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[bug fix] Compute fp16 batch norm in fp32 instead of downcasting the params #54

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Merged
gokulkrishna98 merged 1 commit into from
Aug 7, 2026
Merged

[bug fix] Compute fp16 batch norm in fp32 instead of downcasting the params #54

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22 changes: 18 additions & 4 deletions coreai_torch/_aten_to_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -706,16 +706,27 @@ def replace_batch_norm(
def batch_norm(
input: Value, gamma: Value, beta: Value, mean: Value, variance: Value
) -> Value:
# Match ATen: compute in fp32 and narrow only the result. Downcasting
# the fp32 params to fp16 loses precision and overflows to inf.
input_compute, compute_type, needs_downcast = prepare_compute_type_for_norm(
input, ele_type, loc
)

eps_casted = coreai.constant(node.args[6])

eps_casted = coreai.cast(eps_casted, ele_type)
def to_compute_type(v: Value) -> Value:
if v.type.element_type == compute_type:
return v
return coreai.cast(v, compute_type)

eps_casted = to_compute_type(eps_casted)

expand_dims = [0] + list(
range(2, x_type.rank)
) # expand [C] to [1, C, 1, ...] for broadcasting

def expand_and_cast(v: Value) -> Value:
return coreai.cast(coreai.expand_dims(v, expand_dims), ele_type)
return to_compute_type(coreai.expand_dims(v, expand_dims))

weight, bias, running_mean, running_var = (
expand_and_cast(v) for v in [gamma, beta, mean, variance]
Expand All @@ -724,12 +735,15 @@ def expand_and_cast(v: Value) -> Value:
# ((x - mean) / sqrt(var + eps)) * weight + bias
std = coreai.sqrt(coreai.broadcasting_add(running_var, eps_casted))
normalized = coreai.broadcasting_divide(
coreai.broadcasting_sub(input, running_mean), std
coreai.broadcasting_sub(input_compute, running_mean), std
)

return coreai.broadcasting_add(
result = coreai.broadcasting_add(
coreai.broadcasting_mul(normalized, weight), bias
)
if needs_downcast:
result = coreai.cast(result, ele_type)
return result

# In inference mode, outputs[1] and [2] are empty placeholder tensors (shape [0]).
np_dtype = _get_coreai_to_numpy_dtype()[ele_type]
Expand Down
59 changes: 44 additions & 15 deletions tests/ops/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -692,17 +692,9 @@ async def test_nan_propagation(self) -> None:
await validate_numerical_output(model=model, y=y, x=x)


@pytest.mark.parametrize(
"x",
[
torch.rand(2, 3, 8, 8),
torch.rand(2, 3, 8, 8, dtype=torch.float16), # fp16
],
)
@pytest.mark.parametrize(
"dynamic_dims", [tuple(), (0,), (2,), (3,), (0, 2), (0, 3), (0, 2, 3)]
)
async def test_batchnorm(x: Tensor, dynamic_dims: tuple[int]) -> None:
class TestBatchNorm:
"""Tests for native_batch_norm → coreai batch_norm composite."""

class BatchNormModel(nn.Module):
def __init__(self) -> None:
super().__init__()
Expand All @@ -711,10 +703,47 @@ def __init__(self) -> None:
def forward(self, x: Tensor) -> Tensor:
return self.bn(x)

model = BatchNormModel().eval()
dim_names = {0: "batch", 1: "channels", 2: "height", 3: "width"}
dynamic_shapes = make_dynamic_shapes(x={d: dim_names[d] for d in dynamic_dims})
await validate_numerical_output(model=model, x=x, dynamic_shapes=dynamic_shapes)
@pytest.mark.parametrize(
"x",
[
torch.rand(2, 3, 8, 8),
torch.rand(2, 3, 8, 8, dtype=torch.float16), # fp16
],
)
@pytest.mark.parametrize(
"dynamic_dims", [tuple(), (0,), (2,), (3,), (0, 2), (0, 3), (0, 2, 3)]
)
async def test_basic(self, x: Tensor, dynamic_dims: tuple[int]) -> None:
model = self.BatchNormModel().eval()
dim_names = {0: "batch", 1: "channels", 2: "height", 3: "width"}
dynamic_shapes = make_dynamic_shapes(x={d: dim_names[d] for d in dynamic_dims})
await validate_numerical_output(model=model, x=x, dynamic_shapes=dynamic_shapes)

