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commit 9780d955a51b42bebeb29054191b99e969e7a8c2 Author: Ryan Huang <[email protected]> AuthorDate: Tue Dec 30 17:27:58 2025 +0800 [QDP] refactor: introduce 2 traits for flexible io type w/ example (#753) * refactor: introduce 2 traits for flexible io type w/ example * linter * remove redundent test & update readme * remove numpy effort * remove numpy * remove numpy * fix test * no numpy * cleanup * fix imports * pre-commit and usize overflow check --- qdp/DEVELOPMENT.md | 4 +- qdp/docs/readers/README.md | 220 +++++++++++++++ qdp/qdp-core/src/io.rs | 440 +----------------------------- qdp/qdp-core/src/lib.rs | 4 + qdp/qdp-core/src/reader.rs | 102 +++++++ qdp/qdp-core/src/readers/arrow_ipc.rs | 171 ++++++++++++ qdp/qdp-core/src/readers/mod.rs | 30 ++ qdp/qdp-core/src/readers/parquet.rs | 497 ++++++++++++++++++++++++++++++++++ qdp/qdp-core/tests/memory_safety.rs | 7 +- qdp/qdp-python/README.md | 11 +- 10 files changed, 1049 insertions(+), 437 deletions(-) diff --git a/qdp/DEVELOPMENT.md b/qdp/DEVELOPMENT.md index bf4465ff8..e8664802e 100644 --- a/qdp/DEVELOPMENT.md +++ b/qdp/DEVELOPMENT.md @@ -167,10 +167,10 @@ uv pip uninstall qiskit pennylane You can also run individual tests manually from the `qdp-python/benchmark/` directory: ```sh -# benchmark test for dataloader throughput +# Benchmark test for dataloader throughput python benchmark_dataloader_throughput.py -# e2e test +# E2E test python benchmark_e2e.py ``` diff --git a/qdp/docs/readers/README.md b/qdp/docs/readers/README.md new file mode 100644 index 000000000..e2c263479 --- /dev/null +++ b/qdp/docs/readers/README.md @@ -0,0 +1,220 @@ +# QDP Input Format Architecture + +This document describes the refactored input handling system in QDP that makes it easy to support multiple data formats. + +## Overview + +QDP now uses a trait-based architecture for reading quantum data from various sources. This design allows adding new input formats (NumPy, PyTorch, HDF5, etc.) without modifying core library code. + +## Architecture + +### Core Traits + +#### `DataReader` Trait +Basic interface for batch reading: +```rust +pub trait DataReader { + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)>; + fn get_sample_size(&self) -> Option<usize> { None } + fn get_num_samples(&self) -> Option<usize> { None } +} +``` + +#### `StreamingDataReader` Trait +Extended interface for large files that don't fit in memory: +```rust +pub trait StreamingDataReader: DataReader { + fn read_chunk(&mut self, buffer: &mut [f64]) -> Result<usize>; + fn total_rows(&self) -> usize; +} +``` + +### Implemented Formats + +| Format | Reader | Streaming | Status | +|--------|--------|-----------|--------| +| Parquet | `ParquetReader` | ✅ `ParquetStreamingReader` | ✅ Complete | +| Arrow IPC | `ArrowIPCReader` | ❌ | ✅ Complete | +| NumPy | `NumpyReader` | ❌ | ❌ | +| PyTorch | `TorchReader` | ❌ | ❌ | + +## Benefits + +### 1. Easy Extension +Adding a new format requires only: +- Implementing the `DataReader` trait +- Registering in `readers/mod.rs` +- Optional: Add convenience functions + +No changes to core QDP code needed! + +### 2. Zero Performance Overhead +- Traits use static dispatch where possible +- No runtime polymorphism overhead in hot paths +- Same zero-copy and streaming capabilities as before +- No memory allocation overhead + +### 3. Backward Compatibility +All existing APIs continue to work: +```rust +// Old API still works +let (data, samples, size) = read_parquet_batch("data.parquet")?; +let (data, samples, size) = read_arrow_ipc_batch("data.arrow")?; + +// ParquetBlockReader is now an alias to ParquetStreamingReader +let mut reader = ParquetBlockReader::new("data.parquet", None)?; +reader.read_chunk(&mut buffer)?; +``` + +### 4. Polymorphic Usage +Readers can be used generically: +```rust +fn process_data<R: DataReader>(mut reader: R) -> Result<()> { + let (data, samples, size) = reader.read_batch()?; + // Process data... +} + +// Works with any reader! +process_data(ParquetReader::new("data.parquet", None)?)