Qualcomm AI Engine Direct - Mimi Enablement Stage 2

Author: winskuo-quic

backends/qualcomm/_passes/__init__.py

@@ -11,6 +11,7 @@
 from .convert_conv1d_to_conv2d import ConvertConv1dToConv2d
 from .convert_upsample_bicubic2d import ConvertUpsampleBicubicWithBilinear
 from .decompose_any import DecomposeAny
+from .decompose_cdist import DecomposeCDist
 from .decompose_einsum import DecomposeEinsum
 from .decompose_expm1 import DecomposeExpM1
 from .decompose_linalg_vector_norm import DecomposeLinalgVectorNorm
@@ -27,6 +28,7 @@
 from .recompose_pixel_unshuffle import RecomposePixelUnshuffle
 from .recompose_rms_norm import RecomposeRmsNorm
 from .reduce_dynamic_range import ReduceDynamicRange
+from .remove_0d_tensor import Remove0DTensor
 from .remove_redundancy import RemoveRedundancy
 from .replace_arange_args import ReplaceArangeArgs
 from .replace_index_put_input import ReplaceIndexPutInput
@@ -40,8 +42,9 @@
     AnnotateUnbind,
     ConvertBmmToMatmul,
     ConvertConv1dToConv2d,
-    DecomposeAny,
     ConvertUpsampleBicubicWithBilinear,
+    DecomposeAny,
+    DecomposeCDist,
     DecomposeEinsum,
     DecomposeExpM1,
     DecomposeLinalgVectorNorm,
@@ -58,6 +61,7 @@
     RecomposePixelUnshuffle,
     RecomposeRmsNorm,
     ReduceDynamicRange,
+    Remove0DTensor,
     RemoveRedundancy,
     ReplaceArangeArgs,
     ReplaceIndexPutInput,

backends/qualcomm/_passes/annotate_stack.py

@@ -17,14 +17,16 @@ class AnnotateStack(ExportPass):
     generated after quantization process.
     """
 
-    decomp_ops = [torch.ops.aten.unbind.int]
+    decomp_ops = [torch.ops.aten.stack.default]
 
     def __init__(self, edge_program: torch.export.ExportedProgram):
         super(AnnotateStack, self).__init__()
         self.edge_program = edge_program
 
     def _annotate_stack(self, graph_module: torch.fx.GraphModule):
-        partitions = get_source_partitions(graph_module.graph, [torch.stack, "stack"])
+        partitions = get_source_partitions(
+            graph_module.graph, [torch.stack, torch.ops.aten.stack.default, "stack"]
+        )
         for _, src_partitions in partitions.items():
             for src_partition in src_partitions:
                 output = src_partition.output_nodes[0]

backends/qualcomm/_passes/annotate_unbind.py

@@ -24,7 +24,9 @@ def __init__(self, edge_program: torch.export.ExportedProgram):
         self.edge_program = edge_program
 
     def _annotate_unbind(self, graph_module: torch.fx.GraphModule):
-        partitions = get_source_partitions(graph_module.graph, [torch.unbind, "unbind"])
+        partitions = get_source_partitions(
+            graph_module.graph, [torch.unbind, torch.ops.aten.unbind.int, "unbind"]
+        )
         for _, src_partitions in partitions.items():
             for src_partition in src_partitions:
                 if src_partition.input_nodes[0].target in dq_ops:

backends/qualcomm/_passes/convert_conv1d_to_conv2d.py

@@ -7,6 +7,7 @@
 import torch
 import torch.nn as nn
 from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter
+from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 
@@ -43,6 +44,7 @@ def call(self, graph_module: torch.fx.GraphModule):
                     unsqueeze_node.meta = copy_meta(
                         input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
                     )
+
                     with graph_module.graph.inserting_after(unsqueeze_node):
 
                         filter_node = node.args[1]
@@ -92,6 +94,14 @@ def call(self, graph_module: torch.fx.GraphModule):
                                 ),
                             )
                             squeeze_node.meta = copy_meta(node.meta)
+
+                            if QCOM_REQUANTIZE in input_node.meta:
+                                input_node.meta.pop(QCOM_REQUANTIZE)
+                            if QCOM_REQUANTIZE in node.meta:
+                                squeeze_node.meta[QCOM_REQUANTIZE] = node.meta[
+                                    QCOM_REQUANTIZE
+                                ]
+                                conv2d_node.meta.pop(QCOM_REQUANTIZE, None)
                 for user in node.users.copy():
                     user.replace_input_with(node, squeeze_node)
         graph.eliminate_dead_code()

