Move quantize IO passes from BoltNN to ExecuTorch

Summary: Rationale: code sharing. rather than rewriting

Differential Revision: D65188297

Author: digantdesai

exir/passes/TARGETS

@@ -16,6 +16,7 @@ python_library(
         ":normalize_transpose_pass",
         ":prim_ops_py_registry",
         ":quant_fusion_pass",
+        ":quantize_io_pass",
         ":remove_noop_pass",
         ":replace_aten_with_edge_pass",
         ":replace_broken_ops_with_function_ops_pass",
@@ -143,6 +144,19 @@ python_library(
     ],
 )
 
+python_library(
+    name = "quantize_io_pass",
+    srcs = [
+        "quantize_io_pass.py",
+    ],
+    deps = [
+        "fbsource//third-party/pypi/numpy:numpy",
+        "//caffe2:torch",
+        "//executorch/exir:pass_base",
+        "//executorch/exir/dialects:lib",
+    ],
+)
+
 python_library(
     name = "memory_planning_pass",
     srcs = [

exir/passes/quantize_io_pass.py

@@ -0,0 +1,217 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+import logging
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+
+import torch
+
+from executorch.exir import EdgeProgramManager
+from executorch.exir.dialects._ops import ops as exir_ops
+
+from executorch.exir.pass_base import ExportPass
+from executorch.exir.tensor import scalar_type_enum
+from torch.fx.passes.infra.pass_base import PassResult
+
+logger = logging.getLogger(__name__)
+
+def quantize_input(
+    exported_program, input_index, qparams: Optional[Dict[str, Any]] = None
+):
+    """
+    Modify the program to expect quantized input at given index. The input is expected
+    to be quantizing this input as the first step. Must be called before
+    permute_input_layout. Returns the scale, zero point, qmin, qmax, and dtype of the
+    expected quantization.
+    """
+    graph = exported_program.graph_module.graph
+    name = exported_program.graph_signature.user_inputs[input_index]
+    placeholders = [n for n in graph.nodes if n.op == "placeholder" and n.name == name]
+    assert placeholders
+    target_placeholder = placeholders[0]
+
+    if len(target_placeholder.users) != 1:
+        raise ValueError(f"Input {input_index} has more than one users")
+    quantize = next(iter(target_placeholder.users))
+    if (
+        quantize.target
+        != exir_ops.edge.quantized_decomposed.quantize_per_tensor.default
+    ):
+        raise ValueError(f"Input {input_index} is not used by a quantize op")
+
+    # If user specified qparams are different from args of quantize op, we do requantization instead of eliminating quantize op
+    need_requant = False
+    if qparams is not None:
+        assert all(
+            qparam in qparams for qparam in ["scale", "zp", "dtype"]
+        ), "dtype/scale/zp must be specified in qparam for input requantization"
+        if qparams["dtype"] != quantize.args[5]:
+            if any(
+                dtype
+                not in [torch.int8, torch.uint8, torch.bool, torch.int16, torch.uint16]
+                for dtype in [qparams["dtype"], quantize.args[5]]
+            ):
+                raise ValueError(
+                    f"Only limited data types are supported for requantization, but got {qparams['dtype']} -> {quantize.args[5]}"
+                )
+
+            need_requant = True
+        elif (
+            not np.isclose(qparams["scale"], quantize.args[1])
+            or qparams["zp"] != quantize.args[2]
+        ):
+            need_requant = True
+
+    if need_requant:
+        assert qparams is not None
+        dtype = qparams["dtype"]
+        qmin = torch.iinfo(dtype).min
+        qmax = torch.iinfo(dtype).max
+        scale = qparams["scale"]
+        zero_point = qparams["zp"]
+        quant_args = (scale, zero_point, qmin, qmax, dtype)
+        logger.info(
+            f"Modifying program to requantize quantized input at index {input_index}"
+        )
+        logger.info(f"Quantization parameters: {quant_args}")
+
+        with exported_program.graph_module.graph.inserting_before(quantize):
+            input_dequant = exported_program.graph_module.graph.call_function(
+                exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+                args=(
+                    target_placeholder,
+                    *quant_args,
+                ),
+            )
+            input_dequant.meta["input_qparams"] = [
+                {
+                    "scale": scale,
+                    "zero_point": zero_point,
+                    "qmin": qmin,
+                    "qmax": qmax,
+                    "dtype": dtype,
+                }
+            ]
+            input_dequant.meta["val"] = quantize.meta["val"].to(torch.float32)
+            target_placeholder.meta["val"] = target_placeholder.meta["val"].to(dtype)
+            quantize.replace_input_with(target_placeholder, input_dequant)
+    else:
+        quant_args = quantize.args[1:]
