pad invalid work (glm)

CONTINUE.md

@@ -0,0 +1,122 @@
+# CONTINUE.md: PAD with Invalid instead of 0
+
+## Goal
+Make the low-level `Ops.PAD` pad with `Invalid` instead of `0`, while keeping
+the external `Tensor.pad` behavior unchanged.
+
+## Changes made (all 3 files are modified, see `git diff`)
+
+### 1. `tinygrad/schedule/indexing.py:92` — core change
+`convert_pad_to_where_to_keep_behavior_local` now uses `UOp.const(x.dtype, Invalid)`
+instead of `UOp.const(x.dtype, 0)` as the else value. This is what makes `Ops.PAD`
+pad with Invalid.
+
+### 2. `tinygrad/uop/symbolic.py:87-99` — Invalid propagation rules
+Added two new rules to `pm_data_invalid` so that `where(invalid_gate, a, const_b)`
+uses `b` (the const) in don't-care positions instead of poisoning to Invalid.
+This is needed so that `_pad_constant`'s mask `where(pad(ones_bool), base, value)`
+works — the mask is a `where(valid, True, Invalid)` gate, and the else `value`
+is a const.
+
+The rules are restricted to only match when the gate's valid value is a **const**
+(`UPat.cvar("x")`), to distinguish pad masks (where valid=True, a const) from
+gather masks (where valid=loaded_data, not a const). Without this restriction,
+`test_tensor_index` breaks because gather masks also create `where(cond, x, Invalid)`
+but need to keep poisoning.
+
+### 3. `tinygrad/mixin/op.py:280-289` — `_pad_constant` fix
+Swapped the `value == 0` early return for `value is Invalid` early return.
+When `value is Invalid`, just return `base` (which already has Invalid from
+`Ops.PAD`). For all other values (including 0), use the mask approach:
+`where(pad(ones_bool), base, const_value)`.
+
+## Current state
+- `test/unit/test_invalid_tensor.py` — **all 22 pass**
+- `test/unit/test_function.py` — **5 failures**, all multi-shard tests
+
+## The remaining bug: `cat` + multi-shard
+
+`cat` (op.py:716) uses `pad` + `usum` (element-wise ADD) to combine tensors:
+```python
+padded = [t.pad(...) for i,t in enumerate(tensors)]
+return padded[0].usum(*padded[1:])
+```
+
+When two shards are cat'd, each is padded and then summed. The valid masks
+are **complementary** (shard 0 valid in positions 0-1, shard 1 valid in 2-3).
+
+`_pad_constant` creates `where(mask_pad, data_pad, 0)` where:
+- `mask_pad = where(valid, True, Invalid)` — gate's valid value is const `True`
+- `data_pad = where(valid, data, Invalid)` — gate's valid value is loaded `data` (NOT const)
+
+The new const-specific rule handles the mask pad correctly. But for the data pad,
+the gate's valid value (`data`) is not a const, so the **non-const** lift-out rule
+fires: `where(valid, where(valid, data, Invalid), 0)` → `where(valid, where(valid, data, 0), Invalid)`.
+
+The `Invalid` else poisons the ADD. The binary Invalid rule lifts both gates out:
+`where(c6, data0, Invalid) + where(c8, data1, Invalid)` → `where(c6&c8, data0+data1, Invalid)`.
+
+Since `c6` and `c8` are complementary, `c6&c8` is always False → result is all Invalid → 0.
+
+### Master comparison
+On master, `convert_pad_to_where` uses `0` (not Invalid), so the ADD is just
+`where(c6, data0, 0) + where(c8, data1, 0)` with no Invalid, no lifting, works fine.
+
+### Debug output (with changes)
+```
+c16 = c6.where(c11.index(c13), 0)        # where(c6, load0, 0) — correct
+c22 = c6.where(0, c17.index(c20))         # where(c6, 0, load1) — correct
+c25 = (c6&c8).where((c16+c22), Invalid)   # WRONG: c6&c8 always False → all Invalid
+```
+
+### Master debug output
+```
+c13 = c6.where(c8.index(c10), 0)          # where(c6, load0, 0)
+c21 = c6.where(0, c14.index(c19))          # where(c6, 0, load1)
+c22 = c13+c21                              # plain ADD, no wrapper — correct
+```
+
+## Suggested fix approaches
+
+### Option A: General WHERE simplification rule
+Add a rule: `where(a, where(a, x, _), c)` → `where(a, x, c)`.
+When the outer and inner conditions are the same UOp, the inner else is
+unreachable. This would simplify `where(valid, where(valid, data, Invalid), 0)`
+→ `where(valid, data, 0)` before the lift-out rule can fire.
+Check if this rule already exists in `symbolic.py` — it may need to be added
+before the lift-out rules.
+
+### Option B: Don't use Ops.PAD for data in `_pad_constant`
+When `value is not Invalid`, avoid creating `Ops.PAD` on the data. Use `cat`
+or `expand` to create the padded tensor directly, bypassing the Invalid
+propagation entirely.
+
+### Option C: Make the lift-out rule use the outer else value
+Change the non-const lift-out rule: when `where(a, where(cond, x, Invalid), c)`
+and `c` is a const, use `c` as the else instead of `Invalid`. This is what the
+const-specific rule does, but it needs to also handle non-const gate valid values.
+
+## Test commands
+```bash
+# invalid tensor tests (currently pass)
+python -m pytest test/unit/test_invalid_tensor.py -x -q -n12
+
+# function tests (5 multi-shard failures)
+python -m pytest test/unit/test_function.py -x -q -n12
+
+# the specific failing test
+python -m pytest test/unit/test_function.py::TestFunctionMulti::test_simple_multi_sharded -x -q
+
+# debug the failing case
+DEBUG=6 python -c "
+from tinygrad import Tensor
+a = Tensor([1,2,3,4]).shard(['CPU', 'CPU:1'], axis=0)
+print(a.numpy())  # should be [1,2,3,4], gets [0,0,0,0]
+"
+```
+
+## Lint/typecheck
+```bash
+python -m mypy tinygrad/
+python -m ruff check .
+```

