imalsogreg commented on a change in pull request #7085:
URL: https://github.com/apache/tvm/pull/7085#discussion_r570241645
##########
File path: python/tvm/relay/analysis/__init__.py
##########
@@ -26,7 +26,7 @@
from . import call_graph
from .call_graph import CallGraph
-# Feature
+# # Feature
Review comment:
Two `#`'s?
##########
File path: python/tvm/relay/build_module.py
##########
@@ -193,6 +193,18 @@ def get_params(self):
ret[key] = value.data
return ret
+@register_func("tvm.relay.build")
+def _rust_build_module(mod, target=None, target_host=None, params=None,
mod_name="default"):
+ print(mod)
+ print("\n")
+ rt_mod = build(mod, target, target_host, params, mod_name).module
+ print(rt_mod)
+ print(rt_mod["default"])
Review comment:
leftover prints, or something we want to keep?
##########
File path: rust/tvm/README.md
##########
@@ -15,221 +15,40 @@
<!--- specific language governing permissions and limitations -->
<!--- under the License. -->
-# TVM Runtime Frontend Support
+# TVM
-This crate provides an idiomatic Rust API for
[TVM](https://github.com/apache/tvm) runtime frontend. Currently this requires
**Nightly Rust** and tested on `rustc 1.32.0-nightly`
+This crate provides an idiomatic Rust API for
[TVM](https://github.com/apache/tvm).
+The code works on **Stable Rust** and is tested against `rustc 1.47`.
-## What Does This Crate Offer?
-
-Here is a major workflow
-
-1. Train your **Deep Learning** model using any major framework such as
[PyTorch](https://pytorch.org/), [Apache MXNet](https://mxnet.apache.org/) or
[TensorFlow](https://www.tensorflow.org/)
-2. Use **TVM** to build optimized model artifacts on a supported context such
as CPU, GPU, OpenCL and specialized accelerators.
-3. Deploy your models using **Rust** :heart:
-
-### Example: Deploy Image Classification from Pretrained Resnet18 on ImageNet1k
-
-Please checkout [examples/resnet](examples/resnet) for the complete end-to-end
example.
-
-Here's a Python snippet for downloading and building a pretrained Resnet18 via
Apache MXNet and TVM
-
-```python
-block = get_model('resnet18_v1', pretrained=True)
-
-sym, params = relay.frontend.from_mxnet(block, shape_dict)
-# compile the model
-with relay.build_config(opt_level=opt_level):
- graph, lib, params = relay.build(
- net, target, params=params)
-# same the model artifacts
-lib.save(os.path.join(target_dir, "deploy_lib.o"))
-cc.create_shared(os.path.join(target_dir, "deploy_lib.so"),
- [os.path.join(target_dir, "deploy_lib.o")])
-
-with open(os.path.join(target_dir, "deploy_graph.json"), "w") as fo:
- fo.write(graph.json())
-with open(os.path.join(target_dir,"deploy_param.params"), "wb") as fo:
- fo.write(relay.save_param_dict(params))
-```
+You can find the API Documentation
[here](https://tvm.apache.org/docs/api/rust/tvm/index.html).
-Now, we need to input the artifacts to create and run the *Graph Runtime* to
detect our input cat image
-
-
+## What Does This Crate Offer?
-as demostrated in the following Rust snippet
+The goal of this crate is to provide bindings to both the TVM compiler and
runtime
+APIs. First train your **Deep Learning** model using any major framework such
as
+[PyTorch](https://pytorch.org/), [Apache MXNet](https://mxnet.apache.org/) or
[TensorFlow](https://www.tensorflow.org/).
+Then use **TVM** to build and deploy optimized model artifacts on a supported
devices such as CPU, GPU, OpenCL and specialized accelerators.
-```rust
- let graph = fs::read_to_string("deploy_graph.json")?;
- // load the built module
- let lib = Module::load(&Path::new("deploy_lib.so"))?;
- // get the global TVM graph runtime function
- let runtime_create_fn = Function::get("tvm.graph_runtime.create",
true).unwrap();
- let runtime_create_fn_ret = call_packed!(
- runtime_create_fn,
- &graph,
- &lib,
- &ctx.device_type,
- &ctx.device_id
- )?;
- // get graph runtime module
- let graph_runtime_module: Module = runtime_create_fn_ret.try_into()?;
- // get the registered `load_params` from runtime module
- let ref load_param_fn = graph_runtime_module
- .get_function("load_params", false)
- .unwrap();
- // parse parameters and convert to TVMByteArray
- let params: Vec<u8> = fs::read("deploy_param.params")?;
- let barr = TVMByteArray::from(¶ms);
- // load the parameters
- call_packed!(load_param_fn, &barr)?;
- // get the set_input function
- let ref set_input_fn = graph_runtime_module
- .get_function("set_input", false)
- .unwrap();
+The Rust bindings are composed of a few crates:
+- The [tvm](https://tvm.apache.org/docs/api/rust/tvm/index.html) crate which
exposes Rust bindings to
+ both the compiler and runtime.
+- The [tvm_macros](https://tvm.apache.org/docs/api/rust/tvm/index.html) crate
which provides macros
+ which generate unsafe boilerplate for TVM's data structures.
+- The [tvm_rt](https://tvm.apache.org/docs/api/rust/tvm_rt/index.html) crate
which exposes Rust
Review comment:
Just wondering as a naive reader, why there is a `tvm_rt` crate when
`tvm` exposes bindings to the runtime. (Is there _extra_ runtime stuff in
`tmv_rt`, vs. `tvm_rt` being a subset of `tvm` for users that don't want to
pull in the compiler bindings?
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