Remove outdated comments and names in OSS

Differential Revision: D80181207

Pull Request resolved: #13419

Author: mcremon-meta

backends/cadence/aot/decompose_ops.py

@@ -7,9 +7,7 @@
 
 
 # This file contains all the functions that decompose one op into simpler ops in the
-# graph. The functions decomposing ops for models deployed with Jarvis are grouped
-# together in class 'DecomposeOpsInGraph'. Some examples of functions in the class are
-# 1. functions that decompose an ATen gelu op into an equivalent series of simpler ops
+# graph.
 
 # pyre-strict
 

backends/cadence/aot/ops_registrations.py

@@ -964,7 +964,7 @@ def transposed_convolution_meta(
 ) -> torch.Tensor:
     # The native definition of torch transposed conv will have weight shape as
     # (in_channels, out_channels/groups, *kernel_size).
-    # However, the two channel position is flipped in the Jarvis pass of replacing it
+    # However, the two channel position is flipped in the Cadence pass of replacing it
     # with cadence::transposed_convolution here: https://fburl.com/code/d2s7pkyy
     out_channels, _input_channels, *kernel_size = weight.shape
     out_channels *= groups

backends/cadence/aot/remove_ops.py

@@ -7,16 +7,6 @@
 # pyre-strict
 
 
-# This file contains functions to remove operators from the graph. The removed
-# ops should belong to either of the following categories:
-# 1. The op should be redundant for inference (e.g., dropout). Such ops are grouped
-# together in 'RemoveRedundantOps'. Anyone running inference can add this class
-# in their pass list, and it should semantic-preserving transformation.
-# 2. The op should be redundant for Jarvis (e.g., contiguous). Such ops are grouped
-# together in 'CadenceRemoveNops'. The ops removed in this class might not be nop
-# in a context outside of Jarvis', so exercise caution while invoking this in a
-# pass list outside of Jarvis.
-
 import logging
 from dataclasses import dataclass, field
 from typing import cast, List, Optional, Sequence, Set
@@ -152,7 +142,7 @@ def call_operator(
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
 class RemoveToOpsPass(ExportPass):
-    # aten.to.* as of now are all nops for Jarvis
+    # aten.to.* as of now are all nops
     def call_operator(
         self,
         op,  # pyre-ignore
@@ -413,7 +403,7 @@ def call_operator(
 class RemoveAliasCopyOpPass(ExportPass):
     """
 
-    alias_copy is a no-op for Jarvis and can be removed.
+    alias_copy is a no-op and can be removed.
     """
 
     def call_operator(
@@ -936,10 +926,6 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
         return super().call(graph_module)
 
 
-# The following class consolidates functions to remove ops that are redundant
-# in Jarvis. Currently, each function in this class iterates over each node of
-# the graph module once. In future, we could consolidate them into a monolithic
-# function.
 class CadenceRemoveNops:
     passes = [
         SimplifySliceOpPass,

backends/cadence/aot/replace_ops.py

@@ -7,12 +7,7 @@
 
 
 # This file contains all the functions that replace one op with another in the
-# graph. The functions replacing ops for models deployed with Jarvis are grouped
-# together in class 'ReplaceOpsInGraph'. Some examples of functions in the class are
-# 1. functions that replace an ATen op with a custom op that accepts extra arguments
-# 2. functions that replace in-place variants of ATen ops with out-of-place version.
-# 3. functions that replace an ATen op with another semantically equivalent ATen op.
-# 4. functions that concretize optional args.
+# graph.
 
 # pyre-unsafe
 
@@ -54,7 +49,7 @@
 from torch.fx.node import Argument
 
 # A map to represent ops that:
-# (a) are functionally equivalent wrt. Jarvis; and
+# (a) are functionally equivalent; and
 # (b) have identical arguments
 # An op whose target is 'key' in this dict can be replaced by the functionally euivalent
 # op whose target is 'value'. The replacement would just involve changing the op target.
@@ -650,7 +645,7 @@ def call_operator(self, op, args, kwargs, meta):
 
 # Make that pass runnable standalone at opt level 0.
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceAtenConvolutionWithJarvisConvolutionPass(ExportPass):
+class ReplaceAtenConvolutionWithCadenceConvolutionPass(ExportPass):
     """
     Replace aten convolution op with jarvis-specific convolution op, since the
     aten version is not supported by jarvis.
@@ -784,7 +779,7 @@ class ReplaceConvWithChannelLastConv:
     tensors. However, if the input and output to the convolution op are originally
     in NWHC layout, and are then permuted to conform to NCHW layout, we can fuse
     the two permute ops with the convolution op, and call the NHWC layout
-    convolution op in Jarvis.
+    convolution op.
     """
 
     def __init__(self):
@@ -821,7 +816,7 @@ def conv_layout_is_nhwc(self, node: torch.fx.Node) -> bool:
         out_shape = get_shape(self.graph_module, node)
         assert out_shape is not None
         out_dims = len(out_shape)
-        assert out_dims in {3, 4}, "Jarvis only supports conv1d and conv2d"
+        assert out_dims in {3, 4}, "Only supports conv1d and conv2d"
         conv1d = out_dims == 3
 
