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204 lines
10 KiB
Python
204 lines
10 KiB
Python
import time, inspect
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from collections import deque
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from tinygrad.uop.ops import UOp, Ops, UOpMetaClass, rewrite_group, graph_rewrite, gate_kernel_sink, KernelInfo
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from tinygrad.uop.spec import type_verify, spec_tensor
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from tinygrad.helpers import DEBUG, cpu_profile, TracingKey, SPEC, pluralize, SCACHE, BASEDIR, partition, dedup
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# **** schedule linearizer
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# unwrap VIEW/CAST/etc to find the actual data source (kernel output, buffer, or multi-device op)
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def _unwrap_src(s: UOp) -> UOp:
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while len(s.src) and s.op not in {Ops.AFTER, Ops.BUFFER, Ops.PARAM, Ops.MSELECT, Ops.MSTACK, Ops.BIND}: s = s.src[0]
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return s
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# a buffer state is AFTER | BUFFER | PARAM. MSELECT/MSTACK join per-device states, BIND is not a buffer dependency
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def _states(s: UOp) -> list[UOp]:
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s = _unwrap_src(s)
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if s.op in {Ops.MSELECT, Ops.MSTACK}: return [st for ss in s.src for st in _states(ss)]
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if s.op is Ops.BIND: return []
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assert s.op in {Ops.AFTER, Ops.BUFFER, Ops.PARAM}, f"input to kernel must resolve to a buffer state, not {s.op}"
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return [s]
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def _split_after(after: UOp) -> tuple[tuple[UOp, ...], tuple[UOp, ...]]:
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kernels, remaining = partition(after.src[1:], lambda s: s.op in {Ops.CALL, Ops.END})
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deps, remaining = partition(remaining, lambda s: s.op is Ops.AFTER)
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if invalid := [s for s in remaining if s.op is not Ops.STORE]:
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raise AssertionError(f"AFTER source should be CALL, END, STORE, or AFTER, not {invalid[0].op}")
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return tuple(kernels), tuple(deps)
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def create_schedule(sched_sink:UOp) -> UOp:
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with cpu_profile(TracingKey("toposort sched_sink")):
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# build kernel dependency graph: edges from producer kernel to consumer kernels
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children: dict[UOp, list[UOp]] = {}
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in_degree: dict[UOp, int] = {}
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writes: dict[UOp, list[tuple[UOp, UOp, tuple[UOp, ...]]]] = {} # buffer -> (AFTER, prior state, new kernels)
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reads: list[tuple[UOp, UOp, UOp]] = [] # (reader AFTER, reader kernel, buffer state read)
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for u in sched_sink.toposort(gate_kernel_sink):
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if u.op is not Ops.AFTER: continue
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kernels, after_deps = _split_after(u)
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prev_state = _unwrap_src(u.src[0])
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prev_kernels = set(_split_after(prev_state)[0]) if prev_state.op is Ops.AFTER else set()
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writes.setdefault(u.buf_uop, []).append((u, prev_state, tuple(k for k in kernels if k not in prev_kernels)))
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for k in kernels:
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in_degree.setdefault(k, 0)
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if k.op is Ops.END: assert k.src[0].op is Ops.CALL, f"END src[0] should be KERNEL, not {k.src[0].op}"
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kernel_deps = k.src[0].src[1:] if k.op is Ops.END else k.src[1:]
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read_states = [st for s in kernel_deps for st in _states(s)]
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reads += [(u, k, st) for st in read_states]
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# RAW deps: a kernel runs after the kernels that produced the states it reads or joins
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for st in read_states + [st for s in after_deps for st in _states(s)]:
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if st.op is Ops.AFTER:
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for t in _split_after(st)[0]:
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children.setdefault(t, []).append(k)
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in_degree[k] += 1
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# WAR deps: a kernel reading buffer state S must run before another write that supersedes S. an AFTER only
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# supersedes its immediate prior state; join members already present in that prior state are ordering deps, not writes
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for u, k, s in reads:
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for a, prev_state, write_kernels in writes.get(s.buf_uop, []):
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if a is u or prev_state is not s: continue
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for t in write_kernels:
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if t is not k and t not in k.backward_slice:
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children.setdefault(k, []).append(t)
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in_degree[t] += 1
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with cpu_profile(TracingKey("linearize schedule")):
