def synthetic_pattern_variable_program(include_types=True):
"""A program that tests product types.
Args:
include_types: If False, we omit types on the variables, requiring a type
inference pass.
Returns:
program: `instructions.Program`.
"""
block = instructions.Block([
instructions.PrimOp(["inp"], "many", lambda x: (x + 1,
(x + 2, x + 3))),
instructions.PrimOp(["many"], ["one", "two"], lambda x: x),
], instructions.halt_op())
leaf = instructions.TensorType(np.int64, ())
the_vars = {
"inp": instructions.Type(leaf),
"many": instructions.Type((leaf, (leaf, leaf))),
"one": instructions.Type(leaf),
"two": instructions.Type((leaf, leaf)),
}
if not include_types:
_strip_types(the_vars)
return instructions.Program(instructions.ControlFlowGraph([block]), [],
the_vars, ["inp"], "two")
def single_if_program():
"""Single if program: 'if (input > 1) ans = 2; else ans = 0; return ans;'.
Returns:
program: `instructions.Program` with a simple conditional.
"""
entry = instructions.Block()
then_ = instructions.Block()
else_ = instructions.Block()
entry.assign_instructions([
instructions.PrimOp(["input"], "cond", lambda n: n > 1),
instructions.BranchOp("cond", then_, else_),
])
then_.assign_instructions([
instructions.PrimOp([], "answer", lambda: 2),
instructions.halt_op(),
])
else_.assign_instructions([
instructions.PrimOp([], "answer", lambda: 0),
instructions.halt_op(),
])
single_if_blocks = [entry, then_, else_]
# pylint: disable=bad-whitespace
single_if_vars = {
"input": instructions.single_type(np.int64, ()),
"cond": instructions.single_type(np.bool, ()),
"answer": instructions.single_type(np.int64, ()),
}
return instructions.Program(
instructions.ControlFlowGraph(single_if_blocks), [], single_if_vars,
["input"], "answer")
def shape_sequence_program(shape_sequence):
"""Program that writes into `answer` zeros having a sequence of shapes.
This enables us to test that the final inferred shape is the broadcast of all
intermediate shapes.
Args:
shape_sequence: The sequence of intermediate shapes.
Returns:
program: `instructions.Program` which returns an arbitrary value.
"""
block_ops = []
def op(shape, ans):
return np.zeros(shape, dtype=np.array(ans).dtype),
for shape in shape_sequence:
# We use a partial instead of a lambda in order to capture a copy of shape.
block_ops.append(
instructions.PrimOp(['ans'], ['ans'], functools.partial(op,
shape)))
shape_seq_block = instructions.Block(block_ops, instructions.halt_op())
shape_seq_vars = {
'ans': instructions.Type(None),
instructions.pc_var: instructions.single_type(np.int64, ()),
}
return instructions.Program(
instructions.ControlFlowGraph([shape_seq_block]), [], shape_seq_vars,
['ans'], ['ans'])
def constant_program():
"""Constant program: 'ans=1; ans=2; return ans;'.
Returns:
program: `instructions.Program` which returns a constant value.
"""
constant_block = instructions.Block([
instructions.PrimOp([], "answer", lambda: 1),
instructions.PrimOp([], "answer", lambda: 2),
], instructions.halt_op())
constant_vars = {
"answer": instructions.single_type(np.int64, ()),
}
return instructions.Program(
instructions.ControlFlowGraph([constant_block]), [], constant_vars,
["answer"], "answer")
def synthetic_pattern_program():
"""A program that tests pattern matching of `PrimOp` outputs.
Returns:
program: `instructions.Program`.
"""
block = instructions.Block([
instructions.PrimOp([], ("one", ("five", "three")), lambda: (1,
(2, 3))),
instructions.PrimOp([], (("four", "five"), "six"), lambda:
((4, 5), 6)),
], instructions.halt_op())
the_vars = {
"one": instructions.single_type(np.int64, ()),
"three": instructions.single_type(np.int64, ()),
"four": instructions.single_type(np.int64, ()),
"five": instructions.single_type(np.int64, ()),
"six": instructions.single_type(np.int64, ()),
}
return instructions.Program(instructions.ControlFlowGraph([block]), [],
the_vars, [],
(("one", "three"), "four", ("five", "six")))
def pea_nuts_program(latent_shape, choose_depth, step_state):
"""Synthetic program usable for benchmarking VM performance.
This program is intended to resemble the control flow and scaling
parameters of the NUTS algorithm, without any of the complexity.
