[RFC][BYOC] Marvell ML/AI Accelerator Integration #48
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Hello, How can we request a reviewer to review our RFC? Thanks,
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@ssdurako @ccjoechou 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 @comaniac @mbs-octoml @Mousius @junrushao1994 for further comments on BYOC, device planning, and support for custom executors
| 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 | ||
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| * Note: Mrvl-ML/AI backend compiler, which does accelerator-specific optimization and code generation, is not included |
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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?
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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?)
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cc @jroesch can we unblock their rust debugging? @ccjoechou i'm not as familiar with the rust stuff in TVM, but we should transfer ownership of any rust packages to a TVM account. apologies for any oversight there.
| ball; and it can be used to read in input file(s) and the model binary to run inference for the Mrvl subgraph | ||
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| * 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 |
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just checking if this was the end of the sentence here
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ok--suggest to either add a period or maybe reword as "For this RFC, we will focus only on models that use float16 quantization mode."
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| * 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 |
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could you clarify this sentence?
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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.
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you can add them to assets/ and then link similar to https://github.com/apache/tvm-rfcs/blob/main/rfcs/0050-roadmaps.md (see the Raw source for example how to link it).
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| ``` | ||
| 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] */; |
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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)
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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.
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got it. so just to clarify--you're proposing to fuse these merge-composite IR functions at the Relay level into a single e.g. Relay @main? I think another strategy would be to run a TIR-only pass after scheduling. curious if that may work to accomplish the same goals? the benefit there is that you can also operate, at that time, on the logical TIR buffers.
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We have not spend time on the TIR flow and passes - we will.
One quick question, is TIR buffer and its data-layout can lead how inputs/outputs of Marvell sub-graphs and LLVM-non-Marvell sub-graphs are communicated during inference runtime?
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Saw your other feedback regarding RFC-#10 and we will review the RFC. Thanks.
| return mod_new | ||
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| * Marvell-specific graph executor codegen, We have defined call backs and extension functions in the following files: |
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could you motivate this further? it's hard to understand why you need to output your own JSON format without some explanation here.
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oh the email version of this question was linking to a different RFC segment (the light green section above) -- led to me to answer differently in my in-line reply to your email.
Sorry and since I noticed here and can see the correct light green block above corresponding to your question, so let me reply here again properly.
We are using the TVM graph executor codegen to process BYOC-Marvell-relay-seq-generated Marvell-part of the IR sub-graph, which includes Marvell-specific GraphInputNode object(s) & attributes and Marvell-specific GraphOpNode object(s) and attributes. When processing the Marvell sub-graph in the TVM graph executor codegen, we need to specialize the generation code in order to dump extra Marvell-specified attributes to node-JSON file (also in a more readable format). The original code can't do what we need; hence, we are using derive classes and call back functions in C++ to overwrite defaults here.
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ok got it, i think that makes sense to me. i think the main question i have here is the mechanism by which you guys export the Marvell GraphExecutor sub-graph.
| # Rationale and alternatives | ||
| [rationale-and-alternatives]: #rationale-and-alternatives | ||
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| * We follow the TVM BYOC framework to enable BYOC Marvell flow without impacting any TVM core features. |
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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?
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the email version of this question was linking to a different RFC segment (the light green section above) -- led to me to answer differently in my in-line reply to your email.
Sorry and since I noticed here and can see the correct light green block above corresponding to your question, so let me reply here again properly.
Please check my reply to the previous question (e.g., we need Marvell-specific GraphOpNode and GraphInputNode in order to dump out Marvell specific attributes to node-JSON file).
Also, since we are also using our build-from-outside-typical TVM flow Marvell compiler backend component, which can do additional compile-time optimizations and is reading in graph-executor-generated JSON meta data, currently, we don't think using runtime.Module to generate Marvell-specific JSON meta files of network is the way to go here.
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@areusch: We may not know enough but like to learn more about the runtime.Module subject (its flow and how to add specialization). Can you provide an example or an existing tvm/tests-folder suite as a pointer for us to run an existing runtime using tvm repo (without relying on specific USE_ flags being ON), if there is such example?
| * 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. |
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i think it would help to spell out why you guys need to be able to identify each expression here.
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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 (which were provided by driver and/or hardware) and their corresponding user frontend model’s operators (e.g., each expression which customer knows)
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so is the idea that the exported graph contains the en_id and then someone can trace that back to an annotated Relay program? what's the procedure by which en_id could be used?
