Update on "implement position encoding for shifted tokens"

In AttentionSink, it uses tokens' positions in the KVCache instead of the actual text. When tokens get shifted in KVCache, it needs to update q and k's position embedding.

In the original [implementation](https://github.com/mit-han-lab/streaming-llm) of AttentionSink with Rope, it caches the original q and k in KVCache and apply position embedding during inference.

This PR adds `RopeWithAttentionSink`. It assumes that q and k are already encoded with their original position. When we shift tokens, we reapply the position delta. This has two benefits:
- minimize our code since our existing `llama_transformer` applies rope embedding before doing KVCache update
- avoid performance regression when tokens are not shifted because we don't need to reapply position encoding in KVCache for them

Differential Revision: [D65366440](https://our.internmc.facebook.com/intern/diff/D65366440/)

[ghstack-poisoned]

Author: helunwencser

.ci/scripts/gather_test_models.py

@@ -20,16 +20,16 @@
 CUSTOM_RUNNERS = {
     "linux": {
         # This one runs OOM on smaller runner, the root cause is unclear (T163016365)
-        "w2l": "linux.12xlarge",
-        "ic4": "linux.12xlarge",
-        "resnet50": "linux.12xlarge",
-        "llava": "linux.12xlarge",
-        "llama3_2_vision_encoder": "linux.12xlarge",
-        # "llama3_2_text_decoder": "linux.12xlarge",  # TODO: re-enable test when Huy's change is in / model gets smaller.
+        "w2l": "linux.4xlarge.memory",
+        "ic4": "linux.4xlarge.memory",
+        "resnet50": "linux.4xlarge.memory",
+        "llava": "linux.4xlarge.memory",
+        "llama3_2_vision_encoder": "linux.4xlarge.memory",
+        "llama3_2_text_decoder": "linux.4xlarge.memory",
         # This one causes timeout on smaller runner, the root cause is unclear (T161064121)
-        "dl3": "linux.12xlarge",
-        "emformer_join": "linux.12xlarge",
-        "emformer_predict": "linux.12xlarge",
+        "dl3": "linux.4xlarge.memory",
+        "emformer_join": "linux.4xlarge.memory",
+        "emformer_predict": "linux.4xlarge.memory",
     }
 }
 
@@ -39,10 +39,12 @@
     "linux": {
         "mobilebert": 90,
         "emformer_predict": 360,
+        "llama3_2_text_decoder": 360,
     },
     "macos": {
         "mobilebert": 90,
         "emformer_predict": 360,
+        "llama3_2_text_decoder": 360,
     },
 }
 

.ci/scripts/setup-macos.sh

@@ -49,6 +49,9 @@ install_buck() {
 
   rm "${BUCK2}"
   popd
+
+  # Kill all running buck2 daemon for a fresh start
+  buck2 killall || true
 }
 
 function write_sccache_stub() {

.ci/scripts/test_llama.sh

@@ -27,6 +27,10 @@ while [[ $# -gt 0 ]]; do
       MODE="$2" # portable or xnnpack+custom or xnnpack+custom+qe
       shift 2
       ;;
+    -pt2e_quantize)
+      PT2E_QUANTIZE="$2"
+      shift 2
+      ;;
     -upload)
       UPLOAD_DIR="$2"
       shift 2
@@ -44,6 +48,9 @@ MODE=${MODE:-"xnnpack+custom"}
 # Default UPLOAD_DIR to empty string if not set
 UPLOAD_DIR="${UPLOAD_DIR:-}"
 
+# Default PT2E_QUANTIZE to empty string if not set
+PT2E_QUANTIZE="${PT2E_QUANTIZE:-}"
+
 if [[ $# -lt 4 ]]; then # Assuming 4 mandatory args
     echo "Expecting atleast 4 positional arguments"
     echo "Usage: [...]"
@@ -234,6 +241,10 @@ if [[ "${COREML}" == "ON" ]]; then
 fi
 if [[ "${QNN}" == "ON" ]]; then
   EXPORT_ARGS="${EXPORT_ARGS} -kv -v --qnn --disable_dynamic_shape"
+  echo "PT2E_QUANTIZE is ${PT2E_QUANTIZE}"
+  if [[ "${PT2E_QUANTIZE}" == "qnn_16a16w" ]]; then
+    EXPORT_ARGS+=" --tokenizer_path tokenizer.model --pt2e_quantize qnn_16a16w --calibration_tasks wikitext --calibration_limit 1 --calibration_seq_length 128 --calibration_data Once "
+  fi
 fi
 # Add dynamically linked library location
 $PYTHON_EXECUTABLE -m examples.models.llama.export_llama ${EXPORT_ARGS}