tinygrad/llm/cli.py

@@ -6,26 +6,6 @@ from tinygrad.helpers import partition, DEBUG, Timing, GlobalCounters, stderr_lo
 from tinygrad.viz.serve import TCPServerWithReuse, HTTPRequestHandler
 from tinygrad.llm.model import Transformer
 
-def holdback(s:str, tag:str) -> int:
-  # length of the suffix of s that is a prefix of tag (the tag may be split across streamed pieces)
-  return max((i for i in range(1, min(len(s), len(tag))+1) if tag.startswith(s[-i:])), default=0)
-
-def parse_tool_call(s:str) -> tuple[str, typing.Any]|None:
-  s = s.strip()
-  if s.startswith("{"):  # hermes JSON format: {"name": ..., "arguments": {...}}
-    try:
-      call = json.loads(s)
-      return call["name"], call.get("arguments", call.get("parameters", {}))
-    except (json.JSONDecodeError, KeyError): return None
-  # XML format: <function=name>\n<parameter=key>\nvalue\n</parameter>...</function>
-  if (fm := re.match(r"<function=([^>]+)>\s*(.*?)\s*(?:</function>)?$", s, re.DOTALL)):
-    args = {}
-    for pm in re.finditer(r"<parameter=([^>]+)>\s*(.*?)\s*</parameter>", fm.group(2), re.DOTALL):
-      try: args[pm.group(1)] = json.loads(pm.group(2))
-      except json.JSONDecodeError: args[pm.group(1)] = pm.group(2)
-    return fm.group(1), args
-  return None
-
 class SimpleTokenizer:
   def __init__(self, normal_tokens:dict[str, int], special_tokens:dict[str, int], preset:str="llama3",
                bos_id:int|None=None, eos_id:int=0, eot_id:int|None=None):
@@ -133,75 +113,26 @@ class Handler(HTTPRequestHandler):
   def do_GET(self):
     if self.path == "/v1/models": self.send_data(json.dumps({"object":"list","data":[{"id":self.server.model_name,"object":"model"}]}).encode())
     else: self.send_data((pathlib.Path(__file__).parent / "chat.html").read_bytes(), content_type="text/html")
-  def run_model(self, ids:list[int], model_name:str, include_usage=False, max_tokens:int|None=None, temperature:float=0.0,
-                parse_tool_calls=False, prefill_think=False):
+  def run_model(self, ids:list[int], model_name:str, include_usage=False, max_tokens:int|None=None, temperature:float=0.0):
     model, tok = self.server.model, self.server.tok
     cache_start_pos = model.get_start_pos(ids)
     stderr_log(f"{self.path}  {colored('--', 'BLACK')}  "
                f"in:{colored(f'{cache_start_pos:5d}', 'green')} +{len(ids)-cache_start_pos:5d}  {colored('--', 'BLACK')}  ")
     tmpl = {"id":f"chatcmpl-{uuid.uuid4().hex[:24]}", "object":"chat.completion.chunk", "created":int(time.time()), "model":model_name}
-    def chunk(d:dict): return {"choices": [{"index":0, "delta":d, "finish_reason":None}], **tmpl}
-    yield chunk({"role":"assistant", "content":""})
+    yield {"choices": [{"index":0, "delta":{"role":"assistant","content":""}, "finish_reason":None}], **tmpl}
     out: list[int] = []
     finish_reason = "stop"
     st = time.perf_counter()
     dec = tok.stream_decoder()
-    mode, buf, tool_text = ("reasoning" if prefill_think else "undecided"), "", ""
-    def route(piece:str, final:bool=False):
-      nonlocal mode, buf, tool_text
-      if mode == "undecided":  # decide whether the output starts with a think block
-        buf += piece
-        if not final and len(buf) < len("<think>") and "<think>".startswith(buf): return
-        mode, piece, buf = ("reasoning", buf[len("<think>"):], "") if buf.startswith("<think>") else ("content", buf, "")
-      if mode == "reasoning":
-        buf += piece
-        if "</think>" in buf:
-          before, piece = buf.split("</think>", 1)
-          if before: yield chunk({"reasoning_content":before})
-          buf, mode, piece = "", "content", piece.lstrip("\n")
-        else:
-          hold = 0 if final else holdback(buf, "</think>")
-          if (flush := buf[:len(buf)-hold]): yield chunk({"reasoning_content":flush})
-          buf = buf[len(buf)-hold:]
-          return
-      if not parse_tool_calls:
-        if piece: yield chunk({"content":piece})
-      else:
-        tool_text += piece
-        if tool_text.startswith("<tool_call>"): return
-        if "<tool_call>" in tool_text:
-          before, tool_text = tool_text.split("<tool_call>", 1)
-          if before: yield chunk({"content":before})
-          tool_text = "<tool_call>" + tool_text
-        else:
-          # hold back any suffix that could be the start of a "<tool_call>" tag split across tokens
-          hold = 0 if final else holdback(tool_text, "<tool_call>")
-          if (flush := tool_text[:len(tool_text)-hold]): yield chunk({"content":flush})
-          tool_text = tool_text[len(tool_text)-hold:]
     for next_id in model.generate(ids, temperature=temperature):