Hence the name.
Each batch member looks like:
state = ... # shape latent_shape
def recur(depth, state):
if depth > 1:
state1 = recur(depth - 1, state)
state2 = state1 + 1
state3 = recur(depth - 1, state2)
ans = state3 + 1
else:
ans = step_state(state) # To simulate NUTS, something heavy
return ans
while count > 0:
count = count - 1
depth = choose_depth(count)
state = recur(depth, state)
Args:
latent_shape: Python `tuple` of `int` giving the event shape of the
latent state.
choose_depth: Python `Tensor -> Tensor` callable. The input
`Tensor` will have shape `[batch_size]` (i.e., scalar event
shape), and give the iteration of the outer while loop the
thread is in. The `choose_depth` function must return a `Tensor`
of shape `[batch_size]` giving the depth, for each thread,
to which to call `recur` in this iteration.
step_state: Python `Tensor -> Tensor` callable. The input and
output `Tensor`s will have shape `[batch_size] + latent_shape`.
This function is expected to update the state, and represents
the "real work" versus which the VM overhead is being measured.
Returns:
program: `instructions.Program` that runs the above benchmark.
"""
entry = instructions.Block()
top_body = instructions.Block()
finish_body = instructions.Block()
enter_recur = instructions.Block()
recur_body_1 = instructions.Block()
recur_body_2 = instructions.Block()
recur_body_3 = instructions.Block()
recur_base_case = instructions.Block()
# pylint: disable=bad-whitespace
entry.assign_instructions([
instructions.PrimOp(["count"], "cond",
lambda count: count > 0), # cond = count > 0
instructions.BranchOp("cond", top_body,
instructions.halt()), # if cond
])
top_body.assign_instructions([
instructions.PopOp(["cond"]), # done with cond now
instructions.PrimOp(["count"], "ctm1",
lambda count: count - 1), # ctm1 = count - 1
instructions.PopOp(["count"]), # done with count now
instructions.push_op(["ctm1"], ["count"]), # count = ctm1
instructions.PopOp(["ctm1"]), # done with ctm1
instructions.PrimOp(["count"], "depth",
choose_depth), # depth = choose_depth(count)
instructions.push_op(
["depth", "state"],
["depth", "state"]), # state = recur(depth, state)
instructions.PopOp(["depth", "state"]), # done with depth, state
instructions.PushGotoOp(finish_body, enter_recur),
])
finish_body.assign_instructions([
instructions.push_op(["ans"], ["state"]), # ...
instructions.PopOp(["ans"]), # pop callee's "ans"
instructions.GotoOp(entry), # end of while body
])
# Definition of recur begins here
enter_recur.assign_instructions([
instructions.PrimOp(["depth"], "cond1",
lambda depth: depth > 0), # cond1 = depth > 0
instructions.BranchOp("cond1", recur_body_1,
recur_base_case), # if cond1
])
recur_body_1.assign_instructions([
instructions.PopOp(["cond1"]), # done with cond1 now
instructions.PrimOp(["depth"], "dm1",
lambda depth: depth - 1), # dm1 = depth - 1
instructions.PopOp(["depth"]), # done with depth
instructions.push_op(
["dm1", "state"],
["depth", "state"]), # state1 = recur(dm1, state)
instructions.PopOp(["state"]), # done with state
instructions.PushGotoOp(recur_body_2, enter_recur),
])
recur_body_2.assign_instructions([
instructions.push_op(["ans"], ["state1"]), # ...
instructions.PopOp(["ans"]), # pop callee's "ans"
instructions.PrimOp(["state1"], "state2",
lambda state: state + 1), # state2 = state1 + 1
instructions.PopOp(["state1"]), # done with state1
instructions.push_op(
["dm1", "state2"],
["depth", "state"]), # state3 = recur(dm1, state2)
instructions.PopOp(["dm1", "state2"]), # done with dm1, state2
instructions.PushGotoOp(recur_body_3, enter_recur),
])
recur_body_3.assign_instructions([
instructions.push_op(["ans"], ["state3"]), # ...
instructions.PopOp(["ans"]), # pop callee's "ans"
instructions.PrimOp(["state3"], "ans",
lambda state: state + 1), # ans = state3 + 1
instructions.PopOp(["state3"]), # done with state3
instructions.IndirectGotoOp(), # return ans
])
recur_base_case.assign_instructions([
instructions.PopOp(["cond1", "depth"]), # done with cond1, depth
instructions.PrimOp(["state"], "ans",
step_state), # ans = step_state(state)
instructions.PopOp(["state"]), # done with state
instructions.IndirectGotoOp(), # return ans
])
pea_nuts_graph = instructions.ControlFlowGraph([
entry,
top_body,
finish_body,
enter_recur,
recur_body_1,
recur_body_2,
recur_body_3,
recur_base_case,
])
# pylint: disable=bad-whitespace
pea_nuts_vars = {
"count": instructions.single_type(np.int64, ()),
"cond": instructions.single_type(np.bool, ()),
"cond1": instructions.single_type(np.bool, ()),
"ctm1": instructions.single_type(np.int64, ()),
"depth": instructions.single_type(np.int64, ()),
"dm1": instructions.single_type(np.int64, ()),
"state": instructions.single_type(np.float32, latent_shape),
"state1": instructions.single_type(np.float32, latent_shape),
"state2": instructions.single_type(np.float32, latent_shape),
"state3": instructions.single_type(np.float32, latent_shape),
"ans": instructions.single_type(np.float32, latent_shape),
}
return instructions.Program(pea_nuts_graph, [], pea_nuts_vars,
["count", "state"], "state")
def fibonacci_function_calls(include_types=True, dtype=np.int64):
"""The Fibonacci program again, but with `instructions.FunctionCallOp`.
Computes fib(n): fib(0) = fib(1) = 1.
Args:
include_types: If False, we omit types on the variables, requiring a type
inference pass.
dtype: The dtype to use for `n`-like internal state variables.
Returns:
program: Full-powered `instructions.Program` that computes fib(n).
"""
enter_fib = instructions.Block(name="enter_fib")
recur = instructions.Block(name="recur")
finish = instructions.Block(name="finish")
fibonacci_type = lambda types: types[0]
fibonacci_func = instructions.Function(None, ["n"],
"ans",
fibonacci_type,
name="fibonacci")
# pylint: disable=bad-whitespace
# Definition of fibonacci function
enter_fib.assign_instructions([
instructions.PrimOp(["n"], "cond", lambda n: n > 1), # cond = n > 1
instructions.BranchOp("cond", recur, finish), # if cond
])
recur.assign_instructions([
instructions.PrimOp(["n"], "nm1", lambda n: n - 1), # nm1 = n - 1
instructions.FunctionCallOp(fibonacci_func, ["nm1"],
"fibm1"), # fibm1 = fibonacci(nm1)
instructions.PrimOp(["n"], "nm2", lambda n: n - 2), # nm2 = n - 2
instructions.FunctionCallOp(fibonacci_func, ["nm2"],
"fibm2"), # fibm2 = fibonacci(nm2)
instructions.PrimOp(["fibm1", "fibm2"], "ans",
lambda x, y: x + y), # ans = fibm1 + fibm2
instructions.halt_op(), # return ans
])
finish.assign_instructions([ # else:
instructions.PrimOp([], "ans", lambda: 1), # ans = 1
instructions.halt_op(), # return ans
])
fibonacci_blocks = [enter_fib, recur, finish]
fibonacci_func.graph = instructions.ControlFlowGraph(fibonacci_blocks)
fibonacci_main_blocks = [
instructions.Block([
instructions.FunctionCallOp(fibonacci_func, ["n1"], "ans"),
],
instructions.halt_op(),
name="main_entry"),
]
# pylint: disable=bad-whitespace
fibonacci_vars = {
"n": instructions.single_type(dtype, ()),
"n1": instructions.single_type(dtype, ()),
"cond": instructions.single_type(np.bool, ()),
"nm1": instructions.single_type(dtype, ()),
"fibm1": instructions.single_type(dtype, ()),
"nm2": instructions.single_type(dtype, ()),
"fibm2": instructions.single_type(dtype, ()),
"ans": instructions.single_type(dtype, ()),
}
if not include_types:
_strip_types(fibonacci_vars)
return instructions.Program(
instructions.ControlFlowGraph(fibonacci_main_blocks), [fibonacci_func],
fibonacci_vars, ["n1"], "ans")
def is_even_function_calls(include_types=True, dtype=np.int64):
"""The is-even program, via "even-odd" recursion.
Computes True if the input is even, False if the input is odd, by a pair of
mutually recursive functions is_even and is_odd, which return True and False
respectively for <1-valued inputs.
Tests out mutual recursion.
Args:
include_types: If False, we omit types on the variables, requiring a type
inference pass.
dtype: The dtype to use for `n`-like internal state variables.
