Add torchao kernels to llama runner

.gitmodules

@@ -64,3 +64,6 @@
 [submodule "third-party/pybind11"]
 	path = third-party/pybind11
 	url = https://github.com/pybind/pybind11.git
+[submodule "third-party/ao"]
+	path = third-party/ao
+	url = https://github.com/pytorch/ao.git

examples/models/llama/CMakeLists.txt

@@ -37,6 +37,8 @@ cmake_dependent_option(
   "NOT EXECUTORCH_BUILD_ARM_BAREMETAL" OFF
 )
 
+option(EXECUTORCH_BUILD_TORCHAO "Build the torchao kernels" OFF)
+
 if(NOT PYTHON_EXECUTABLE)
   set(PYTHON_EXECUTABLE python3)
 endif()
@@ -121,6 +123,13 @@ if(EXECUTORCH_BUILD_KERNELS_CUSTOM)
   list(APPEND link_libraries custom_ops)
 endif()
 
+if(EXECUTORCH_BUILD_TORCHAO)
+  set(TORCHAO_BUILD_EXECUTORCH_OPS ON)
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../../../third-party/ao/torchao/experimental ${CMAKE_CURRENT_BINARY_DIR}/../../../third-party/ao/torchao/experimental)
+  target_link_options_shared_lib(torchao_ops_executorch)
+  list(APPEND link_libraries torchao_ops_executorch)
+endif()
+
 set(XNNPACK_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/../../../backends/xnnpack)
 # Extra compile option and include dir for pthreadpool
 if(EXECUTORCH_BUILD_PTHREADPOOL)

examples/models/llama/export_llama_lib.py

@@ -12,14 +12,14 @@
 import copy
 import json
 import logging
+import re
 import shlex
 from enum import Enum
 from json import JSONDecodeError
 from pathlib import Path
 from typing import Callable, List, Optional, Union
 
 import pkg_resources
-
 import torch
 
 from executorch.devtools.etrecord import generate_etrecord
@@ -153,12 +153,12 @@ def build_args_parser() -> argparse.ArgumentParser:
         ],
         help="Use PT2E quantization. Comma separated options. e.g. xnnpack_dynamic (for per channel 8 bit weight), xnnpack_dynamic_qc4 (for per channel 4 bit weight), embedding.",
     )
+
     parser.add_argument(
         "-qmode",
         "--quantization_mode",
-        type=str,
+        type=_qmode_type,
         default=None,
-        choices=["int8", "8da4w", "8da4w-gptq", "vulkan_4w"],
         help="type of quantization",
     )
 
@@ -568,6 +568,23 @@ def get_quantizer_and_quant_params(args):
     return pt2e_quant_params, quantizers, quant_dtype
 
 
+def _qmode_type(value):
+    choices = ["int8", "8da4w", "8da4w-gptq", "vulkan_4w"]
+    patterns = [r"torchao:8da(\d+)w"]
+
+    if value in choices:
+        return value
+
+    for pattern in patterns:
+        matches = re.findall(pattern, value)
+        if len(matches) == 1:
+            return value
+
+    raise argparse.ArgumentTypeError(
+        f"Got qmode {value}, but expected one of {choices}, or one of the regex patterns {patterns}."
+    )
+
+
 def _validate_args(args):
     """
     TODO: Combine all the backends under --backend args
@@ -581,6 +598,19 @@ def _validate_args(args):
     if args.num_sharding > 0 and not args.qnn:
         raise ValueError("Model shard is only supported with qnn backend now.")
 
+    if (
+        args.quantization_mode is not None
+        and args.quantization_mode.startswith("torchao:")
+    ) or (
+        args.embedding_quantize is not None
+        and args.embedding_quantize.startswith("torchao:")
+    ):
+        if args.enable_dynamic_shape:
+            raise ValueError(
+                "Dynamic shape is not currently supported with torchao ops. Please use --disable_dynamic_shape."
+                "If you need this feature, please file an issue."
+            )
+
 
 def _export_llama(modelname, args) -> LLMEdgeManager:  # noqa: C901
     _validate_args(args)

examples/models/llama/install_requirements.sh

@@ -10,8 +10,7 @@
 pip install snakeviz sentencepiece
 
 # Install torchao.
-TORCHAO_VERSION=$(cat "$(dirname "$0")"/../../../.ci/docker/ci_commit_pins/torchao.txt)
-pip install --no-use-pep517 "git+https://github.com/pytorch/ao.git@${TORCHAO_VERSION}"
+pip install "$(dirname "$0")/../../../third-party/ao"
 