       if len(out) == 0: stderr_log(f"prefill:{(len(ids)-cache_start_pos)/((pt:=time.perf_counter())-st):4.0f} tok/s  {colored('--', 'BLACK')}  ")
       if tok.is_end(next_id): break
       out.append(next_id)
-      yield from route(dec(next_id))
+      yield {"choices": [{"index":0, "delta":{"content":dec(next_id)}, "finish_reason":None}], **tmpl}
       if max_tokens is not None and len(out) >= max_tokens:
         finish_reason = "length"
         break
-    yield from route(dec(), final=True)
-    if parse_tool_calls:
-      tool_calls = []
-      calls = [(m.group(1), m.group(0)) for m in re.finditer(r"<tool_call>\s*(.*?)\s*</tool_call>", tool_text, re.DOTALL)]
-      if not calls and tool_text.startswith("<tool_call>"): calls = [(tool_text[len("<tool_call>"):], tool_text)]  # unclosed tag
-      for i, (inner, raw) in enumerate(calls):
-        if (parsed := parse_tool_call(inner)) is None:
-          stderr_log(f"failed to parse tool call: {inner[:200]}")
-          yield chunk({"content":raw})  # don't silently drop output the client can't use
-        else:
-          name, args = parsed
-          tool_calls.append({"index":i, "id":f"call_{uuid.uuid4().hex[:24]}", "type":"function",
-                             "function":{"name":name, "arguments":args if isinstance(args, str) else json.dumps(args)}})
-      if tool_calls:
-        yield chunk({"tool_calls":tool_calls})
-        if finish_reason == "stop": finish_reason = "tool_calls"
+    if (tail := dec()): yield {"choices": [{"index":0, "delta":{"content":tail}, "finish_reason":None}], **tmpl}
     yield {"choices": [{"index":0, "delta":{},"finish_reason":finish_reason}], **tmpl}
     if include_usage:
       yield {"choices": [], "usage": {"prompt_tokens": len(ids), "completion_tokens": len(out), "total_tokens": len(ids) + len(out)}, **tmpl}
@@ -215,65 +146,39 @@ class Handler(HTTPRequestHandler):
     body: dict[str, typing.Any] = json.loads(raw_body.decode("utf-8"))
     if DEBUG >= 1: print(json.dumps(body, indent=2))
     if self.path == "/v1/chat/completions":
-      messages, tools = body["messages"], body.get("tools")
-      prefill_think = False
-      if self.server.template is not None:
-        # the chat template expects tool_call arguments as dicts, OpenAI clients send them as JSON strings
-        norm = []
-        for m in messages:
-          if m.get("tool_calls"):
-            m = dict(m)
-            m["tool_calls"] = [{**tc, "function":{**tc["function"], "arguments":json.loads(a) if isinstance((a:=tc["function"]["arguments"]), str)
-                                else a}} if "function" in tc else tc for tc in m["tool_calls"]]
-          norm.append(m)
-        rendered = self.server.template.render(messages=norm, tools=tools, add_generation_prompt=norm[-1]["role"] != "assistant")
-        prefill_think = rendered.rstrip().endswith("<think>")
-        ids: list[int] = tok.encode(rendered)
-        if (prefix := tok.prefix()) and ids[:len(prefix)] != prefix: ids = prefix + ids
-        if norm[-1]["role"] == "assistant":  # last assistant message is treated as prefill, drop its end-of-turn tokens
-          end = tok.end_turn()
-          if len(ids) >= len(end) and ids[-len(end):] == end: ids = ids[:-len(end)]
-      else:
-        if tools: stderr_log("warning: ignoring tools, install jinja2 to enable tool calling via the model's chat template")
-        ids = tok.prefix()
-        for i, msg in enumerate(messages):
-          ids += tok.role(msg["role"])
-          content = msg["content"]
-          if isinstance(content, str): ids += tok.encode(content)
-          elif isinstance(content, list):
-            for c in content:
-              if c["type"] == "text": ids += tok.encode(c["text"])
-              else: raise RuntimeError(f"unhandled type: {c['type']}")
-          else: raise RuntimeError(f"unknown content type: {type(content)}")
-          if msg["role"] == "assistant" and i == len(messages) - 1: break
-          ids += tok.end_turn()
-        else: ids += tok.role("assistant")
+      # extract tokens, last assistant message is treated as prefill
+      ids: list[int] = tok.prefix()
+      for i, msg in enumerate(body["messages"]):
+        ids += tok.role(msg["role"])
+        content = msg["content"]
+        if isinstance(content, str): ids += tok.encode(content)
+        elif isinstance(content, list):
+          for c in content:
+            if c["type"] == "text": ids += tok.encode(c["text"])
+            else: raise RuntimeError(f"unhandled type: {c['type']}")
+        else: raise RuntimeError(f"unknown content type: {type(content)}")
+        if msg["role"] == "assistant" and i == len(body["messages"]) - 1: break
+        ids += tok.end_turn()
+      else: ids += tok.role("assistant")
 
