[torchfuzz] add nn functional ops (#164434)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164434
Approved by: https://github.com/pianpwk
ghstack dependencies: #164432

tools/experimental/dynamic_shapes/torchfuzz/checks.py

@@ -43,8 +43,8 @@ class EagerVsFullGraphDynamicCompileWithNumericsCheck(Check):
             "diff = (out_eager_sum - out_compiled_sum).abs().item()",
             "rel_diff = diff / (out_eager_sum.abs().item() + 1e-12) * 100",
             "print(f'Relative diff (sum): {rel_diff:.6f}%')",
-            "if rel_diff > 5:",
-            "    print(f'❌ Forward output sums differ significantly (relative)!')",
+            "if rel_diff > 5 and diff > 1:",
+            "    print(f'❌ Forward output sums differ significantly (relative and absolute)!')",
             "    print('out_eager_sum:', out_eager_sum.item())",
             "    print('out_compiled_sum:', out_compiled_sum.item())",
             "    print('Absolute diff:', diff)",

tools/experimental/dynamic_shapes/torchfuzz/codegen.py

@@ -124,6 +124,12 @@ class DefaultFuzzTemplate(FuzzTemplate):
                 "torch.addmm",
                 "torch.bmm",
                 "torch.matmul",
+                "torch.nn.functional.embedding",
+                "torch.nn.functional.linear",
+                "torch.nn.functional.relu",
+                "torch.nn.functional.softmax",
+                "torch.nn.functional.dropout",
+                "torch.nn.functional.layer_norm",
             ],
             check=EagerVsFullGraphDynamicCompileWithNumericsCheck(),
         )
@@ -184,9 +190,25 @@ class DefaultFuzzTemplate(FuzzTemplate):
                         storage_size = 1
 
                     stride_str = str(spec.stride)
-                    code_lines.append(
-                        f"{arg_name} = torch.as_strided(torch.randn({storage_size}).to({dtype_str}), {size_str}, {stride_str})"
-                    )
+
+                    # Special handling for integer tensors which might be used as indices
+                    if spec.dtype in [torch.int32, torch.int64]:
+                        # For integer tensors, generate valid indices with headroom for arithmetic
+                        # Use smaller range [5, 30] to allow for multiplication and other operations
+                        # This prevents indices from becoming too large after arithmetic
+                        min_val = (
+                            5  # Minimum to avoid negative results after subtraction
+                        )
+                        max_val = (
+                            30  # Maximum to avoid out-of-bounds after multiplication
+                        )
+                        code_lines.append(
+                            f"{arg_name} = torch.as_strided(torch.randint({min_val}, {max_val}, ({storage_size},)).to({dtype_str}), {size_str}, {stride_str})"
+                        )
+                    else:
+                        code_lines.append(
+                            f"{arg_name} = torch.as_strided(torch.randn({storage_size}).to({dtype_str}), {size_str}, {stride_str})"
+                        )
 
         return code_lines
 

tools/experimental/dynamic_shapes/torchfuzz/operators/__init__.py

@@ -17,6 +17,14 @@ from torchfuzz.operators.matrix_multiply import (
     MatmulOperator,
     MMOperator,
 )
+from torchfuzz.operators.nn_functional import (
+    DropoutOperator,
+    EmbeddingOperator,
+    LayerNormOperator,
+    LinearOperator,
+    ReLUOperator,
+    SoftmaxOperator,
+)
 from torchfuzz.operators.registry import get_operator, list_operators, register_operator
 from torchfuzz.operators.scalar_pointwise import (
     ScalarAddOperator,
@@ -58,6 +66,12 @@ __all__ = [
     "AddmmOperator",
     "BmmOperator",
     "MatmulOperator",
+    "EmbeddingOperator",
+    "LinearOperator",
+    "ReLUOperator",
+    "SoftmaxOperator",
+    "DropoutOperator",
+    "LayerNormOperator",
     "get_operator",
     "register_operator",
     "list_operators",

tools/experimental/dynamic_shapes/torchfuzz/operators/constant.py

@@ -98,6 +98,21 @@ class ConstantOperator(Operator):
             else:
                 # For non-empty tensors, use the first element as fill value
                 fill_value = actual_tensor.flatten()[0].item()
+
+                # For integer types, clamp the value to a smaller range to avoid
+                # issues when used in arithmetic with embedding indices
+                import torch
+
+                if output_spec.dtype in [
+                    torch.int8,
+                    torch.int16,
+                    torch.int32,
+                    torch.int64,
+                ]:
+                    # Clamp integer values to [0, 3] to avoid index overflow in multiplication
+                    # Even with multiplication, indices should stay in reasonable range
+                    fill_value = max(0, min(3, abs(fill_value)))
+
                 tensor_creation = (
                     f"torch.full({size_str}, {fill_value}, dtype={dtype_str})"
                 )

