schedule: cache unbinds for consistent cache keys (#13662)

* schedule: cache unbinds for consistent cache keys

different bound variable values (e.g. kv cache positions) now produce
the same schedule cache key by unbinding BIND(DEFINE_VAR, CONST) before
computing the cache key and rebinding after lookup.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* schedule: cache unbinds for consistent cache keys

When scheduling, BIND(DEFINE_VAR, CONST) nodes are now unbound to
tagged DEFINE_VARs before computing the cache key. This ensures that
the same computation with different bound values (e.g., different
KV cache positions in LLM) gets the same cache key and reuses the
cached schedule.

The fix:
- pm_pre_sched_cache: replaces BIND with tagged DEFINE_VAR
- pm_post_sched_cache: restores tagged DEFINE_VAR back to original BIND
- pm_remove_rangeify_tags: excludes DEFINE_VAR to preserve tags through rangeify
- var_vals extracted from BINDs before cache key computation

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* schedule: fix BIND handling and add CLAUDE.md

- Handle BIND to RANGE in create_schedule (not matched by CONST pattern)
- Assert all BINDs on same variable have same value
- Add CLAUDE.md codebase guide

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

test/unit/test_schedule_cache.py

@@ -1,8 +1,31 @@
 import unittest
-from tinygrad import Tensor
+from tinygrad import Tensor, Variable
 from tinygrad.engine.schedule import schedule_cache
 
 class TestScheduleCache(unittest.TestCase):
+  def test_bound_variable_reuses_cache(self):
+    schedule_cache.clear()
+    v = Variable('v', 1, 100)
+    x = Tensor.ones(10).contiguous().realize()
+
+    # first run with v=5
+    t1 = (x + Tensor(v.bind(5))).sum()
+    self.assertEqual(t1.item(), 60.0)
+    cache_size_after_first = len(schedule_cache)
+
+    # second run with v=10 should reuse cache
+    t2 = (x + Tensor(v.bind(10))).sum()
+    self.assertEqual(t2.item(), 110.0)
+    self.assertEqual(len(schedule_cache), cache_size_after_first)
+
+  def test_bound_variable_var_vals(self):
+    v = Variable('pos', 1, 100)
+    x = Tensor.ones(10).contiguous().realize()
+
+    t = x + Tensor(v.bind(42))
+    _, var_vals = t.schedule_with_vars()
+    self.assertEqual(var_vals, {'pos': 42})
+
   def test_simple(self):
     a = Tensor.ones(10).contiguous()
     b = Tensor.ones(10).contiguous()