       # reply
       max_tokens = body.get("max_completion_tokens") or body.get("max_tokens")
       chunks = self.run_model(ids, body["model"], not body.get("stream") or body.get("stream_options",{}).get("include_usage", False),
-                              max_tokens=max_tokens, temperature=float(body.get("temperature", 0.0)), parse_tool_calls=bool(tools),
-                              prefill_think=prefill_think)
+                              max_tokens=max_tokens, temperature=float(body.get("temperature", 0.0)))
       if body.get("stream"): self.stream_json(chunks)
       else:
-        out, reasoning, tool_calls, finish_reason = [], [], [], "stop"
+        out, finish_reason = [], "stop"
         for c in chunks:
-          if c["choices"] and (delta := c["choices"][0].get("delta", {})):
-            if delta.get("content"): out.append(delta["content"])
-            if delta.get("reasoning_content"): reasoning.append(delta["reasoning_content"])
-            if delta.get("tool_calls"): tool_calls.extend(delta["tool_calls"])
+          if c["choices"] and c["choices"][0].get("delta", {}).get("content"): out.append(c["choices"][0]["delta"]["content"])
           if c["choices"] and c["choices"][0].get("finish_reason"): finish_reason = c["choices"][0]["finish_reason"]
-        message: dict[str, typing.Any] = {"role":"assistant", "content":"".join(out) or None}
-        if reasoning: message["reasoning_content"] = "".join(reasoning)
-        if tool_calls: message["tool_calls"] = [{k:v for k, v in tc.items() if k != "index"} for tc in tool_calls]
         self.send_data(json.dumps({**c, "object":"chat.completion",
-          "choices":[{"index":0, "message":message, "finish_reason":finish_reason}]}).encode())
+          "choices":[{"index":0, "message":{"role":"assistant","content":"".join(out)}, "finish_reason":finish_reason}]}).encode())
     else:
       raise RuntimeError(f"unhandled path {self.path}")
 
