| # Parses derivatives.yaml into autograd functions |
| # |
| # Each autograd function is represented by `DifferentiabilityInfo` containing |
| # a list of `Derivative`. See `torchgen.api.autograd` for the data models. |
| from collections import defaultdict |
| import re |
| from typing import Counter, Sequence, Any, Tuple, List, Set, Dict, Match, Optional |
| import yaml |
| |
| from torchgen.api.autograd import ( |
| Derivative, |
| DifferentiabilityInfo, |
| SavedAttribute, |
| ForwardDerivative, |
| ) |
| from torchgen.api.types import ( |
| Binding, |
| CppSignatureGroup, |
| NamedCType, |
| BaseCType, |
| VectorCType, |
| intArrayRefT, |
| tensorOptionsT, |
| typeAndSizeT, |
| longT, |
| boolT, |
| layoutT, |
| tensorGeometryT, |
| scalarTypeT, |
| SpecialArgName, |
| OptionalCType, |
| stringT, |
| ) |
| from torchgen.api import cpp |
| from torchgen.gen import ( |
| parse_native_yaml, |
| get_grouped_by_view_native_functions, |
| ) |
| from torchgen.context import with_native_function |
| from torchgen.model import ( |
| FunctionSchema, |
| NativeFunction, |
| Variant, |
| Type, |
| NativeFunctionsViewGroup, |
| OperatorName, |
| ) |
| from torchgen.utils import IDENT_REGEX, split_name_params, YamlLoader, concatMap |
| |
| _GLOBAL_LOAD_DERIVATIVE_CACHE = {} |
| |
| # This function directly adds derivative entries for {view}_copy variants of each view op. |
| # Since every {view} and {view}_copy op shares the same derivative formula, |
| # we generate them here instead of duplicating them in the yaml. |
| # See Note [Codegen'd {view}_copy Operators] |
| def add_view_copy_derivatives( |
| infos: List[DifferentiabilityInfo], view_groups: List[NativeFunctionsViewGroup] |
| ) -> List[DifferentiabilityInfo]: |
| # Get the map from each view op's name to its corresponding view group |
| view_name_to_group: Dict[OperatorName, NativeFunctionsViewGroup] = { |
| g.view.func.name: g for g in view_groups |
| } |
| |
| view_copy_differentiability_infos = [] |
| for info in infos: |
| maybe_view_group = view_name_to_group.get(info.func.func.name, None) |
| if maybe_view_group is not None and maybe_view_group.view_copy is not None: |
| view_copy_info = info.create_view_copy_from_view_derivative( |
| maybe_view_group |
| ) |
| if view_copy_info is not None: |
| view_copy_differentiability_infos.append(view_copy_info) |
| |
| return view_copy_differentiability_infos |
| |
| |
| def load_derivatives( |
| derivatives_yaml_path: str, native_yaml_path: str, tags_yaml_path: str |
| ) -> Sequence[DifferentiabilityInfo]: |
| # Do some caching as this is a deterministic function |
| global _GLOBAL_LOAD_DERIVATIVE_CACHE |
| key = (derivatives_yaml_path, native_yaml_path) |
| if key not in _GLOBAL_LOAD_DERIVATIVE_CACHE: |
| |
| with open(derivatives_yaml_path, "r") as f: |
| definitions = yaml.load(f, Loader=YamlLoader) |
| |
| funcs = parse_native_yaml(native_yaml_path, tags_yaml_path).native_functions |
| # From the parsed native functions, separate out the (generated) view_copy functions, |
| # so we can generate derivatives for them separately. |
| native_functions_with_view_groups = get_grouped_by_view_native_functions(funcs) |
| native_functions_without_view_copies = concatMap( |
| # We need to pull out the view_inplace ops too, since they might have their own derivative entries. |
| lambda g: [g] |
| if isinstance(g, NativeFunction) |
| else list(g.functions(include_copy=False)), |
| native_functions_with_view_groups, |
| ) |
| view_groups = [ |
| g |
| for g in native_functions_with_view_groups |
| if isinstance(g, NativeFunctionsViewGroup) |
| ] |
| |
| # What's the difference between function schema v.s. signature? |
| # function schema is the complete declaration including mutability annotation / default value and etc. |
| # signature is the canonical schema for a group of functions (in-place/out/functional variants) |
