[torchfuzz] Make scalar and tensor distribution configurable (#164034)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164034
Approved by: https://github.com/pianpwk

tools/experimental/dynamic_shapes/torchfuzz/codegen.py

@@ -29,6 +29,81 @@ class FuzzTemplate:
             torch.bool,
         ]
 
+    def spec_distribution(self):
+        """
+        Define the distribution for generating random Specs.
+
+        Returns:
+            Dict with keys:
+            - 'tensor_prob': Probability of generating TensorSpec (0.0 to 1.0)
+            - 'scalar_prob': Probability of generating ScalarSpec (0.0 to 1.0)
+            - 'allow_tensors': Whether TensorSpec generation is allowed (boolean)
+            - 'allow_scalars': Whether ScalarSpec generation is allowed (boolean)
+        """
+        return {
+            "tensor_prob": 0.8,
+            "scalar_prob": 0.2,
+            "allow_tensors": True,
+            "allow_scalars": True,
+        }
+
+    def fuzz_spec_custom(self):
+        """
+        Generate a random Spec based on this template's distribution preferences.
+
+        Returns:
+            Spec: Either a TensorSpec or ScalarSpec according to template's distribution
+        """
+        import random
+
+        from torchfuzz.tensor_fuzzer import fuzz_torch_tensor_type
+
+        # Get template's distribution configuration
+        distribution = self.spec_distribution()
+
+        # Get random dtype based on template
+        dtype = fuzz_torch_tensor_type("default")
+
+        # Validate distribution configuration
+        allow_tensors = distribution.get("allow_tensors", True)
+        allow_scalars = distribution.get("allow_scalars", True)
+
+        if not allow_tensors and not allow_scalars:
+            raise ValueError("Template must allow at least one of tensors or scalars")
+
+        # Determine which type to generate
+        if not allow_scalars:
+            # Only tensors allowed
+            return self._generate_tensor_spec(dtype)
+        elif not allow_tensors:
+            # Only scalars allowed
+            return self._generate_scalar_spec(dtype)
+        else:
+            # Both allowed, use probability distribution
+            tensor_prob = distribution.get("tensor_prob", 0.8)
+            if random.random() < tensor_prob:
+                return self._generate_tensor_spec(dtype)
+            else:
+                return self._generate_scalar_spec(dtype)
+
+    def _generate_tensor_spec(self, dtype):
+        """Generate a TensorSpec with the given dtype."""
+        from torchfuzz.tensor_fuzzer import (
+            fuzz_tensor_size,
+            fuzz_valid_stride,
+            TensorSpec,
+        )
+
+        size = fuzz_tensor_size()
+        stride = fuzz_valid_stride(size)
+        return TensorSpec(size=size, stride=stride, dtype=dtype)
+
+    def _generate_scalar_spec(self, dtype):
+        """Generate a ScalarSpec with the given dtype."""
+        from torchfuzz.tensor_fuzzer import ScalarSpec
+
+        return ScalarSpec(dtype=dtype)
+
 
 class DefaultFuzzTemplate(FuzzTemplate):
     def __init__(self):
@@ -44,6 +119,15 @@ class DefaultFuzzTemplate(FuzzTemplate):
             check=EagerVsFullGraphDynamicCompileCheck(),
         )
 
+    def spec_distribution(self):
+        """Default template: tensor-only (no scalars)."""
+        return {
+            "tensor_prob": 1.0,
+            "scalar_prob": 0.0,
+            "allow_tensors": True,
+            "allow_scalars": False,
+        }
+
     def imports_codegen(self):
         return [
             "import torch",
@@ -129,6 +213,15 @@ class DTensorFuzzTemplate(FuzzTemplate):
             torch.bool,
         ]
 
+    def spec_distribution(self):
+        """DTensor template: tensor-only (no scalars)."""
+        return {
+            "tensor_prob": 1.0,
+            "scalar_prob": 0.0,
+            "allow_tensors": True,
+            "allow_scalars": False,
+        }
+
     def imports_codegen(self):
         return [
             "import torch",
@@ -259,6 +352,15 @@ class UnbackedFuzzTemplate(FuzzTemplate):
             torch.int64,
         ]
 
+    def spec_distribution(self):
+        """Unbacked template: 50% tensors, 50% scalars."""
+        return {
+            "tensor_prob": 0.5,
+            "scalar_prob": 0.5,
+            "allow_tensors": True,
+            "allow_scalars": True,
+        }
+
     def imports_codegen(self):
         return [
             "import torch",

tools/experimental/dynamic_shapes/torchfuzz/ops_fuzzer.py

@@ -216,31 +216,47 @@ class OperationGraph:
 
 def fuzz_spec(template: str = "default") -> Spec:
     """
-    Generate a random Spec (either TensorSpec or ScalarSpec) using tensor fuzzing functions.
+    Generate a random Spec (either TensorSpec or ScalarSpec) using template's distribution preferences.
-
-    Utilizes:
-    - fuzz_torch_tensor_type() for random dtype
-    - fuzz_tensor_size() for random tensor size
-    - fuzz_valid_stride() for random valid strides
 
     Args:
-        template: Template name to determine supported dtypes
+        template: Template name to determine configuration and distribution
 
     Returns:
-        Spec: Either a TensorSpec (80% probability) or ScalarSpec (20% probability) with random properties
+        Spec: Either a TensorSpec or ScalarSpec according to template's distribution
     """
-    # Get random dtype based on template
+    # Try to use template's custom distribution if available
-    dtype = fuzz_torch_tensor_type(template)
+    try:
+        # Instantiate template
+        if template == "dtensor":
+            from torchfuzz.codegen import DTensorFuzzTemplate
 
-    # 20% probability of returning ScalarSpec
+            fuzz_template = DTensorFuzzTemplate()
-    if random.random() < 0.2:
+        elif template == "unbacked":
-        return ScalarSpec(dtype=dtype)
+            from torchfuzz.codegen import UnbackedFuzzTemplate
 
-    # 80% probability of returning TensorSpec
+            fuzz_template = UnbackedFuzzTemplate()
-    # Get random size and corresponding stride
+        else:
-    size = fuzz_tensor_size()
+            from torchfuzz.codegen import DefaultFuzzTemplate
-    stride = fuzz_valid_stride(size)
+
-    return TensorSpec(size=size, stride=stride, dtype=dtype)
+            fuzz_template = DefaultFuzzTemplate()
+
+        # Use template's custom spec generation
+        return fuzz_template.fuzz_spec_custom()
+
+    except Exception:
+        # Fallback to original hardcoded behavior if template fails
+        # Get random dtype based on template
+        dtype = fuzz_torch_tensor_type(template)
+
+        # 20% probability of returning ScalarSpec
+        if random.random() < 0.2:
+            return ScalarSpec(dtype=dtype)
+
+        # 80% probability of returning TensorSpec
+        # Get random size and corresponding stride
+        size = fuzz_tensor_size()
+        stride = fuzz_valid_stride(size)
+        return TensorSpec(size=size, stride=stride, dtype=dtype)
 
 
 def fuzz_op(