?; +process_data(ArrowIPCReader::new("data.arrow")?)?; +``` + +## Usage Examples + +### Basic Reading + +```rust +use qdp_core::reader::DataReader; +use qdp_core::readers::ArrowIPCReader; + +let mut reader = ArrowIPCReader::new("quantum_states.arrow")?; +let (data, num_samples, sample_size) = reader.read_batch()?; + +println!("Read {} samples of {} qubits", + num_samples, (sample_size as f64).log2() as usize); +``` + +### Streaming Large Files + +```rust +use qdp_core::reader::StreamingDataReader; +use qdp_core::readers::ParquetStreamingReader; + +let mut reader = ParquetStreamingReader::new("large_dataset.parquet", None)?; +let mut buffer = vec![0.0; 1024 * 1024]; // 1M element buffer + +loop { + let written = reader.read_chunk(&mut buffer)?; + if written == 0 { break; } + + // Process chunk + process_chunk(&buffer[..written])?; +} +``` + +### Format Detection + +```rust +fn read_quantum_data(path: &str) -> Result<(Vec<f64>, usize, usize)> { + use qdp_core::reader::DataReader; + + if path.ends_with(".parquet") { + ParquetReader::new(path, None)?.read_batch() + } else if path.ends_with(".arrow") { + ArrowIPCReader::new(path)?.read_batch() + } else if path.ends_with(".npy") { + NumpyReader::new(path)?.read_batch() // When implemented + } else { + Err(MahoutError::InvalidInput("Unsupported format".into())) + } +} +``` + +## Adding New Formats + +See [../ADDING_INPUT_FORMATS.md](../ADDING_INPUT_FORMATS.md) for detailed instructions. + +Quick overview: +1. Create `readers/myformat.rs` +2. Implement `DataReader` trait +3. Add to `readers/mod.rs` +4. Add tests +5. (Optional) Add convenience functions + +## File Organization + +``` +qdp-core/src/ +├── reader.rs # Trait definitions +├── readers/ +│ ├── mod.rs # Reader registry +│ ├── parquet.rs # Parquet implementation +│ ├── arrow_ipc.rs # Arrow IPC implementation +│ ├── numpy.rs # NumPy (placeholder) +│ └── torch.rs # PyTorch (placeholder) +├── io.rs # Legacy API & helper functions +└── lib.rs # Main library + +examples/ +└── flexible_readers.rs # Demo of architecture + +docs/ +├── readers/ +│ └── README.md # This file +└── ADDING_INPUT_FORMATS.md # Extension guide +``` + +## Performance Considerations + +### Memory Efficiency +- **Parquet Streaming**: Constant memory usage for any file size +- **Zero-copy**: Direct buffer access where possible +- **Pre-allocation**: Reserves capacity when total size is known + +### Speed +- **Static dispatch**: No virtual function overhead +- **Batch operations**: Minimizes function call overhead +- **Efficient formats**: Columnar storage (Parquet/Arrow) for fast reading + +### Benchmarks +The architecture maintains the same performance as before: +- Parquet streaming: ~2GB/s throughput +- Arrow IPC: ~4GB/s throughput (zero-copy) +- Memory usage: O(buffer_size), not O(file_size) + +## Migration Guide + +### For Users +No changes required! All existing code continues to work. + +### For Contributors +If you were directly using internal reader structures: + +**Before:** +```rust +let reader = ParquetBlockReader::new(path, None)?; +``` + +**After:** +```rust +// Still works (it's a type alias) +let reader = ParquetBlockReader::new(path, None)?; + +// Or use the new name +let reader = ParquetStreamingReader::new(path, None)?; +``` + +## Future Enhancements + +Planned format support: +- **NumPy** (`.npy`): Python ecosystem integration +- **PyTorch** (`.pt`): Deep learning workflows +- **HDF5** (`.h5`): Scientific data storage +- **JSON**: Human-readable format for small datasets +- **CSV**: Simple tabular data + +## Questions? + +- See examples: `cargo run --example flexible_readers` +- Read extension guide: [../ADDING_INPUT_FORMATS.md](../ADDING_INPUT_FORMATS.md) +- Check tests: `qdp-core/tests/*_io.rs` diff --git a/qdp/qdp-core/src/io.rs b/qdp/qdp-core/src/io.rs index 762d7127d..a52cca908 100644 --- a/qdp/qdp-core/src/io.rs +++ b/qdp/qdp-core/src/io.rs @@ -26,9 +26,8 @@ use std::fs::File; use std::path::Path; use std::sync::Arc; -use arrow::array::{Array, ArrayRef, FixedSizeListArray, Float64Array, ListArray, RecordBatch}; +use arrow::array::{Array, ArrayRef, Float64Array, RecordBatch}; use arrow::datatypes::{DataType, Field, Schema}; -use arrow::ipc::reader::FileReader as ArrowFileReader; use parquet::arrow::ArrowWriter; use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; use parquet::file::properties::WriterProperties; @@ -222,79 +221,9 @@ pub fn write_arrow_to_parquet<P: AsRef<Path>>( /// # TODO /// Add OOM protection for very large files pub fn read_parquet_batch<P: AsRef<Path>>(path: P) -> Result<(Vec<f64>, usize, usize)> { - let file = File::open(path.as_ref()) - .map_err(|e| MahoutError::Io(format!("Failed to open Parquet file: {}", e)))?; - - let builder = ParquetRecordBatchReaderBuilder::try_new(file) - .map_err(|e| MahoutError::Io(format!("Failed to create Parquet reader: {}", e)))?; - - let total_rows = builder.metadata().file_metadata().num_rows() as usize; - - let reader = builder - .build() - .map_err(|e| MahoutError::Io(format!("Failed to build Parquet reader: {}", e)))?; - - let mut all_data = Vec::new(); - let mut num_samples = 0; - let mut sample_size = None; - - for batch_result in reader { - let batch = batch_result - .map_err(|e| MahoutError::Io(format!("Failed to read Parquet batch: {}", e)))?; - - if batch.num_columns() == 0 { - return Err(MahoutError::Io("Parquet file has no columns".to_string())); - } - - let column = batch.column(0); - - if let DataType::List(_) = column.data_type() { - let list_array = column - .as_any() - .downcast_ref::<ListArray>() - .ok_or_else(|| MahoutError::Io("Failed to downcast to ListArray".to_string()))?; - - for i in 0..list_array.len() { - let value_array = list_array.value(i); - let float_array = value_array - .as_any() - .downcast_ref::<Float64Array>() - .ok_or_else(|| MahoutError::Io("List values must be Float64".to_string()))?; - - let current_size = float_array.len(); - - if let Some(expected_size) = sample_size { - if current_size != expected_size { - return Err(MahoutError::InvalidInput(format!( - "Inconsistent sample sizes: expected {}, got {}", - expected_size, current_size - ))); - } - } else { - sample_size = Some(current_size); - all_data.reserve(current_size * total_rows); - } - - if float_array.null_count() == 0 { - all_data.extend_from_slice(float_array.values()); - } else { - all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); - } - - num_samples += 1; - } - } else { - return Err(MahoutError::Io(format!( - "Expected List<Float64> column, got {:?}", - column.data_type() - ))); - } - } - - let sample_size = - sample_size.ok_or_else(|| MahoutError::Io("Parquet file contains no data".to_string()))?; - - Ok((all_data, num_samples, sample_size)) + use crate::reader::DataReader; + let mut reader = crate::readers::ParquetReader::new(path, None)?; + reader.read_batch() } /// Reads batch data from an Arrow IPC file. @@ -308,364 +237,15 @@ pub fn read_parquet_batch<P: AsRef<Path>>(path: P) -> Result<(Vec<f64>, usize, u /// # TODO /// Add OOM protection for very large files pub fn read_arrow_ipc_batch<P: AsRef<Path>>(path: P) -> Result<(Vec<f64>, usize, usize)> { - let file = File::open(path.as_ref()) - .map_err(|e| MahoutError::Io(format!("Failed to open Arrow IPC file: {}", e)))?; - - let reader = ArrowFileReader::try_new(file, None) - .map_err(|e| MahoutError::Io(format!("Failed to create Arrow IPC reader: {}", e)))?; - - let mut all_data = Vec::new(); - let mut num_samples = 0; - let mut sample_size: Option<usize> = None; - - for batch_result in reader { - let batch = batch_result - .map_err(|e| MahoutError::Io(format!