Add torchchat quantizer (pytorch#897)

Summary:
Pull Request resolved: pytorch#897

This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api).  See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API.

I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat.  They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper.

jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you.  I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion.

We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel).  From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further.

Reviewed By: digantdesai

Differential Revision: D62394341

torchao/experimental/kernels/cpu/aarch64/CMakeLists.txt

@@ -6,8 +6,8 @@
 
 add_library(
   kernel_aarch64
-  ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/reduction/find_min_and_max.cpp
-  ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/reduction/compute_sum.cpp
-  ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/quantization/quantize.cpp
-  ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp
+  ${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/aarch64/reduction/find_min_and_max.cpp
+  ${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/aarch64/reduction/compute_sum.cpp
+  ${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/aarch64/quantization/quantize.cpp
+  ${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp
 )

torchao/experimental/kernels/cpu/linear/examples/torch_custom_op/CMakeLists.txt

@@ -13,12 +13,12 @@ set(CMAKE_BUILD_TYPE Release)
 add_compile_options("-Wall" "-Werror")
 
 include(CMakePrintHelpers)
-message("TORCHAO_LIBRARIES: ${TORCHAO_LIBRARIES}")
-include_directories(${TORCHAO_LIBRARIES})
+message("TORCHAO_INCLUDE_DIRS: ${TORCHAO_INCLUDE_DIRS}")
+include_directories(${TORCHAO_INCLUDE_DIRS})
 
-add_subdirectory(${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64 ${CMAKE_CURRENT_BINARY_DIR}/kernel_aarch64)
+add_subdirectory(${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/aarch64 ${CMAKE_CURRENT_BINARY_DIR}/kernel_aarch64)
 
-include(${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/Utils.cmake)
+include(${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/Utils.cmake)
 
 set(PLATFORM "ATEN" CACHE STRING "Choose platform surface: ATEN, EXECUTORCH")
 string(TOUPPER ${PLATFORM} PLATFORM_TO_UPPER)

torchao/experimental/kernels/cpu/linear/examples/torch_custom_op/build_custom_op.sh

@@ -6,14 +6,14 @@
 # LICENSE file in the root directory of this source tree.
 
 SCRIPT_DIR=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &> /dev/null && pwd)
-export TORCHAO_LIBRARIES=${SCRIPT_DIR}/../../../../../../..
+export TORCHAO_INCLUDE_DIRS=${SCRIPT_DIR}/../../../../../../..
 
 export CMAKE_PREFIX_PATH="$(python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)')"
 echo "CMAKE_PREFIX_PATH: ${CMAKE_PREFIX_PATH}"
-export CMAKE_OUT=/tmp/cmake-out/torch_ao/examples/torch_custom_op
-cmake -DTORCHAO_LIBRARIES=${TORCHAO_LIBRARIES} \
+export CMAKE_OUT=/tmp/cmake-out/torchao
+cmake -DTORCHAO_INCLUDE_DIRS=${TORCHAO_INCLUDE_DIRS} \
     -DCMAKE_PREFIX_PATH=${CMAKE_PREFIX_PATH} \
     -DPLATFORM="ATEN" \
-    -S ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/linear/examples/torch_custom_op \
+    -S ${TORCHAO_INCLUDE_DIRS}/torchao/experimental/kernels/cpu/linear/examples/torch_custom_op \
     -B ${CMAKE_OUT}
 cmake --build  ${CMAKE_OUT}

torchao/experimental/kernels/cpu/linear/examples/torch_custom_op/run_custom_op.py

@@ -5,12 +5,18 @@
 # LICENSE file in the root directory of this source tree.
 
 import copy
+import glob
+
+import sys
 
 import torch
-from torch_custom_op import (
-    linear_a8sz_w_lowbit_reference_impl,
-    replace_linear_with_quantized_linear,
-)
+
+sys.path.insert(0, "../../../../..")
+from quant_api import Int8DynActIntxWeightQuantizer
+
+libs = glob.glob("/tmp/cmake-out/torchao/liblowbit_op_aten.*")
+libs = list(filter(lambda l: (l.endswith("so") or l.endswith("dylib")), libs))
+torch.ops.load_library(libs[0])
 
 group_size = 256
 m = 1
@@ -27,15 +33,15 @@
 
 print("Quantizing random model")
 quantized_model = copy.deepcopy(model)
-quantized_model = quantized_model.eval()
-replace_linear_with_quantized_linear(
-    quantized_model,
-    kwargs={
-        "group_size": group_size,
-        "nbit": nbit,
-        "has_weight_zeros": has_weight_zeros,
-    },
+quantizer = Int8DynActIntxWeightQuantizer(
+    device="cpu",
+    precision=torch.float32,
+    bitwidth=nbit,
+    groupsize=group_size,
+    has_weight_zeros=has_weight_zeros,
 )
+quantized_model = quantizer.quantize(quantized_model)
+quantized_model = quantized_model.eval()
 