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yes and I have update the RFC to include more information regarding exprnode_id and its usages.
| community has any better or work-in-progress resolution. | ||
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| * 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 |
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could you explain the nature of the problem that requires the client to know the absolute path?
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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 -- unless the client knows the uploaded file's
path on server.
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could you try calling tvm.rpc.server.workpath on the RPC server? https://github.com/apache/tvm/blob/main/python/tvm/rpc/server.py#L62
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yes and we will find time to check.
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BTW, in our use case, we need the server path to be know on the client side so that the client is the master who controls activities to be running on the server side.
| 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. | ||
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| * In order for us to generate the constants-JSON file, we must “NOT” remove external params, which were stored in |
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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?
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We are using relay to generate JSON meta files representing the given network model in a way our (compiler-) backend code can process directly at compile time (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 optimizations including manipulating constants, we need constants JSON.
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it might be possible for you to do this in TIR, if you're able to leverage tir.constant. you would need to use https://github.com/apache/tvm-rfcs/blob/main/rfcs/0010-target-registered-compiler-flow-customisation.md, so I'm not sure if that's appropriate here.
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We will review RFC-#10 to find out. Thanks.
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| * Do Marvell-specific layout conversions to transform IR graph in order to meet requirements of the accelerator | ||
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| * Do Marvell-specific composite-merging/fusing to transform IR graph in order to utilize available HW capability |
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Hi, thanks for the RFC. My team at OctoML is looking at bringing some training features to the BYOC world (a la https://arxiv.org/pdf/2111.00655.pdf), so I'm looking at this RFC with that future in mind. Can you expand on:
- Is the fusion using the existing MergeComposite / AnnotateTarget/ MergeCompilerRegions(maybe) / PartitionGraph sequence?
- Other than the global layout xform, which necessarily must be done before any fusion etc, are there any other xforms before the above partitioning takes place?
- Can you explain the need to limit to one kernel for each of your byoc and the default tvm? Perhaps it's an artifact of how you're later trying to capture the byoc output in json graph form? Ideally the BYOC target.ext.name function could be run multiple times, the resulting runtime::Module would be accumulated in the IRModule, and the runtime::Modules later merged. Perhaps supporting that would actually be easier and would remove the at-most-one kernel limit?
- Ideally there'd be a single entry point for 'partition for marvel', after which the regular TVM build would deal with fusion, lowering and codegen for everything that's left (ie overall model - kernels you already partitioned out). I may not be following the explanation but it seems you're proposing the driver splits things more explicitly.
- Like @areusch I'm a bit confused by the special handling of the graph. Perhaps it would be worth going through the tensorrt BYOC integration as a reference example since it too collects a JSON representation of the to-be-complied fused sub-graph (we invoke the TensorRT build function at runtime not compile time), but it does so on top of existing machinery.
Let me know if it would be easier to discuss this on a PR rather than here, then we could come back to here.
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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 pattern
- 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].
We have also up-steamed the TVM GitHub’s PR-9730 as a POC (can be downloaded via git clone https://github.com/ccjoechou/tvm.git and changes are on the byoc-mrvl branch). Please see the tvm/python/tvm/relay/op/contrib/mrvl.py file's partition_for_mrvl() function's seq setup there.
There is also the test_mrvl suite, which can be run to generate JSON files for ssd-resnet50 network.
[Using our definition of sub-graph -- not the TVM partition's definition of sub-graph] Yes, limitation regarding at-most one mrvl-sub-graph and at most-one llvm sub-graph can be relaxed later on when we have runtime & driver hookups ready + our driver & firmware of our HW accelerator are also ready to handle multiple sub-graphs. We will be spending time on this area in the next few months.
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@mbs-octoml: Thanks for replying. Please also see my comments to @areusch's reply below including several in-line write-ups since they can be providing information regarding to your questions too. Please let me know if anything can be clarified on the TVM GitHub PR-9730 front.
Currently, we are also running parts of the tvm/Jenkinsfile stages and their steps locally using our own Jenkins server. However, we are having problem to debug rust/cargo issue (the tvm/scripts/task_rust.sh suite). It will be great, if you can provide us additional information regarding how to build our "local" tvm-build package (I can git clone current OctoML GitHub tvm-build repo) and then how we can adjust the tvm/rust/Cargo.toml file to use our "local" tvm-build package.
Also, any tips and pointers regarding how to debug rust/cargo build.
