Update name from xtensa to cadence

Summary: As titled.

Differential Revision: D55998135

examples/xtensa/aot/export_example.py → examples/cadence/aot/export_example.py

@@ -28,7 +28,7 @@
 logging.basicConfig(level=logging.INFO, format=FORMAT)
 
 
-def export_xtensa_model(model, example_inputs):
+def export_model(model, example_inputs):
     # Quantizer
     quantizer = XtensaBaseQuantizer()
 
@@ -46,20 +46,20 @@ def export_xtensa_model(model, example_inputs):
     patterns = [q.pattern for q in quantizer.quantizers]
     QuantFusion(patterns)(converted_model)
 
-    # Get edge program (note: the name will change to export_to_xtensa in future PRs)
+    # Get edge program (note: the name will change to export_to_cadence in future PRs)
     edge_prog_manager = export_to_edge(converted_model, example_inputs, pt2_quant=True)
 
     # Run a couple required passes for quant/dequant ops
-    xtensa_prog_manager = edge_prog_manager.transform(
+    cadence_prog_manager = edge_prog_manager.transform(
         [ReplacePT2QuantWithXtensaQuant(), ReplacePT2DequantWithXtensaDequant()],
         check_ir_validity=False,
     )
 
-    exec_prog = xtensa_prog_manager.to_executorch()
+    exec_prog = cadence_prog_manager.to_executorch()
 
     logging.info(
         f"Final exported graph module:\n{exec_prog.exported_program().graph_module}"
     )
 
-    # Save the program as XtensaDemoModel.pte
-    save_pte_program(exec_prog, "XtensaDemoModel")
+    # Save the program as CadenceDemoModel.pte
+    save_pte_program(exec_prog, "CadenceDemoModel")

examples/xtensa/aot/quantizer.py → examples/cadence/aot/quantizer.py

@@ -437,7 +437,7 @@ def get_anchors(
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_linear.default
+        return torch.ops.cadence.quantized_linear.default
 
 
 class LinearFunctionalPattern(QuantizationPattern):
@@ -457,7 +457,7 @@ def get_anchors(
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_linear.default
+        return torch.ops.cadence.quantized_linear.default
 
 
 class LayerNormPattern(QuantizationPattern):
@@ -476,7 +476,7 @@ def get_anchors(self, gm, fused_partition) -> PartitionAnchors:
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_layer_norm.default
+        return torch.ops.cadence.quantized_layer_norm.default
 
 
 class Conv1dPattern(QuantizationPattern):
@@ -503,7 +503,7 @@ def get_anchors(
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_conv.default
+        return torch.ops.cadence.quantized_conv.default
 
 
 class Conv2dPattern(QuantizationPattern):
@@ -530,7 +530,7 @@ def get_anchors(
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_conv.default
+        return torch.ops.cadence.quantized_conv.default
 
 
 class AddmmPattern(QuantizationPattern):
@@ -550,7 +550,7 @@ def get_anchors(
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_linear.default
+        return torch.ops.cadence.quantized_linear.default
 
 
 class ReluPattern(QuantizationPattern):
@@ -573,7 +573,7 @@ def get_anchors(
         )
 
     def replacement_op(self):
-        return torch.ops.xtensa.quantized_relu.default
+        return torch.ops.cadence.quantized_relu.default
 
 
 class GenericQuantizer(Quantizer):
@@ -823,15 +823,15 @@ def mark_fused(cls, nodes) -> bool:
 
 class ReplacePT2QuantWithXtensaQuant(ExportPass):
     """
-    Replace the pt2 quantization ops with custom xtensa quantization ops.
+    Replace the pt2 quantization ops with custom cadence quantization ops.
     """
 
     def call_operator(self, op, args, kwargs, meta):
         if op not in {exir_ops.edge.quantized_decomposed.quantize_per_tensor.default}:
             return super().call_operator(op, args, kwargs, meta)
 
         return super().call_operator(
-            exir_ops.edge.xtensa.quantize_per_tensor.default,
+            exir_ops.edge.cadence.quantize_per_tensor.default,
             args,
             kwargs,
             meta,
@@ -840,15 +840,15 @@ def call_operator(self, op, args, kwargs, meta):
 
 class ReplacePT2DequantWithXtensaDequant(ExportPass):
     """
-    Replace the pt2 dequantization ops with custom xtensa dequantization ops.
+    Replace the pt2 dequantization ops with custom cadence dequantization ops.
     """
 
     def call_operator(self, op, args, kwargs, meta):
         if op not in {exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default}:
             return super().call_operator(op, args, kwargs, meta)
 
         return super().call_operator(
-            exir_ops.edge.xtensa.dequantize_per_tensor.default,
+            exir_ops.edge.cadence.dequantize_per_tensor.default,
             args,
             kwargs,
             meta,

examples/xtensa/ops/CMakeLists.txt → examples/cadence/ops/CMakeLists.txt

@@ -31,14 +31,14 @@ set(_aten_ops__srcs
     "${CMAKE_CURRENT_SOURCE_DIR}/op_view_copy.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/util/broadcast_util.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/util/repeat_util.cpp")
-add_library(aten_ops_xtensa ${_aten_ops__srcs})
-target_link_libraries(aten_ops_xtensa PUBLIC executorch)
-target_link_libraries(aten_ops_xtensa PRIVATE xtensa_kernels)
+add_library(aten_ops_cadence ${_aten_ops__srcs})
+target_link_libraries(aten_ops_cadence PUBLIC executorch)
+target_link_libraries(aten_ops_cadence PRIVATE cadence_kernels)
 