 # Install lm-eval for Model Evaluation with lm-evalution-harness
 # Install tiktoken for tokenizer

examples/models/llama/source_transformation/quantize.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import logging
+import re
 from functools import partial
 from pathlib import Path
 from typing import Any, Dict, Optional
@@ -70,6 +72,26 @@ def quantize(  # noqa C901
     if qmode == "int8":
         # Add quantization mode options here: group size, bit width, etc.
         return WeightOnlyInt8QuantHandler(model).quantized_model()
+    elif qmode.startswith("torchao:"):
+        pattern = r"torchao:8da(\d+)w"
+        matches = re.findall(pattern, qmode)
+        assert len(matches) == 1, f"Expected 1 match for pattern but got {len(matches)}"
+        bitwidth = int(matches[0][0])
+        _load_torchao_ops_aten()
+        from torchao.experimental.quant_api import Int8DynActIntxWeightLinearQuantizer
+
+        with torch.no_grad():
+            model = Int8DynActIntxWeightLinearQuantizer(
+                device="cpu",
+                precision=torch.float32,
+                groupsize=group_size,
+                bitwidth=bitwidth,
+                has_weight_zeros=False,
+            ).quantize(model)
+
+        if verbose:
+            print("quantized model:", model)
+        return model
     elif qmode == "8da4w":
         # Check for required args
         if group_size is None:
@@ -79,6 +101,7 @@ def quantize(  # noqa C901
         model = Int8DynActInt4WeightQuantizer(
             precision=torch_dtype, groupsize=group_size
         ).quantize(model)
+
         if verbose:
             print("quantized model:", model)
         return model
@@ -692,6 +715,25 @@ def forward(self, indices: torch.Tensor) -> torch.Tensor:
 
 
 def get_quant_embedding_transform(args):
+    if args.embedding_quantize.startswith("torchao:"):
+        bitwidth, group_size = args.embedding_quantize.split(":")[1].split(",")
+        group_size = int(group_size)
+        bitwidth = int(bitwidth)
+        _load_torchao_ops_aten()
+        from torchao.experimental.quant_api import IntxWeightEmbeddingQuantizer
+
+        def _torchao_embedding_quantizer(model):
+            with torch.no_grad():
+                model = IntxWeightEmbeddingQuantizer(
+                    device="cpu",
+                    precision=torch.float32,
+                    bitwidth=bitwidth,
+                    groupsize=group_size,
+                ).quantize(model)
+            return model
+
+        return _torchao_embedding_quantizer
+
     bitwidth, group_size = args.embedding_quantize.split(",")
     if group_size == "none" or group_size == "None" or group_size == "0":
         group_size = None
@@ -733,4 +775,23 @@ def get_quant_weight_transform(args, dtype_override, verbose):
     )
 
 
+def _load_torchao_ops_aten():
+    import glob
+    import os
+
+    libs = glob.glob(
+        os.path.abspath(
+            os.path.join(
+                os.environ.get("CMAKE_INSTALL_PREFIX", ""),
+                "lib/libtorchao_ops_aten.*",
+            )
+        )
+    )
+    assert (
+        len(libs) == 1
+    ), f"Expected 1 library but got {len(libs)}.  If you installed the torchao ops in a non-standard location, please set CMAKE_INSTALL_PREFIX correctly."
+    logging.info(f"Loading custom ops library: {libs[0]}")
+    torch.ops.load_library(libs[0])
+
+
 ############################ Source Transform End #######################

examples/models/llama3_2_vision/install_requirements.sh

@@ -9,5 +9,4 @@
 pip install --pre torchtune --extra-index-url https://download.pytorch.org/whl/nightly/cpu --no-cache-dir
 
 # Install torchao.
-TORCHAO_VERSION=$(cat "$(dirname "$0")"/../../../.ci/docker/ci_commit_pins/torchao.txt)
-pip install --no-use-pep517 "git+https://github.com/pytorch/ao.git@${TORCHAO_VERSION}"
+pip install "$(dirname "$0")/../../../third-party/ao"

examples/models/phi-3-mini-lora/install_requirements.sh

@@ -10,5 +10,4 @@ pip install torchtune
 pip install tiktoken
 
 # Install torchao.
-TORCHAO_VERSION=$(cat "$(dirname "$0")"/../../../.ci/docker/ci_commit_pins/torchao.txt)
-pip install --no-use-pep517 "git+https://github.com/pytorch/ao.git@${TORCHAO_VERSION}"
+pip install "$(dirname "$0")/../../../third-party/ao"