Thanks.
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@areusch - Thanks for replying.
Please see my comments below regarding your questions.
1. 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
1. 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
2. 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.
3. 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)
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@areusch requested changes on this pull request.
@ssdurako<https://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=> @ccjoechou<https://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 @comaniac<https://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-octoml<https://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=> @Mousius<https://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=> @junrushao1994<https://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
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In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.md<https://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
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+* 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?)]
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In rfcs/0048-BYOC-Marvell-ML-accelerator-integration.md<https://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.md<https://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.md<https://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.md<https://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.md<https://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.md<https://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.md<https://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.md<https://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.md<https://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.md<https://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.]
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@ccjoechou sorry for the delay, I'll try to be a bit quicker replying here.
I read your PoC PR a bit and I see you guys are indeed following the BYOC flow. I think that part makes sense to me. the main question I have is around enshrining the external JSON in BuildResult. I think it's a little unclear how the "external graph JSON" is consumed in your PoC. Do I understand right that you guys want to pass this to an external compiler? I'm wondering if something like Model Library Format is the right thing here (note though, that it doesn't support BYOC right now...so we would be wanting to adopt an approach here that was easy to port forward to that).
| 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 | ||
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| * Note: Mrvl-ML/AI backend compiler, which does accelerator-specific optimization and code generation, is not included |
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cc @jroesch can we unblock their rust debugging? @ccjoechou i'm not as familiar with the rust stuff in TVM, but we should transfer ownership of any rust packages to a TVM account. apologies for any oversight there.
| ball; and it can be used to read in input file(s) and the model binary to run inference for the Mrvl subgraph | ||
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| * 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 |
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ok--suggest to either add a period or maybe reword as "For this RFC, we will focus only on models that use float16 quantization mode."
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| * 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 |
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you can add them to assets/ and then link similar to https://github.com/apache/tvm-rfcs/blob/main/rfcs/0050-roadmaps.md (see the Raw source for example how to link it).
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| 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] */; |
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got it. so just to clarify--you're proposing to fuse these merge-composite IR functions at the Relay level into a single e.g. Relay @main? I think another strategy would be to run a TIR-only pass after scheduling. curious if that may work to accomplish the same goals? the benefit there is that you can also operate, at that time, on the logical TIR buffers.
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| * 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 |
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ok. i'd like to suggest for clarity's sake we use exprnode_id here.
| return mod_new | ||
| ``` | ||
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| * Marvell-specific graph executor codegen, We have defined call backs and extension functions in the following files: |
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ok got it, i think that makes sense to me. i think the main question i have here is the mechanism by which you guys export the Marvell GraphExecutor sub-graph.
| * 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. |
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so is the idea that the exported graph contains the en_id and then someone can trace that back to an annotated Relay program? what's the procedure by which en_id could be used?
| community has any better or work-in-progress resolution. | ||
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| * 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 |
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could you try calling tvm.rpc.server.workpath on the RPC server? https://github.com/apache/tvm/blob/main/python/tvm/rpc/server.py#L62
| 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. | ||
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| * In order for us to generate the constants-JSON file, we must “NOT” remove external params, which were stored in |
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it might be possible for you to do this in TIR, if you're able to leverage tir.constant. you would need to use https://github.com/apache/tvm-rfcs/blob/main/rfcs/0010-target-registered-compiler-flow-customisation.md, so I'm not sure if that's appropriate here.
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@areusch: Thanks again for your latest responses. Don't worry about timing (we all have our real jobs to do too). |
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@areusch: Forgot to answer your first question. |
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@ccjoechou great thanks! i'll take another look here when you're done with the updates. |
Integrate Marvell’s ML/AI accelerator with TVM BYOC framework in order to bring the TVM ecosystem to Marvell customers.
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Hi @areusch: |
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@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. |
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@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?
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Integrate Marvell’s ML/AI accelerator with TVM BYOC framework in order to bring the TVM ecosystem to Marvell customers.
* [RFC][Runtime] Bring `PackedFunc` into TVM Object System * Apply suggestions from code review Fix a typo Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com> * Apply suggestions from RFC review Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com>
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@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 |
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@areusch: No worries. I saw lots TVM emails coming from you & others working on other also important stuffs. We will wait for your feedback. |
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@ccjoechou I took another look. I think I'm still hung up on how you want to use partition_for_mrvl--are you trying to develop a separate compilation flow, or reuse the TVM compilation flow? part of the missing piece is that I think the PoC doesn't demonstrate how the compilation artifacts are passed down to the runtime: https://github.com/apache/tvm/pull/9730/files#diff-7561f47a7ba9997bdd34dec8311c0070985abc9703ced5ce52087fb0639c79fdR206
i can't really tell how this is done from the RFC either.