 class LLMServer(TCPServerWithReuse):
-  def __init__(self, server_address:tuple, model:Transformer, model_name:str, tok:SimpleTokenizer, template:typing.Any=None):
-    self.model, self.model_name, self.tok, self.template = model, model_name, tok, template
+  def __init__(self, server_address:tuple, model:Transformer, model_name:str, tok:SimpleTokenizer):
+    self.model, self.model_name, self.tok = model, model_name, tok
     super().__init__(server_address, Handler)
 
 def main():
@@ -289,26 +194,11 @@ def main():
   model, kv = Transformer.from_gguf(fetch(models.get(args.model, args.model)), args.max_context)
   model_name = kv.get('general.name') or kv.get('general.basename') or args.model
   file_sizes = [y.nbytes() for y in UOp.sink(*[x.uop for x in nn.state.get_parameters(model)]).toposort() if y.op is Ops.BUFFER]
-  print(f"using model \"{model_name}\" with {sum(file_sizes):,} bytes and {sum(x.numel() for x in nn.state.get_parameters(model)):,} params, "
-        f"max context {args.max_context} on {nn.state.get_parameters(model)[0].device}")
+  print(f"using model \"{model_name}\" with {sum(file_sizes):,} bytes and {sum(x.numel() for x in nn.state.get_parameters(model)):,} params")
 
   # get tokenizer
   tok = SimpleTokenizer.from_gguf_kv(kv)
 
-  # compile the chat template if jinja2 is available (enables tool calling and model-specific formatting)
-  template = None
-  if (ct := kv.get('tokenizer.chat_template')) is not None:
-    try:
-      import jinja2
-      env = jinja2.Environment()
-      env.filters['tojson'] = lambda obj, **kwargs: json.dumps(obj)  # jinja2's tojson escapes <>& for HTML safety
-      env.globals['raise_exception'] = lambda msg: (_ for _ in ()).throw(RuntimeError(msg))
-      env.globals['strftime_now'] = lambda fmt: time.strftime(fmt)
-      for name, key in {'bos_token':'bos', 'eos_token':'eos', 'unk_token':'unknown', 'pad_token':'padding', 'sep_token':'separator'}.items():
-        if (tid := kv.get(f'tokenizer.ggml.{key}_token_id')) is not None: env.globals[name] = tok.decode([tid])
-      template = env.from_string(ct)
-    except ImportError: stderr_log("warning: jinja2 is not installed, the model's chat template is disabled")
-
   # warmup the JIT
   if args.warmup or args.serve:
     # run 2 tokens through the model twice to capture the JIT before serving
@@ -316,7 +206,7 @@ def main():
       for _ in range(2): list(zip(range(2), model.generate([0])))
 
   # start server
-  if args.serve: LLMServer(('', args.serve), model, model_name, tok, template).serve_forever()
+  if args.serve: LLMServer(('', args.serve), model, model_name, tok).serve_forever()
 