tools/experimental/dynamic_shapes/torchfuzz/operators/nn_functional.py

@@ -0,0 +1,446 @@
+"""Neural network functional operator implementations."""
+
+import random
+from typing import Optional
+
+import torch
+
+from torchfuzz.operators.base import Operator
+from torchfuzz.tensor_fuzzer import Spec, TensorSpec
+
+
+class EmbeddingOperator(Operator):
+    """Operator for torch.nn.functional.embedding."""
+
+    def __init__(self):
+        super().__init__("torch.nn.functional.embedding")
+
+    @property
+    def torch_op_name(self) -> Optional[str]:
+        """Return the torch operation name."""
+        return "torch.nn.functional.embedding"
+
+    def can_produce(self, output_spec: Spec) -> bool:
+        """Embedding can produce tensor outputs with floating point dtypes."""
+        if not isinstance(output_spec, TensorSpec):
+            return False
+        # Embedding needs at least 1 dimension (embedding_dim)
+        if len(output_spec.size) == 0:
+            return False
+        # Embedding outputs are typically float tensors
+        return output_spec.dtype in [
+            torch.float32,
+            torch.float64,
+            torch.float16,
+            torch.bfloat16,
+        ]
+
+    def fuzz_inputs_specs(self, output_spec: Spec) -> list[Spec]:
+        """Generate input specs for embedding operation.
+
+        Embedding requires:
+        - weight tensor: (num_embeddings, embedding_dim)
+        - input tensor: integer indices (any shape, but output shape + [embedding_dim])
+        """
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("EmbeddingOperator can only produce TensorSpec outputs")
+
+        # Output shape should be input_shape + [embedding_dim]
+        if len(output_spec.size) == 0:
+            raise ValueError("Embedding output must have at least 1 dimension")
+
+        embedding_dim = output_spec.size[-1]
+        input_shape = output_spec.size[:-1]  # Remove last dimension for embedding_dim
+
+        # Generate reasonable vocab size that's larger than our index generation range
+        # This ensures that indices generated in range [0, 100) will always be valid
+        num_embeddings = random.randint(150, 500)  # Always larger than max index (100)
+
+        # Weight tensor: (num_embeddings, embedding_dim)
+        weight_spec = TensorSpec(
+            size=(num_embeddings, embedding_dim),
+            stride=(embedding_dim, 1),
+            dtype=output_spec.dtype,
+        )
+
+        # Input tensor: integer indices with shape that produces the output shape
+        input_spec = TensorSpec(
+            size=input_shape,
+            stride=self._calculate_stride(input_shape),
+            dtype=torch.int64,  # Indices are typically int64
+        )
+
+        return [weight_spec, input_spec]
+
+    def _calculate_stride(self, size):
+        """Calculate stride for a given size."""
+        if not size:
+            return ()
+        stride = []
+        current_stride = 1
+        for dim_size in reversed(size):
+            stride.append(current_stride)
+            current_stride *= dim_size
+        return tuple(reversed(stride))
+
+    def codegen(
+        self, output_name: str, input_names: list[str], output_spec: Spec
+    ) -> str:
+        """Generate code for embedding operation."""
+        if len(input_names) != 2:
+            raise ValueError("Embedding requires exactly 2 inputs: weight and input")
+
+        weight_name, input_name = input_names
+        # Ensure indices are integer type and clamped to valid range
+        # This handles any arithmetic operations that might produce out-of-bounds indices
+        return f"{output_name} = torch.nn.functional.embedding(torch.clamp({input_name}.to(torch.int64), 0, {weight_name}.size(0)-1), {weight_name})"
+
+
+class LinearOperator(Operator):
+    """Operator for torch.nn.functional.linear."""
+
+    def __init__(self):
+        super().__init__("torch.nn.functional.linear")
+
+    @property
+    def torch_op_name(self) -> Optional[str]:
+        """Return the torch operation name."""
+        return "torch.nn.functional.linear"
+
+    def can_produce(self, output_spec: Spec) -> bool:
+        """Linear can produce tensor outputs with floating point dtypes."""
+        if not isinstance(output_spec, TensorSpec):
+            return False
+        # Linear needs at least 1 dimension (output features)
+        if len(output_spec.size) == 0:
+            return False
+        return output_spec.dtype in [