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queue: deque[UOp] = deque(k for k,v in in_degree.items() if v == 0)
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linearized: list[UOp] = []
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while len(queue):
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rk = queue.popleft()
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if rk.op is Ops.LINEAR:
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linearized.extend(rk.src)
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else:
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k = rk.src[0] if rk.op is Ops.END else rk
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assert k.op is Ops.CALL, f"unexpected op in queue: {k.op}"
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buf_uops = tuple(_unwrap_src(s).buf_uop for s in k.src[1:] if s.op is not Ops.BIND)
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linearized.append(k.src[0].call(*buf_uops))
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for x in children.get(rk, []):
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in_degree[x] -= 1
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if in_degree[x] == 0: queue.append(x)
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if any(in_degree.values()): raise RuntimeError("cycle detected in assign graph")
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return UOp(Ops.LINEAR, src=tuple(linearized))
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from tinygrad.schedule.memory import memory_plan_rewrite
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from tinygrad.engine.realize import capturing, pm_flatten_linear
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from tinygrad.schedule.rangeify import get_kernel_graph
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from tinygrad.helpers import CAPTURING
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from tinygrad.uop.ops import PatternMatcher, UPat, ParamArg
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from tinygrad.dtype import AddrSpace
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def create_new_buffer(ctx:tuple[dict[UOp, UOp], tuple[UOp, ...]], b:UOp):
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if (ret:=ctx[0].get(b, None)) is None: ctx[0][b] = ret = UOp.new_buffer(b.device, b.max_numel(), b.dtype)
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return ret
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pm_post_sched_cache = PatternMatcher([
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# only resolve buffer PARAMs (slot>=0); ALU/shape vars use slot=-1 and must not be swapped for call args
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(UPat(Ops.PARAM, name="x"), lambda ctx,x: ctx[1][x.arg.slot] if x.arg.slot >= 0 else None),
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# create new BUFFERs
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(UPat(Ops.BUFFER, src=(UPat(),), name="b"), lambda ctx,b:
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create_new_buffer(ctx, b) if isinstance(b.arg, ParamArg) and b.addrspace is AddrSpace.GLOBAL else None),
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])
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def resolve_linear_call(linear_call:UOp):
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linear = graph_rewrite(linear_call.src[0], pm_post_sched_cache, ctx=({}, linear_call.src[1:]), walk=True, name="params to buffers")
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binds = {f"p{i}":x.src[0] for i,x in enumerate(linear_call.src[1:]) if x.op is Ops.BIND}
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return linear.substitute({v:binds[v.expr] for v in linear.variables() if v.expr in binds}, enter_calls=True, name="resolve scalar params")
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pm_resolve_linear_call = PatternMatcher([
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# call LINEAR is resolved here
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(UPat(Ops.CALL, src=(UPat(Ops.LINEAR),), name="linear_call", allow_any_len=True), resolve_linear_call),
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])+pm_flatten_linear
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schedule_cache: dict[bytes, UOp] = {}
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# ctx is just for DEBUG on inner
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def lower_sink_to_linear(function:UOp) -> UOp|None:
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st = time.perf_counter()
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if isinstance(function.arg, KernelInfo): return None
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cache_key = function.key
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if not SCACHE or (sc_ret:=schedule_cache.get(cache_key, None)) is None:
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if SPEC: type_verify(function, spec_tensor)
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# support recursive CALLs
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linear = create_schedule(get_kernel_graph(function))
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if SCACHE: schedule_cache[cache_key] = linear
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else:
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# schedule cache hit
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linear = sc_ret
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if (DEBUG >= 1 and len(linear.src) > 1) or DEBUG >= 3:
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for frm in inspect.stack():
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if frm.filename == "<string>": continue
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if frm.filename.startswith(str(BASEDIR / "apps")): break
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if not frm.filename.startswith(str(BASEDIR)) and not frm.filename.endswith("/contextlib.py"): break
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else:
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frm = None
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print(f"scheduled {len(linear.src):5d} kernels in {(time.perf_counter()-st)*1000:8.2f} ms"+\
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f" | {' cache hit' if SCACHE and sc_ret is not None else 'CACHE MISS'} {cache_key.hex()[:8]}"+\
