Update AOTI package

pytorch · Oct 2, 2024 · 271f1d1 · 271f1d1
1 parent edaa15c
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diff --git a/.ci/scripts/validate.sh b/.ci/scripts/validate.sh
@@ -133,60 +133,60 @@ function generate_aoti_model_output() {
         echo "******************************************"
         echo "************** non-quantized *************"
         echo "******************************************"
-        python3 -W ignore torchchat.py export --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path "${MODEL_DIR}/${MODEL_NAME}.so" --device "$TARGET_DEVICE" || exit 1
-        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --dso-path "$MODEL_DIR/${MODEL_NAME}.so" --prompt "$PROMPT" --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+        python3 -W ignore torchchat.py export --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path "${MODEL_DIR}/${MODEL_NAME}.pt2" --device "$TARGET_DEVICE" || exit 1
+        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --aoti-package-path "$MODEL_DIR/${MODEL_NAME}.pt2" --prompt "$PROMPT" --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
         .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
 
         echo "******************************************"
         echo "******* Emb: channel-wise quantized ******"
         echo "******************************************"
-        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 8, "groupsize": 0}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 8, "groupsize": 0}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
         .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
 
         echo "******************************************"
         echo "******** Emb: group-wise quantized *******"
         echo "******************************************"
-        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 8, "groupsize": 8}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 8, "groupsize": 8}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
         .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
 
         echo "***********************************************"
         echo "******* Emb: 4bit channel-wise quantized ******"
         echo "***********************************************"
-        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 4, "groupsize": 0, "packed": "True"}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 4, "groupsize": 0, "packed": "True"}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
         .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
 
         echo "***********************************************"
         echo "******** Emb: 4bit group-wise quantized *******"
         echo "***********************************************"
-        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 4, "groupsize": 8, "packed": "True"}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+        python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"embedding" : {"bitwidth": 4, "groupsize": 8, "packed": "True"}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+        python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
         .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
 
         if [ "${EXCLUDE_INT8_QUANT:-false}" == false ]; then
             echo "******************************************"
             echo "******* INT8 channel-wise quantized ******"
             echo "******************************************"
-            python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"linear:int8" : {"bitwidth": 8, "groupsize": 0}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-            python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+            python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"linear:int8" : {"bitwidth": 8, "groupsize": 0}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+            python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
             .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
 
             echo "******************************************"
             echo "******** INT8 group-wise quantized *******"
             echo "******************************************"
-            python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"linear:int8" : {"bitwidth": 8, "groupsize": 8}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-            python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+            python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"linear:int8" : {"bitwidth": 8, "groupsize": 8}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+            python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
             .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
         fi
         echo "******************************************"
         echo "******** INT4 group-wise quantized *******"
         echo "******************************************"
         if [[ "$TARGET_DEVICE" != "cuda" || "$DTYPE" == "bfloat16" ]]; then
             # For CUDA, only bfloat16 makes sense for int4 mm kernel
-            python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"linear:int4" : {"groupsize": 32}}' --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" || exit 1
-            python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
+            python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant '{"linear:int4" : {"groupsize": 32}}' --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" || exit 1
+            python3 -W ignore torchchat.py generate --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --temperature 0 --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" > "$MODEL_DIR/output_aoti" || exit 1
             .ci/scripts/check_gibberish "$MODEL_DIR/output_aoti"
         fi
     done
@@ -285,8 +285,8 @@ function eval_model_sanity_check() {
             echo "******** INT4 group-wise quantized (AOTI) *******"
             echo "*************************************************"
             if [ "$DTYPE" != "float16" ]; then
-                python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant "$QUANT_OPTIONS" --checkpoint-path "$CHECKPOINT_PATH" --output-dso-path ${MODEL_DIR}/${MODEL_NAME}.so --dynamic-shapes --device "$TARGET_DEVICE" || exit 1
-                python3 -W ignore torchchat.py eval --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --dso-path ${MODEL_DIR}/${MODEL_NAME}.so --device "$TARGET_DEVICE" --limit 5 > "$MODEL_DIR/output_eval_aoti" || exit 1
+                python3 -W ignore torchchat.py export --dtype ${DTYPE} --quant "$QUANT_OPTIONS" --checkpoint-path "$CHECKPOINT_PATH" --output-aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --dynamic-shapes --device "$TARGET_DEVICE" || exit 1
+                python3 -W ignore torchchat.py eval --dtype ${DTYPE} --checkpoint-path "$CHECKPOINT_PATH" --aoti-package-path ${MODEL_DIR}/${MODEL_NAME}.pt2 --device "$TARGET_DEVICE" --limit 5 > "$MODEL_DIR/output_eval_aoti" || exit 1
                 cat "$MODEL_DIR/output_eval_aoti"
             fi;
         fi;