+            torch.float32,
+            torch.float64,
+            torch.float16,
+            torch.bfloat16,
+        ]
+
+    def fuzz_inputs_specs(self, output_spec: Spec) -> list[Spec]:
+        """Generate input specs for linear operation.
+
+        Linear transformation: y = xW^T + b
+        - input: (..., in_features)
+        - weight: (out_features, in_features)
+        - bias: (out_features,) [optional]
+        - output: (..., out_features)
+        """
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("LinearOperator can only produce TensorSpec outputs")
+
+        if len(output_spec.size) == 0:
+            raise ValueError("Linear output must have at least 1 dimension")
+
+        out_features = output_spec.size[-1]
+        batch_shape = output_spec.size[:-1]
+
+        # Generate reasonable input features size
+        in_features = random.randint(8, 256)
+
+        # Input tensor: (..., in_features)
+        input_shape = batch_shape + (in_features,)
+        input_spec = TensorSpec(
+            size=input_shape,
+            stride=self._calculate_stride(input_shape),
+            dtype=output_spec.dtype,
+        )
+
+        # Weight tensor: (out_features, in_features)
+        weight_spec = TensorSpec(
+            size=(out_features, in_features),
+            stride=(in_features, 1),
+            dtype=output_spec.dtype,
+        )
+
+        # Bias tensor: (out_features,) - make bias optional with 50% probability
+        if random.random() < 0.5:
+            bias_spec = TensorSpec(
+                size=(out_features,), stride=(1,), dtype=output_spec.dtype
+            )
+            return [input_spec, weight_spec, bias_spec]
+        else:
+            return [input_spec, weight_spec]
+
+    def _calculate_stride(self, size):
+        """Calculate stride for a given size."""
+        if not size:
+            return ()
+        stride = []
+        current_stride = 1
+        for dim_size in reversed(size):
+            stride.append(current_stride)
+            current_stride *= dim_size
+        return tuple(reversed(stride))
+
+    def codegen(
+        self, output_name: str, input_names: list[str], output_spec: Spec
+    ) -> str:
+        """Generate code for linear operation."""
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("LinearOperator can only produce TensorSpec outputs")
+
+        # Ensure dtype compatibility by converting all inputs to the expected output dtype
+        target_dtype = str(output_spec.dtype)
+
+        if len(input_names) == 2:
+            input_name, weight_name = input_names
+            return f"{output_name} = torch.nn.functional.linear({input_name}.to({target_dtype}), {weight_name}.to({target_dtype}))"
+        elif len(input_names) == 3:
+            input_name, weight_name, bias_name = input_names
+            return f"{output_name} = torch.nn.functional.linear({input_name}.to({target_dtype}), {weight_name}.to({target_dtype}), {bias_name}.to({target_dtype}))"
+        else:
+            raise ValueError(
+                "Linear requires 2 or 3 inputs: input, weight, and optional bias"
+            )
+
+
+class ReLUOperator(Operator):
+    """Operator for torch.nn.functional.relu."""
+
+    def __init__(self):
+        super().__init__("torch.nn.functional.relu")
+
+    @property
+    def torch_op_name(self) -> Optional[str]:
+        """Return the torch operation name."""
+        return "torch.nn.functional.relu"
+
+    def can_produce(self, output_spec: Spec) -> bool:
+        """ReLU can produce tensor outputs with floating point dtypes."""
+        if not isinstance(output_spec, TensorSpec):
+            return False
+        return output_spec.dtype in [
+            torch.float32,
+            torch.float64,
+            torch.float16,
+            torch.bfloat16,
+        ]
+
+    def fuzz_inputs_specs(self, output_spec: Spec) -> list[Spec]:
+        """Generate input specs for ReLU operation.
+
+        ReLU is element-wise, so input shape matches output shape.
+        """
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("ReLUOperator can only produce TensorSpec outputs")
+
+        # Input tensor has same shape and dtype as output
+        input_spec = TensorSpec(
+            size=output_spec.size, stride=output_spec.stride, dtype=output_spec.dtype
+        )
+
+        return [input_spec]
+
+    def codegen(
+        self, output_name: str, input_names: list[str], output_spec: Spec