| # that are semantically related. |
| functions_by_signature: Dict[ |
| FunctionSchema, List[NativeFunction] |
| ] = defaultdict(list) |
| functions_by_schema: Dict[str, NativeFunction] = dict() |
| for function in native_functions_without_view_copies: |
| functions_by_signature[function.func.signature()].append(function) |
| assert str(function.func) not in functions_by_schema |
| functions_by_schema[str(function.func)] = function |
| |
| # Keep track of how many of which ops we've seen so we can |
| # disambiguate them with a numeric suffix. |
| op_counter = Counter[str]() |
| |
| infos = [ |
| create_differentiability_info( |
| defn, functions_by_signature, functions_by_schema, op_counter |
| ) |
| for defn in definitions |
| ] |
| infos += add_view_copy_derivatives(infos, view_groups) |
| |
| _GLOBAL_LOAD_DERIVATIVE_CACHE[key] = infos |
| |
| return _GLOBAL_LOAD_DERIVATIVE_CACHE[key] |
| |
| |
| @with_native_function |
| def cpp_arguments(f: NativeFunction) -> Sequence[Binding]: |
| return CppSignatureGroup.from_native_function(f, method=False).signature.arguments() |
| |
| |
| def create_derivative( |
| f: NativeFunction, |
| formula: str, |
| var_names: Tuple[str, ...], |
| available_named_gradients: Sequence[str], |
| ) -> Derivative: |
| original_formula = formula |
| arguments: List[NamedCType] = [ |
| a.nctype.remove_const_ref() for a in cpp_arguments(f) |
| ] |
| |
| return_names = tuple(n if n != "self" else "result" for n in cpp.return_names(f)) |
| return_types = tuple(cpp.return_type(r).remove_const_ref() for r in f.func.returns) |
| |
| named_returns = [ |
| NamedCType(name, type) for name, type in zip(return_names, return_types) |
| ] |
| |
| formula, saved_inputs = saved_variables(formula, arguments, var_names) |
| formula, saved_outputs = saved_variables(formula, named_returns, var_names) |
| |
| used_named_gradients = { |
| name |
| for name in available_named_gradients |
| if re.search(IDENT_REGEX.format(name), formula) |
| } |
| |
| # Check that the referenced derivatives in the formula are in bounds |
| for i in used_gradient_indices(formula): |
| if i >= len(f.func.returns): |
| raise RuntimeError( |
| f"Out of bounds grads access: derivative formula for {cpp.name(f.func)} " |
| f"used grads[{i}], but the forward only returns {len(f.func.returns)} outputs." |
| ) |
| |
| return Derivative( |
| formula=formula, |
| original_formula=original_formula, |
| var_names=var_names, |
| saved_inputs=saved_inputs, |
| saved_outputs=saved_outputs, |
| named_gradients=used_named_gradients, |
| ) |
| |
| |
| def create_forward_derivative( |
| f: NativeFunction, formula: str, names: Tuple[str, ...] |
| ) -> ForwardDerivative: |
| var_names = names |
| var_types: Optional[Tuple[Type, ...]] = None |
| for r in f.func.returns: |
| if r.name in var_names: |
| if var_types is None: |
| var_types = tuple() |
| var_types = var_types + (r.type,) |
| |
| # Handle default return names |
| if var_types is None: |
| if var_names == ("result",): |
| assert len(f.func.returns) == 1 |
| var_types = (f.func.returns[0].type,) |
| else: |
| for var_name in var_names: |
| res = re.findall(r"^result(\d+)$", var_name) |
| if len(res) == 1: |
| if var_types is None: |
| var_types = tuple() |
| arg_idx = int(res[0]) |
| var_types = var_types + (f.func.returns[arg_idx].type,) |
| |
| assert var_types is not None, "No matching output for forward derivative definition" |
| return ForwardDerivative( |
| formula=formula, |
| var_names=var_names, |
| var_types=var_types, |
| required_inputs_fw_grad=None, |
| required_inputs_primal=None, |
| required_original_self_value=False, |
| is_reusing_outplace_formula=False, |
| ) |
| |
| |
| def postprocess_forward_derivatives( |
| f: NativeFunction, |
| defn_name: str, |
| all_arg_names: List[str], |