         # Get the possible targets for the nodes in pt_nodes. Since conv1d has
@@ -951,7 +946,7 @@ class ReplaceConvWithChannelLastConvPass(ExportPass):
     """
 
     def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
-        result = ReplaceAtenConvolutionWithJarvisConvolutionPass()(graph_module)
+        result = ReplaceAtenConvolutionWithCadenceConvolutionPass()(graph_module)
         assert result is not None
         ReplaceConvWithChannelLastConv()(result.graph_module)
         return result
@@ -1871,9 +1866,9 @@ def call_operator(self, op, args, kwargs, meta):
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceAtenAvgPoolWithJarvisAvgPoolPass(ExportPass):
+class ReplaceAtenAvgPoolWithCadenceAvgPoolPass(ExportPass):
     """
-    Replace the aten avg_pool op with the jarvis custom avg_pool2d op.
+    Replace the aten avg_pool op with the cadence custom avg_pool2d op.
     """
 
     def call_operator(self, op, args, kwargs, meta):
@@ -2435,7 +2430,7 @@ class CadenceReplaceOpsInGraph:
         ReplacePadWithCatPass,
         ReplaceConstantPadNdWithSlicePass,
         ReplaceConvWithChannelLastConvPass,
-        ReplaceAtenConvolutionWithJarvisConvolutionPass,
+        ReplaceAtenConvolutionWithCadenceConvolutionPass,
         ForceChannelLastForConvPass,
         ReplaceTrivialConvWithLinear,
         ReplaceConvWithIm2RowAndLinear,
@@ -2454,7 +2449,7 @@ class CadenceReplaceOpsInGraph:
         ReplacePT2DequantWithCadenceDequantPass,
         ReplaceSingleElementTensorArgumentsFromFullOpWithScalarPass,
         ReplaceAdaptiveAvgPoolWithAtenAvgPoolPass,
-        ReplaceAtenAvgPoolWithJarvisAvgPoolPass,
+        ReplaceAtenAvgPoolWithCadenceAvgPoolPass,
         ReplaceWhereWithFullArgsWithWhereScalar,
         ReplaceAtenApproxGeluWithApproxGeluPass,
         ReplaceSplitWithSlicePass,

backends/cadence/aot/tests/test_replace_ops_passes.py

@@ -22,7 +22,7 @@
     ReplaceAdaptiveAvgPoolWithAtenAvgPoolPass,
     ReplaceAddMMWithLinearPass,
     ReplaceAtenApproxGeluWithApproxGeluPass,
-    ReplaceAtenConvolutionWithJarvisConvolutionPass,
+    ReplaceAtenConvolutionWithCadenceConvolutionPass,
     ReplaceConstantPadNdWithSlicePass,
     ReplaceConvolutionOptionalArgsWithConcreteArgsPass,
     ReplaceConvWithIm2RowAndLinear,
@@ -411,7 +411,7 @@ def test_replace_transposed_conv_with_linear(
         builder.output([convolution])
         original_gm = builder.get_graph_module()
 
-        p1 = ReplaceAtenConvolutionWithJarvisConvolutionPass()
+        p1 = ReplaceAtenConvolutionWithCadenceConvolutionPass()
         p2 = ReplaceTransposedConvWithLinearPass()
         graph_after_passes = cast(
             PassResult, p2(cast(PassResult, p1(original_gm)).graph_module)
@@ -969,7 +969,7 @@ def test_replace_conv1d_with_linear(self) -> None:
             args=(x, weights, bias, [1], [0], [1], 1, False),
         )
         # First, replace the aten convolution with a cadence.convolution op
-        p1 = ReplaceAtenConvolutionWithJarvisConvolutionPass()
+        p1 = ReplaceAtenConvolutionWithCadenceConvolutionPass()
         temp_graph = cast(PassResult, p1(original_gm)).graph_module
         # temp_graph = p1(original_gm).graph_module
         self.assertIsNotNone(temp_graph)
@@ -1003,7 +1003,7 @@ def test_replace_conv2d_with_linear(self) -> None:
             args=(x, weights, bias, [1, 1], [0, 0], [1, 1], 1, False),
         )
         # First, replace the aten convolution with a cadence.convolution op
-        p1 = ReplaceAtenConvolutionWithJarvisConvolutionPass()
+        p1 = ReplaceAtenConvolutionWithCadenceConvolutionPass()
         temp_graph = cast(PassResult, p1(original_gm)).graph_module
         self.assertIsNotNone(temp_graph)