backends/qualcomm/_passes/decompose_cdist.py

@@ -0,0 +1,81 @@
+# Copyright (c) Qualcomm Innovation Center, Inc.
+# 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.exir.pass_base import ExportPass, PassResult
+
+
+class CDist(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, y):
+        # Step 1: Compute differences
+        diff = x.unsqueeze(-2) - y.unsqueeze(-3)
+
+        # Step 2: Square differences
+        sq_diff = diff**2
+
+        # Step 3: Sum of squares
+        sum_sq_diff = sq_diff.sum(dim=-1)
+
+        # Step 4: Square root
+        distances = torch.sqrt(sum_sq_diff)
+
+        return distances
+
+
+class DecomposeCDist(ExportPass):
+    """
+    Decompose for math equivalent op.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        graph = graph_module.graph
+        for node in graph.nodes:
+            model = CDist()
+            if torch.ops.aten.cdist.default == node.target:
+                if len(node.args) > 2:
+                    assert (
+                        node.args[2] == 2
+                    ), "Currently only p=2 is supported for CDist Decomposition"
+                decomposed_module = torch.export.export(
+                    model,
+                    (node.args[0].meta["val"], node.args[1].meta["val"]),
+                    strict=True,
+                ).module()
+                with graph.inserting_before(node):
+                    # remap is used to map original node values to new node values,
+                    # which ensures that reference to nodes are correctly updated in the new graph
+                    remap = {"x": node.args[0], "y": node.args[1]}
+
+                    for decomposed_node in decomposed_module.graph.nodes:
+                        # no need to copy existent 'output'
+                        if decomposed_node.op == "output":
+                            for user in node.users.copy():
+                                # remap
+                                user.replace_input_with(
+                                    node,
+                                    remap[decomposed_node.args[0][0]],
+                                )
+                        # no need to copy existent placeholders
+                        elif decomposed_node.op == "placeholder":
+                            # replace node map from string to graph node
+                            remap[decomposed_node] = remap.pop(decomposed_node.name)
+                        else:
+                            remap[decomposed_node] = graph.node_copy(
+                                decomposed_node,
+                                arg_transform=lambda x, remap=remap: remap[x],
+                            )
+
+                    graph.erase_node(node)
+
+        graph.eliminate_dead_code()
+        graph_module.recompile()
+        return PassResult(graph_module, True)

backends/qualcomm/_passes/lift_constant_scalar_operands.py

@@ -53,7 +53,13 @@ class TensorOpInfo:
 }
 
 
-SKIP_LIFT_OPS = {aten.full_like.default, aten.arange.start_step}
+SKIP_LIFT_OPS = {
+    aten.full_like.default,
+    aten.arange.start_step,
+    aten.arange.default,
+    aten.scalar_tensor.default,
+    aten.elu.default,
+}
 
 
 class LiftConstantScalarOperands(ExportPass):

backends/qualcomm/_passes/qnn_pass_manager.py

@@ -16,6 +16,7 @@
     ConvertConv1dToConv2d,
     ConvertUpsampleBicubicWithBilinear,
     DecomposeAny,
+    DecomposeCDist,
     DecomposeEinsum,
     DecomposeExpM1,
     DecomposeLinalgVectorNorm,
@@ -32,6 +33,7 @@
     RecomposePixelUnshuffle,
     RecomposeRmsNorm,
     ReduceDynamicRange,
+    Remove0DTensor,
     RemoveRedundancy,
     ReplaceArangeArgs,
     ReplaceIndexPutInput,
@@ -71,7 +73,7 @@ def get_capture_program_passes():
     # If a pass is activated, it will be executed by default.
     default_passes_and_setting = [
         (AnnotateQuantAttrs, True),
-        (AnnotateStack, False),
+        (AnnotateStack, True),
         (AnnotateUnbind, True),
         (ConvertBmmToMatmul, True),
         (ConvertConv1dToConv2d, True),
@@ -84,6 +86,7 @@ def get_capture_program_passes():
         (LayoutTransform, True),
         (RecomposePixelUnshuffle, True),
         (RecomposeRmsNorm, False),
+        (Remove0DTensor, True),
         (RemoveRedundancy, True),
         (ReplaceIndexPutInput, True),
         (TagQuantIO, False),
@@ -176,7 +179,23 @@ def transform_for_to_edge_pipeline(
 