 # Let files say "include <executorch/path/to/header.h>".
 set(_common_include_directories ${EXECUTORCH_ROOT}/..)
 
-target_include_directories(aten_ops_xtensa PUBLIC ${ROOT_DIR}/..
+target_include_directories(aten_ops_cadence PUBLIC ${ROOT_DIR}/..
                                                   ${CMAKE_BINARY_DIR}
                                                   ${_common_include_directories})
 
@@ -52,7 +52,7 @@ target_include_directories(custom_ops PUBLIC ${ROOT_DIR}/..
                                              ${_common_include_directories})
 
 target_link_libraries(custom_ops PUBLIC executorch)
-target_link_libraries(custom_ops PRIVATE xtensa_kernels)
+target_link_libraries(custom_ops PRIVATE cadence_kernels)
 
 # Generate C++ bindings to register kernels into both PyTorch (for AOT) and
 # Executorch (for runtime). Here select all ops in functions.yaml
@@ -62,6 +62,6 @@ generate_bindings_for_kernels(
 message("Generated files ${gen_command_sources}")
 
 gen_operators_lib(
-  "xtensa_ops_lib"
+  "cadence_ops_lib"
   KERNEL_LIBS custom_ops
-  DEPS aten_ops_xtensa)
+  DEPS aten_ops_cadence)

examples/xtensa/ops/functions.yaml → examples/cadence/ops/functions.yaml

@@ -33,29 +33,29 @@
       kernel_name: torch::executor::view_copy_out
 
 # custom ops
-- func: xtensa::quantize_per_tensor.out(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
+- func: cadence::quantize_per_tensor.out(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
   variants: function
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::quantize_per_tensor_out
 
-- func: xtensa::dequantize_per_tensor.out(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
+- func: cadence::dequantize_per_tensor.out(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
   variants: function
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::dequantize_per_tensor_out
 
-- func: xtensa::quantized_conv.out(Tensor input, Tensor weight, Tensor bias, int[] stride, SymInt[] padding, int[] dilation, int groups, int input_zero_point, Tensor weight_zero_point, Tensor bias_scale, float out_scale, int out_zero_point, Tensor out_multiplier, Tensor out_shift, bool channel_last=False, *, Tensor(a!) out) -> Tensor(a!)
+- func: cadence::quantized_conv.out(Tensor input, Tensor weight, Tensor bias, int[] stride, SymInt[] padding, int[] dilation, int groups, int input_zero_point, Tensor weight_zero_point, Tensor bias_scale, float out_scale, int out_zero_point, Tensor out_multiplier, Tensor out_shift, bool channel_last=False, *, Tensor(a!) out) -> Tensor(a!)
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::quantized_conv_out
 
-- func: xtensa::quantized_linear.out(Tensor src, Tensor weight, Tensor bias, float src_scale, int src_zero_point, float weight_scale, int weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, *, Tensor(a!) out) -> Tensor(a!)
+- func: cadence::quantized_linear.out(Tensor src, Tensor weight, Tensor bias, float src_scale, int src_zero_point, float weight_scale, int weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, *, Tensor(a!) out) -> Tensor(a!)
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::quantized_linear_out
 
-- func: xtensa::quantized_relu.out(Tensor X, Tensor X_zero_point, *, Tensor(a!) out) -> Tensor(a!)
+- func: cadence::quantized_relu.out(Tensor X, Tensor X_zero_point, *, Tensor(a!) out) -> Tensor(a!)
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::quantized_relu_out

examples/xtensa/tests/quantized_conv1d_example.py → examples/cadence/tests/quantized_conv1d_example.py

@@ -12,7 +12,7 @@
 
 import torch
 
-from ..aot.export_example import export_xtensa_model
+from ..aot.export_example import export_model
 
 
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -55,4 +55,4 @@ def forward(self, x: torch.Tensor):
 
     example_inputs = (torch.randn(shape),)
 
-    export_xtensa_model(model, example_inputs)
+    export_model(model, example_inputs)

examples/xtensa/tests/quantized_linear_example.py → examples/cadence/tests/quantized_linear_example.py

@@ -12,7 +12,7 @@
 
 import torch
 
-from ..aot.export_example import export_xtensa_model
+from ..aot.export_example import export_model
 
 
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -39,4 +39,4 @@ def forward(self, x: torch.Tensor):
 
     example_inputs = (torch.ones(shape),)
 
-    export_xtensa_model(model, example_inputs)
+    export_model(model, example_inputs)

examples/xtensa/tests/rnnt_predictor_quantized_example.py → examples/cadence/tests/rnnt_predictor_quantized_example.py

@@ -14,7 +14,7 @@
 
 from typing import Tuple
 
-from ..aot.export_example import export_xtensa_model
+from ..aot.export_example import export_model
 
 
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -66,4 +66,4 @@ def forward(
         predictor_lengths,
     )
 
-    export_xtensa_model(model, example_inputs)
+    export_model(model, example_inputs)