Update base for Update on "add eval for attention sink"

This PR adds the function to evaluate the model's perplexity when AttentionSink is enabled.

This is mostly copied from https://github.com/mit-han-lab/streaming-llm/blob/main/examples/eval_long_ppl.py which is used by the AttentionSink paper to evaluate the model's perplexity when AttentionSink is enabled.

Differential Revision: [D66474732](https://our.internmc.facebook.com/intern/diff/D66474732/)

Perplexity measured for llama 3.2 1B and 1B_Instruct model up to 40k tokens with AttentionSink enabled:

<img width="966" alt="Screenshot 2024-11-25 at 2 46 04 PM" src="https://github.com/user-attachments/assets/ba7118f9-b5d7-4de8-b1fa-7d2ba0646515">


[ghstack-poisoned]

.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")]);
 }

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

@@ -146,7 +146,6 @@ - (void)testMV3ProgramProfiling {
             XCTAssertNotNil(profilingResult[make_path_with_output_name("aten__native_batch_norm_legit_no_training_default_13_cast_fp16")]);
             XCTAssertNotNil(profilingResult[make_path_with_output_name("_inversed_aten_div_tensor_24_cast_fp16")]);
             XCTAssertNotNil(profilingResult[make_path_with_output_name("aten_mean_dim_7_cast_fp16")]);
-            XCTAssertNotNil(profilingResult[make_path_with_output_name("aten_clamp_default_54_cast_fp16")]);
             XCTAssertNotNil(profilingResult[make_path_with_output_name("aten__native_batch_norm_legit_no_training_default_22_cast_fp16")]);
             XCTAssertNotNil(profilingResult[make_path_with_output_name("aten_mul_tensor_27_cast_fp16")]);
         };

backends/arm/_passes/cast_int64_pass.py

@@ -5,8 +5,15 @@
 
 # pyre-unsafe
 
+import logging
+
 import torch
+from executorch.backends.arm._passes.arm_pass_utils import is_param_node
 from executorch.exir.pass_base import ExportPass, PassResult
+from torch._export.utils import is_buffer
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
 
 
 class CastInt64ToInt32Pass(ExportPass):
@@ -18,17 +25,31 @@ def _to_int32(self, graph_module: torch.fx.GraphModule):
         for node in graph_module.graph.nodes:
             fake_tensor = node.meta["val"]
             if isinstance(fake_tensor, torch._subclasses.fake_tensor.FakeTensor):
-                if node.meta["val"].dtype == torch.int64:
-                    node.meta["val"] = node.meta["val"].to(torch.int32)
-                    buffer_name = (
-                        self.exported_program.graph_signature.inputs_to_buffers[
-                            node.name
-                        ]
-                    )
-                    new_tensor = self.exported_program.state_dict[buffer_name].to(
-                        torch.int32
-                    )
-                    self.exported_program.state_dict[buffer_name] = new_tensor
+                if node.meta["val"].dtype == torch.int64 and is_param_node(
+                    self.exported_program, node
+                ):
+                    if is_buffer(self.exported_program, node):
+                        node.meta["val"] = node.meta["val"].to(torch.int32)
+                        buffer_name = (
+                            self.exported_program.graph_signature.inputs_to_buffers[
+                                node.name
+                            ]
+                        )
+                        buffer = self.exported_program.state_dict[node.name]
+                        logger.warning(
+                            f"Casting buffer {node.name} from torch.int64 to torch.int32"
+                            f" defined in {node.meta['stack_trace']}"
+                        )
+                        if torch.min(buffer) < torch.iinfo(torch.int32).min:
+                            raise RuntimeError(
+                                f"Buffer {node.name} has value < {torch.iinfo(torch.int32).min}"
+                            )
+                        if torch.max(buffer) > torch.iinfo(torch.int32).max:
+                            raise RuntimeError(
+                                f"Buffer {node.name} has value > {torch.iinfo(torch.int32).max}"
+                            )
+                        buffer_int32 = buffer.to(torch.int32)
+                        self.exported_program.state_dict[buffer_name] = buffer_int32
 
     def call(self, graph_module: torch.fx.GraphModule):
         self._to_int32(graph_module)

backends/arm/_passes/scalars_to_attribute_pass.py

@@ -51,6 +51,11 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 if isinstance(arg, Node):
                     new_args.append(arg)
                     continue
+                if isinstance(arg, int) and not torch.is_floating_point(
+                    get_first_fake_tensor(n)
+                ):
+                    new_args.append(arg)
+                    continue
 
                 prefix = "_tensor_constant_"
                 get_new_attr_name = get_new_attr_name_with_prefix(prefix)