VulkanQuantizer for weight-only quantization on linear

Differential Revision: D61243540

Pull Request resolved: #4707

Author: nathanaelsee

backends/vulkan/quantizer/TARGETS

@@ -0,0 +1,13 @@
+load("@fbcode_macros//build_defs:python_library.bzl", "python_library")
+
+oncall("executorch")
+
+python_library(
+    name = "vulkan_quantizer",
+    srcs = [
+        "vulkan_quantizer.py",
+    ],
+    deps = [
+        "//caffe2:torch",
+    ],
+)

backends/vulkan/quantizer/vulkan_quantizer.py

@@ -0,0 +1,120 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+from __future__ import annotations
+
+import functools
+from typing import Any, Callable, Dict, Optional
+
+import torch
+from torch.ao.quantization.observer import MinMaxObserver, PerChannelMinMaxObserver
+from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+from torch.ao.quantization.quantizer import QuantizationSpec, Quantizer
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    _convert_scalars_to_attrs,
+    OP_TO_ANNOTATOR,
+    propagate_annotation,
+    QuantizationConfig,
+)
+from torch.fx import Node
+
+
+__all__ = [
+    "VulkanQuantizer",
+    "get_weight_quantization_config",
+]
+
+
+@functools.lru_cache
+def get_weight_quantization_config(
+    is_per_channel: bool = True,
+    weight_qmin: int = -128,
+    weight_qmax: int = 127,
+) -> QuantizationConfig:
+
+    weight_qscheme = (
+        torch.per_channel_symmetric if is_per_channel else torch.per_tensor_symmetric
+    )
+    weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = (
+        PerChannelMinMaxObserver if is_per_channel else MinMaxObserver
+    )
+    extra_args: Dict[str, Any] = {"eps": 2**-12}
+
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=weight_qmin,
+        quant_max=weight_qmax,
+        qscheme=weight_qscheme,
+        ch_axis=0,
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    quantization_config = QuantizationConfig(
+        input_activation=None,
+        output_activation=None,
+        weight=weight_quantization_spec,
+        bias=None,
+        is_qat=False,
+    )
+    return quantization_config
+
+
+_SUPPORTED_OPS = [
+    "linear",
+]
+
+
+class VulkanQuantizer(Quantizer):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.global_config: Optional[QuantizationConfig] = None
+
+    def set_global(self, quantization_config: QuantizationConfig) -> VulkanQuantizer:
+        self.global_config = quantization_config
+        return self
+
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        """Transforms scalar values to tensor attributes"""
+        return _convert_scalars_to_attrs(model)
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        # currently only support static quant on Vulkan
+        model = self._annotate_for_static_quantization_config(model)
+        propagate_annotation(model)
+        return model
+
+    def _annotate_all_static_patterns(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[Callable[[Node], bool]] = None,
+    ) -> torch.fx.GraphModule:
+        if quantization_config is None:
+            return model
+
+        for op in _SUPPORTED_OPS:
+            OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        return model
+
+    def _annotate_for_static_quantization_config(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        self._annotate_all_static_patterns(
+            model,
+            self.global_config,
+        )
+        return model
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass