def test_dynamic_shape(self):
with kernel_arena_scope():
dN = te.VarHandle("n", te.Dtype.Int)
A = te.Placeholder('A', te.Dtype.Double, [dN])
B = te.Placeholder('B', te.Dtype.Double, [dN])
def compute(i):
return A.load([i]) - B.load([i])
C = te.Compute('C', [te.DimArg(dN, 'i')], compute)
loopnest = te.LoopNest([C])
loopnest.prepare_for_codegen()
stmt = te.simplify(loopnest.root_stmt())
cg = te.construct_codegen('ir_eval', stmt, [A, B, C, dN])
def test_with_shape(n):
tA = torch.randn(n, dtype=torch.double)
tB = torch.randn(n, dtype=torch.double)
tC = torch.empty(n, dtype=torch.double)
cg.call([tA, tB, tC, n])
torch.testing.assert_allclose(tA - tB, tC)
test_with_shape(8)
test_with_shape(31)
def construct_te_fn(op, n: int, dtype=torch.float32):
A = torch._C._te.BufHandle("A", [n], dtype)
def compute(i):
return op(A.load([i]))
C = te.Compute("C", [n], compute)
loopnest = te.LoopNest([C])
loopnest.prepare_for_codegen()
stmt = te.simplify(loopnest.root_stmt())
return te.construct_codegen("ir_eval", stmt, [A, C])
def construct_adder(n: int, dtype=torch.float32):
A = te.BufHandle('A', [n], dtype)
B = te.BufHandle('B', [n], dtype)
def compute(i):
return A.load([i]) + B.load([i])
C = te.Compute('C', [n], compute)
loopnest = te.LoopNest([C])
loopnest.prepare_for_codegen()
stmt = te.simplify(loopnest.root_stmt())
return te.construct_codegen('ir_eval', stmt, [A, B, C])
def construct_adder(n: int, dtype=te.Dtype.Float):
dN = te.ExprHandle.int(n)
A = te.Placeholder('A', dtype, [dN])
B = te.Placeholder('B', dtype, [dN])
def compute(i):
return A.load([i]) + B.load([i])
C = te.Compute('C', [te.DimArg(dN, 'i')], compute)
loopnest = te.LoopNest([C])
loopnest.prepare_for_codegen()
stmt = te.simplify(loopnest.root_stmt())
return te.construct_codegen('ir_eval', stmt, [A, B, C])
def nnc_compile(model: torch.nn.Module, example_inputs) -> torch.nn.Module:
"""
nnc_compile(model, example_inputs) returns a function with the same args
as `model.forward`, with an extra argument corresponding to where the
output is stored. This function takes the inputs (which must be PyTorch
tensors with the same shapes as example_inputs), and passes them to an
NNC executor.
"""
fx_model = fx.symbolic_trace(model)
ShapeProp(fx_model).propagate(*example_inputs)
# This env maps from nodes to `te.ExprHandle`, which represent the output
# of an NNC computation.
env = {}
def get_te_shapes(node):
return [te.ExprHandle.int(i) for i in node.shape]
def get_nnc_type(dtype):
if dtype == torch.float:
return te.Dtype.Float
elif dtype == torch.long:
return te.Dtype.Long
else:
raise RuntimeError("nyi")
def get_te_type(node):
return get_nnc_type(node.dtype)
def gen_compute(args):
te_args = [env[arg.name] for arg in args]
def lookup_env(l):
return fx.node.map_aggregate(
l, lambda x: env[x.name] if isinstance(x, fx.Node) else x)
def fetch_attr(target: str):
target_atoms = target.split('.')
attr_itr = fx_model
for i, atom in enumerate(target_atoms):
if not hasattr(attr_itr, atom):
raise RuntimeError(
f"Node referenced nonexistant target {'.'.join(target_atoms[:i])}"
)
attr_itr = getattr(attr_itr, atom)
return attr_itr
outs = None
inputs = []
module_attrs = []
for node in fx_model.graph.nodes:
if node.op == 'placeholder':
# We simply map the input placeholder to a `te.Placeholder`, which
# also represents an input to the NNC computation.
shapes = get_te_shapes(node)
env[node.name] = te.Placeholder(node.name, get_te_type(node),
shapes)
inputs.append(env[node.name])
elif node.op == 'call_function':
# This does the bulk of the work - we call `lower_function`, which
# returns a `te.ExprHandle` (the output of a NNC computation), and
# put it in our environment.
result = lower_function(node, node.target, lookup_env(node.args),
node.args)
env[node.name] = result
elif node.op == 'output':
outs = list(lookup_env(node.args))
elif node.op == 'get_attr':
# As NNC doesn't have any concept of state, we pull out the module
# attributes and pass them in as inputs to NNC.
module_attrs.append(node)
env[node.name] = te.Placeholder(node.name, get_te_type(node),
shapes)
else:
raise RuntimeError("not yet implemented")
loopnest = te.LoopNest(outs)
loopnest.prepare_for_codegen()
stmt = te.simplify(loopnest.root_stmt())
cg = te.construct_codegen('llvm', stmt, [
te.BufferArg(x)
for x in [env[i.name] for i in module_attrs] + inputs + outs
])
def f(inps):
module_stuff = [fetch_attr(i.target) for i in module_attrs]
cg.call(module_stuff + list(inps))
return f
def nnc_compile(fx_model: fx.GraphModule,
example_inputs,
get_loopnest=False) -> torch.nn.Module:
"""
nnc_compile(model, example_inputs) returns a function with the same args
as `model.forward`, with an extra argument corresponding to where the
output is stored. This function takes the inputs (which must be PyTorch
tensors with the same shapes as example_inputs), and passes them to an
NNC executor.
"""
# fx_model = fx.symbolic_trace(model)
t = fx_model.graph.flatten_inps(*example_inputs)
ShapeProp(fx_model).propagate(*fx_model.graph.flatten_inps(
*example_inputs))
# This env maps from nodes to `te.ExprHandle`, which represent the output
# of an NNC computation.
env = {}
def get_te_type(node):
return get_nnc_type(node.meta['tensor_meta'].dtype)
def gen_compute(args):
te_args = [env[arg.name] for arg in args]
def lookup_env(l):
res = fx.node.map_aggregate(
l, lambda x: env[x.name] if isinstance(x, fx.Node) else x)
return res
def fetch_attr(target: str):
target_atoms = target.split('.')
attr_itr = fx_model
for i, atom in enumerate(target_atoms):
if not hasattr(attr_itr, atom):
raise RuntimeError(
f"Node referenced nonexistant target {'.'.join(target_atoms[:i])}"
)
attr_itr = getattr(attr_itr, atom)
return attr_itr
outs = None
inputs = []
module_attrs = []
compute_stmts = []
for node in fx_model.graph.nodes:
if node.op == 'placeholder':
# We simply map the input placeholder to a `te.Placeholder`, which
# also represents an input to the NNC computation.
if 'tensor_meta' not in node.meta:
continue
shapes = get_te_shapes(node.meta['tensor_meta'].shape)
placeholder = te.Placeholder(node.name, get_te_type(node), shapes)
env[node.name] = placeholder.data()
inputs.append(placeholder)
elif node.op == 'call_function':
# This does the bulk of the work - we call `lower_function`, which
# returns a `te.ExprHandle` (the output of a NNC computation), and
# put it in our environment.
if 'tensor_meta' in node.meta:
# todo: fix kwargs handling
if node.kwargs:
raise RuntimeError("kwargs nyi")
buf, stmt = lower_function(node, node.target,
lookup_env(node.args), node.args)
# if isinstance(stmt, list)
compute_stmts.extend(stmt)
env[node.name] = buf
elif node.target == getattr or node.target == operator.getitem:
# todo: handle non-tensor computations correctly
continue
elif node.op == 'output':
args = node.args
args = pytree.tree_map(
lambda x: list(x) if isinstance(
x, fx.immutable_collections.immutable_list) else x, args)
flat_args, _ = pytree.tree_flatten(list(args))
te_args = lookup_env(flat_args)
outs = (list(te_args), [(i.meta['tensor_meta'].shape,
i.meta['tensor_meta'].dtype)
for i in flat_args])
elif node.op == 'get_attr':
# As NNC doesn't have any concept of state, we pull out the module
# attributes and pass them in as inputs to NNC.
module_attrs.append(node)
shapes = get_te_shapes(
process_shape(node.meta['tensor_meta'].shape))
placeholder = te.Placeholder(node.name, get_te_type(node), shapes)
env[node.name] = placeholder.data()
else:
print(node.op, node.target)
raise RuntimeError("not yet implemented")
loopnest = te.LoopNest(te.Stmt(compute_stmts), outs[0])
if get_loopnest:
return loopnest
# loopnest.inline_intermediate_bufs(True)
loopnest.simplify()
loopnest.prepare_for_codegen()
stmt = te.simplify(loopnest.root_stmt())
cg = te.construct_codegen('llvm', stmt, [
te.BufferArg(x)
for x in [env[i.name] for i in module_attrs] + inputs + outs[0]
])
if module_attrs:
module_stuff = [
fetch_attr(i.target).contiguous().data for i in module_attrs
]
else:
module_stuff = []
def f(*inps, out_tensors=None):
inps = fx_model.graph.flatten_inps(*inps)
if out_tensors is None:
results = [
torch.empty(shape, dtype=dtype) for shape, dtype in outs[1]
]
# results = alloc_results
else:
results = out_tensors
full_inps = module_stuff + list(inps) + results
cg.call(full_inps)
results = fx_model.graph.unflatten_outs(results)
return results
if len(results) == 1:
return results[0]
return results
return f
