jax-ml/jax · error · ValueError
M mismatch: {m} != {m2}
Error message
M mismatch: {m} != {m2} What it means
The low-level mma helper requires the M dimension of the accumulator to match the M of operand a (a.shape[0] == acc.shape[0]). A mismatch means the shapes don't form a valid matmul tile.
Source
Thrown at jax/experimental/mosaic/gpu/mma.py:192
Args:
acc: A `FragmentedArray` with a `TiledLayout` generated from
`MMALayouts.acc`.
a: A `FragmentedArray` with a `TiledLayout` generated from
`MMALayouts.lhs`.
b: A `FragmentedArray` with a `TiledLayout` generated from `MMALayouts.rhs`.
Returns:
A new `FragmentedArray` with the result of the computation with
the same type as `acc`.
"""
(m, k) = a.shape
(k2, n) = b.shape
(m2, n2) = acc.shape
if m != m2:
raise ValueError(f"M mismatch: {m} != {m2}")
if n != n2:
raise ValueError(f"N mismatch: {n} != {n2}")
if k != k2:
raise ValueError(f"K mismatch: {k} != {k2}")
# todo(cperivol): A tile shape can have dimensions that are higher
# multiples of the mma op size as long as those dimensions are not
# sharded across warps.
i4 = ir.IntegerType.get_signless(4)
i8 = ir.IntegerType.get_signless(8)
i32 = ir.IntegerType.get_signless(32)
bf16 = ir.BF16Type.get()
f16 = ir.F16Type.get()
f8e4m3fn = ir.Float8E4M3FNType.get()
f8e5m2 = ir.Float8E5M2Type.get()
if (element_type := a.mlir_dtype) != b.mlir_dtype:
raise ValueError(f"Dtype mismatch: {a.mlir_dtype} != {b.mlir_dtype}")
if element_type not in (bf16, f16, f8e4m3fn, f8e5m2, i8, i4):View on GitHub (pinned to 1e1c6a8fc0)
Solutions
- Allocate acc with shape (a.shape[0], b.shape[1])
- Fix the operand tiling so M matches the accumulator
- Zero-init a correctly shaped accumulator each matmul
Example fix
// before acc = fa.from_tensor(jnp.zeros((32, n), jnp.float32)) acc = mma.mma(a_64xk, b, acc) // after acc = fa.from_tensor(jnp.zeros((64, n), jnp.float32)) acc = mma.mma(a_64xk, b, acc)
Defensive patterns
Strategy: validation
Validate before calling
assert a.shape[0] == acc.shape[0], 'M mismatch'
Prevention
- Allocate accumulators with shape (a.shape[0], b.shape[1]) at kernel start
When it happens
Trigger: Calling mma(a, b, acc) where acc has fewer/more rows than a, e.g. acc of shape (32, N) with a of shape (64, K).
Common situations: Accumulator allocated with a different tile shape than the operands, or reusing an accumulator across matmuls of different M.
Related errors
- N mismatch: {n} != {n2}
- K mismatch: {k} != {k2}
- Logical shape {self.logical_shape} cannot be bigger than con
- Accumulator and LHS have incompatible shapes. Expected LHS t
- Accumulator and RHS have incompatible shapes. Expected RHS t
AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27).
Data as JSON: /api/errors/b161cd4e41ae5a72.
Report an issue: GitHub.