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.prim_op(["input"], "cond", lambda n: n > 1), instructions.BranchOp("cond", then_, else_), ]) then_.assign_instructions([ instructions.prim_op([], "answer", lambda: 2), instructions.halt_op(), ]) else_.assign_instructions([ instructions.prim_op([], "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 if_(self, condition, then_name=None, else_name=None, continue_name=None): """Records a conditional operation and `true` first branch. The `false` branch, if present, must be guarded by a call to `else_`, below. Example: ```python ab = dsl.ProgramBuilder() ab.var.true = ab.const(True) with ab.if_(ab.var.true): ... # The body of the `with` statement gives the `true` branch with ab.else_(): # The else_ clause is optional ... ``` Args: condition: Python string giving the boolean variable that holds the branch condition. then_name: Optional Python string naming the true branch when the program is printed. else_name: Optional Python string naming the false branch when the program is printed. continue_name: Optional Python string naming the continuation after the if when the program is printed. Yields: Nothing. Raises: ValueError: If trying to condition on a variable that has not been written to. """ # Should I have _prepare_for_instruction here? self._prepare_for_instruction() if condition not in self._var_defs: raise ValueError( 'Undefined variable {} used as if condition.'.format( condition)) then_block = self._fresh_block(name=then_name) else_block = self._fresh_block(name=else_name) after_else_block = self._fresh_block(name=continue_name) self._append_block(then_block, prev_terminator=inst.BranchOp( str(condition), then_block, else_block)) yield # In case the enclosed code ended in a dangling if, close it self._prepare_for_instruction() # FIXME: Always adding this goto risks polluting the output with # excess gotos. They can probably be cleaned up during label # resolution. self._append_block(else_block, prev_terminator=inst.GotoOp(after_else_block)) self._pending_after_else_block = after_else_block
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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["n"], "cond", lambda n: n > 1), # cond = n > 1 instructions.BranchOp("cond", recur, finish), # if cond ]) recur.assign_instructions([ instructions.prim_op(["n"], "nm1", lambda n: n - 1), # nm1 = n - 1 instructions.FunctionCallOp(fibonacci_func, ["nm1"], "fibm1"), # fibm1 = fibonacci(nm1) instructions.prim_op(["n"], "nm2", lambda n: n - 2), # nm2 = n - 2 instructions.FunctionCallOp(fibonacci_func, ["nm2"], "fibm2"), # fibm2 = fibonacci(nm2) instructions.prim_op(["fibm1", "fibm2"], "ans", lambda x, y: x + y), # ans = fibm1 + fibm2 instructions.halt_op(), # return ans ]) finish.assign_instructions([ # else: instructions.prim_op([], "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.prim_op(["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.prim_op([], "ans", lambda: True), # ans = True instructions.halt_op(), # return ans ]) recur_is_even.assign_instructions([ # else instructions.PopOp(["cond"]), # done with cond now instructions.prim_op(["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.prim_op(["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.prim_op([], "ans", lambda: False), # ans = False instructions.halt_op(), # return ans ]) recur_is_odd.assign_instructions([ # else instructions.PopOp(["cond"]), # done with cond now instructions.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op(["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.prim_op([], "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")