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[Testing] Generalize matmul truncation numerical testing #1152

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[Testing] Generalize matmul truncation numerical testing #1152

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17 changes: 16 additions & 1 deletion build_tools/ci/cpu_comparison/matmul_template/matmul_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,19 @@ def get_higher_order_element_type(element_type):


def generate_matmul_test(
output_fn, input_fn, m, n, k, lhs_rhs_type, acc_type, b=0, m0=0, n0=0, k0=0
output_fn,
input_fn,
m,
n,
k,
lhs_rhs_type,
acc_type,
b=0,
m0=0,
n0=0,
k0=0,
trunci_scale=None,
trunci_shift=None,
):
"""
Generate mlir file (output_fn) from the template file (input_fn).
Expand All @@ -34,6 +46,9 @@ def generate_matmul_test(
# This is only used for batch matmul.
replace["B"] = b

replace["TRUNCI_SCALE"] = trunci_scale
replace["TRUNCI_SHIFT"] = trunci_shift

# m0, n0, k0 are only used for matmul4d as inner dim sizes.
replace["M0"] = m0
replace["N0"] = n0
Expand Down
13 changes: 7 additions & 6 deletions build_tools/ci/cpu_comparison/matmul_template/matmul_trunci_scaling_MxK_KxN.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -2,15 +2,16 @@
// input ${K}x${N}x${TYPE1}

// Matmul + Trunci variant with scaling.
// In an actual quantized model, truncating from a higher bitwidth to a lower precision bitwidth
// won't work and we need to scale.
// Since the output of the Matmul here is an integer cannot be multiplied with a floating point
// scale factor, we need to represent the scale factor with a multiplier and a shift operator instead.
// In an actual quantized model, truncating from a higher bitwidth to a lower
// precision bitwidth won't work and we need to scale. Since the output of the
// matmul here is an integer cannot be multiplied with a floating point
// scale factor, we need to represent the scale factor with a multiplier and a
// shift operator instead.
func.func @matmul_trunci(%arg0: tensor<${M}x${K}x${TYPE1}>, %arg1: tensor<${K}x${N}x${TYPE1}>) -> tensor<${M}x${N}x${TYPE1}>
{
%cst = arith.constant ${ZERO} : ${TYPE2}
%cst_mul = arith.constant 10 : ${TYPE_MUL_RESULT}
%cst_shift = arith.constant 7 : ${TYPE_MUL_RESULT}
%cst_mul = arith.constant ${TRUNCI_SCALE} : ${TYPE_MUL_RESULT}
%cst_shift = arith.constant ${TRUNCI_SHIFT} : ${TYPE_MUL_RESULT}
%0 = tensor.empty() : tensor<${M}x${N}x${TYPE2}>
%i8out = tensor.empty() : tensor<${M}x${N}x${TYPE1}>
%1 = linalg.fill ins(%cst : ${TYPE2}) outs(%0 : tensor<${M}x${N}x${TYPE2}>) -> tensor<${M}x${N}x${TYPE2}>
Expand Down
50 changes: 30 additions & 20 deletions build_tools/ci/cpu_comparison/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -733,10 +733,9 @@ def __init__(
K,
input_type,
acc_type,
lhs,
rhs,
expected_out,
test_params=None,
trunci_scale=10,
trunci_shift=7,
):
super().__init__(
name=f"matmul_scale_trunci_{M}_{N}_{K}_{input_type}_{acc_type}",
Expand All @@ -749,23 +748,42 @@ def __init__(
)
self.labels.append("MatmulScaleTrunci")

# Assertions on shapes: Check that lhs is MxK, rhs is KxN, and expected_out is MxN
assert lhs.shape == (M, K)
assert rhs.shape == (K, N)
assert expected_out.shape == (M, N)
self.trunci_scale = trunci_scale
self.trunci_shift = trunci_shift
self.lhs = np.random.randint(0, 3, (self.M, self.K)).astype(np.int8)
self.rhs = np.random.randint(0, 3, (self.K, self.N)).astype(np.int8)
self.expected_out = np.right_shift(
(
(self.lhs.astype(np.int32) @ self.rhs.astype(np.int32))
* self.trunci_scale
),
self.trunci_shift,
).astype(np.int8)

self.lhs = lhs
self.rhs = rhs
self.expected_out = expected_out
# Assertions on shapes: Check that lhs is MxK, rhs is KxN, and expected_out is MxN
assert self.expected_out.shape == (M, N)

def _execute(self, config):
matmul_template_dir = config.file_dir / "matmul_template"
template_name = matmul_template_dir / "matmul_trunci_scaling_MxK_KxN.mlir"
self.generate(config, template_name)

generate_matmul_test(
self.get_filename(config),
template_name,
k=self.K,
m=self.M,
n=self.N,
lhs_rhs_type=self.input_type,
acc_type=self.acc_type,
trunci_scale=self.trunci_scale,
trunci_shift=self.trunci_shift,
)

filename = self.get_filename(config)
input_args = generate_inputs(
filename, self.get_dir(config), 1, {1: self.lhs, 2: self.rhs}
)

aie_vs_baseline(
config=config,
aie_compilation_flags=self.aie_compilation_flags,
Expand Down Expand Up @@ -1576,9 +1594,6 @@ def __init__(self):
128,
"i8",
"i32",
2 * np.ones([256, 128], dtype=np.int8),
3 * np.ones([128, 256], dtype=np.int8),
60 * np.ones([256, 256], dtype=np.int8),
test_params=TestParams(
name_suffix="scaling",
tile_pipeline="pack-peel-4-level-tiling",
Expand All @@ -1599,9 +1614,6 @@ def __init__(self):
128,
"i8",
"i32",
2 * np.ones([256, 128], dtype=np.int8),
3 * np.ones([128, 256], dtype=np.int8),
60 * np.ones([256, 256], dtype=np.int8),
test_params=TestParams(
tile_pipeline="pack-peel-4-level-tiling",
run_on_target=["npu1_4col"],
Expand All @@ -1620,9 +1632,6 @@ def __init__(self):
128,
"i8",
"i32",
2 * np.ones([256, 128], dtype=np.int8),
3 * np.ones([128, 256], dtype=np.int8),
60 * np.ones([256, 256], dtype=np.int8),
test_params=TestParams(
tile_pipeline="pack-peel-4-level-tiling",
run_on_target=["npu4"],
Expand All @@ -1636,6 +1645,7 @@ def __init__(self):
),
)
)

# Matmul with truncf test(s):
for tile_pipeline in ["pack-peel", "pack-peel-4-level-tiling"]:
self.register(
Expand Down
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