| derivatives: List[Derivative], |
| forward_derivatives: List[ForwardDerivative], |
| args_with_derivatives: Sequence[Binding], |
| ) -> List[ForwardDerivative]: |
| def find_required_inputs(formula: str, postfix: str) -> Tuple[str, ...]: |
| required_inputs = set() |
| for arg in args_with_derivatives: |
| if arg.type == "at::TensorList": |
| # The functions taking TensorList handle everything internally |
| continue |
| arg_name = arg.name |
| |
| found = re.search(IDENT_REGEX.format(arg_name), formula) |
| if found: |
| raise RuntimeError( |
| f"The forward formula for {defn_name} is using the base name of the {arg_name} " |
| f"argument which is ambiguous. You should use {arg_name}_p to access the primal " |
| f"value and {arg_name}_t to access the tangent." |
| ) |
| |
| found = re.search(IDENT_REGEX.format(arg_name + postfix), formula) |
| if found: |
| required_inputs.add(arg_name) |
| |
| return tuple(required_inputs) |
| |
| updated_derivatives: List[ForwardDerivative] = [] |
| |
| for defn in forward_derivatives: |
| formula = defn.formula |
| required_inputs_tangent = find_required_inputs(formula, "_t") |
| if formula == "auto_element_wise": |
| if ( |
| (not len(args_with_derivatives) == 1) |
| or len(forward_derivatives) > 1 |
| or len(forward_derivatives[0].var_names) > 1 |
| ): |
| raise RuntimeError( |
| f"Derivative definition of {defn_name} in derivatives.yaml defines the " |
| "forward definition of gradient as element_wise but this only " |
| "works for functions with a single differentiable input and a " |
| "single differentiable output." |
| ) |
| if not len(derivatives) == 1: |
| raise RuntimeError( |
| f"Derivative definition of {defn_name} in derivatives.yaml defines the " |
| "forward definition of gradient as element_wise but it does not " |
| "defines the gradient formula for its argument which is required." |
| ) |
| # This transformation is based on the observation that for element-wise functions, the Jacobian |
| # matrix is diagonal and thus doing J * v is the same as (v^T J)^T (in practice, we ignore the transpositions) |
| # For the complex case, we use hermitian transpose and get (v.conj() J).conj() |
| # So here we are going to re-use the backward formula and replace two things: |
| # 1) all occurrences of "grad" with "foo_t.conj()", where foo is the name of the unique differentiable input. |
| # 2) all usage of an original input "foo" with its primal value "foo_p". |
| # 3) conjugate the final result |
| # For example, for abs, the backward formula is: |
| # grad * self.sgn() |
| # And this function generates a forward formula that is: |
| # (self_t.conj() * self_p.sgn()).conj() |
| |
| backward_formula = derivatives[0].original_formula |
| input_name = args_with_derivatives[0].name |
| |
| # Do replacement 1) of the grad |
| def repl(m: Any) -> str: |
| return f"{m.group(1)}{input_name}_t.conj(){m.group(2)}" |
| |
| fw_formula = re.sub(IDENT_REGEX.format("grad"), repl, backward_formula) |
| |
| # Do replacement 2) of the input variables |
| for arg in args_with_derivatives: |
| arg_name = arg.name |
| |
| def repl(m: Any) -> str: |
| return f"{m.group(1)}{arg_name}_p{m.group(2)}" |
| |
| fw_formula = re.sub(IDENT_REGEX.format(arg_name), repl, fw_formula) |
| |
| # Do the final conjugate 3) |
| fw_formula = f"({fw_formula}).conj()" |
| |
| # Since there is a single differentiable inputs and we necessarily need its tangent we can |
| # simply require all differentiable input's tangent. |
| required_inputs_tangent = tuple(all_arg_names) |
| formula = fw_formula |
| elif formula == "auto_linear": |
| if ( |
| len(forward_derivatives) > 1 |
| or len(forward_derivatives[0].var_names) > 1 |
| ): |
| raise RuntimeError( |
| f"Derivative definition of {defn_name} in derivatives.yaml defines the " |
| "forward definition of gradient as linear but this only works " |