         return exported_program
 
+    # Before quantizer
+    def transform_for_annotation_pipeline(self, graph_module: GraphModule):
+        self.add_pass(ReduceDynamicRange())
+        self.add_pass(RecomposePixelUnshuffle(quantization_capture=True))
+        self.add_pass(ReplaceArangeArgs())
+        self.add_pass(DecomposeCDist())
+        self.add_pass(DecomposeScaledDotProductAttention())
+        self.add_pass(DecomposeSilu())
+        self.add_pass(DecomposeEinsum())
+        self.add_pass(DecomposeExpM1())
+        self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
+        self.add_pass(ReplaceInfValues())
+        self.add_pass(LiftConstantScalarOperands())
+        return self._transform(graph_module)
+
     def transform_for_export_pipeline(self, exported_program: ExportedProgram):
+        self.add_pass(DecomposeCDist())
         self.add_pass(DecomposeScaledDotProductAttention())
         self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
         self.add_pass(DecomposeExpM1())
@@ -191,16 +210,3 @@ def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
         self.add_pass(LayoutTransform(exported_program, insert_permute=True))
         self.add_pass(FuseConsecutiveTranspose())
         return self._transform(exported_program.graph_module)
-
-    def transform_for_annotation_pipeline(self, graph_module: GraphModule):
-        self.add_pass(ReduceDynamicRange())
-        self.add_pass(RecomposePixelUnshuffle(quantization_capture=True))
-        self.add_pass(ReplaceArangeArgs())
-        self.add_pass(DecomposeScaledDotProductAttention())
-        self.add_pass(DecomposeSilu())
-        self.add_pass(DecomposeEinsum())
-        self.add_pass(DecomposeExpM1())
-        self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True))
-        self.add_pass(ReplaceInfValues())
-        self.add_pass(LiftConstantScalarOperands())
-        return self._transform(graph_module)

backends/qualcomm/_passes/remove_0d_tensor.py

@@ -0,0 +1,36 @@
+# Copyright (c) Qualcomm Innovation Center, Inc.
+# 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.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+
+
+class Remove0DTensor(ExportPass):
+    """
+    QNN does not allow 0D tensor, we remove the node that will output an 0D tensor.
+    Before adding operations to the list of nodes to be removed, please ensure that it will not change the logic.
+    """
+
+    remove_ops = {
+        exir_ops.edge.aten.select.int,
+        exir_ops.edge.aten.select_copy.int,
+    }
+
+    def __init__(self, quantization_capture=False) -> None:
+        super().__init__()
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        graph = graph_module.graph
+        for node in graph.nodes:
+            if node.target in self.remove_ops and len(node.meta["val"].shape) == 0:
+                for user_n in list(node.users.keys()):
+                    user_n.replace_input_with(node, node.args[0])
+                graph.erase_node(node)
+
+        graph.eliminate_dead_code()
+        graph_module.recompile()
+        return PassResult(graph_module, True)

backends/qualcomm/partition/qnn_partitioner.py

@@ -34,7 +34,7 @@
     not_supported_operator,
     to_be_implemented_operator,
 )
-from .utils import generate_qnn_executorch_option, get_skip_decomp_table
+from .utils import filter_fn, generate_qnn_executorch_option, get_skip_decomp_table
 
 
 class QnnOperatorSupport(OperatorSupportBase):
@@ -181,5 +181,4 @@ def ops_to_not_decompose(
         self, ep: ExportedProgram
     ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]:
         do_not_decompose = get_skip_decomp_table()
-
-        return do_not_decompose, None
+        return (do_not_decompose, filter_fn)

backends/qualcomm/partition/utils.py

@@ -24,6 +24,21 @@ def generate_qnn_executorch_option(
     return qnn_compile_spec_buffer
 
 
+# Logic to determine whether to skip decompose and has higher priority than get_skip_decomp_table()
+def filter_fn(node: torch.fx.Node) -> bool:
+    # QNN does not support int32/int64 IO for the following OPs.
+    potential_i32_i64_io_ops = [
+        torch.ops.aten.stack.default,
+        torch.ops.aten.unbind.int,
+    ]
+    if node.target in potential_i32_i64_io_ops and node.meta["val"].dtype in [
+        torch.int32,
+        torch.int64,
+    ]:
+        return False
+    return True
+
+
 def get_skip_decomp_table() -> List[torch._ops.OperatorBase]:
     do_not_decompose = [
         torch.ops.aten.adaptive_avg_pool2d.default,
@@ -41,7 +56,7 @@ def get_skip_decomp_table() -> List[torch._ops.OperatorBase]:
         torch.ops.aten.stack.default,
         torch.ops.aten.upsample_bicubic2d.vec,
         # This request is ignored because it is in a blocklist. Refer to exir/program/_program.py
-        # torch.ops.aten.unbind.int,
+        torch.ops.aten.unbind.int,
         torch.ops.pt2e_quant.quantize_affine.default,
         torch.ops.pt2e_quant.dequantize_affine.default,
     ]