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| 1 | +# Copyright (c) Qualcomm Innovation Center, Inc. |
| 2 | +# All rights reserved |
| 3 | +# |
| 4 | +# This source code is licensed under the BSD-style license found in the |
| 5 | +# LICENSE file in the root directory of this source tree. |
| 6 | + |
| 7 | +import torch |
| 8 | +from executorch.exir.dialects._ops import ops as exir_ops |
| 9 | +from executorch.exir.dialects.edge._ops import EdgeOpOverload |
| 10 | +from executorch.exir.pass_base import ExportPass, PassResult |
| 11 | +from torchao.quantization.pt2e.utils import get_new_attr_name_with_prefix |
| 12 | + |
| 13 | +from .utils import copy_meta, get_const_node |
| 14 | + |
| 15 | + |
| 16 | +class DecomposeVar(ExportPass): |
| 17 | + """ |
| 18 | + Decompose aten.var.correction and aten.var.dim into supported primitives: |
| 19 | + var(x, dim) = mean((x - mean(x, dim, keepdim=True))^2, dim, keepdim) |
| 20 | + * N / (N - correction) |
| 21 | +
|
| 22 | + For var.correction: |
| 23 | + correction is an optional Scalar (default=1, i.e. Bessel's correction) |
| 24 | + For var.dim: |
| 25 | + unbiased=True maps to correction=1, unbiased=False maps to correction=0 |
| 26 | + """ |
| 27 | + |
| 28 | + def __init__(self): |
| 29 | + super(DecomposeVar, self).__init__() |
| 30 | + self.var_targets = { |
| 31 | + torch.ops.aten.var.correction, |
| 32 | + torch.ops.aten.var.dim, |
| 33 | + exir_ops.edge.aten.var.correction, |
| 34 | + exir_ops.edge.aten.var.dim, |
| 35 | + } |
| 36 | + |
| 37 | + def _get_correction(self, node): |
| 38 | + """Extract the correction factor from node args based on op variant.""" |
| 39 | + target = node.target |
| 40 | + if target in ( |
| 41 | + torch.ops.aten.var.correction, |
| 42 | + exir_ops.edge.aten.var.correction, |
| 43 | + ): |
| 44 | + # var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) |
| 45 | + # correction is a kwarg, but in the graph it may appear in kwargs |
| 46 | + correction = node.kwargs.get("correction", None) |
| 47 | + if correction is None: |
| 48 | + correction = 1.0 |
| 49 | + return float(correction) |
| 50 | + else: |
| 51 | + # var.dim(Tensor self, int[1]? dim=None, bool unbiased=True, bool keepdim=False) |
| 52 | + unbiased = node.args[2] if len(node.args) > 2 else True |
| 53 | + return 1.0 if unbiased else 0.0 |
| 54 | + |
| 55 | + def _get_dim_and_keepdim(self, node): |
| 56 | + """Extract dim and keepdim from node args based on op variant.""" |
| 57 | + target = node.target |
| 58 | + if target in ( |
| 59 | + torch.ops.aten.var.correction, |
| 60 | + exir_ops.edge.aten.var.correction, |
| 61 | + ): |
| 62 | + # var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) |
| 63 | + dim = node.args[1] if len(node.args) > 1 else None |
| 64 | + keepdim = node.kwargs.get("keepdim", False) |
| 65 | + return dim, keepdim |
| 66 | + else: |
| 67 | + # var.dim(Tensor self, int[1]? dim=None, bool unbiased=True, bool keepdim=False) |
| 68 | + dim = node.args[1] if len(node.args) > 1 else None |
| 69 | + keepdim = node.args[3] if len(node.args) > 3 else False |
| 70 | + return dim, keepdim |
| 71 | + |
| 72 | + def call(self, graph_module: torch.fx.GraphModule): |
| 73 | + graph = graph_module.graph |
| 74 | + const_cache = {} |
| 75 | + |
| 76 | + for node in list(graph.nodes): |
| 77 | + if node.op == "call_function" and node.target in self.var_targets: |
| 78 | + x_node = node.args[0] |
