my email.

Feb 24 '22 23:02 ccjoechou

@ccjoechou Summarizing our discussion a bit:

Marvell is interested in being able to arbitrarily partition a Relay graph into hardware-accelerated and non-hardware-acclerated parts
- The boundaries between these parts are to be determined by Marvell backend; therefore, some additional control is needed over the default behavior provided by MergeComposite
- @mbs-octoml suggests that they use the StopFusion annotation to manually enforce the boundaries. These annotations could be added programmatically via a Relay IRModule pass. StopFusion is used in FuseOps pass to avoid fusion.
- Using this approach, the Marvell partitioning pass defined here could be simplified and the existing fusion pass could be used.
Marvell needs to be able to determine which:
- Imported ONNX operator is responsible for a given Relay node
- Relay node is responsible for a TIR CallNode
This needs to happen at two times:
1. At compile time, to serve as a reference to the boundary nodes between a hardware-accelerated and non-hardware-accelerated subgraph
2. At runtime, to determine which backend operator to call
A follow-up question here from me: at runtime, couldn’t you just emit the call to the correct backend operator? I wonder if the reason this mapping was needed was due to previous difficulties configuring the TVM partitioner (it would sometimes fuse across a desired boundary). Is it possible to avoid the need for this reference at runtime given the improved partitioning approach mentioned above?

That doesn't solve the problem of needing to identify a Relay node at compile time. However, if we can reduce this need to a purely compile-time need, perhaps we can develop an alternate way to refer to a Relay node given Relay source code other than adding an id to the IR. cc @tqchen @junrushao1994 in case they have ideas here.
- Marvell proposes to add a Relay attribute exprnode_id and export this from the compiled artifact to identify the relay nodes which are fused into a particular subgraph
- More broadly, source maps (e.g. mapping TIR to Relay to frontend operator) would help here.
Right now the RFC proposes to create a new GraphExecutorCodegen. It might not be necessary to do this if we could export the exprnode_id for Relay operators passed to BYOC. A suggestion is to create a Marvell-specific runtime::Module modeled after CUDAModule which contains several distinct pieces of generated code. The exprnode_ids could be kept separate from any binary instructions if encoded this way. This pattern is common amongst GPU-offloaded runtime::Module.
- Additionally, note the SaveToFile override which is invoked when Module.save() is called from Python. This can allow you walk the runtime::Module tree from Python and collect the various exprnode_ids into a single e.g. JSON blob.
@jroesch to comment on rust CI failures
Marvell would like to contribute a simulator which can run in TVM CI to test their accelerator. We discussed either adding the sim to ci-cpu or a new ci-marvell, the method to do this, and limitations of TVM CI.
Marvell runs a patched version of the TVM CI internally. A primary reason why patching is needed is because many tests in the TVM CI require an internet connection to e.g. download models, but their CI is run in a sandbox. It would be particularly helpful to mark such tests e.g. via pytest.mark in order to make these easy to skip. We also discussed pre-populating the download_testdata cache and patching pytest.skip into download_testdata on their internal fork. cc @leandron @driazati @konturn for visibility and in case they have ideas here.

Mar 02 '22 06:03 areusch