Combine tflite getting started guide into a single page (#8546)
diff --git a/docs/website/docs/getting-started/tflite-cmd.md b/docs/website/docs/getting-started/tflite-cmd.md
deleted file mode 100644
index 20219c8..0000000
--- a/docs/website/docs/getting-started/tflite-cmd.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# TFLite via Command Line
-
-IREE's tooling is divided into two components: import and compilation.
-
-1. The import tool converts the TFLite flatbuffer to an IREE compatible form,
-validating that only IREE compatible operations remain. Containing a combination of TOSA
-and IREE operations.
-2. The compilation stage generates the bytecode module for a list of targets, which can
-be executed by IREE.
-
-These two stages can be completed entirely via the command line.
-
-```shell
-WORKDIR="/tmp/workdir"
-TFLITE_URL="https://storage.googleapis.com/iree-model-artifacts/tflite-integration-tests/posenet_i8.tflite"
-TFLITE_PATH=${WORKDIR}/model.tflite
-IMPORT_PATH=${WORKDIR}/tosa.mlir
-MODULE_PATH=${WORKDIR}/module.vmfb
-
-# Fetch the sample model
-wget ${TFLITE_URL} -O ${TFLITE_PATH}
-
-# Import the sample model to an IREE compatible form
-iree-import-tflite ${TFLITE_PATH} -o ${IMPORT_PATH}
-
-# Compile for the CPU backend
-iree-compile \
- --iree-mlir-to-vm-bytecode-module \
- --iree-input-type=tosa \
- --iree-hal-target-backends=dylib-llvm-aot \
- ${IMPORT_PATH} \
- -o ${MODULE_PATH}
-```
diff --git a/docs/website/docs/getting-started/tflite-python.md b/docs/website/docs/getting-started/tflite-python.md
deleted file mode 100644
index a46ef10..0000000
--- a/docs/website/docs/getting-started/tflite-python.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# TFLite via Python
-
-The example below demonstrates downloading, compiling, and executing a TFLite
-model using the Python API. This includes some initial setup to declare global
-variables, download the sample module, and download the sample inputs.
-
-Declaration of absolute paths for the sample repo and import all required libraries.
-The default setup uses the CPU backend as the only target. This can be reconfigured
-to select alternative targets.
-
-```python
-import iree.compiler.tflite as iree_tflite_compile
-import iree.runtime as iree_rt
-import numpy
-import os
-import urllib.request
-
-from PIL import Image
-
-workdir = "/tmp/workdir"
-os.makedirs(workdir, exist_ok=True)
-
-tfliteFile = "/".join([workdir, "model.tflite"])
-jpgFile = "/".join([workdir, "input.jpg"])
-tfliteIR = "/".join([workdir, "tflite.mlir"])
-tosaIR = "/".join([workdir, "tosa.mlir"])
-bytecodeModule = "/".join([workdir, "iree.vmfb"])
-
-backends = ["dylib-llvm-aot"]
-config = "dylib"
-```
-
-The TFLite sample model and input are downloaded locally.
-
-```python
-tfliteUrl = "https://storage.googleapis.com/iree-model-artifacts/tflite-integration-tests/posenet_i8.tflite"
-jpgUrl = "https://storage.googleapis.com/iree-model-artifacts/tflite-integration-tests/posenet_i8_input.jpg"
-
-urllib.request.urlretrieve(tfliteUrl, tfliteFile)
-urllib.request.urlretrieve(jpgUrl, jpgFile)
-```
-
-Once downloaded we can compile the model for the selected backends. Both the TFLite and TOSA representations
-of the model are saved for debugging purposes. This is optional and can be omitted.
-
-```python
-iree_tflite_compile.compile_file(
- tfliteFile,
- input_type="tosa",
- output_file=bytecodeModule,
- save_temp_tfl_input=tfliteIR,
- save_temp_iree_input=tosaIR,
- target_backends=backends,
- import_only=False)
-```
-
-After compilation is completed we configure the VmModule using the dylib configuration and compiled
-IREE module.
-
-```python
-config = iree_rt.Config("dylib")
-context = iree_rt.SystemContext(config=config)
-with open(bytecodeModule, 'rb') as f:
- vm_module = iree_rt.VmModule.from_flatbuffer(f.read())
- context.add_vm_module(vm_module)
-```
-
-Finally, the IREE module is loaded and ready for execution. Here we load the sample image, manipulate to
-the expected input size, and execute the module. By default TFLite models include a single
-function named 'main'. The final results are printed.