@pytest.mark.parametrize(
"running_var, running_mean, weight",
[
# running_var past the fp16 max (65504) becomes inf when the params are
# downcast before the sqrt, which silently zeroes the whole output.
([1e6, 2e5, 9e4], [0.0, 1.0, -2.0], [2000.0, 1000.0, 500.0]),
# Tiny variances make eps and the param mantissas precision-critical.
([1e-6, 4e-5, 2.0], [0.5, -0.25, 100.0], [1.0, 2.0, 0.5]),
],
)
async def test_fp16_input_fp32_params(
self,
running_var: list[float],
running_mean: list[float],
weight: list[float],
) -> None:
"""An fp16 activation with fp32 params must be computed in fp32."""
model = self.BatchNormModel().eval()
model.bn.running_var.data = torch.tensor(running_var)
model.bn.running_mean.data = torch.tensor(running_mean)
model.bn.weight.data = torch.tensor(weight)
model.bn.bias.data = torch.tensor([0.0, 1.0, -1.0])

x = (torch.rand(2, 3, 4, 4) * 2 - 1).half()
await validate_numerical_output(model=model, x=x)


@pytest.mark.parametrize("dynamic", [False, True])
Expand Down
53 changes: 45 additions & 8 deletions tests/ops/test_ops_ir.py
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Original file line number Diff line number Diff line change
Expand Up @@ -577,16 +577,16 @@ def forward(self, x: Tensor) -> Tensor:


class TestBatchNormIR:
def test_static_composite(self) -> None:
class BatchNormModel(nn.Module):
def __init__(self):
super().__init__()
self.bn = nn.BatchNorm2d(3)
class BatchNormModel(nn.Module):
def __init__(self):
super().__init__()
self.bn = nn.BatchNorm2d(3)

def forward(self, x: Tensor) -> Tensor:
return self.bn(x)
def forward(self, x: Tensor) -> Tensor:
return self.bn(x)

ir = get_ir(BatchNormModel().eval(), x=torch.rand(1, 3, 4, 4))
def test_static_composite(self) -> None:
ir = get_ir(self.BatchNormModel().eval(), x=torch.rand(1, 3, 4, 4))
filecheck_pattern(
ir,
check_file="""
Expand Down Expand Up @@ -616,6 +616,43 @@ def forward(self, x: Tensor) -> Tensor:
""",
)