+    ) -> str:
+        """Generate code for ReLU operation."""
+        if len(input_names) != 1:
+            raise ValueError("ReLU requires exactly 1 input")
+
+        input_name = input_names[0]
+        return f"{output_name} = torch.nn.functional.relu({input_name})"
+
+
+class SoftmaxOperator(Operator):
+    """Operator for torch.nn.functional.softmax."""
+
+    def __init__(self):
+        super().__init__("torch.nn.functional.softmax")
+
+    @property
+    def torch_op_name(self) -> Optional[str]:
+        """Return the torch operation name."""
+        return "torch.nn.functional.softmax"
+
+    def can_produce(self, output_spec: Spec) -> bool:
+        """Softmax can produce tensor outputs with floating point dtypes."""
+        if not isinstance(output_spec, TensorSpec):
+            return False
+        # Softmax needs at least 1 dimension to apply softmax along a dimension
+        if len(output_spec.size) == 0:
+            return False
+        return output_spec.dtype in [
+            torch.float32,
+            torch.float64,
+            torch.float16,
+            torch.bfloat16,
+        ]
+
+    def fuzz_inputs_specs(self, output_spec: Spec) -> list[Spec]:
+        """Generate input specs for softmax operation.
+
+        Softmax is element-wise along a dimension, input shape matches output shape.
+        """
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("SoftmaxOperator can only produce TensorSpec outputs")
+
+        # Input tensor has same shape and dtype as output
+        input_spec = TensorSpec(
+            size=output_spec.size, stride=output_spec.stride, dtype=output_spec.dtype
+        )
+
+        return [input_spec]
+
+    def codegen(
+        self, output_name: str, input_names: list[str], output_spec: Spec
+    ) -> str:
+        """Generate code for softmax operation."""
+        if len(input_names) != 1:
+            raise ValueError("Softmax requires exactly 1 input")
+
+        input_name = input_names[0]
+        # Use dim=-1 as default (last dimension)
+        return f"{output_name} = torch.nn.functional.softmax({input_name}, dim=-1)"
+
+
+class DropoutOperator(Operator):
+    """Operator for torch.nn.functional.dropout."""
+
+    def __init__(self):
+        super().__init__("torch.nn.functional.dropout")
+
+    @property
+    def torch_op_name(self) -> Optional[str]:
+        """Return the torch operation name."""
+        return "torch.nn.functional.dropout"
+
+    def can_produce(self, output_spec: Spec) -> bool:
+        """Dropout can produce tensor outputs with floating point dtypes."""
+        if not isinstance(output_spec, TensorSpec):
+            return False
+        return output_spec.dtype in [
+            torch.float32,
+            torch.float64,
+            torch.float16,
+            torch.bfloat16,
+        ]
+
+    def fuzz_inputs_specs(self, output_spec: Spec) -> list[Spec]:
+        """Generate input specs for dropout operation.
+
+        Dropout is element-wise, input shape matches output shape.
+        """
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("DropoutOperator can only produce TensorSpec outputs")
+
+        # Input tensor has same shape and dtype as output
+        input_spec = TensorSpec(
+            size=output_spec.size, stride=output_spec.stride, dtype=output_spec.dtype
+        )
+
+        return [input_spec]
+
+    def codegen(
+        self, output_name: str, input_names: list[str], output_spec: Spec
+    ) -> str:
+        """Generate code for dropout operation."""
+        if len(input_names) != 1:
+            raise ValueError("Dropout requires exactly 1 input")
+
+        input_name = input_names[0]
+        # Use training=False to make it deterministic for testing
+        return f"{output_name} = torch.nn.functional.dropout({input_name}, p=0.1, training=False)"
+
+
+class LayerNormOperator(Operator):
+    """Operator for torch.nn.functional.layer_norm."""
+
+    def __init__(self):
+        super().__init__("torch.nn.functional.layer_norm")
+
+    @property
+    def torch_op_name(self) -> Optional[str]:
+        """Return the torch operation name."""
+        return "torch.nn.functional.layer_norm"
+
+    def can_produce(self, output_spec: Spec) -> bool:
+        """LayerNorm can produce tensor outputs with floating point dtypes."""
+        if not isinstance(output_spec, TensorSpec):
+            return False
+        # LayerNorm needs at least 1 dimension to normalize over