.github/workflows/apple.yml

@@ -42,6 +42,8 @@ jobs:
 
   build-demo-ios:
     name: build-demo-ios
+    # NB: Don't run this on fork PRs because they won't have access to the secret and would fail anyway
+    if: ${{ !github.event.pull_request.head.repo.fork }}
     uses: pytorch/test-infra/.github/workflows/macos_job.yml@main
     secrets: inherit
     with:
@@ -190,6 +192,8 @@ jobs:
         ) done
 
   upload-frameworks-ios:
+    # NB: Don't run this on fork PRs because they won't have access to the secret and would fail anyway
+    if: ${{ !github.event.pull_request.head.repo.fork }}
     runs-on: ubuntu-22.04
     needs: [build-frameworks-ios, set-version]
     timeout-minutes: 30
@@ -278,6 +282,8 @@ jobs:
 
   build-benchmark-app:
     name: build-benchmark-app
+    # NB: Don't run this on fork PRs because they won't have access to the secret and would fail anyway
+    if: ${{ !github.event.pull_request.head.repo.fork }}
     uses: pytorch/test-infra/.github/workflows/macos_job.yml@main
     secrets: inherit
     with:

.github/workflows/build-wheels-linux.yml

@@ -27,6 +27,7 @@ jobs:
       test-infra-ref: main
       with-cuda: disabled
       with-rocm: disabled
+      python-versions: '["3.10", "3.11", "3.12"]'
 
   build:
     needs: generate-matrix

.github/workflows/build-wheels-m1.yml

@@ -27,6 +27,7 @@ jobs:
       test-infra-ref: main
       with-cuda: disabled
       with-rocm: disabled
+      python-versions: '["3.10", "3.11", "3.12"]'
 
   build:
     needs: generate-matrix

.github/workflows/pull.yml

@@ -332,7 +332,7 @@ jobs:
       docker-image: executorch-ubuntu-22.04-clang12
 
   unittest-arm:
-    uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+    uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
     with:
       runner: linux.2xlarge
       docker-image: executorch-ubuntu-22.04-arm-sdk
@@ -368,6 +368,7 @@ jobs:
     strategy:
       matrix:
         dtype: [fp32]
+        pt2e_quantize: [qnn_16a16w, qnn_8a8w]
         mode: [qnn]
       fail-fast: false
     with:
@@ -384,6 +385,7 @@ jobs:
         DTYPE=${{ matrix.dtype }}
         BUILD_TOOL="cmake"
         MODE=${{ matrix.mode }}
+        PT2E_QUANTIZE=${{ matrix.pt2e_quantize }}
 
         PYTHON_EXECUTABLE=python bash .ci/scripts/setup-qnn-deps.sh
         PYTHON_EXECUTABLE=python bash .ci/scripts/build-qnn-sdk.sh
@@ -393,7 +395,7 @@ jobs:
         # Install requirements for export_llama
         PYTHON_EXECUTABLE=python bash examples/models/llama/install_requirements.sh
         # Test llama2
-        PYTHON_EXECUTABLE=python bash .ci/scripts/test_llama.sh -model stories110M -build_tool "${BUILD_TOOL}" -dtype "${DTYPE}" -mode "${MODE}"
+        PYTHON_EXECUTABLE=python bash .ci/scripts/test_llama.sh -model stories110M -build_tool "${BUILD_TOOL}" -mode "${MODE}" -dtype "${DTYPE}" -pt2e_quantize "${PT2E_QUANTIZE}"
 
   test-phi-3-mini-runner-linux:
     name: test-phi-3-mini-runner-linux

.github/workflows/trunk.yml

@@ -131,7 +131,7 @@ jobs:
 