def test_fp16_input_computes_in_fp32(self) -> None:
"""fp16 input is promoted, the fp32 params are used as-is, and only the
result is narrowed back to fp16."""
ir = get_ir(
self.BatchNormModel().eval(), x=torch.rand(1, 3, 4, 4, dtype=torch.float16)
)
filecheck_pattern(
ir,
check_file="""
// CHECK-LABEL: module {
// CHECK-NEXT: coreai.graph private noinline @batch_norm_{{.*}}(%[[ARG0:.*]]: tensor<1x3x4x4xf16> {coreai.name = "input"}, %[[ARG1:.*]]: tensor<3xf32> {coreai.name = "gamma"}, %[[ARG2:.*]]: tensor<3xf32> {coreai.name = "beta"}, %[[ARG3:.*]]: tensor<3xf32> {coreai.name = "mean"}, %[[ARG4:.*]]: tensor<3xf32> {coreai.name = "variance"}) -> tensor<1x3x4x4xf16> attributes {__coreai_pure__, composite_decl = #coreai.composite_declaration<"batch_norm" = {input_names = ["input", "gamma", "beta", "mean", "variance"], op_attrs = {eps = 9.99999974E-6 : f32, version = 1 : si64}, output_names = ["output"]}>, template_op = "batch_norm"} {
// CHECK-NEXT: %[[V0:.*]] = coreai.constant dense<[1, 3, 1, 1]> : tensor<4xui32>
// CHECK-NEXT: %[[V1:.*]] = coreai.constant dense<9.99999974E-6> : tensor<f32>
// CHECK-NEXT: %[[V2:.*]] = coreai.cast %[[ARG0]] : tensor<1x3x4x4xf16> to tensor<1x3x4x4xf32>
// CHECK-NEXT: %[[V3:.*]] = coreai.reshape %[[ARG1]], %[[V0]] : (tensor<3xf32>, tensor<4xui32>) -> tensor<1x3x1x1xf32>
// CHECK-NEXT: %[[V4:.*]] = coreai.reshape %[[ARG2]], %[[V0]] : (tensor<3xf32>, tensor<4xui32>) -> tensor<1x3x1x1xf32>
// CHECK-NEXT: %[[V5:.*]] = coreai.reshape %[[ARG3]], %[[V0]] : (tensor<3xf32>, tensor<4xui32>) -> tensor<1x3x1x1xf32>
// CHECK-NEXT: %[[V6:.*]] = coreai.decomposable.broadcasting_add %[[ARG4]], %[[V1]] : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
// CHECK-NEXT: %[[V7:.*]] = coreai.reshape %[[V6]], %[[V0]] : (tensor<3xf32>, tensor<4xui32>) -> tensor<1x3x1x1xf32>
// CHECK-NEXT: %[[V8:.*]] = coreai.sqrt %[[V7]] : tensor<1x3x1x1xf32> -> tensor<1x3x1x1xf32>
// CHECK-NEXT: %[[V9:.*]] = coreai.decomposable.broadcasting_sub %[[V2]], %[[V5]] : (tensor<1x3x4x4xf32>, tensor<1x3x1x1xf32>) -> tensor<1x3x4x4xf32>
// CHECK-NEXT: %[[V10:.*]] = coreai.decomposable.broadcasting_divide %[[V9]], %[[V8]] : (tensor<1x3x4x4xf32>, tensor<1x3x1x1xf32>) -> tensor<1x3x4x4xf32>
// CHECK-NEXT: %[[V11:.*]] = coreai.decomposable.broadcasting_mul %[[V10]], %[[V3]] : (tensor<1x3x4x4xf32>, tensor<1x3x1x1xf32>) -> tensor<1x3x4x4xf32>
// CHECK-NEXT: %[[V12:.*]] = coreai.decomposable.broadcasting_add %[[V11]], %[[V4]] : (tensor<1x3x4x4xf32>, tensor<1x3x1x1xf32>) -> tensor<1x3x4x4xf32>
// CHECK-NEXT: %[[V13:.*]] = coreai.cast %[[V12]] : tensor<1x3x4x4xf32> to tensor<1x3x4x4xf16>
// CHECK-NEXT: coreai.output %[[V13]] : tensor<1x3x4x4xf16>
// CHECK-NEXT: }
// CHECK-NEXT: coreai.graph @main(%[[ARG0]]: tensor<1x3x4x4xf16> {coreai.name = "x"}) -> (tensor<1x3x4x4xf16> {coreai.name = "{{.*}}"}) attributes {__coreai_pure__} {
// CHECK-NEXT: %[[V0]] = coreai.constant dense<1.000000e+00> : tensor<3xf32>
// CHECK-NEXT: %[[V1]] = coreai.constant dense<0.000000e+00> : tensor<3xf32>
// CHECK-NEXT: %[[V2]] = coreai.invoke @batch_norm_{{.*}}(%[[ARG0]], %[[V0]], %[[V1]], %[[V1]], %[[V0]]) : (tensor<1x3x4x4xf16>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<1x3x4x4xf16>
// CHECK-NEXT: coreai.output %[[V2]] : tensor<1x3x4x4xf16>
// CHECK-NEXT: }
// CHECK-NEXT: }
""",
)


class TestToCopyIR:
def test_cast_f32_to_f16(self) -> None:
Expand Down