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impl ValidatedWeight<RowMajor> {
const MAX_ZERO_PCT: f32 = 80.0;
const MIN_L2_NORM: f32 = 1e-6;
/// Construct a validated row-major weight matrix.
///
/// This is the ONLY constructor. There is no way to create a
/// `ValidatedWeight<ColumnMajor>` because `ColumnMajor` does not exist.
///
/// # Errors
///
/// Returns `ContractValidationError` if validation fails.
pub fn new(
data: Vec<f32>,
out_dim: usize,
in_dim: usize,
name: &str,
) -> Result<Self, ContractValidationError> {
// Gate 1: Shape validation
let expected_len = out_dim * in_dim;
if data.len() != expected_len {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-LAYOUT-CONTRACT-001".to_string(),
message: format!(
"Shape mismatch: got {} elements, expected {} ({}x{})",
data.len(),
expected_len,
out_dim,
in_dim
),
});
}
let stats = TensorStats::compute(&data);
// Gate 2: Density validation
if stats.zero_pct() > Self::MAX_ZERO_PCT {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-DATA-QUALITY-001".to_string(),
message: format!(
"DENSITY FAILURE: {:.1}% zeros (max {}%)",
stats.zero_pct(),
Self::MAX_ZERO_PCT
),
});
}
// Gate 3: NaN validation
if stats.nan_count > 0 {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-DATA-QUALITY-002".to_string(),
message: format!("Contains {} NaN values", stats.nan_count),
});
}
// Gate 4: Inf validation
if stats.inf_count > 0 {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-DATA-QUALITY-002".to_string(),
message: format!("Contains {} Inf values", stats.inf_count),
});
}
// Gate 5: L2 norm validation
if stats.l2_norm < Self::MIN_L2_NORM {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-DATA-QUALITY-003".to_string(),
message: "L2 norm ~0: tensor is effectively empty".to_string(),
});
}
Ok(Self {
data,
out_dim,
in_dim,
name: name.to_string(),
stats,
_layout: PhantomData,
})
}
/// Access the validated data
#[must_use]
pub fn data(&self) -> &[f32] {
&self.data
}
/// Consume and return the inner data
#[must_use]
pub fn into_inner(self) -> Vec<f32> {
self.data
}
/// Get output dimension
#[must_use]
pub fn out_dim(&self) -> usize {
self.out_dim
}
/// Get input dimension
#[must_use]
pub fn in_dim(&self) -> usize {
self.in_dim
}
/// Get tensor name
#[must_use]
pub fn name(&self) -> &str {
&self.name
}
/// Get validation statistics
#[must_use]
pub fn stats(&self) -> &TensorStats {
&self.stats
}
}
// =============================================================================
// VALIDATED VECTOR (for 1D tensors like layer norms)
// =============================================================================
/// Validated 1D tensor (bias, norm weights)
#[derive(Debug, Clone)]
pub struct ValidatedVector {
data: Vec<f32>,
name: String,
stats: TensorStats,
}
impl ValidatedVector {
/// Construct a validated vector
///
/// # Errors
///
/// Returns `ContractValidationError` if validation fails.
pub fn new(
data: Vec<f32>,
expected_len: usize,
name: &str,
) -> Result<Self, ContractValidationError> {
// Gate 0: Zero-length guard (PMAT-332)
// A zero-length norm weight is never valid — it means the tensor is missing.
if expected_len == 0 {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-LAYOUT-CONTRACT-003".to_string(),
message: "Zero-length vector: expected_len must be > 0".to_string(),
});
}
// Gate 1: Length validation
if data.len() != expected_len {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-LAYOUT-CONTRACT-003".to_string(),
message: format!(
"Length mismatch: got {}, expected {}",
data.len(),
expected_len
),
});
}
let stats = TensorStats::compute(&data);
// Gate 2: NaN validation
if stats.nan_count > 0 {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-DATA-QUALITY-002".to_string(),
message: format!("Contains {} NaN values", stats.nan_count),
});
}
// Gate 3: Inf validation
if stats.inf_count > 0 {
return Err(ContractValidationError {
tensor_name: name.to_string(),
rule_id: "F-DATA-QUALITY-002".to_string(),
message: format!("Contains {} Inf values", stats.inf_count),
});
}
Ok(Self {
data,
name: name.to_string(),
stats,
})
}
/// Access the validated data
#[must_use]
pub fn data(&self) -> &[f32] {
&self.data
}
/// Consume and return the inner data
#[must_use]
pub fn into_inner(self) -> Vec<f32> {
self.data
}
/// Get tensor name
#[must_use]
pub fn name(&self) -> &str {
&self.name
}
/// Get validation statistics
#[must_use]
pub fn stats(&self) -> &TensorStats {
&self.stats
}
}