diff --git a/.github/workflows/pull.yml b/.github/workflows/pull.yml
@@ -378,8 +378,8 @@ jobs:
 
         echo "::group::Run inference with quantize file"
         if [ $(uname -s) == Darwin ]; then
-          python3 torchchat.py export --output-dso-path /tmp/model.so --quantize torchchat/quant_config/cuda.json --checkpoint "./checkpoints/${REPO_NAME}/model.pth"
-             python3 torchchat.py generate --dso-path /tmp/model.so --checkpoint "./checkpoints/${REPO_NAME}/model.pth"~
+          python3 torchchat.py export --output-aoti-package-path /tmp/model.pt2 --quantize torchchat/quant_config/cuda.json --checkpoint "./checkpoints/${REPO_NAME}/model.pth"
+             python3 torchchat.py generate --aoti-package-path /tmp/model.pt2 --checkpoint "./checkpoints/${REPO_NAME}/model.pth"~
         fi
         echo "::endgroup::"
 
@@ -1023,8 +1023,8 @@ jobs:
 
           for dtype in fp32 fp16 bf16 fast fast16; do
             echo "Running export + runner with dtype=$dtype"
-            python torchchat.py export --checkpoint-path ${MODEL_DIR}/stories15M.pt --dtype $dtype --output-dso-path /tmp/model.so
-            ./cmake-out/aoti_run /tmp/model.so -z ${MODEL_DIR}/tokenizer.model -i "${PROMPT}"
+            python torchchat.py export --checkpoint-path ${MODEL_DIR}/stories15M.pt --dtype $dtype --output-aoti-package-path /tmp/model.pt2
+            ./cmake-out/aoti_run /tmp/model.pt2 -z ${MODEL_DIR}/tokenizer.model -i "${PROMPT}"
           done
 
           echo "Tests complete."

diff --git a/.github/workflows/runner-cuda-dtype.yml b/.github/workflows/runner-cuda-dtype.yml
@@ -56,9 +56,9 @@ jobs:
         for DTYPE in bfloat16; do
             python torchchat.py generate --dtype ${DTYPE} --checkpoint-path ${MODEL_DIR}/stories15M.pt --temperature 0 --prompt "${PROMPT}" --device cuda
 
-            python torchchat.py export --checkpoint-path ${MODEL_DIR}/stories15M.pt --output-dso-path /tmp/model.so
+            python torchchat.py export --checkpoint-path ${MODEL_DIR}/stories15M.pt --output-pt2-path /tmp/model.pt2
 
-            ./cmake-out/aoti_run /tmp/model.so -d CUDA -z ${MODEL_DIR}/tokenizer.model -i "${PROMPT}"
+            ./cmake-out/aoti_run /tmp/model.pt2 -d CUDA -z ${MODEL_DIR}/tokenizer.model -i "${PROMPT}"
 
         done
 

diff --git a/README.md b/README.md
@@ -292,13 +292,14 @@ Use the "Max Response Tokens" slider to limit the maximum number of tokens gener
 ## Desktop/Server Execution
 
 ### AOTI (AOT Inductor)
-[AOTI](https://pytorch.org/blog/pytorch2-2/) compiles models before execution for faster inference. The process creates a [DSO](https://en.wikipedia.org/wiki/Shared_library) model (represented by a file with extension `.so`)
+[AOTI](https://pytorch.org/blog/pytorch2-2/) compiles models before execution for faster inference. The process creates a zipped PT2 file containing all the artifacts generated by AOTInductor, and a [.so](https://en.wikipedia.org/wiki/Shared_library) file with the runnable contents
 that is then loaded for inference. This can be done with both Python and C++ enviroments.
 