-
-```python
-im = numpy.array(Image.open(jpgFile).resize((192, 192))).reshape((1, 192, 192, 3))
-args = [im]
-
-invoke = context.modules.module["main"]
-iree_results = invoke(*args)
-print(iree_results)
-```
diff --git a/docs/website/docs/getting-started/tflite.md b/docs/website/docs/getting-started/tflite.md
index c276350..9f56962 100644
--- a/docs/website/docs/getting-started/tflite.md
+++ b/docs/website/docs/getting-started/tflite.md
@@ -15,8 +15,118 @@
iree-tools-tflite
```
-- [Command Line](./tflite-cmd.md)
-- [Python API](./tflite-python.md)
+## Importing and Compiling
+IREE's tooling is divided into two components: import and compilation.
+
+1. The import tool converts the TFLite flatbuffer to an IREE compatible form,
+validating that only IREE compatible operations remain. Containing a combination of TOSA
+and IREE operations.
+2. The compilation stage generates the bytecode module for a list of targets, which can
+be executed by IREE.
+
+### Using Command Line Tools
+These two stages can be completed entirely via the command line.
+
+```shell
+WORKDIR="/tmp/workdir"
+TFLITE_URL="https://storage.googleapis.com/iree-model-artifacts/tflite-integration-tests/posenet_i8.tflite"
+TFLITE_PATH=${WORKDIR}/model.tflite
+IMPORT_PATH=${WORKDIR}/tosa.mlir
+MODULE_PATH=${WORKDIR}/module.vmfb
+
+# Fetch the sample model
+wget ${TFLITE_URL} -O ${TFLITE_PATH}
+
+# Import the sample model to an IREE compatible form
+iree-import-tflite ${TFLITE_PATH} -o ${IMPORT_PATH}
+
+# Compile for the CPU backend
+iree-compile \
+ --iree-mlir-to-vm-bytecode-module \
+ --iree-input-type=tosa \
+ --iree-hal-target-backends=dylib-llvm-aot \
+ ${IMPORT_PATH} \
+ -o ${MODULE_PATH}
+```
+
+### Using the Python API
+The example below demonstrates downloading, compiling, and executing a TFLite
+model using the Python API. This includes some initial setup to declare global
+variables, download the sample module, and download the sample inputs.
+
+Declaration of absolute paths for the sample repo and import all required libraries.
+The default setup uses the CPU backend as the only target. This can be reconfigured
+to select alternative targets.
+
+```python
+import iree.compiler.tflite as iree_tflite_compile
+import iree.runtime as iree_rt
+import numpy
+import os
+import urllib.request
+
+from PIL import Image
+
+workdir = "/tmp/workdir"
+os.makedirs(workdir, exist_ok=True)
+
+tfliteFile = "/".join([workdir, "model.tflite"])
+jpgFile = "/".join([workdir, "input.jpg"])
+tfliteIR = "/".join([workdir, "tflite.mlir"])
+tosaIR = "/".join([workdir, "tosa.mlir"])
+bytecodeModule = "/".join([workdir, "iree.vmfb"])
+
+backends = ["dylib-llvm-aot"]
+config = "dylib"
+```
+
+The TFLite sample model and input are downloaded locally.
+
+```python
+tfliteUrl = "https://storage.googleapis.com/iree-model-artifacts/tflite-integration-tests/posenet_i8.tflite"
+jpgUrl = "https://storage.googleapis.com/iree-model-artifacts/tflite-integration-tests/posenet_i8_input.jpg"
+
+urllib.request.urlretrieve(tfliteUrl, tfliteFile)
+urllib.request.urlretrieve(jpgUrl, jpgFile)
+```
+
+Once downloaded we can compile the model for the selected backends. Both the TFLite and TOSA representations
+of the model are saved for debugging purposes. This is optional and can be omitted.
+
+```python
+iree_tflite_compile.compile_file(
+ tfliteFile,
+ input_type="tosa",
+ output_file=bytecodeModule,
+ save_temp_tfl_input=tfliteIR,
+ save_temp_iree_input=tosaIR,
+ target_backends=backends,
+ import_only=False)
+```
+
+After compilation is completed we configure the VmModule using the dylib configuration and compiled
+IREE module.
+
+```python
+config = iree_rt.Config("dylib")
+context = iree_rt.SystemContext(config=config)
+with open(bytecodeModule, 'rb') as f:
+ vm_module = iree_rt.VmModule.from_flatbuffer(f.read())
+ context.add_vm_module(vm_module)
+```
+
+Finally, the IREE module is loaded and ready for execution. Here we load the sample image, manipulate to
+the expected input size, and execute the module. By default TFLite models include a single
+function named 'main'. The final results are printed.
+
+```python
+im = numpy.array(Image.open(jpgFile).resize((192, 192))).reshape((1, 192, 192, 3))
+args = [im]
+
+invoke = context.modules.module["main"]
+iree_results = invoke(*args)
+print(iree_results)
+```
## Troubleshooting