I would generally prefer if we could avoid subclassing pieces of the core compilation flow. this makes it harder for us to maintain the core compilation flow. I think it'd be helpful to discuss why you guys need the additional attributes in graph.json and see whether we could augment our existing graph.json with those attributes, rather than building a parallel flow. I'd also still like to understand more about your partitioning scheme.
would it help to do something more high-bandwidth, either over discord or zoom?
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| To exercise the TVM-BYOC-Marvell flow, we have provided a tests/python/contrib/test\_mrvl folder with test\_mrvl\_codegen.py and infrastructure.py files so that they shows how to exercise the TVM-BYOC-Marvell flow for a pre-trained SSD-ResNet50 model. In addition, Marvell are also planning to provide the Marvell backend compiler (mrvl-tvmircomp) and the Marvell HW accelerator software simulator (mlModel) so that they can be used to read in JSON files generated by the TVM-BYOC-Marvell flow to run inference to get results. | ||
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| In the uploaded appache/tvm-PR-9730 branch, |
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could you finish this sentence or rm?
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| ``` | ||
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| First, we can download a pre-trained SSD-ResNet50 model from the MXNet-gluoncv site; then, call the mrvl.partition\_for\_mrvl() function to trigger the TVM-BYOC-Marvell flow; and finally, call relay.build() function and mrvl.dump\_json\_meta\_data\_files() function to generate a pair of JSON files for each Marvell sub-graph identified by the TVM-BYOC-Marvell flow. |
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suggest to use numbered list:
| First, we can download a pre-trained SSD-ResNet50 model from the MXNet-gluoncv site; then, call the mrvl.partition\_for\_mrvl() function to trigger the TVM-BYOC-Marvell flow; and finally, call relay.build() function and mrvl.dump\_json\_meta\_data\_files() function to generate a pair of JSON files for each Marvell sub-graph identified by the TVM-BYOC-Marvell flow. | |
| The above code snippet does the following: | |
| 1. Download a pre-trained SSD-ResNet50 model from the MXNet-gluoncv site | |
| 2. Call the `mrvl.partition_for_mrvl()` function to partition the graph into Marvell and non-Marvell pieces and trigger the TVM-BYOC-Marvell flow | |
| 3. Call relay.build() function and mrvl.dump\_json\_meta\_data\_files() function to generate a pair of JSON files for each Marvell sub-graph identified by the TVM-BYOC-Marvell flow. |
| ) = keras.datasets.fashion_mnist.load_data() | ||
| ``` | ||
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| In the code snippet below, we call onnx.load() and relay.frontend.from\_onnx() to generate TVM mod and params. Then, they are used by the mrvl.partition\_for\_mrvl() function and the mrvl.dump\_json\_meta\_data\_files() function provided for the TVM-BYOC-Marvell flow to generate Nodes-JSON file (nodes\_json\_filename) and Constants-JSON file (consts\_json\_filename). |
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in the PoC PR, partition_for_mrvl is registered in python/tvm/driver/tvmc/composite_target.py along with the other BYOC partitioners, but its signature differs significantly (from the de-facto partition_func(IRModule) -> IRModule):
"""Partition the graph greedily offloading supported
operators to Mrvl
Parameters
----------
mod : Module
The module to run passes on.
params : Optional[Dict[str, NDArray]]
Constant input parameters.
Returns
-------
mod_mrvl : annotated and partitioned module - part 1, the mrvl sub graph
mod_other : annotated and partitioned module - part 2, if any, the rest sub graph
params : TBA
opt_level : TBA
disabled_pass_list : TBA
mod : TBA
mrvl_layers_in_mrvl_subgraph : TBA
"""
what's your intention here? in order to register this function in REGISTERED_CODEGEN, you'll need to make that signature match up. however, i think from my reading, what's happening here is you're invoking a fair bit of the compilation pipeline underneath a hard-coded PassContext, then returning a fair bit of extra information here. some of this information looks fairly specific to the Marvell lowering flow.
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@areusch: |
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@ccjoechou Summarizing our discussion a bit:
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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 (apache/tvm#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.