[Kernel][Misc] dynamo support for ScalarType (#7594)

csrc/core/scalar_type.hpp

@@ -313,6 +313,8 @@ class ScalarType {
 //  have ScalarType inherit from torch::CustomClassHolder and have a constexpr
 //  constructor at the same time (torch::CustomClassHolder does not have a
 //  constexpr destructor)
+// See also:
+// https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA
 class ScalarTypeTorch : public torch::CustomClassHolder, public ScalarType {
  public:
   ScalarTypeTorch(int64_t exponent, int64_t mantissa, int64_t bias,
@@ -382,6 +384,29 @@ class ScalarTypeTorch : public torch::CustomClassHolder, public ScalarType {
         exponent, mantissa, finite_values_only, NanRepr(nan_repr)));
   }
 
+  // This needs to be implemented and throw a TypeError in order for
+  // PyTorch's opcheck to work on ops that use ScalarTypes.
+  int64_t len() const {
+    throw c10::TypeError("__len__ not implemented");
+    return 0;
+  }
+
+  // Serialize a ScalarType into a tuple of pairs.  Where each pair
+  // is a (fieldname, value).
+  // For simplicity, we are just going to convert to a ScalarTypeId.
+  std::tuple<std::tuple<std::string, int64_t>> obj_flatten() const {
+    return {{"ScalarType", id()}};
+  }
+
+  // Deserialize a scalar type that has been serialized by obj_flatten,
+  // ostensibly from a tuple of (member name, value) pairs, but in reality
+  // just a ScalarTypeId.
+  static SelfPtr obj_unflatten(
+      std::tuple<std::tuple<std::string, int64_t>> const& flat_type) {
+    return c10::make_intrusive<Self>(
+        from_id(std::get<1>(std::get<0>(flat_type))));
+  }
+
   template <typename T>
   static void bind_readonly_property(torch::class_<Self>& cls,
                                      std::string const& name, T Base::*field) {
@@ -457,6 +482,7 @@ class ScalarTypeTorch : public torch::CustomClassHolder, public ScalarType {
                         self.get()->min());
     });
 
+    bind_function(cls, "__len__", &ScalarTypeTorch::len);
     bind_function(cls, "__str__", &Base::str);
     bind_function(cls, "__eq__", [](SelfPtr const& self, SelfPtr const& other) {
       return *self == *other;
@@ -465,6 +491,10 @@ class ScalarTypeTorch : public torch::CustomClassHolder, public ScalarType {
       return "ScalarType." + self.get()->str();
     });
 
+    bind_function(cls, "__obj_flatten__", &ScalarTypeTorch::obj_flatten);
+    bind_static_function(cls, "__obj_unflatten__",
+                         &ScalarTypeTorch::obj_unflatten);
+
     // Bind static functions (convenience constructors)
     bind_static_function(cls, "int_", &ScalarTypeTorch::int_);
     bind_static_function(cls, "uint", &ScalarTypeTorch::uint);

vllm/_core_ext.py

 import importlib.util
 from enum import Enum
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Tuple, Union
 
 import torch
 
@@ -31,14 +31,14 @@ if TYPE_CHECKING or not core_C_available:
     @dataclass(frozen=True)
     class ScalarType:
         """
-        ScalarType can represent a wide range of floating point and integer 
-        types, in particular it can be used to represent sub-byte data types 
-        (something that torch.dtype currently does not support). It is also 
+        ScalarType can represent a wide range of floating point and integer
+        types, in particular it can be used to represent sub-byte data types
+        (something that torch.dtype currently does not support). It is also
         capable of  representing types with a bias, i.e.:
-          `stored_value = value + bias`, 
-        this is useful for quantized types (e.g. standard GPTQ 4bit uses a bias 
-        of 8). The implementation for this class can be found in 
-        csrc/core/scalar_type.hpp, these type signatures should be kept in sync 
+          `stored_value = value + bias`,
+        this is useful for quantized types (e.g. standard GPTQ 4bit uses a bias
+        of 8). The implementation for this class can be found in
+        csrc/core/scalar_type.hpp, these type signatures should be kept in sync
         with that file.
         """
 
@@ -51,15 +51,15 @@ if TYPE_CHECKING or not core_C_available:
         mantissa: int
         """
         Number of bits in the mantissa if this is a floating point type,
-        or the number bits representing an integer excluding the sign bit if 
+        or the number bits representing an integer excluding the sign bit if
         this an integer type.
         """
 
         bias: int
         """
-        bias used to encode the values in this scalar type 
-        (value = stored_value - bias, default 0) for example if we store the 
-        type as an unsigned integer with a bias of 128 then the value 0 will be 
+        bias used to encode the values in this scalar type
+        (value = stored_value - bias, default 0) for example if we store the
+        type as an unsigned integer with a bias of 128 then the value 0 will be
         stored as 128 and -1 will be stored as 127 and 1 will be stored as 129.
         """
 
@@ -73,7 +73,7 @@ if TYPE_CHECKING or not core_C_available:
 
         nan_repr: int = NanRepr.IEEE_754.value
         """
-        How NaNs are represent in this scalar type, returns NanRepr value. 
+        How NaNs are represent in this scalar type, returns NanRepr value.
         (not applicable for integer types)
         """
 
@@ -83,14 +83,14 @@ if TYPE_CHECKING or not core_C_available:
 
         def min(self) -> Union[int, float]:
             """
-            Min representable value for this scalar type. 
+            Min representable value for this scalar type.
             (accounting for bias if there is one)
             """
             raise NotImplementedError
 
         def max(self) -> Union[int, float]:
             """
-            Max representable value for this scalar type. 
+            Max representable value for this scalar type.
             (accounting for bias if there is one)
             """
             raise NotImplementedError
@@ -103,28 +103,28 @@ if TYPE_CHECKING or not core_C_available:
             """
             ...
 
