support qnn mean (dim=None) (pytorch#14675)

Summary:

Address mean op lower failure. When dim is not specified, it will take mean across all axes. For QNN, we need to get axes based on input shape

Differential Revision: D83520776

Author: cccclai

backends/qualcomm/builders/op_mean_dim.py

@@ -40,7 +40,20 @@ def define_node(
         )
 
         # mean dims and keep dims
-        mean_dims = cast(List[int], node.args[1])
+        rank = len(input_node.meta["val"].shape)
+
+        if rank == 0:
+            raise RuntimeError("Mean doesn't support 0d input, please report a bug in https://github.com/pytorch/executorch/issues")
+
+        dim_arg = node.args[1]
+
+        if dim_arg is None or len(dim_arg) == 0:
+            mean_dims = list(range(rank))   # reduce over all dims
+        elif isinstance(dim_arg, int):
+            mean_dims = [dim_arg]
+        else:
+            mean_dims = list(dim_arg)
+        print("mean_dims: ", mean_dims, "rank: ", rank)
         mean_dims = [
             mean_dim % len(input_node.meta["val"].shape) for mean_dim in mean_dims
         ]

backends/qualcomm/tests/models.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-
+from typing import Optional, Union, Tuple, List
 
 # module with related operator only
 
@@ -1332,20 +1332,20 @@ def forward(self, x):
         return self.max_pool2d(x)
 
 
-class MeanWKeppDim(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return torch.mean(x, (-1, -2), keepdim=True)
-
-
-class MeanWOKeppDim(torch.nn.Module):
-    def __init__(self):
+class Mean(torch.nn.Module):
+    def __init__(
+        self,
+        dim: Optional[Union[int, Tuple[int, ...], List[int]]] = None,
+        keepdim: bool = False,
+        dtype: Optional[torch.dtype] = None,
+    ):
         super().__init__()
+        self.dim = dim
+        self.keepdim = keepdim
+        self.dtype = dtype
 
     def forward(self, x):
-        return torch.mean(x, (-1, -2))
+        return torch.mean(x, dim=self.dim, keepdim=self.keepdim, dtype=self.dtype)
 
 
 class MaskedFill(torch.nn.Module):

backends/qualcomm/tests/test_qnn_delegate.py

@@ -1018,12 +1018,60 @@ def test_qnn_backend_max_pool2d(self):
         sample_input = (torch.randn(4, 3, 24, 24),)
         self.lower_module_and_test_output(module, sample_input)
 
-    def test_qnn_backend_mean_dim(self):
-        modules = [MeanWKeppDim(), MeanWOKeppDim()]  # noqa: F405
-        sample_input = (torch.randn([2, 5, 1, 3]),)
-        for i, module in enumerate(modules):
+    def test_qnn_backend_mean(self):
+        test_comb = [
+            # Reduce over last two dims, keepdim=True
+            {
+                QCOM_MODULE: Mean(dim=(-1, -2), keepdim=True),
+                QCOM_SAMPLE_INPUTS: (torch.randn([2, 5, 1, 3]),),
+            },
+            # Reduce over last two dims, keepdim=False
+            {
+                QCOM_MODULE: Mean(dim=(-1, -2), keepdim=False),
+                QCOM_SAMPLE_INPUTS: (torch.randn([2, 5, 1, 3]),),
+            },
+            # Default: reduce all dims
+            {
+                QCOM_MODULE: Mean(),
+                QCOM_SAMPLE_INPUTS: (torch.randn(10, 10),),
+            },
+            # TODO: To be enabled via reshape input to 1d tensor
+            # # Scalar case
+            # {
+            #     QCOM_MODULE: Mean(),
+            #     QCOM_SAMPLE_INPUTS: (torch.tensor(5.0),),
+            # },
+            # Edge case: dim is a empty list
+            {
+                QCOM_MODULE: Mean(dim=[]),
+                QCOM_SAMPLE_INPUTS: (torch.randn(4, 6, 8),),
+            },
+            # Edge case: reduce along dim=0 (batch dimension)
+            {
+                QCOM_MODULE: Mean(dim=0),
+                QCOM_SAMPLE_INPUTS: (torch.randn(4, 6, 8),),
+            },
+            # Edge case: reduce along dim=0 with keepdim=True
+            {
+                QCOM_MODULE: Mean(dim=0, keepdim=True),
+                QCOM_SAMPLE_INPUTS: (torch.randn(4, 6, 8),),
+            },
+            # Edge case: reduce along multiple dims
+            {
+                QCOM_MODULE: Mean(dim=(0, 2)),
+                QCOM_SAMPLE_INPUTS: (torch.randn(3, 4, 5),),
+            },
+            # Edge case: high-dimensional tensor
+            {
+                QCOM_MODULE: Mean(dim=(1, 3), keepdim=True),
+                QCOM_SAMPLE_INPUTS: (torch.randn(2, 3, 4, 5, 6),),
+            },
+        ]
+
+        for i, test in enumerate(test_comb):
             with self.subTest(i=i):
-                self.lower_module_and_test_output(module, sample_input)
+                print("running test i: ", i)
+                self.lower_module_and_test_output(test[QCOM_MODULE], test[QCOM_SAMPLE_INPUTS])
 