   # do benchmark
   if args.benchmark is not None:

tinygrad/mixin/op.py

@@ -284,8 +284,8 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     X = self.shrink(tuple((-smin(pB,0),smin(pA+s,s)) for (pB,pA),s in zip(pX, self.shape))) if has_neg else self
     pads = tuple((smax(pB,0), smax(pA,0)) for pB,pA in pX) if has_neg else pX
     base = MovementMixin.pad(X, pads)
-    if value == 0: return base
-    if value is not Invalid: base = base.cast(least_upper_dtype(base.dtype, dtypes.from_py(value)))
+    if value is Invalid: return base
+    if value != 0: base = base.cast(least_upper_dtype(base.dtype, dtypes.from_py(value)))
     return MovementMixin.pad(X.const_like(1).cast(dtypes.bool), pads).where(base, base.const_like(value))
 
   def _pad_circular(self, pX:tuple[tuple[sint, sint], ...]) -> Self:

tinygrad/schedule/indexing.py

@@ -1,7 +1,7 @@
 from typing import Iterator
 import functools, itertools
 from dataclasses import dataclass, field, replace
-from tinygrad.dtype import dtypes, AddrSpace
+from tinygrad.dtype import dtypes, AddrSpace, Invalid
 from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, graph_rewrite, sint, AxisType, profile_matches
 from tinygrad.uop.ops import consumer_map_from_toposort, gate_kernel_sink
 from tinygrad.uop.symbolic import symbolic, pm_simplify_valid, pm_drop_and_clauses
@@ -89,7 +89,7 @@ def convert_pad_to_where_to_keep_behavior_local(ctx:IndexingContext, x:UOp):
   if x not in ctx.range_map: return None
   bx = create_bufferize_and_index_based_on_ranges(ctx, x)
   valid: UOp = UOp.const(dtypes.bool, True).uprod([r.get_valid() for r in ctx.range_map[x][0]])
-  return valid.where(bx.src[0], UOp.const(x.dtype, 0))
+  return valid.where(bx.src[0], UOp.const(x.dtype, Invalid))
 
 def convert_reduce_to_reduce_with_ranges(ctx:IndexingContext, x:UOp):
   if x.arg[1] == 0: return None

tinygrad/uop/symbolic.py

@@ -85,12 +85,19 @@ pm_data_invalid = PatternMatcher([
   (UPat(GroupOp.Binary, src=(UPat.var("y"), invalid_gate), name="alu"), lambda cond,x,y,alu,i: cond.where(y.alu(alu.op,x), i.cast(alu.dtype))),
   (UPat(GroupOp.Binary-GroupOp.Comparison, src=[invalid_pat, UPat()]), lambda i: i),
   # an Invalid condition poisons the whole where; a gated Invalid condition lifts the gate out
+  # when the gate's valid value is a const (e.g. a pad mask: where(valid, True, Invalid)),
+  # use the else value in don't-care positions so masks work
   (invalid_pat.where(UPat.var("a"), UPat()), lambda i,a: i.cast(a.dtype)),
+  (UPat.var("cond").where(UPat.cvar("x"), invalid_pat).where(UPat.var("a"), UPat.cvar("b")),
+   lambda cond,x,i,a,b: cond.where(x.where(a,b), b)),
   (invalid_gate.where(UPat.var("a"), UPat.var("b")), lambda cond,x,i,a,b: cond.where(x.where(a,b), i.cast(a.dtype))),
   # normalize where(cond, Invalid, val) -> where(~cond, val, Invalid)
   (UPat.var("cond").where(invalid_pat, UPat.var("val")), lambda cond, i, val: cond.logical_not().where(val, i) if val.arg != Invalid else i),
   # lift Invalid out: a.where(cond.where(x, Invalid), c) -> (~a|cond).where(a.where(x, c), Invalid)
   # when a is cond, ~a|cond is True and would drop the Invalid gate (losing the valid), so keep cond as the gate
+  # when c is a const and the gate's valid value is a const (pad mask), use c in don't-care positions
+  (UPat.var("a").where(UPat.var("cond").where(UPat.cvar("x"), invalid_pat), UPat.cvar("c")),
+   lambda cond,i,x,a,c: (cond if a is cond else (a.logical_not()|cond)).where(a.where(x,c), c) if c.arg != Invalid else None),
   (UPat.var("a").where(invalid_gate, UPat.var("c")), lambda cond,i,x,a,c:
    (cond if a is cond else (a.logical_not()|cond)).where(a.where(x,c), i) if c.arg != Invalid else None),
   (UPat.var("a").where(UPat.var("b"), invalid_gate), lambda cond,i,x,a,b: (a|cond).where(a.where(b, x), i) if b.arg != Invalid else None),