| "for functions with a single differentiable output." |
| ) |
| # This transformation is based on the observation that linear functions can be written as: |
| # y = f(x) = A * x |
| # For some matrix A and the Jacobian of the function f is also A. |
| # So doing J * v = A * v = f(v). |
| # Hence to do the jvp, we simply need to evaluate the function at the point v instead of x. |
| # We do this by calling the forward again by replacing any occurrence of the differentiable |
| # input "foo" by it's tangent "foo_t". |
| # Note that multiple inputs are not a problem as long as the function is truly linear wrt to |
| # the vector where all the differentiable inputs are stacked. |
| |
| diff_arg_names = [arg.name for arg in args_with_derivatives] |
| assert len(diff_arg_names) > 0 |
| |
| # Do replacement of input variables |
| new_args = [] |
| for arg_name in all_arg_names: |
| if arg_name in diff_arg_names: |
| arg_name = arg_name + "_t" |
| new_args.append(arg_name) |
| |
| # TODO we are trolling |
| if f.func.is_symint_fn(): |
| defn_name += "_symint" |
| |
| # Call into the forward again. We need two cases here to handle both Tensor methods and at:: functions. |
| if Variant.function in f.variants: |
| fw_formula = "at::{}({})".format(defn_name, ", ".join(new_args)) |
| else: |
| assert Variant.method in f.variants |
| fw_formula = "{}.{}({})".format( |
| new_args[0], defn_name, ", ".join(new_args[1:]) |
| ) |
| |
| # All of the input tangents are always used so all of them are required here. |
| required_inputs_tangent = tuple(diff_arg_names) |
| formula = fw_formula |
| |
| # At this point, the formula is final and is not modified anymore. |
| |
| # During forward formula, we use the primal instead of the input Tensors. |
| # This call inspects the formula to find for which input's primal are used. |
| required_inputs_primal = find_required_inputs(formula, "_p") |
| |
| updated_derivatives.append( |
| ForwardDerivative( |
| formula=formula, |
| var_names=defn.var_names, |
| var_types=defn.var_types, |
| required_inputs_fw_grad=required_inputs_tangent, |
| required_inputs_primal=required_inputs_primal, |
| required_original_self_value=False, |
| is_reusing_outplace_formula=False, |
| ) |
| ) |
| |
| return updated_derivatives |
| |
| |
| def is_forward_derivative_definition( |
| all_arg_names: List[str], names: Tuple[str, ...] |
| ) -> bool: |
| for name in names: |
| if name not in all_arg_names: |
| return True |
| else: |
| return False |
| raise RuntimeError("Expected `names` to be non-empty") |
| |
| |
| def create_differentiability_info( |
| defn: Dict[Any, Any], |
| functions_by_signature: Dict[FunctionSchema, List[NativeFunction]], |
| functions_by_schema: Dict[str, NativeFunction], |
| op_counter: Counter[str], |
| ) -> DifferentiabilityInfo: |
| """Processes a single entry `defn` in derivatives.yaml""" |
| |
| def canonical_function( |
| functions: Sequence[NativeFunction], name: str |
| ) -> NativeFunction: |
| for f in functions: |
| if ( |
| not f.func.is_functional_fn() |
| and not f.func.is_out_fn() |
| and name == str(f.func.name.name) |
| ): |
| return f |
| # some functions only have in-place variants |
| assert name + "_" == cpp.name(functions[0].func) |
| return functions[0] |
| |
| def split_names(raw_names: str) -> Tuple[str, ...]: |
| """Given "foo, bar", return ["foo", "bar"].""" |
| return tuple(x.strip() for x in raw_names.split(",")) |
| |
| def check_grad_usage(defn_name: str, derivatives: Sequence[Derivative]) -> None: |
| """ |
| Check for some subtle mistakes one might make when writing derivatives. |
| These mistakes will compile, but will be latent until a function is |
| used with double backwards. |
| """ |
| |
| uses_grad = False # true if any derivative uses "grad" |
| num_grads_uses = 0 # count of uses of "grads" or "grads[INDEX]" |