Returns:
program: Full-powered `instructions.Program` that computes is_even(n).
"""
def pred_type(t):
return instructions.TensorType(np.bool, t[0].shape)
# Forward declaration of is_odd.
is_odd_func = instructions.Function(None, ["n"], "ans", pred_type)
enter_is_even = instructions.Block()
finish_is_even = instructions.Block()
recur_is_even = instructions.Block()
is_even_func = instructions.Function(None, ["n"], "ans", pred_type)
# pylint: disable=bad-whitespace
# Definition of is_even function
enter_is_even.assign_instructions([
instructions.PrimOp(["n"], "cond", lambda n: n < 1), # cond = n < 1
instructions.BranchOp("cond", finish_is_even,
recur_is_even), # if cond
])
finish_is_even.assign_instructions([
instructions.PopOp(["n", "cond"]), # done with n, cond
instructions.PrimOp([], "ans", lambda: True), # ans = True
instructions.halt_op(), # return ans
])
recur_is_even.assign_instructions([ # else
instructions.PopOp(["cond"]), # done with cond now
instructions.PrimOp(["n"], "nm1", lambda n: n - 1), # nm1 = n - 1
instructions.PopOp(["n"]), # done with n
instructions.FunctionCallOp(is_odd_func, ["nm1"],
"ans"), # ans = is_odd(nm1)
instructions.PopOp(["nm1"]), # done with nm1
instructions.halt_op(), # return ans
])
is_even_blocks = [enter_is_even, finish_is_even, recur_is_even]
is_even_func.graph = instructions.ControlFlowGraph(is_even_blocks)
enter_is_odd = instructions.Block()
finish_is_odd = instructions.Block()
recur_is_odd = instructions.Block()
# pylint: disable=bad-whitespace
# Definition of is_odd function
enter_is_odd.assign_instructions([
instructions.PrimOp(["n"], "cond", lambda n: n < 1), # cond = n < 1
instructions.BranchOp("cond", finish_is_odd, recur_is_odd), # if cond
])
finish_is_odd.assign_instructions([
instructions.PopOp(["n", "cond"]), # done with n, cond
instructions.PrimOp([], "ans", lambda: False), # ans = False
instructions.halt_op(), # return ans
])
recur_is_odd.assign_instructions([ # else
instructions.PopOp(["cond"]), # done with cond now
instructions.PrimOp(["n"], "nm1", lambda n: n - 1), # nm1 = n - 1
instructions.PopOp(["n"]), # done with n
instructions.FunctionCallOp(is_even_func, ["nm1"],
"ans"), # ans = is_even(nm1)
instructions.PopOp(["nm1"]), # done with nm1
instructions.halt_op(), # return ans
])
is_odd_blocks = [enter_is_odd, finish_is_odd, recur_is_odd]
is_odd_func.graph = instructions.ControlFlowGraph(is_odd_blocks)
is_even_main_blocks = [
instructions.Block([
instructions.FunctionCallOp(is_even_func, ["n1"], "ans"),
], instructions.halt_op()),
]
# pylint: disable=bad-whitespace
is_even_vars = {
"n": instructions.single_type(dtype, ()),
"n1": instructions.single_type(dtype, ()),
"cond": instructions.single_type(np.bool, ()),
"nm1": instructions.single_type(dtype, ()),
"ans": instructions.single_type(np.bool, ()),
}
if not include_types:
_strip_types(is_even_vars)
return instructions.Program(
instructions.ControlFlowGraph(is_even_main_blocks),
[is_even_func, is_odd_func], is_even_vars, ["n1"], "ans")
def fibonacci_program():
"""More complicated, fibonacci program: computes fib(n): fib(0) = fib(1) = 1.
Returns:
program: Full-powered `instructions.Program` that computes fib(n).
