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FuseDequantLinearPass to convert dq -> linear into weight_int8packed_mm
Differential Revision: D60945766 Pull Request resolved: pytorch#4708
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# 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. | ||
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# pyre-strict | ||
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import torch | ||
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from executorch.exir.dialects._ops import ops as exir_ops | ||
from executorch.exir.pass_base import ExportPass, PassResult | ||
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class FuseDequantLinearPass(ExportPass): | ||
""" | ||
Fuses weight dequantize_per_channel nodes with linear nodes into | ||
weight_int8pack_mm nodes, for 8-bit weight-only quantization. | ||
Replaces dq(weight) -> linear(activation, dq) with weight_int8pack_mm | ||
Replaces dq(weight) -> linear(activation, dq, bias) with weight_int8pack_mm -> add | ||
""" | ||
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def fuse_dequant_with_linear( | ||
self, | ||
graph_module: torch.fx.GraphModule, | ||
dequant_node: torch.fx.Node, | ||
linear_node: torch.fx.Node, | ||
) -> None: | ||
activations = linear_node.args[0] | ||
bias = None | ||
if len(linear_node.args) > 2: | ||
bias = linear_node.args[2] | ||
quant_weight = dequant_node.args[0] | ||
scale = dequant_node.args[1] | ||
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with graph_module.graph.inserting_before(linear_node): | ||
weight_int8pack_mm_node = graph_module.graph.create_node( | ||
"call_function", | ||
exir_ops.edge.aten._weight_int8pack_mm.default, | ||
(activations, quant_weight, scale), | ||
) | ||
if bias: | ||
add_node = graph_module.graph.create_node( | ||
"call_function", | ||
exir_ops.edge.aten.add.Tensor, | ||
(weight_int8pack_mm_node, bias), | ||
) | ||
linear_node.replace_all_uses_with(add_node) | ||
else: | ||
linear_node.replace_all_uses_with(weight_int8pack_mm_node) | ||
graph_module.graph.erase_node(linear_node) | ||
graph_module.graph.erase_node(dequant_node) | ||
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def is_node_target( | ||
self, node: torch.fx.Node, target: torch._ops.OperatorBase | ||
) -> bool: | ||
return node.op == "call_function" and node.target == target | ||
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def call(self, graph_module: torch.fx.GraphModule) -> PassResult: | ||
for node in graph_module.graph.nodes: | ||
if self.is_node_target(node, exir_ops.edge.aten.linear.default): | ||
weight_node = node.args[1] | ||
if self.is_node_target( | ||
weight_node, | ||
exir_ops.edge.quantized_decomposed.dequantize_per_channel.default, | ||
): | ||
# only fuse if weight tensor is int8 packed | ||
quant_weight = weight_node.args[0] | ||
if quant_weight.meta["val"].dtype != torch.int8: | ||
continue | ||
self.fuse_dequant_with_linear(graph_module, weight_node, node) | ||
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graph_module.recompile() | ||
graph_module = super().call(graph_module).graph_module | ||
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return PassResult(graph_module, True) |