| uses_named_grads = False # true if any derivative uses "grad_{name}" |
| used_grads_indices: List[int] = [] # which indices of grads are used |
| for d in derivatives: |
| formula = d.formula |
| uses_grad = uses_grad or bool( |
| re.findall(IDENT_REGEX.format("grad"), formula) |
| ) |
| num_grads_uses += len(re.findall(IDENT_REGEX.format("grads"), formula)) |
| uses_named_grads = uses_named_grads or bool(d.named_gradients) |
| used_grads_indices.extend(used_gradient_indices(formula)) |
| # This is a basic sanity check: the number of places we see |
| # "grads" should be no fewer than the number of indices we see |
| # inside "grads". They may not be equal because we may use |
| # "grads" without an index. |
| assert num_grads_uses >= len(used_grads_indices) |
| # Thus if the number is equal, every use of grads is also |
| # indexed. |
| only_used_grads_indices = num_grads_uses == len(used_grads_indices) |
| |
| if uses_grad and num_grads_uses > 0: |
| raise RuntimeError( |
| f"Derivative definition of {defn_name} in derivatives.yaml illegally " |
| "mixes use of 'grad' and 'grads'. Consider replacing " |
| "occurrences of 'grad' with 'grads[0]'" |
| ) |
| |
| if only_used_grads_indices and set(used_grads_indices) == {0}: |
| raise RuntimeError( |
| f"Derivative definition of {defn_name} in derivatives.yaml solely " |
| "refers to 'grads[0]'. If the first output is indeed the " |
| "only differentiable output, replace 'grads[0]' with 'grad'; " |
| "otherwise, there is a likely error in your derivatives " |
| "declaration." |
| ) |
| |
| if uses_named_grads and (uses_grad or num_grads_uses > 0): |
| raise RuntimeError( |
| f"Derivative definition of {defn_name} in derivatives.yaml illegally " |
| 'mixes use of "grad_RETURN_NAME" and "grad" or "grads[x]". Use ' |
| "only one method for identifying gradients." |
| ) |
| |
| @with_native_function |
| def set_up_derivatives( |
| f: NativeFunction, |
| ) -> Tuple[ |
| Sequence[Derivative], |
| Sequence[ForwardDerivative], |
| Sequence[Binding], |
| Sequence[str], |
| Sequence[str], |
| ]: |
| # Set up the derivative information |
| derivatives: List[Derivative] = [] |
| forward_derivatives: List[ForwardDerivative] = [] |
| non_differentiable_arg_names: List[str] = [] |
| args_with_derivatives_set: Set[str] = set() |
| |
| all_arg_names = [a.name for a in cpp_arguments(f)] |
| all_ret_names = [ |
| r.name for r in f.func.returns |
| ] # only used for the assert below |
| # output_differentiability is captured from the enclosed |
| # scope. Don't modify it. |
| # |
| # If it is not present, then no output is explicitly |
| # undifferentiable. |
| # |
| # It may be present and shorter than the length of return |
| # values. If that's the case, any return value that does not |
| # have a corresponding entry is considered not differentiable. |
| differentiability = output_differentiability or [True] * len(f.func.returns) |
| # A return is available as a named gradient ... |
| available_named_gradients = [ |
| f"grad_{ret.name}" |
| for ret, differentiable in zip(f.func.returns, differentiability) |
| # if it has not been explicitly made undifferentiable |
| if differentiable |
| # and if it has a name |
| and ret.name is not None |
| # and if its type is differentiable |
| and ret.type.is_tensor_like() |
| ] |
| |
| for raw_names in sorted(defn.keys()): |
| formula = defn[raw_names] |
| names = split_names(raw_names) |
| |
| for name in names: |
| assert not (name in all_arg_names and name in all_ret_names), ( |
| f"While processing the derivative formula for '{f.func.name}' wrt '{name}', " |
| f"expected '{name}' to not be both an input arg and named return. " |
| ) |
| |
| if is_forward_derivative_definition(all_arg_names, names): |
| forward_derivatives.append(create_forward_derivative(f, formula, names)) |
| else: |
