patschmidt2 commented on issue #16566:
URL: https://github.com/apache/tvm/issues/16566#issuecomment-2066754902
@LeiWang1999 I tried using your commit but I still receive an error:
```
Error message: The stmt tir.For#0 doesn't match the tensor intrin
The pattern attempting to be matched:
for y_o in range(T.int64(32)):
k_o = T.int64()
with T.block("res_update"):
v_y_o_i = T.axis.spatial(T.int64(32), y_o)
v_k_o_i = T.axis.reduce(T.int64(64), k_o)
res = T.Buffer((T.int64(1), T.int64(32)), "int8")
v_x_o_o = T.int64()
p0 = T.Buffer((T.int64(1), T.int64(64)), "int8")
fused_constant = T.Buffer((64, 32), "int8")
fused_constant_1 = T.Buffer((32,), "int32")
T.reads(res[v_x_o_o, v_y_o_i], p0[v_x_o_o, v_k_o_i],
fused_constant[v_k_o_i, v_y_o_i], fused_constant_1[v_y_o_i])
T.writes(res[v_x_o_o, v_y_o_i])
T.block_attr({"scale": T.float32(0.0007562367245554924)})
res[v_x_o_o, v_y_o_i] = res[v_x_o_o, v_y_o_i] + (p0[v_x_o_o,
v_k_o_i] * fused_constant[v_k_o_i, v_y_o_i] + T.Cast("int8",
fused_constant_1[v_y_o_i]))
Does not match the tensorize description:
for j in range(T.int64(32)):
k = T.int64()
with T.block(""):
vii = T.axis.spatial(T.int64(1), T.int64(0))
vjj = T.axis.spatial(T.int64(32), j)
vkk = T.axis.reduce(T.int64(64), k)
C = T.Buffer((1, T.int64(32)), "int8", offset_factor=1)
A = T.Buffer((1, T.int64(64)), "int8", offset_factor=1)
B = T.Buffer((T.int64(64), T.int64(32)), "int8", offset_factor=1)
Bias = T.Buffer((T.int64(32),), "int32", offset_factor=1)
T.reads(C[T.int64(0), vjj], A[T.int64(0), vkk], B[vkk, vjj],
Bias[vjj])
T.writes(C[T.int64(0), vjj])
C[T.int64(0), vjj] = C[T.int64(0), vjj] + (A[T.int64(0), vkk] *
B[vkk, vjj] + T.Cast("int8", Bias[vjj]))
CompareArray array size mismatch. lhs.size()=2 vs rhs.size()=3
BlockRealizeNode iter_values do not match: op->iter_values=[y_o, k_o] vs
rhs->iter_values=[T.int64(0), j, k]
```
So that is still the original error. I have found a way to change the
`DeriveBlockBinding` function in order to keep all iter_values but that leads
to this new error:
```
Error message: The stmt tir.For#0 doesn't match the tensor intrin
The pattern attempting to be matched:
for y_o in range(T.int64(32)):
x_o = T.int64()
k_o = T.int64()
with T.block("res_update"):
v_x_o_i = T.axis.spatial(T.int64(1), x_o)
v_y_o_i = T.axis.spatial(T.int64(32), y_o)
v_k_o_i = T.axis.reduce(T.int64(64), k_o)
res = T.Buffer((T.int64(1), T.int64(32)), "int8")
p0 = T.Buffer((T.int64(1), T.int64(64)), "int8")
fused_constant = T.Buffer((64, 32), "int8")
fused_constant_1 = T.Buffer((32,), "int32")
T.reads(res[T.int64(0), v_y_o_i], p0[T.int64(0), v_k_o_i],
fused_constant[v_k_o_i, v_y_o_i], fused_constant_1[v_y_o_i])
T.writes(res[T.int64(0), v_y_o_i])
T.block_attr({"scale": T.float32(0.00083669449668377638)})
res[T.int64(0), v_y_o_i] = res[T.int64(0), v_y_o_i] +
(p0[T.int64(0), v_k_o_i] * fused_constant[v_k_o_i, v_y_o_i] + T.Cast("int8",
fused_constant_1[v_y_o_i]))
Does not match the tensorize description:
for j in range(T.int64(32)):
k = T.int64()
with T.block(""):
vii = T.axis.spatial(T.int64(1), T.int64(0))
vjj = T.axis.spatial(T.int64(32), j)
vkk = T.axis.reduce(T.int64(64), k)
C = T.Buffer((1, T.int64(32)), "int8", offset_factor=1)
A = T.Buffer((1, T.int64(64)), "int8", offset_factor=1)
B = T.Buffer((T.int64(64), T.int64(32)), "int8", offset_factor=1)
Bias = T.Buffer((T.int64(32),), "int32", offset_factor=1)
T.reads(C[T.int64(0), vjj], A[T.int64(0), vkk], B[vkk, vjj],
Bias[vjj])
T.writes(C[T.int64(0), vjj])
C[T.int64(0), vjj] = C[T.int64(0), vjj] + (A[T.int64(0), vkk] *
B[vkk, vjj] + T.Cast("int8", Bias[vjj]))
Expression mismatch: x_o vs T.int64(0)
BlockRealizeNode iter_values do not match: op->iter_values=[x_o, y_o, k_o]
vs rhs->iter_values=[T.int64(0), j, k]
```
The variable is preserved, but the Simplifier applied to the intrin
description replaces the unit loop with a constant. Is there a similar
simplifier that I could apply to the Block? Because that seems to be the only
difference remaining here.
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