-        def is_floating_point(self):
+        def is_floating_point(self) -> bool:
             "If the type is a floating point type"
             return self.exponent != 0
 
-        def is_integer(self):
+        def is_integer(self) -> bool:
             "If the type is an integer type"
             return self.exponent == 0
 
-        def has_bias(self):
+        def has_bias(self) -> bool:
             "If the type has a non-zero bias"
             return self.bias != 0
 
-        def has_infs(self):
+        def has_infs(self) -> bool:
             "If the type is floating point and supports infinity"
             return not self._finite_values_only
 
-        def has_nans(self):
+        def has_nans(self) -> bool:
             return self.nan_repr != NanRepr.NONE.value
 
         def is_ieee_754(self) -> bool:
             """
-            If the type is a floating point type that follows IEEE 754 
+            If the type is a floating point type that follows IEEE 754
             conventions
             """
             return self.nan_repr == NanRepr.IEEE_754.value and \
@@ -136,6 +136,11 @@ if TYPE_CHECKING or not core_C_available:
         def __repr__(self) -> str:
             raise NotImplementedError
 
+        # __len__ needs to be defined (and has to throw TypeError) for pytorch's
+        # opcheck to work.
+        def __len__(self) -> int:
+            raise TypeError
+
         #
         # Convenience Constructors
         #
@@ -153,16 +158,16 @@ if TYPE_CHECKING or not core_C_available:
         @classmethod
         def float_IEEE754(cls, exponent: int, mantissa: int) -> 'ScalarType':
             """
-            Create a standard floating point type 
+            Create a standard floating point type
             (i.e. follows IEEE 754 conventions).
             """
             return cls(exponent, mantissa, 0, True)
 
         @classmethod
         def float_(cls, exponent: int, mantissa: int, finite_values_only: bool,
-                   nan_repr: int):
+                   nan_repr: int) -> 'ScalarType':
             """
-            Create a non-standard floating point type 
+            Create a non-standard floating point type
             (i.e. does not follow IEEE 754 conventions).
             """
             return cls(exponent, mantissa, 0, True, finite_values_only,
@@ -175,3 +180,93 @@ elif core_C_available:
         logger.warning("Failed to import from vllm._core_C with %r", e)
 
     ScalarType = torch.classes._core_C.ScalarType
+
+    # Needed for dynamo support of ScalarType.
+    @torch._library.register_fake_class("_core_C::ScalarType")
+    class FakeScalarType:
+
+        def __init__(self, scalar_type):
+            self.ScalarType = scalar_type
+
+        def bias_getter(self) -> int:
+            return self.ScalarType.bias
+
+        def exponent_getter(self) -> int:
+            return self.ScalarType.exponent
+
+        def mantissa_getter(self) -> int:
+            return self.ScalarType.mantissa
+
+        def signed_getter(self) -> bool:
+            return self.ScalarType.signed
+
+        def size_bits_getter(self) -> int:
+            return self.ScalarType.size_bits
+
+        @property
+        def size_bits(self) -> int:
+            return self.ScalarType.size_bits
+
+        def min(self) -> Union[int, float]:
+            return self.ScalarType.min()
+
+        def max(self) -> Union[int, float]:
+            return self.ScalarType.max()
+
+        def is_signed(self) -> bool:
+            return self.ScalarType.is_signed()
+
+        def is_floating_point(self) -> bool:
+            return self.ScalarType.is_floating_point()
+
+        def is_integer(self) -> bool:
+            return self.ScalarType.is_integer()
+
+        def has_bias(self) -> bool:
+            return self.ScalarType.has_bias()
+
+        def has_infs(self) -> bool:
+            return self.ScalarType.has_infs()
+
+        def has_nans(self) -> bool:
+            return self.ScalarType.has_nans()
+
+        def is_ieee_754(self) -> bool:
+            return self.ScalarType.is_ieee_754()
+
+        def __str__(self) -> str:
+            return self.ScalarType.__str__()
+
+        def __repr__(self) -> str:
+            return self.ScalarType.__repr__()
+
+        def __len__(self) -> int:
+            return self.ScalarType.__len__()
+
+        def __obj_flatten__(self) -> Tuple[Tuple[str, Any], ...]:
+            return torch.classes._core_C.ScalarType.__obj_flatten__(
+                self.ScalarType)
+
+        @classmethod
+        def __obj_unflatten__(
+                cls, flat_type: Tuple[Tuple[str, Any], ...]) -> 'ScalarType':
+            return cls(
+                torch.classes._core_C.ScalarType.__obj_unflatten__(flat_type))
+
+        @classmethod
+        def int_(cls, size_bits: int, bias: Optional[int]) -> 'ScalarType':
+            return ScalarType.int_(size_bits, bias)
+
+        @classmethod
+        def uint(cls, size_bits: int, bias: Optional[int]) -> 'ScalarType':
+            return ScalarType.uint(size_bits, bias)
+
+        @classmethod
+        def float_IEEE754(cls, exponent: int, mantissa: int) -> 'ScalarType':
+            return ScalarType.float_IEEE754(exponent, mantissa)
+
+        @classmethod
+        def float_(cls, exponent: int, mantissa: int, finite_values_only: bool,
+                   nan_repr: int) -> 'ScalarType':
+            return ScalarType.float_(exponent, mantissa, finite_values_only,
+                                     nan_repr)