| if formula.lower().strip() == "non_differentiable": |
| non_differentiable_arg_names += names |
| else: |
| derivative = create_derivative( |
| f, formula, names, available_named_gradients |
| ) |
| derivatives.append(derivative) |
| args_with_derivatives_set |= set(names) |
| |
| overlap = args_with_derivatives_set.intersection(non_differentiable_arg_names) |
| if overlap: |
| raise RuntimeError( |
| f"derivatives definition for {defn} have overlapped non_differentiable " |
| f"and differentiable variables: {overlap}" |
| ) |
| |
| # Next, let us determine the list of inputs in order. |
| # TODO: do we need eagerly calculate and save it here? Can it be derived |
| # from NativeFunction and `derivatives` on callsites instead? |
| args_with_derivatives = [ |
| a for a in cpp_arguments(f) if a.name in args_with_derivatives_set |
| ] |
| |
| # Postprocess forward derivatives definitions now that we know the differentiable arguments |
| forward_derivatives = postprocess_forward_derivatives( |
| f, |
| defn_name, |
| all_arg_names, |
| derivatives, |
| forward_derivatives, |
| args_with_derivatives, |
| ) |
| |
| # Test to see if the use of 'grads' makes sense. |
| check_grad_usage(defn_name, derivatives) |
| |
| return ( |
| derivatives, |
| forward_derivatives, |
| args_with_derivatives, |
| non_differentiable_arg_names, |
| available_named_gradients, |
| ) |
| |
| # NB: Removes 'name' from defn dictionary |
| specification = defn.pop("name") |
| defn_name, _ = split_name_params(specification) |
| # NB: Removes 'output_differentiability' from defn dictionary |
| # `None` means all differentiable. |
| output_differentiability = defn.pop("output_differentiability", None) |
| output_differentiability_conditions = None |
| if output_differentiability and any( |
| [isinstance(diff, str) for diff in output_differentiability] |
| ): |
| if len(output_differentiability) != 1: |
| raise RuntimeError( |
| f"Not supported: for {specification}," |
| f"output_differentiability must either be " |
| f"List[bool] or a List[str] where each str is a " |
| f"condition. In the case where it is a condition, " |
| f"we only support single-output functions. " |
| f"Please file us an issue. " |
| ) |
| output_differentiability_conditions = output_differentiability |
| output_differentiability = [True] |
| |
| schema_function = functions_by_schema.get(specification) |
| if not schema_function: |
| avail = "\n".join( |
| k for k, v in functions_by_schema.items() if cpp.name(v.func) == defn_name |
| ) |
| raise RuntimeError( |
| f"could not find ATen function for schema: {specification} " |
| f". Available signatures:\n{avail}" |
| ) |
| |
| # now map this to the legacy schema; this isn't technically necessary, but we'd need some logic here |
| # to map in-place schemas to the out-of-place variants. |
| # TODO: maybe the logic to handle the legacy schema is no longer necessary? |
| signature = schema_function.func.signature() |
| functions = functions_by_signature[signature] |
| if len(functions) == 0: |
| avail = "\n".join( |
| str(k) |
| for k, v in functions_by_signature.items() |
| if cpp.name(k) == defn_name |
| ) |
| raise RuntimeError( |
| f"could not find ATen function for legacy signature: {signature} " |
| f"corresponding to schema {specification}. Please report a bug to PyTorch. " |
| f"Available signatures:\n{avail}" |
| ) |
| |
| canonical = canonical_function(functions, defn_name) |
| if "grad_input_mask" in (a.name for a in cpp_arguments(canonical)): |
| raise RuntimeError( |
| f"Schema for {defn_name} has an argument named grad_input_mask, " |
| "but this name would be shadowed by our codegen. " |
| "Please use a different name in native_functions.yaml." |
| ) |
| |
| if "result" in (a.name for a in cpp_arguments(canonical)): |
| raise RuntimeError( |
| f"Schema for {defn_name} has an argument named result, " |