"""
entry = instructions.Block(name="entry")
enter_fib = instructions.Block(name="enter_fib")
recur1 = instructions.Block(name="recur1")
recur2 = instructions.Block(name="recur2")
recur3 = instructions.Block(name="recur3")
finish = instructions.Block(name="finish")
# pylint: disable=bad-whitespace
entry.assign_instructions([
instructions.PushGotoOp(instructions.halt(), enter_fib),
])
# Definition of fibonacci function starts here
enter_fib.assign_instructions([
instructions.PrimOp(["n"], "cond", lambda n: n > 1), # cond = n > 1
instructions.BranchOp("cond", recur1, finish), # if cond
])
recur1.assign_instructions([
instructions.PopOp(["cond"]), # done with cond now
instructions.PrimOp(["n"], "nm1", lambda n: n - 1), # nm1 = n - 1
instructions.push_op(["nm1"], ["n"]), # fibm1 = fibonacci(nm1)
instructions.PopOp(["nm1"]), # done with nm1
instructions.PushGotoOp(recur2, enter_fib),
])
recur2.assign_instructions([
instructions.push_op(["ans"], ["fibm1"]), # ...
instructions.PopOp(["ans"]), # pop callee's "ans"
instructions.PrimOp(["n"], "nm2", lambda n: n - 2), # nm2 = n - 2
instructions.PopOp(["n"]), # done with n
instructions.push_op(["nm2"], ["n"]), # fibm2 = fibonacci(nm2)
instructions.PopOp(["nm2"]), # done with nm2
instructions.PushGotoOp(recur3, enter_fib),
])
recur3.assign_instructions([
instructions.push_op(["ans"], ["fibm2"]), # ...
instructions.PopOp(["ans"]), # pop callee's "ans"
instructions.PrimOp(["fibm1", "fibm2"], "ans",
lambda x, y: x + y), # ans = fibm1 + fibm2
instructions.PopOp(["fibm1", "fibm2"]), # done with fibm1, fibm2
instructions.IndirectGotoOp(), # return ans
])
finish.assign_instructions([ # else:
instructions.PopOp(["n", "cond"]), # done with n, cond
instructions.PrimOp([], "ans", lambda: 1), # ans = 1
instructions.IndirectGotoOp(), # return ans
])
fibonacci_blocks = [entry, enter_fib, recur1, recur2, recur3, finish]
# pylint: disable=bad-whitespace
fibonacci_vars = {
"n": instructions.single_type(np.int64, ()),
"cond": instructions.single_type(np.bool, ()),
"nm1": instructions.single_type(np.int64, ()),
"fibm1": instructions.single_type(np.int64, ()),
"nm2": instructions.single_type(np.int64, ()),
"fibm2": instructions.single_type(np.int64, ()),
"ans": instructions.single_type(np.int64, ()),
}
return instructions.Program(
instructions.ControlFlowGraph(fibonacci_blocks), [], fibonacci_vars,
["n"], "ans")
def primop(self, f, vars_in=None, vars_out=None):
"""Records a primitive operation.
Example:
```
ab = dsl.ProgramBuilder()
ab.var.five = ab.const(5)
# Implicit output binding
ab.var.ten = ab.primop(lambda five: five + five)
# Explicit output binding
ab.primop(lambda: (5, 10), vars_out=[ab.var.five, ab.var.ten])
```
Args:
f: A Python callable, the primitive operation to perform. Can be
an inline lambda expression in simple cases. Must return a list or
tuple of results, one for each intended output variable.
vars_in: A list of Python strings, giving the auto-batched variables
to pass into the callable when invoking it. If absent, `primop`
will try to infer it by inspecting the argument list of the callable
and matching against variables bound in the local scope.
vars_out: A pattern of Python strings, giving the auto-batched variable(s)
to which to write the result of the callable. Defaults to the empty
list.
Raises:
ValueError: If the definition is invalid, if the primop references
undefined auto-batched variables, or if auto-detection of input
variables fails.
Returns:
op: An `instructions.PrimOp` instance representing this operation. If one
subsequently assigns this to a local, via `ProgramBuilder.var.foo = op`,
that local becomes the output pattern.
"""
self._prepare_for_instruction()
if vars_out is None:
vars_out = []
if vars_in is None:
# Deduce the intended variable names from the argument list of the callee.
# Expected use case: the callee is an inline lambda expression.
args, varargs, keywords, _ = inspect.getargspec(f)
vars_in = []
for arg in args:
if arg in self._locals:
vars_in.append(self._locals[arg])
else:
raise ValueError(
'Auto-referencing unbound variable {}.'.format(arg))
if varargs is not None:
raise ValueError('Varargs are not supported for primops')
if keywords is not None:
raise ValueError('kwargs are not supported for primops')
for var in vars_in:
if var not in self._var_defs:
raise ValueError(
'Referencing undefined variable {}.'.format(var))
prim = inst.PrimOp(_str_list(vars_in), inst.pattern_map(str, vars_out),
f)
self._blocks[-1].instructions.append(prim)
for var in inst.pattern_traverse(vars_out):
self._mark_defined(var)
return prim