     @unittest.skip("failed to lower in QNN 2.26")
     def test_qnn_backend_mha(self):
@@ -2666,13 +2714,63 @@ def test_qnn_backend_max_pool2d(self):
         module = self.get_qdq_module(module, sample_input)
         self.lower_module_and_test_output(module, sample_input)
 
-    def test_qnn_backend_mean_dim(self):
-        modules = [MeanWKeppDim(), MeanWOKeppDim()]  # noqa: F405
-        sample_input = (torch.randn([2, 5, 1, 3]),)
-        for i, module in enumerate(modules):
+    def test_qnn_backend_mean(self):
+        test_comb = [
+            # Reduce over last two dims, keepdim=True
+            {
+                QCOM_MODULE: Mean(dim=(-1, -2), keepdim=True),
+                QCOM_SAMPLE_INPUTS: (torch.randn([2, 5, 1, 3]),),
+            },
+            # Reduce over last two dims, keepdim=False
+            {
+                QCOM_MODULE: Mean(dim=(-1, -2), keepdim=False),
+                QCOM_SAMPLE_INPUTS: (torch.randn([2, 5, 1, 3]),),
+            },
+            # Default: reduce all dims
+            {
+                QCOM_MODULE: Mean(),
+                QCOM_SAMPLE_INPUTS: (torch.randn(10, 10),),
+            },
+            # TODO: To be enabled via reshape input to 1d tensor
+            # Scalar case
+            # {
+            #     QCOM_MODULE: Mean(),
+            #     QCOM_SAMPLE_INPUTS: (torch.tensor(5.0),),
+            # },
+            # Edge case: dim is a empty list
+            {
+                QCOM_MODULE: Mean(dim=[]),
+                QCOM_SAMPLE_INPUTS: (torch.randn(4, 6, 8),),
+            },
+            # Edge case: reduce along dim=0 (batch dimension)
+            {
+                QCOM_MODULE: Mean(dim=0),
+                QCOM_SAMPLE_INPUTS: (torch.randn(4, 6, 8),),
+            },
+            # Edge case: reduce along dim=0 with keepdim=True
+            {
+                QCOM_MODULE: Mean(dim=0, keepdim=True),
+                QCOM_SAMPLE_INPUTS: (torch.randn(4, 6, 8),),
+            },
+            # Edge case: reduce along multiple dims
+            {
+                QCOM_MODULE: Mean(dim=(0, 2)),
+                QCOM_SAMPLE_INPUTS: (torch.randn(3, 4, 5),),
+            },
+            # Edge case: high-dimensional tensor
+            {
+                QCOM_MODULE: Mean(dim=(1, 3), keepdim=True),
+                QCOM_SAMPLE_INPUTS: (torch.randn(2, 3, 4, 5, 6),),
+            },
+        ]
+
+        for i, test in enumerate(test_comb):
             with self.subTest(i=i):
-                module = self.get_qdq_module(module, sample_input)
-                self.lower_module_and_test_output(module, sample_input)
+                module = self.get_qdq_module(test[QCOM_MODULE], test[QCOM_SAMPLE_INPUTS])
+                print("quantized module")
+                module.graph.print_tabular()
+                self.lower_module_and_test_output(module, test[QCOM_SAMPLE_INPUTS])
+
 
     def test_qnn_backend_mha(self):
         module = MultiheadAttention()  # noqa: F405