| "but this is only allowed for outputs." |
| "Please use a different name in native_functions.yaml." |
| ) |
| |
| ( |
| derivatives, |
| forward_derivatives, |
| args_with_derivatives, |
| non_differentiable_arg_names, |
| available_named_gradients, |
| ) = set_up_derivatives(canonical) |
| |
| used_named_gradients: Set[str] = set() |
| for d in derivatives: |
| used_named_gradients |= d.named_gradients |
| |
| # only assign an op name if we are actually going to calculate a derivative |
| op = None |
| if args_with_derivatives: |
| op_prefix = _create_op_prefix(defn_name) |
| op = f"{op_prefix}{op_counter[op_prefix]}" |
| op_counter[op_prefix] += 1 |
| |
| return DifferentiabilityInfo( |
| name=defn_name, |
| func=canonical, |
| op=op, |
| derivatives=derivatives, |
| forward_derivatives=forward_derivatives, |
| all_saved_inputs=dedup_vars([v for d in derivatives for v in d.saved_inputs]), |
| all_saved_outputs=dedup_vars([v for d in derivatives for v in d.saved_outputs]), |
| available_named_gradients=available_named_gradients, |
| used_named_gradients=used_named_gradients, |
| args_with_derivatives=args_with_derivatives, |
| non_differentiable_arg_names=non_differentiable_arg_names, |
| output_differentiability=output_differentiability, |
| output_differentiability_conditions=output_differentiability_conditions, |
| ) |
| |
| |
| GRAD_INDEX_REGEX = r"(?:^|\W)grads\[(\d+)\]" |
| |
| |
| def used_gradient_indices(formula: str) -> List[int]: |
| """Determine a list of gradient indices (the i in grads[i]) that |
| are used by the formula. |
| |
| >>> used_gradient_indices("foo(grads[0], grads[1])") |
| [0, 1] |
| """ |
| return [int(i) for i in re.findall(GRAD_INDEX_REGEX, formula)] |
| |
| |
| def saved_variables( |
| formula: str, |
| nctypes: List[NamedCType], |
| var_names: Tuple[str, ...], |
| ) -> Tuple[str, Tuple[SavedAttribute, ...]]: |
| def stride_expr(name: str) -> str: |
| assert var_names == (name,), ( |
| 'Replacement for ".strides()" is currently only supported for single derivatives of the same tensor ' |
| 'that ".strides()" is being called on.' |
| ) |
| return f'strides_or_error({name}, "{name}")' |
| |
| REPLACEMENTS: List[Tuple[str, Dict[str, Any]]] = [ |
| # replace self.sizes() with self_sizes |
| ( |
| r"{}.sizes\(\)", |
| { |
| "suffix": "_sizes", |
| "nctype": lambda name: NamedCType(name, BaseCType(intArrayRefT)), |
| }, |
| ), |
| # replace self->sizes() with self_sizes_opt |
| ( |
| r"{}->sizes\(\)", |
| { |
| "suffix": "_sizes_opt", |
| "nctype": lambda name: NamedCType( |
| name, OptionalCType(BaseCType(intArrayRefT)) |
| ), |
| "expr": lambda name: f"{name}.has_value() ? c10::optional<IntArrayRef>({name}->sizes()) : c10::nullopt", |
| }, |
| ), |
| # replace self.options() with self_options |
| ( |
| r"{}.options\(\)", |
| { |
| "suffix": "_options", |
| "nctype": lambda name: NamedCType(name, BaseCType(tensorOptionsT)), |
| }, |
| ), |
| # replace zeros_like(self) with self_info |
| ( |
| r"zeros_like\({}\)", |
| { |
| "suffix": "_info", |
| "nctype": lambda name: NamedCType(name, BaseCType(typeAndSizeT)), |
| "expr": lambda name: name, # at save-time |
| "res": lambda name: name + "_info.zeros()", # at eval-time |
| }, |
| ), |
| # replace self.size(2) with self_size_2 |
| ( |
| r"{}.size\((\w+)\)", |
| { |
| "suffix": lambda m: "_argsize_{}".format(*m.groups()), |
| "nctype": lambda name: NamedCType(name, BaseCType(longT)), |
| }, |
| ), |
| # replace self.numel() with self_numel |
| ( |
| r"{}.numel\(\)", |
| { |
| "suffix": "_numel", |
| "nctype": lambda name: NamedCType(name, BaseCType(longT)), |
| }, |
| ), |
| # replace to_args_sizes(self) with self_args_sizes |
| ( |
| r"to_args_sizes\({}\)", |
| { |
| "suffix": "_args_sizes", |
| "nctype": lambda name: NamedCType( |
| name, VectorCType(VectorCType(BaseCType(longT))) |
| ), |