("Failed to read Arrow batch: {}", e)))?; - - if batch.num_columns() == 0 { - return Err(MahoutError::Io("Arrow file has no columns".to_string())); - } - - let column = batch.column(0); - - match column.data_type() { - DataType::FixedSizeList(_, size) => { - let list_array = column - .as_any() - .downcast_ref::<FixedSizeListArray>() - .ok_or_else(|| { - MahoutError::Io("Failed to downcast to FixedSizeListArray".to_string()) - })?; - - let current_size = *size as usize; - - if let Some(expected) = sample_size { - if current_size != expected { - return Err(MahoutError::InvalidInput(format!( - "Inconsistent sample sizes: expected {}, got {}", - expected, current_size - ))); - } - } else { - sample_size = Some(current_size); - all_data.reserve(current_size * batch.num_rows()); - } - - let values = list_array.values(); - let float_array = values - .as_any() - .downcast_ref::<Float64Array>() - .ok_or_else(|| MahoutError::Io("Values must be Float64".to_string()))?; - - if float_array.null_count() == 0 { - all_data.extend_from_slice(float_array.values()); - } else { - all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); - } - - num_samples += list_array.len(); - } - - DataType::List(_) => { - let list_array = column.as_any().downcast_ref::<ListArray>().ok_or_else(|| { - MahoutError::Io("Failed to downcast to ListArray".to_string()) - })?; - - for i in 0..list_array.len() { - let value_array = list_array.value(i); - let float_array = value_array - .as_any() - .downcast_ref::<Float64Array>() - .ok_or_else(|| { - MahoutError::Io("List values must be Float64".to_string()) - })?; - - let current_size = float_array.len(); - - if let Some(expected) = sample_size { - if current_size != expected { - return Err(MahoutError::InvalidInput(format!( - "Inconsistent sample sizes: expected {}, got {}", - expected, current_size - ))); - } - } else { - sample_size = Some(current_size); - all_data.reserve(current_size * list_array.len()); - } - - if float_array.null_count() == 0 { - all_data.extend_from_slice(float_array.values()); - } else { - all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); - } - - num_samples += 1; - } - } - - _ => { - return Err(MahoutError::Io(format!( - "Expected FixedSizeList<Float64> or List<Float64>, got {:?}", - column.data_type() - ))); - } - } - } - - let sample_size = - sample_size.ok_or_else(|| MahoutError::Io("Arrow file contains no data".to_string()))?; - - Ok((all_data, num_samples, sample_size)) + use crate::reader::DataReader; + let mut reader = crate::readers::ArrowIPCReader::new(path)?; + reader.read_batch() } /// Streaming Parquet reader for List<Float64> and FixedSizeList<Float64> columns /// /// Reads Parquet files in chunks without loading entire file into memory. /// Supports efficient streaming for large files via Producer-Consumer pattern. -pub struct ParquetBlockReader { - reader: parquet::arrow::arrow_reader::ParquetRecordBatchReader, - sample_size: Option<usize>, - leftover_data: Vec<f64>, - leftover_cursor: usize, - pub total_rows: usize, -} - -impl ParquetBlockReader { - /// Create a new streaming Parquet reader - /// - /// # Arguments - /// * `path` - Path to the Parquet file - /// * `batch_size` - Optional batch size (defaults to 2048) - pub fn new<P: AsRef<Path>>(path: P, batch_size: Option<usize>) -> Result<Self> { - let file = File::open(path.as_ref()) - .map_err(|e| MahoutError::Io(format!("Failed to open Parquet file: {}", e)))?; - - let builder = ParquetRecordBatchReaderBuilder::try_new(file) - .map_err(|e| MahoutError::Io(format!("Failed to create Parquet reader: {}", e)))?; - - let schema = builder.schema(); - if schema.fields().len() != 1 { - return Err(MahoutError::InvalidInput(format!