+        logger.info(f"Modifying program to take quantized input at index {input_index}")
+        logger.info(f"Quantization parameters: {quant_args}")
+
+        target_placeholder.meta["val"] = (
+            exir_ops.edge.quantized_decomposed.quantize_per_tensor.default(
+                target_placeholder.meta["val"], *quant_args
+            )
+        )
+        quantize.replace_all_uses_with(quantize.args[0])
+
+    exported_program.graph_module.graph.eliminate_dead_code()
+    return quant_args
+
+
+def quantize_output(exported_program, output_index):
+    """
+    Modify the program to produce quantized output at given index. The model is expected
+    to be dequantizing this output as the last step. Must be called before
+    permute_output_layout. Returns the scale, zero point, qmin, qmax, and dtype of the
+    output quantization.
+    """
+    graph = exported_program.graph_module.graph
+    outputs = [n for n in graph.nodes if n.op == "output"]
+    if len(outputs) != 1:
+        raise NotImplementedError("Only 1 output node is supported")
+
+    output_node = outputs[0]
+    output_list = list(output_node.args[0])
+    if output_index >= len(output_list):
+        raise ValueError(
+            f"{len(output_list)} outputs available, "
+            + f"output index out of bounds: {output_index}"
+        )
+
+    target_output = output_list[output_index]
+    if (
+        target_output.target
+        != exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default
+    ):
+        raise ValueError("Output {output_index} is not a dequantize op")
+
+    dequant = target_output
+    output_list[output_index] = dequant.args[0]
+    output_node.args = (output_list,)
+    dequant_args = dequant.args[1:]
+    graph.eliminate_dead_code()
+
+    logger.info(
+        f"Modifying program to produce quantized output at index {output_index}"
+    )
+    logger.info(f"Quantization parameters: {dequant_args}")
+    return dequant_args
+
+class QuantizeInputs(ExportPass):
+    def __init__(
+        self,
+        edge_program_manager: EdgeProgramManager,
+        quantized_inputs_idx: Union[Dict[int, Dict[str, Any]], List[int]],
+        method_name: Optional[str] = None,
+    ):
+        super().__init__()
+        self.edge_program_manager = edge_program_manager
+
+        self.quantized_inputs_idx_dict = {}
+        if isinstance(quantized_inputs_idx, dict):
+            self.quantized_inputs_idx_dict = quantized_inputs_idx
+        else:
+            for idx in quantized_inputs_idx:
+                self.quantized_inputs_idx_dict[idx] = None
+        self.param_prefix_name = "" if method_name is None else method_name + "_"
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        for i, qparams in self.quantized_inputs_idx_dict.items():
+            quant_args = quantize_input(
+                self.edge_program_manager.exported_program(), i, qparams
+            )
+
+            if not self.edge_program_manager._config_methods:
+                self.edge_program_manager._config_methods = {}
+
+            self.edge_program_manager._config_methods[self.param_prefix_name + f"input{i}_scale"] = quant_args[0]
+            self.edge_program_manager._config_methods[self.param_prefix_name + f"input{i}_zp"] = quant_args[1]  # pyre-ignore
+            self.edge_program_manager._config_methods[
+                self.param_prefix_name + f"input{i}_quant_min"
+            ] = quant_args[2]
+            self.edge_program_manager._config_methods[
+                self.param_prefix_name + f"input{i}_quant_max"
+            ] = quant_args[3]
+            self.edge_program_manager._config_methods[
+                self.param_prefix_name + f"input{i}_dtype"
+            ] = scalar_type_enum(quant_args[4])
+        return PassResult(graph_module, True)
+
+class QuantizeOutputs(ExportPass):
+    def __init__(
+        self,
+        edge_program_manager: EdgeProgramManager,
+        quantized_outputs_idx_list: List[int],
+        method_name: Optional[str] = None,
+    ):
+        super().__init__()
+        self.edge_program_manager = edge_program_manager
+        self.quantized_outputs_idx_list = quantized_outputs_idx_list
+        self.param_prefix_name = "" if method_name is None else method_name + "_"
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        for i in self.quantized_outputs_idx_list:
+            dequant_args = quantize_output(self.edge_program_manager.exported_program(), i)  # noqa F841
+
+            if not self.edge_program_manager._config_methods:
+                self.edge_program_manager._config_methods = {}
+
+            self.edge_program_manager._config_methods[
+                self.param_prefix_name + f"output{i}_scale"
+            ] = dequant_args[0]
+            self.edge_program_manager._config_methods[self.param_prefix_name + f"output{i}_zp"] = dequant_args[1]  # pyre-ignore
+
+        return PassResult(graph_module, True)