+        if len(output_spec.size) == 0:
+            return False
+        return output_spec.dtype in [
+            torch.float32,
+            torch.float64,
+            torch.float16,
+            torch.bfloat16,
+        ]
+
+    def fuzz_inputs_specs(self, output_spec: Spec) -> list[Spec]:
+        """Generate input specs for layer_norm operation.
+
+        LayerNorm normalizes over the last dimensions specified by normalized_shape.
+        - input: input tensor
+        - weight: (normalized_shape,) [optional]
+        - bias: (normalized_shape,) [optional]
+        """
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("LayerNormOperator can only produce TensorSpec outputs")
+
+        if len(output_spec.size) == 0:
+            raise ValueError("LayerNorm output must have at least 1 dimension")
+
+        # Input tensor has same shape and dtype as output
+        input_spec = TensorSpec(
+            size=output_spec.size, stride=output_spec.stride, dtype=output_spec.dtype
+        )
+
+        # For simplicity, normalize over the last dimension
+        normalized_shape = output_spec.size[-1:]
+
+        # Weight and bias tensors (optional with 70% probability each)
+        specs = [input_spec]
+        if random.random() < 0.7:
+            # LayerNorm weight and bias parameters should match input tensor dtype
+            # for compatibility (conversion will be handled in codegen)
+            weight_spec = TensorSpec(
+                size=normalized_shape, stride=(1,), dtype=output_spec.dtype
+            )
+            specs.append(weight_spec)
+
+            if random.random() < 0.7:
+                bias_spec = TensorSpec(
+                    size=normalized_shape, stride=(1,), dtype=output_spec.dtype
+                )
+                specs.append(bias_spec)
+
+        # Cast to list[Spec] to fix type checking
+        from typing import cast
+
+        return cast(list[Spec], specs)
+
+    def codegen(
+        self, output_name: str, input_names: list[str], output_spec: Spec
+    ) -> str:
+        """Generate code for layer_norm operation."""
+        if len(input_names) < 1 or len(input_names) > 3:
+            raise ValueError(
+                "LayerNorm requires 1-3 inputs: input, optional weight, optional bias"
+            )
+
+        if not isinstance(output_spec, TensorSpec):
+            raise ValueError("LayerNormOperator can only produce TensorSpec outputs")
+
+        # Normalize over the last dimension
+        normalized_shape = f"({output_spec.size[-1]},)"
+
+        # Ensure dtype compatibility by converting all inputs to the expected output dtype
+        target_dtype = str(output_spec.dtype)
+
+        input_name = input_names[0]
+
+        if len(input_names) == 1:
+            return f"{output_name} = torch.nn.functional.layer_norm({input_name}.to({target_dtype}), {normalized_shape})"
+        elif len(input_names) == 2:
+            weight_name = input_names[1]
+            return f"{output_name} = torch.nn.functional.layer_norm({input_name}.to({target_dtype}), {normalized_shape}, weight={weight_name}.to({target_dtype}))"
+        else:  # len(input_names) == 3
+            weight_name, bias_name = input_names[1], input_names[2]
+            return f"{output_name} = torch.nn.functional.layer_norm({input_name}.to({target_dtype}), {normalized_shape}, weight={weight_name}.to({target_dtype}), bias={bias_name}.to({target_dtype}))"

tools/experimental/dynamic_shapes/torchfuzz/operators/registry.py

@@ -20,6 +20,14 @@ from torchfuzz.operators.matrix_multiply import (
     MatmulOperator,
     MMOperator,
 )
+from torchfuzz.operators.nn_functional import (
+    DropoutOperator,
+    EmbeddingOperator,
+    LayerNormOperator,
+    LinearOperator,
+    ReLUOperator,
+    SoftmaxOperator,
+)
 from torchfuzz.operators.nonzero import NonzeroOperator
 from torchfuzz.operators.scalar_pointwise import (
     ScalarAddOperator,
@@ -81,6 +89,14 @@ class OperatorRegistry:
         self.register(BmmOperator())
         self.register(MatmulOperator())
 
+        # Neural network functional operators
+        self.register(EmbeddingOperator())
+        self.register(LinearOperator())
+        self.register(ReLUOperator())
+        self.register(SoftmaxOperator())
+        self.register(DropoutOperator())
+        self.register(LayerNormOperator())
+
     def register(self, operator: Operator):
         """Register an operator in the registry."""
         self._operators[operator.name] = operator