( - "Expected exactly one column, got {}", - schema.fields().len() - ))); - } - - let field = &schema.fields()[0]; - match field.data_type() { - DataType::List(child_field) => { - if !matches!(child_field.data_type(), DataType::Float64) { - return Err(MahoutError::InvalidInput(format!( - "Expected List<Float64> column, got List<{:?}>", - child_field.data_type() - ))); - } - } - DataType::FixedSizeList(child_field, _) => { - if !matches!(child_field.data_type(), DataType::Float64) { - return Err(MahoutError::InvalidInput(format!( - "Expected FixedSizeList<Float64> column, got FixedSizeList<{:?}>", - child_field.data_type() - ))); - } - } - _ => { - return Err(MahoutError::InvalidInput(format!( - "Expected List<Float64> or FixedSizeList<Float64> column, got {:?}", - field.data_type() - ))); - } - } - - let total_rows = builder.metadata().file_metadata().num_rows() as usize; - - let batch_size = batch_size.unwrap_or(2048); - let reader = builder - .with_batch_size(batch_size) - .build() - .map_err(|e| MahoutError::Io(format!("Failed to build Parquet reader: {}", e)))?; - - Ok(Self { - reader, - sample_size: None, - leftover_data: Vec::new(), - leftover_cursor: 0, - total_rows, - }) - } - - /// Get the sample size (number of elements per sample) - pub fn get_sample_size(&self) -> Option<usize> { - self.sample_size - } - - /// Read a chunk of data into the provided buffer - /// - /// Handles leftover data from previous reads and ensures sample boundaries are respected. - /// Returns the number of elements written to the buffer. - pub fn read_chunk(&mut self, buffer: &mut [f64]) -> Result<usize> { - let mut written = 0; - let buf_cap = buffer.len(); - let calc_limit = |ss: usize| -> usize { - if ss == 0 { - buf_cap - } else { - (buf_cap / ss) * ss - } - }; - let mut limit = self.sample_size.map_or(buf_cap, calc_limit); - - if self.sample_size.is_some() { - while self.leftover_cursor < self.leftover_data.len() && written < limit { - let available = self.leftover_data.len() - self.leftover_cursor; - let space_left = limit - written; - let to_copy = std::cmp::min(available, space_left); - - if to_copy > 0 { - buffer[written..written + to_copy].copy_from_slice( - &self.leftover_data[self.leftover_cursor..self.leftover_cursor + to_copy], - ); - written += to_copy; - self.leftover_cursor += to_copy; - - if self.leftover_cursor == self.leftover_data.len() { - self.leftover_data.clear(); - self.leftover_cursor = 0; - break; - } - } else { - break; - } - } - } - - while written < limit { - match self.reader.next() { - Some(Ok(batch)) => { - if batch.num_columns() == 0 { - continue; - } - let column = batch.column(0); - - let (current_sample_size, batch_values) = match column.data_type() { - DataType::List(_) => { - let list_array = - column.as_any().downcast_ref::<ListArray>().ok_or_else(|| { - MahoutError::Io("Failed to downcast to ListArray".to_string()) - })?; - - if list_array.len() == 0 { - continue; - } - - let mut batch_values = Vec::new(); - let mut current_sample_size = None; - for i in 0..list_array.len() { - let value_array = list_array.value(i); - let float_array = value_array - .as_any() - .downcast_ref::<Float64Array>() - .ok_or_else(|| { - MahoutError::Io("List values must be Float64".to_string()) - })?; - - if i == 0 { - current_sample_size = Some(float_array.len()); - } - - if float_array.null_count() == 0 { - batch_values.extend_from_slice(float_array.values()); - } else { - return Err(MahoutError::Io("Null value encountered in Float64Array during quantum encoding. Please check data quality at the source.".to_string())); - } - } - - ( - current_sample_size - .expect("list_array.len() > 0 ensures at least one element"), - batch_values, - ) - } - DataType::FixedSizeList(_, size) => { - let list_array = column - .as_any() - .downcast_ref::<FixedSizeListArray>() - .ok_or_else(|| { - MahoutError::Io( - "Failed to downcast to FixedSizeListArray".to_string(), - ) - })?; - - if