[pallas] size emit_pipeline scratch from reshape-merged block-size products

A reshape([-1, d]) that merges tiled dims gives a leading size that is a sympy
product of block-size symbols (u0*u1*u2). _resolve_shape only resolved a single
block symbol and fell back to int(s) = the full-extent size hint, over-sizing
the loop-carried VMEM scratch for emit_pipeline/fori_loop and crashing at
runtime. Resolve compound expressions by substituting each block size.

stack-info: PR: #2769, branch: choijon5/stack/93

Author: choijon5

helion/language/_tracing_ops.py

@@ -17,6 +17,7 @@
 from .._compiler.ast_extension import expr_from_string
 from .._compiler.ast_extension import statement_from_string
 from .._compiler.compile_environment import CompileEnvironment
+from .._compiler.device_function import find_block_size_symbols
 from .._compiler.dtype_utils import cast_ast
 from .._compiler.host_function import HostFunction
 from .._compiler.variable_origin import BlockSizeOrigin
@@ -520,6 +521,44 @@ def _scratch_write_stmt(state: CodegenState, sname: str, val: ast.AST) -> ast.AS
     return statement_from_string(f"{sname}[{idx}] = {{val}}", val=val)
 
 
+def _resolve_dim_size(
+    s: object,
+    env: CompileEnvironment,
+    config: Config,
+) -> int | None:
+    """Resolve a tensor-dim size to a concrete int from ``config``, else ``None``.
+
+    Handles a single tile dim via ``resolve_block_id`` and ``reshape``-merged
+    dims (a sympy product/sum/power of block symbols) by substituting each block
+    size. The ``int(s)`` fallback would otherwise return the full-extent size
+    hint and over-size loop-carried scratch.
+    """
+    bid = env.resolve_block_id(s)
+    if bid is not None:
+        bs = env.block_sizes[bid].from_config(config)
+        return bs if isinstance(bs, int) else None
+
+    if isinstance(s, int):
+        return s
+    expr = s._sympy_() if isinstance(s, torch.SymInt) else s
+    if not isinstance(expr, sympy.Expr):
+        return None
+    if expr.is_Integer:
+        return int(expr)
+
+    block_mapping, non_block_symbols = find_block_size_symbols(expr)
+    if non_block_symbols:
+        return None
+    subs: dict[sympy.Symbol, sympy.Integer] = {}
+    for symbol, block_id in block_mapping.items():
+        bs = env.block_sizes[block_id].from_config(config)
+        if not isinstance(bs, int):
+            return None
+        subs[symbol] = sympy.Integer(bs)
+    resolved = expr.xreplace(subs)
+    return int(resolved) if resolved.is_Integer else None
+
+
 def _resolve_shape(
     proxy: torch.Tensor,
     env: CompileEnvironment,
@@ -528,11 +567,9 @@ def _resolve_shape(
     """Resolve symbolic tile sizes to concrete block sizes from config."""
     resolved = []
     for s in proxy.shape:
-        bid = env.resolve_block_id(s)
-        if bid is not None:
-            bs = env.block_sizes[bid].from_config(config)
-            assert isinstance(bs, int)
-            resolved.append(bs)
+        size = _resolve_dim_size(s, env, config)
+        if size is not None:
+            resolved.append(size)
         else:
             resolved.append(int(s))
     return tuple(resolved)

test/test_pallas.py

@@ -2170,6 +2170,69 @@ def test_attention_unroll_fp32(self) -> None:
         self.assertIn("torch.empty_like(q_view, device='meta')", _code)
         self.assertIn("out = _launcher(", _code)
 
+    def test_attention_reshape_merge_scratch_size(self) -> None:
+        """Reshape-merged tiled dims size loop-carried scratch by block product.
+
+        A ``reshape([-1, d])`` that merges several tiled dims gives a leading
+        size that is a *product* of block-size symbols. The scratch must resolve
+        to that product (here m_block=64), not the symbol's size hint (the full
+        2*2*64=262144 extent) which would over-size the buffer and crash.
+        """
+
+        @helion.kernel(backend="pallas", static_shapes=True)
+        def attn_merge(
+            q_in: torch.Tensor, k_in: torch.Tensor, v_in: torch.Tensor
+        ) -> torch.Tensor:
+            bq, hq, m, n = (
+                q_in.size(0),
+                q_in.size(1),
+                q_in.size(2),
+                k_in.size(2),
+            )
+            d = hl.specialize(q_in.size(3))
+            out = torch.empty_like(q_in)
+            scale = (1.0 / math.sqrt(d)) * 1.44269504
+            for tb, th, tm in hl.tile([bq, hq, m]):
+                qt = q_in[tb, th, tm, :].reshape([-1, d])
+                m_i = hl.full([qt.size(0)], float("-inf"), dtype=torch.float32)
+                l_i = hl.full([qt.size(0)], 1.0, dtype=torch.float32)
+                acc = hl.zeros([qt.size(0), d], dtype=torch.float32)
+                for tn in hl.tile(n):
+                    kt = k_in[tb, th, tn, :].reshape([-1, d])
+                    qk = hl.dot(qt * scale, kt.transpose(0, 1), out_dtype=torch.float32)
+                    m_ij = torch.maximum(m_i, torch.amax(qk, -1))
+                    p = torch.exp2(qk - m_ij[:, None])
+                    l_i = l_i * torch.exp2(m_i - m_ij) + torch.sum(p, -1)
+                    acc = acc * torch.exp2(m_i - m_ij)[:, None]
+                    vt = v_in[tb, th, tn, :].reshape([-1, d])
+                    acc = torch.addmm(acc, p.to(vt.dtype), vt)
+                    m_i = m_ij
+                out[tb, th, tm, :] = (
+                    (acc / l_i[:, None]).to(out.dtype).reshape([1, 1, -1, d])
+                )
+            return out
+
+        query = torch.randn(2, 2, 64, 32, dtype=torch.float32, device=DEVICE)
+        key = torch.randn(2, 2, 64, 32, dtype=torch.float32, device=DEVICE)
+        val = torch.randn(2, 2, 64, 32, dtype=torch.float32, device=DEVICE)
+        code, result = code_and_output(
+            attn_merge,
+            (query, key, val),
+            block_sizes=[1, 1, 64, 32],
+            pallas_loop_type="emit_pipeline",
+        )
+        self.assertIn(
+            "_scratch_shapes=["
+            "((64,), 'jnp.float32', 'vmem'), "
+            "((64,), 'jnp.float32', 'vmem'), "
+            "((64, 32), 'jnp.float32', 'vmem')]",
+            code,
+        )
+        ref = torch.nn.functional.scaled_dot_product_attention(
+            query.float().cpu(), key.float().cpu(), val.float().cpu()
+        ).to(device=DEVICE)
+        torch.testing.assert_close(result, ref, rtol=1e-2, atol=1e-2)
+
     def test_hl_zeros_outer_arithmetic_emit_pipeline(self) -> None:
         """``hl.zeros`` results must support arithmetic at outer (non-inner-loop) scope.