 The following example exports and executes the Llama3.1 8B Instruct
 model.  The first command compiles and performs the actual export.
-```
-python3 torchchat.py export llama3.1 --output-dso-path exportedModels/llama3.1.so
+
+```bash
+python3 torchchat.py export llama3.1 --output-aoti-package-path exportedModels/llama3_1_artifacts.pt2
 ```
 
 > [!NOTE]
@@ -310,12 +311,11 @@ case visit our [customization guide](docs/model_customization.md).
 
 ### Run in a Python Enviroment
 
-To run in a python enviroment, use the generate subcommand like before, but include the dso file.
+To run in a python enviroment, use the generate subcommand like before, but include the pt2 file.
 
+```bash
+python3 torchchat.py generate llama3.1 --aoti-package-path exportedModels/llama3_1_artifacts.pt2 --prompt "Hello my name is"
 ```
-python3 torchchat.py generate llama3.1 --dso-path exportedModels/llama3.1.so --prompt "Hello my name is"
-```
-**Note:** Depending on which accelerator is used to generate the .dso file, the command may need the device specified: `--device (cuda | cpu)`.
 
 
 ### Run using our C++ Runner
@@ -325,11 +325,10 @@ To run in a C++ enviroment, we need to build the runner binary.
 torchchat/utils/scripts/build_native.sh aoti
 ```
 
-Then run the compiled executable, with the exported DSO from earlier.
+Then run the compiled executable, with the pt2.
 ```bash
-cmake-out/aoti_run exportedModels/llama3.1.so -z `python3 torchchat.py where llama3.1`/tokenizer.model -l 3 -i "Once upon a time"
+cmake-out/aoti_run exportedModels/llama3_1_artifacts.pt2 -z `python3 torchchat.py where llama3.1`/tokenizer.model -l 3 -i "Once upon a time"
 ```
-**Note:** Depending on which accelerator is used to generate the .dso file, the runner may need the device specified: `-d (CUDA | CPU)`.
 
 ## Mobile Execution
 

diff --git a/install/install_requirements.sh b/install/install_requirements.sh
@@ -47,10 +47,10 @@ fi
 # NOTE: If a newly-fetched version of the executorch repo changes the value of
 # PYTORCH_NIGHTLY_VERSION, you should re-run this script to install the necessary
 # package versions.
-PYTORCH_NIGHTLY_VERSION=dev20240901
+PYTORCH_NIGHTLY_VERSION=dev20240913
 
 # Nightly version for torchvision
-VISION_NIGHTLY_VERSION=dev20240901
+VISION_NIGHTLY_VERSION=dev20240913
 
 # Nightly version for torchtune
 TUNE_NIGHTLY_VERSION=dev20240928
@@ -73,7 +73,7 @@ fi
 
 # pip packages needed by exir.
 REQUIREMENTS_TO_INSTALL=(
-  torch=="2.5.0.${PYTORCH_NIGHTLY_VERSION}"
+  torch=="2.6.0.${PYTORCH_NIGHTLY_VERSION}"
   torchvision=="0.20.0.${VISION_NIGHTLY_VERSION}"
   torchtune=="0.3.0.${TUNE_NIGHTLY_VERSION}"
 )

diff --git a/runner/run.cpp b/runner/run.cpp
@@ -31,10 +31,7 @@ LICENSE file in the root directory of this source tree.
 #endif
 
 #ifdef __AOTI_MODEL__
-#include <torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h>
-#ifdef USE_CUDA
-#include <torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h>
-#endif
+#include <torch/csrc/inductor/aoti_package/model_package_loader.h>
 torch::Device aoti_device(torch::kCPU);
 
 #else // __ET_MODEL__
@@ -94,7 +91,7 @@ typedef struct {
   RunState state; // buffers for the "wave" of activations in the forward pass
 
 #ifdef __AOTI_MODEL__
-  torch::inductor::AOTIModelContainerRunner* runner;
+  torch::inductor::AOTIModelPackageLoader* runner;
 #else // __ET_MODEL__
   Module* runner;
 #endif
@@ -144,16 +141,8 @@ void build_transformer(
   malloc_run_state(&t->state, &t->config);
 
 #ifdef __AOTI_MODEL__
-#ifdef USE_CUDA
-  if (aoti_device.type() == torch::kCUDA) {
-    t->runner = new torch::inductor::AOTIModelContainerRunnerCuda(model_path);
-    aoti_device = torch::Device(torch::kCUDA);
-  } else {
-#else
-  {
-#endif
-    t->runner = new torch::inductor::AOTIModelContainerRunnerCpu(model_path);
-  }
+  t->runner = new torch::inductor::AOTIModelPackageLoader(model_path);
+  aoti_device = t->runner->get_metadata()["AOTI_DEVICE_KEY"] == "cpu" ? torch::Device(torch::kCPU) : torch::Device(torch::kCUDA);
 #else //__ET_MODEL__
   t->runner = new Module(
       /* path to PTE model */ model_path,