   test-arm-backend-delegation:
     name: test-arm-backend-delegation
-    uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+    uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
     with:
       runner: linux.2xlarge
       docker-image: executorch-ubuntu-22.04-arm-sdk
@@ -157,7 +157,7 @@ jobs:
 
   test-arm-reference-delegation:
     name: test-arm-reference-delegation
-    uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+    uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
     with:
       runner: linux.2xlarge
       docker-image: executorch-ubuntu-22.04-arm-sdk
@@ -351,6 +351,8 @@ jobs:
         done
 
   test-huggingface-transformers:
+    # NB: Don't run this on fork PRs because they won't have access to the secret and would fail anyway
+    if: ${{ !github.event.pull_request.head.repo.fork }}
     name: test-huggingface-transformers
     uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
     secrets: inherit
@@ -441,3 +443,39 @@ jobs:
 
         cmake-out/examples/models/llama/llama_main --model_path=${ET_MODEL_NAME}.pte --tokenizer_path=${TOKENIZER_BIN_FILE} --prompt="My name is"
         echo "::endgroup::"
+
+
+  test-llama-runner-qnn-linux:
+    name: test-llama-runner-qnn-linux
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+    strategy:
+      matrix:
+        dtype: [fp32]
+        pt2e_quantize: [qnn_16a16w, qnn_8a8w]
+        mode: [qnn]
+      fail-fast: false
+    with:
+      runner: linux.2xlarge
+      docker-image: executorch-ubuntu-22.04-qnn-sdk
+      submodules: 'true'
+      ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+      timeout: 900
+      script: |
+        # The generic Linux job chooses to use base env, not the one setup by the image
+        CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")
+        conda activate "${CONDA_ENV}"
+
+        BUILD_TOOL="cmake"
+        DTYPE=${{ matrix.dtype }}
+        MODE=${{ matrix.mode }}
+        PT2E_QUANTIZE=${{ matrix.pt2e_quantize }}
+
+        PYTHON_EXECUTABLE=python bash .ci/scripts/setup-qnn-deps.sh
+        PYTHON_EXECUTABLE=python bash .ci/scripts/build-qnn-sdk.sh
+
+        # Setup executorch
+        PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh "${BUILD_TOOL}"
+        # Install requirements for export_llama
+        PYTHON_EXECUTABLE=python bash examples/models/llama/install_requirements.sh
+        # Test llama2
+        PYTHON_EXECUTABLE=python bash .ci/scripts/test_llama.sh -model stories110M -build_tool "${BUILD_TOOL}" -mode "${MODE}" -dtype "${DTYPE}" -pt2e_quantize "${PT2E_QUANTIZE}"

.gitmodules

@@ -64,6 +64,9 @@
 [submodule "third-party/pybind11"]
 	path = third-party/pybind11
 	url = https://github.com/pybind/pybind11.git
+[submodule "backends/cadence/fusion_g3/third-party/nnlib/nnlib-FusionG3"]
+	path = backends/cadence/fusion_g3/third-party/nnlib/nnlib-FusionG3
+	url = https://github.com/foss-xtensa/nnlib-FusionG3/
 [submodule "third-party/ao"]
 	path = third-party/ao
 	url = https://github.com/pytorch/ao.git

backends/apple/coreml/runtime/test/ETCoreMLModelDebuggerTests.mm

@@ -151,7 +151,6 @@ - (void)testMV3ProgramDebugging {
     XCTAssertNotNil(debuggingResults[make_path_with_output_name("aten__native_batch_norm_legit_no_training_default_13_cast_fp16")]);
     XCTAssertNotNil(debuggingResults[make_path_with_output_name("_inversed_aten_div_tensor_24_cast_fp16")]);
     XCTAssertNotNil(debuggingResults[make_path_with_output_name("aten_mean_dim_7_cast_fp16")]);
-    XCTAssertNotNil(debuggingResults[make_path_with_output_name("aten_clamp_default_54_cast_fp16")]);
     XCTAssertNotNil(debuggingResults[make_path_with_output_name("aten__native_batch_norm_legit_no_training_default_22_cast_fp16")]);
     XCTAssertNotNil(debuggingResults[make_path_with_output_name("aten_mul_tensor_27_cast_fp16")]);
 }