 print("Creating random activations")
 activations = torch.randn(m, k, dtype=torch.float32)
@@ -58,44 +64,3 @@
 print("Running AOTI")
 fn = torch._export.aot_load("/tmp/torch_custom_op_example_model.so", "cpu")
 fn(activations)
-
-
-print("\nChecking correctness on layer 0")
-linear = model[0]
-quantized_linear = quantized_model[0]
-
-with torch.no_grad():
-    result = quantized_linear(activations)
-    expected_result = linear_a8sz_w_lowbit_reference_impl(
-        linear.weight, activations, group_size, nbit, has_weight_zeros
-    )
-    non_quantized_result = linear(activations)
-
-
-# Check that entries in result match entries in expected_result
-num_mismatch_at_low_tol = 0
-num_total = result.reshape(-1).shape[0]
-for i in range(num_total):
-    actual_val = result.reshape(-1)[i]
-    expected_val = expected_result.reshape(-1)[i]
-    if not torch.allclose(actual_val, expected_val):
-        num_mismatch_at_low_tol += 1
-
-        # If results are not close at a relaxed tolerance, exit with failure
-        if not torch.allclose(actual_val, expected_val, atol=1e-6):
-            assert False, "Correctness check failed"
-
-# Assert at most 5% of entries are not close at a low tolerance
-assert num_mismatch_at_low_tol / num_total <= 0.05, "Correctness check failed"
-print(
-    "Correctness check passed.  All results are close, and ",
-    (num_total - num_mismatch_at_low_tol),
-    "/",
-    num_total,
-    " entries are close at a low tolerance.",
-)
-print("Quantization errors:")
-print("\tL1 error: ", torch.mean(torch.abs(result - non_quantized_result)).item())
-print("\tL2 error: ", torch.mean((result - non_quantized_result) ** 2).item())
-print("\tquantized_result[0:5]: ", result[0][0:5])
-print("\tnon_quantized_result[0:5]: ", non_quantized_result[0][0:5])

torchao/experimental/kernels/cpu/linear/examples/torch_custom_op/test_custom_op.py → torchao/experimental/kernels/cpu/linear/examples/torch_custom_op/test_int8_dyn_act_intx_weight_quantizer.py

@@ -4,16 +4,27 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
+import copy
+
+import glob
+
+import sys
 import unittest
 
 import torch
-from torch_custom_op import (
-    linear_a8sz_w_lowbit_reference_impl,
-    replace_linear_with_quantized_linear,
+
+sys.path.insert(0, "../../../../..")
+from quant_api import (
+    _Int8DynActIntxWeightQuantizedLinearFallback,
+    Int8DynActIntxWeightQuantizer,
 )
-import copy
 
-class TestTorchCustomOp(unittest.TestCase):
+libs = glob.glob("/tmp/cmake-out/torchao/liblowbit_op_aten.*")
+libs = list(filter(lambda l: (l.endswith("so") or l.endswith("dylib")), libs))
+torch.ops.load_library(libs[0])
+
+
+class TestInt8DynActIntxWeightQuantizer(unittest.TestCase):
     def test_accuracy(self):
         group_size = 128
         m = 1
@@ -22,24 +33,27 @@ def test_accuracy(self):
         activations = torch.randn(m, k, dtype=torch.float32)
         model = torch.nn.Sequential(*[torch.nn.Linear(k, n, bias=False)])
 
-        for nbit in [2, 3, 4, 5]:
-            for has_weight_zeros in [False, True]:
+        for nbit in [1, 2, 3, 4, 5, 6, 7]:
+            for has_weight_zeros in [True, False]:
+                print(f"Testing nbit={nbit}, has_weight_zeros={has_weight_zeros}")
                 quantized_model = copy.deepcopy(model)
-                replace_linear_with_quantized_linear(
-                    quantized_model,
-                        kwargs={
-                            "group_size": group_size,
-                            "nbit": nbit,
-                            "has_weight_zeros": has_weight_zeros,
-                        },
+                quantizer = Int8DynActIntxWeightQuantizer(
+                    device="cpu",
+                    precision=torch.float32,
+                    bitwidth=nbit,
+                    groupsize=group_size,
+                    has_weight_zeros=has_weight_zeros,
                 )
+                quantized_model = quantizer.quantize(quantized_model)
 
                 with torch.no_grad():
                     result = quantized_model(activations)
-                    expected_result = linear_a8sz_w_lowbit_reference_impl(
-                        model[0].weight, activations, group_size, nbit, has_weight_zeros
+                    reference_impl = _Int8DynActIntxWeightQuantizedLinearFallback()
+                    reference_impl.quantize_and_pack_weights(
+                        model[0].weight, nbit, group_size, has_weight_zeros
                     )
-
+                    expected_result = reference_impl(activations)
+
                 num_mismatch_at_low_tol = 0
                 num_total = result.reshape(-1).shape[0]
                 for i in range(num_total):
@@ -50,7 +64,8 @@ def test_accuracy(self):
                         num_mismatch_at_low_tol += 1
 
                 # Assert at most 5% of entries are not close at a low tolerance
-                self.assertTrue(num_mismatch_at_low_tol / num_total <= 0.05)        
-
-if __name__ == '__main__':
+                self.assertTrue(num_mismatch_at_low_tol / num_total <= 0.05)
+
+
+if __name__ == "__main__":
     unittest.main()