| 79 | + is_edge = isinstance(node.target, EdgeOpOverload) |
| 80 | + meta = node.meta |
| 81 | + |
| 82 | + correction = self._get_correction(node) |
| 83 | + dim, keepdim = self._get_dim_and_keepdim(node) |
| 84 | + |
| 85 | + mean_op = ( |
| 86 | + exir_ops.edge.aten.mean.dim if is_edge else torch.ops.aten.mean.dim |
| 87 | + ) |
| 88 | + sub_op = ( |
| 89 | + exir_ops.edge.aten.sub.Tensor |
| 90 | + if is_edge |
| 91 | + else torch.ops.aten.sub.Tensor |
| 92 | + ) |
| 93 | + mul_op = ( |
| 94 | + exir_ops.edge.aten.mul.Tensor |
| 95 | + if is_edge |
| 96 | + else torch.ops.aten.mul.Tensor |
| 97 | + ) |
| 98 | + |
| 99 | + # Handle dim=None: reduce over all dimensions |
| 100 | + input_shape = node.args[0].meta["val"].shape |
| 101 | + if dim is None: |
| 102 | + dim = list(range(len(input_shape))) |
| 103 | + |
| 104 | + with graph.inserting_before(node): |
| 105 | + x_val = x_node.meta["val"] |
| 106 | + |
| 107 | + # Step 1: mean_x = mean(x, dim, keepdim=True) |
| 108 | + mean_x_node = graph.create_node( |
| 109 | + "call_function", mean_op, (x_node, dim, True) |
| 110 | + ) |
| 111 | + mean_x_node.meta = copy_meta( |
| 112 | + meta, |
| 113 | + callback=lambda m, _x=x_val, _d=dim: { |
| 114 | + **m, |
| 115 | + "val": _x.mean(_d, keepdim=True), |
| 116 | + }, |
| 117 | + ) |
| 118 | + |
| 119 | + # Step 2: diff = x - mean_x |
| 120 | + diff_node = graph.create_node( |
| 121 | + "call_function", sub_op, (x_node, mean_x_node) |
| 122 | + ) |
| 123 | + diff_node.meta = copy_meta( |
| 124 | + meta, callback=lambda m, _x=x_val: {**m, "val": _x} |
| 125 | + ) |
| 126 | + |
| 127 | + # Step 3: sq = diff * diff (more efficient than pow(diff, 2)) |
| 128 | + sq_node = graph.create_node( |
| 129 | + "call_function", mul_op, (diff_node, diff_node) |
| 130 | + ) |
| 131 | + sq_node.meta = copy_meta( |
| 132 | + meta, callback=lambda m, _x=x_val: {**m, "val": _x} |
| 133 | + ) |
| 134 | + |
| 135 | + # Step 4: var = mean(sq, dim, keepdim) |
| 136 | + var_node = graph.create_node( |
| 137 | + "call_function", mean_op, (sq_node, dim, keepdim) |
| 138 | + ) |
| 139 | + var_node.meta = copy_meta(meta) |
| 140 | + |
| 141 | + # Step 5: Apply correction factor if needed |
| 142 | + if correction != 0.0: |
| 143 | + # N = product of sizes along reduced dims |
| 144 | + n = 1 |
| 145 | + for d in dim: |
| 146 | + n *= input_shape[d] |
| 147 | + |
| 148 | + scale = float(n) / float(n - correction) |
| 149 | + |
| 150 | + if is_edge: |
| 151 | + cache_key = ("_var_scale_", scale) |
| 152 | + if cache_key not in const_cache: |
| 153 | + attr_name = get_new_attr_name_with_prefix( |
| 154 | + "_var_scale_const_" |
| 155 | + )(graph_module) |
| 156 | + const_cache[cache_key] = get_const_node( |
| 157 | + graph, graph_module, attr_name, scale, node |
| 158 | + ) |
| 159 | + scale_node = const_cache[cache_key] |
| 160 | + else: |
| 161 | + scale_node = scale |
| 162 | + |
| 163 | + result_node = graph.create_node( |
| 164 | + "call_function", mul_op, (var_node, scale_node) |
| 165 | + ) |
| 166 | + result_node.meta = copy_meta(meta) |
| 167 | + else: |
| 168 | + result_node = var_node |
| 169 | + |
| 170 | + for user in node.users.copy(): |
| 171 | + user.replace_input_with(node, result_node) |
| 172 | + |
| 173 | + graph.eliminate_dead_code() |
| 174 | + graph_module.recompile() |
| 175 | + return PassResult(graph_module, True) |
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