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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # Copyright 2024 Arm Limited and/or its 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. | ||
|
|
||
|
|
||
| import torch | ||
| from executorch.backends.arm._passes.arm_pass_utils import ( | ||
| create_node, | ||
| get_param_tensor, | ||
| insert_q_dq_pair, | ||
| is_param_node, | ||
| ) | ||
| from executorch.backends.arm.tosa_quant_utils import dq_op, q_op | ||
| from executorch.exir import ExportedProgram | ||
| from executorch.exir.dialects._ops import ops as exir_ops | ||
| from executorch.exir.pass_base import ExportPass, PassResult | ||
|
|
||
|
|
||
| class Conv1dUnsqueezePass(ExportPass): | ||
| """ | ||
| This pass is used to change conv1d ops into conv2d since TOSA only | ||
| supports 2d and 3d convolution. This is done by modifying the graph to do the | ||
| following: | ||
| 1) unsqueeze the convolution's input from 3d to 4d | ||
| 2) if the input to unsqueeze is quantized, insert q/dq-pair after unsqueeze | ||
| 3) perform a conv2d (with a modified version of the original conv1d args) | ||
| 4) squeeze the output back down to 3d. | ||
| 5) if all users of squeeze are quantized, insert q/dq-pair before squeeze | ||
| """ | ||
|
|
||
| def __init__(self, exported_program: ExportedProgram) -> None: | ||
| super().__init__() | ||
| self.exported_program = exported_program | ||
|
|
||
| def unsqueeze_kernel_weights(self, kernel_node): | ||
| """ | ||
| Unsqueezes the weights of a conv1d to make it 4 dimensional. | ||
| Args: | ||
| kernel_node: the weights of conv1d node to be unsqueezed | ||
| """ | ||
| kernel_param_3d = get_param_tensor(self.exported_program, kernel_node) | ||
| if kernel_param_3d is None: | ||
| raise AssertionError("Expected param tensor for the kernel node") | ||
|
|
||
| kernel_param_4d = torch.nn.Parameter( | ||
| data=kernel_param_3d.data.contiguous().unsqueeze(dim=-1), | ||
| requires_grad=False, | ||
| ) | ||
|
|
||
| if torch._export.utils.is_param(self.exported_program, kernel_node): | ||
| parameter_name = self.exported_program.graph_signature.inputs_to_parameters[ | ||
| kernel_node.name | ||
| ] | ||
| self.exported_program.state_dict[parameter_name] = kernel_param_4d | ||
| kernel_node.meta["val"] = kernel_node.meta["val"].data.unsqueeze(dim=-1) | ||
| elif torch._export.utils.is_buffer(self.exported_program, kernel_node): | ||
| buffer_name = self.exported_program.graph_signature.inputs_to_buffers[ | ||
| kernel_node.name | ||
| ] | ||
| self.exported_program.state_dict[buffer_name] = kernel_param_4d | ||
| kernel_node.meta["val"] = kernel_node.meta["val"].data.unsqueeze(dim=-1) | ||
| elif torch._export.utils.is_lifted_tensor_constant( | ||
| self.exported_program, kernel_node | ||
| ): | ||
| buffer_name = ( | ||
| self.exported_program.graph_signature.inputs_to_lifted_tensor_constants[ | ||
| kernel_node.name | ||
| ] | ||
| ) | ||
| self.exported_program.constants[buffer_name] = kernel_param_4d | ||
| kernel_node.meta["val"] = kernel_node.meta["val"].data.unsqueeze(dim=-1) | ||
| else: | ||
| setattr( | ||
| kernel_node.graph.owning_module, | ||
| kernel_node.target, | ||
| kernel_param_4d, | ||
| ) | ||
|
|
||
| def call(self, graph_module: torch.fx.GraphModule): | ||
| graph = graph_module.graph | ||
| node_list = list(graph.nodes) | ||
| for node in node_list: | ||
| if node.op == "call_function": | ||
| if node.target == exir_ops.edge.aten.convolution.default: | ||
| stride = list(node.args[3]) | ||
| if len(stride) != 1: | ||
| # skip conv if it is not 1d | ||
| continue | ||
|
|
||
| kernel_node = node.args[1] | ||
| if kernel_node.target == dq_op: | ||
| kernel_node = kernel_node.args[0] | ||
|
|
||
| if not is_param_node(self.exported_program, kernel_node): | ||
| raise AssertionError( | ||
| "Expected op for convolution weight node to be a get_attr node or a parameter" | ||
| ) | ||
|
|
||
| # Modify graph such that the conv changes from 1d to 2d | ||
| self.unsqueeze_kernel_weights(kernel_node) | ||
|
|
||
| # (b) Extend stride, padding, and dilation for extra dim | ||
| node.args = ( | ||
| node.args[0], | ||
| node.args[1], | ||
| node.args[2], | ||
| node.args[3] + [1], # stride | ||
| node.args[4] + [0], # padding | ||
| node.args[5] + [1], # dilation | ||
| node.args[6], | ||
| node.args[7] + [0], | ||
| node.args[8], | ||
| ) | ||
|
|
||
| # c. Add unsqueeze to input (3d -> 4d) and squeeze to output (4d -> 3d) | ||
| # unsqueeze -> conv2d -> squeeze | ||
| with graph.inserting_before(node): | ||
| input_node = node.args[0] | ||
| unsqueeze_before = create_node( | ||
| graph, exir_ops.edge.aten.unsqueeze_copy.default | ||
| ) | ||
| unsqueeze_before.args = ( | ||
| input_node, # Input is node's original input | ||
| -1, # Last Dimension | ||
| ) | ||
| node.replace_input_with(input_node, unsqueeze_before) | ||
|
|
||
| # If Quantized we must insert unsqueeze --> q --> dq --> node | ||
| if input_node.target == dq_op: | ||
| q_params = input_node.args[1:] | ||
| insert_q_dq_pair(graph, unsqueeze_before, q_params) | ||
|
|
||
| with graph.inserting_after(node): | ||
| squeeze_after = create_node( | ||
| graph, | ||
| exir_ops.edge.aten.squeeze_copy.dims, | ||
| ) | ||
| squeeze_after.args = ( | ||
| node, # Input is the conv node | ||
| [-1], # Last dimension | ||
| ) | ||
| original_users = [ | ||
| user for user in node.users if user != squeeze_after | ||
| ] | ||
| for user in original_users: | ||
| user.replace_input_with(node, squeeze_after) | ||
|
|
||
| # If quantized, insert conv2d --> q --> dq --> squeeze | ||
| if all( | ||
| original_user.target == q_op for original_user in original_users | ||
| ): | ||
| q_params = original_users[0].args[1:] | ||
| insert_q_dq_pair(graph, node, q_params) | ||
|
|
||
| graph_module.recompile() | ||
| # Since we are overriding "call", we need to call the parent's "call" | ||
| # to retrace the graph and regenerate metadata | ||
| graph_module = super().call(graph_module).graph_module | ||
|
|
||
| return PassResult(graph_module, True) |
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