| }, |
| ), |
| # replace to_args_scalartypes(self) with self_args_scalartypes |
| ( |
| r"to_args_scalartypes\({}\)", |
| { |
| "suffix": "_args_scalartypes", |
| "nctype": lambda name: NamedCType( |
| name, VectorCType(BaseCType(scalarTypeT)) |
| ), |
| }, |
| ), |
| # replace TensorGeometry(self) with self_geometry |
| ( |
| r"TensorGeometry\({}\)", |
| { |
| "suffix": "_geometry", |
| "nctype": lambda name: NamedCType(name, BaseCType(tensorGeometryT)), |
| }, |
| ), |
| ( |
| r"{}.scalar_type\(\)", |
| { |
| "suffix": "_scalar_type", |
| "nctype": lambda name: NamedCType(name, BaseCType(scalarTypeT)), |
| }, |
| ), |
| # replace self.dim() with self_dim |
| ( |
| r"{}.dim\(\)", |
| { |
| "suffix": "_dim", |
| "nctype": lambda name: NamedCType(name, BaseCType(longT)), |
| }, |
| ), |
| # replace self.strides() with self_strides |
| ( |
| r"{}.strides\(\)", |
| { |
| "suffix": "_strides", |
| "nctype": lambda name: NamedCType(name, BaseCType(intArrayRefT)), |
| "expr": stride_expr, |
| }, |
| ), |
| # replace self.layout() with self_layout |
| ( |
| r"{}.layout\(\)", |
| { |
| "suffix": "_layout", |
| "nctype": lambda name: NamedCType(name, BaseCType(layoutT)), |
| }, |
| ), |
| # replace self.is_conj() with self_conjugate |
| ( |
| r"{}.is_conj\(\)", |
| { |
| "suffix": "_conjugate", |
| "nctype": lambda name: NamedCType(name, BaseCType(boolT)), |
| }, |
| ), |
| ] |
| |
| # find which arguments need to be saved |
| saved: List[SavedAttribute] = [] |
| |
| for nctype in nctypes: |
| name = ( |
| nctype.name.name if isinstance(nctype.name, SpecialArgName) else nctype.name |
| ) |
| # First search the formula for expressions which can be evaluated |
| # when the autograd Function is created to avoid saving variables |
| for regex, info in REPLACEMENTS: |
| |
| def repl(m: Match[str]) -> str: |
| suffix: str = ( |
| info["suffix"](m) if callable(info["suffix"]) else info["suffix"] |
| ) |
| expr: str = info["expr"](name) if "expr" in info else m.group(0) |
| saved.append( |
| SavedAttribute( |
| nctype=info["nctype"](name + suffix), |
| expr=expr, |
| ) |
| ) |
| if "res" in info: |
| replacement: str = info["res"](name) |
| return replacement |
| return name + suffix |
| |
| formula = re.sub(regex.format(name), repl, formula) |
| |
| # c10::optional<std::string> types stored in Backward nodes must be |
| # converted to c10::optional<c10::string_view> before being passed into |
| # the backward function |
| if nctype.type == OptionalCType(BaseCType(stringT)): |
| formula = re.sub( |
| rf"\b{name}\b", |
| f"{name}.has_value() ? c10::optional<c10::string_view>({name}.value()) : c10::nullopt", |
| formula, |
| ) |
| |
| # Find any variables which remain in the formula and save them |
| if re.search(IDENT_REGEX.format(name), formula): |
| saved.append( |
| SavedAttribute( |
| nctype=nctype, |
| expr=name, |
| ) |
| ) |
| |
| return formula, tuple(saved) |
| |
| |
| def _create_op_prefix(name: str) -> str: |
| """Takes a native function name converts to a op prefix name. |
| |
| Note that the "name" parameter must be the native function name |
| without the optional variant suffix, so "add" instead of |
| "add.out". |
| |
| OP names correspond to classes, hence the change to title case. |
| |
| Example:: |
| >>> _create_op_prefix('add') |
| 'AddBackward' |
| """ |
| camel_case = "".join([p.title() for p in name.split("_")]) |
| return (camel_case + "Backward").replace("ForwardBackward", "Backward") |
| |
| |
| def dedup_vars(vars: Sequence[SavedAttribute]) -> Sequence[SavedAttribute]: |
| seen: Set[str] = set() |
| saved: List[SavedAttribute] = [] |
| for var in vars: |
| name = ( |
| var.nctype.name.name |
| if isinstance(var.nctype.name, SpecialArgName) |
| else var.nctype.name |
| ) |
| if name in seen: |
| continue |
| seen.add(name) |
| saved.append(var) |
| return saved |