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f" | {len(UOpMetaClass.ucache):7d} uops in cache"+("" if frm is None else f" | {frm.filename}:{frm.lineno}"))
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return linear
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pm_schedule = PatternMatcher([
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(UPat(Ops.SINK, name="function"), lower_sink_to_linear),
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])
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def assert_all_same_devices(ast:UOp):
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devices = dedup([x.device for x in ast.toposort() if x.op is Ops.PARAM and x.device is not None])
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if len(devices) >= 2: raise RuntimeError(f"all buffers must be on the same device: {devices}")
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def copy_kernel_to_copy_uop(call:UOp, dst:UOp, src:UOp, r:UOp|None=None):
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if dst.device == src.device and not (isinstance(dst.device, str) and dst.device.startswith("DISK")): return None
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return call.replace(src=(UOp(Ops.COPY, src=(src,), arg=dst.device),) + call.src[1:])
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def simplify_copy_kernel(call:UOp, ast:UOp, dst:UOp, src:UOp):
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# NOTE: this is a codegen for SDMA devices
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if dst.device == src.device and not (isinstance(dst.device, str) and dst.device.startswith("DISK")): return None
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from tinygrad.codegen.simplify import pm_flatten_range, pm_simplify_ranges
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from tinygrad.schedule.rangeify import pm_mops
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from tinygrad.uop.symbolic import sym
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sink = graph_rewrite(ast, sym+pm_mops+pm_flatten_range+pm_simplify_ranges, ctx={}, name="simplify ranges in copy")
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return call.replace(src=(sink,) + call.src[1:])
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pm_copy_from_store = PatternMatcher([
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# simplify copy kernels
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(UPat(Ops.CALL, src=(UPat(Ops.SINK, name="ast"), UPat.var("dst"), UPat.var("src")), name="call"), simplify_copy_kernel),
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# replace this with a copy if it's a copy
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(UPat(Ops.CALL, src=(UPat(Ops.PARAM, name="dst").index(UPat(Ops.CONST, arg=0))
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.store(UPat(Ops.PARAM, name="src").index(UPat(Ops.CONST, arg=0))).sink(),),
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name="call", allow_any_len=True), copy_kernel_to_copy_uop),
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(UPat(Ops.CALL, src=(UPat(Ops.PARAM, name="dst").index(UPat(Ops.RANGE, name="r"))
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.store(UPat(Ops.PARAM, name="src").index(UPat(Ops.RANGE, name="r"))).end(UPat(Ops.RANGE, name="r")).sink(),),
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name="call", allow_any_len=True), copy_kernel_to_copy_uop),
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# if it wasn't copy, it currently can't be cross device
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(UPat(Ops.CALL, src=(UPat(Ops.SINK, name="ast"),), allow_any_len=True), assert_all_same_devices),
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])
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@rewrite_group(lambda _,ret: f"Schedule {pluralize('Kernel', len(ret[0].src))}")
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def create_linear_with_vars(big_sink:UOp) -> tuple[UOp, dict[str, int]]:
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# big_sink srcs are all the Tensors
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linear_call = graph_rewrite(big_sink, pm_schedule, name="schedule to linear", enter_calls=True)
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# this recursively resolves the linear_call and allocates buffers
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linear = graph_rewrite(linear_call, pm_resolve_linear_call, name="resolve linear call")
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# create copies
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linear = graph_rewrite(linear, pm_copy_from_store, name="create COPY kernels for SDMA")
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# vars used in the schedule
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used_vars = set().union(*[{v.expr for v in si.src[0].variables()} for si in linear.src])
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# get var_vals
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var_vals: dict[str, int] = {}
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for b in big_sink.src[1:]:
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if b.op is Ops.BIND:
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nm = b.src[0].expr
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if nm not in used_vars: continue
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val = b.src[1].val
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if var_vals.get(nm, val) != val: raise RuntimeError(f"bind mismatch on {nm}, {var_vals[nm]} != {val}")
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var_vals[nm] = val
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# jit captures this schedule, no need to execute.
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if len(capturing) and CAPTURING:
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capturing[0].add_linear(linear, var_vals)
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return UOp(Ops.LINEAR, src=()), var_vals
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held_bufs = ({b for b in linear_call.src[1:] if b.op is Ops.BUFFER} if linear_call.op is Ops.CALL else set())
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return memory_plan_rewrite(linear, held_bufs), var_vals
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