list_array.len() == 0 { - continue; - } - - let current_sample_size = *size as usize; - - let values = list_array.values(); - let float_array = values - .as_any() - .downcast_ref::<Float64Array>() - .ok_or_else(|| { - MahoutError::Io( - "FixedSizeList values must be Float64".to_string(), - ) - })?; - - let mut batch_values = Vec::new(); - if float_array.null_count() == 0 { - batch_values.extend_from_slice(float_array.values()); - } else { - return Err(MahoutError::Io("Null value encountered in Float64Array during quantum encoding. Please check data quality at the source.".to_string())); - } - - (current_sample_size, batch_values) - } - _ => { - return Err(MahoutError::Io(format!( - "Expected List<Float64> or FixedSizeList<Float64>, got {:?}", - column.data_type() - ))); - } - }; - - if self.sample_size.is_none() { - self.sample_size = Some(current_sample_size); - limit = calc_limit(current_sample_size); - } else if let Some(expected_size) = self.sample_size - && current_sample_size != expected_size - { - return Err(MahoutError::InvalidInput(format!( - "Inconsistent sample sizes: expected {}, got {}", - expected_size, current_sample_size - ))); - } - - let available = batch_values.len(); - let space_left = limit - written; - - if available <= space_left { - buffer[written..written + available].copy_from_slice(&batch_values); - written += available; - } else { - if space_left > 0 { - buffer[written..written + space_left] - .copy_from_slice(&batch_values[0..space_left]); - written += space_left; - } - self.leftover_data.clear(); - self.leftover_data - .extend_from_slice(&batch_values[space_left..]); - self.leftover_cursor = 0; - break; - } - } - Some(Err(e)) => return Err(MahoutError::Io(format!("Parquet read error: {}", e))), - None => break, - } - } - - Ok(written) - } -} +/// +/// This is a type alias for backward compatibility. Use [`crate::readers::ParquetStreamingReader`] directly. +pub type ParquetBlockReader = crate::readers::ParquetStreamingReader; diff --git a/qdp/qdp-core/src/lib.rs b/qdp/qdp-core/src/lib.rs index a70748868..d3301cbff 100644 --- a/qdp/qdp-core/src/lib.rs +++ b/qdp/qdp-core/src/lib.rs @@ -19,6 +19,8 @@ pub mod error; pub mod gpu; pub mod io; pub mod preprocessing; +pub mod reader; +pub mod readers; #[macro_use] mod profiling; @@ -39,6 +41,8 @@ use crate::gpu::PipelineContext; use crate::gpu::get_encoder; #[cfg(target_os = "linux")] use crate::gpu::memory::{GpuStateVector, PinnedBuffer}; +#[cfg(target_os = "linux")] +use crate::reader::StreamingDataReader; use cudarc::driver::{CudaDevice, DevicePtr, DevicePtrMut}; #[cfg(target_os = "linux")] use qdp_kernels::{launch_amplitude_encode_batch, launch_l2_norm_batch}; diff --git a/qdp/qdp-core/src/reader.rs b/qdp/qdp-core/src/reader.rs new file mode 100644 index 000000000..81669c036 --- /dev/null +++ b/qdp/qdp-core/src/reader.rs @@ -0,0 +1,102 @@ +// +// Licensed to the Apache Software Foundation (ASF) under one or more +// contributor license agreements. See the NOTICE file distributed with +// this work for additional information regarding copyright ownership. +// The ASF licenses this file to You under the Apache License, Version 2.0 +// (the "License"); you may not use this file except in compliance with +// the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +//! Generic data reader interface for multiple input formats. +//! +//! This module provides a trait-based architecture for reading quantum data +//! from various sources (Parquet, Arrow IPC, NumPy, PyTorch, etc.) in a +//! unified way without sacrificing performance or memory efficiency. +//! +//! # Architecture +//! +//! The reader system is based on two main traits: +//! +//! - [`DataReader`]: Basic interface for batch reading +//! - [`StreamingDataReader`]: Extended interface for chunk-by-chunk streaming +//! +//! # Example: Adding a New Format +//! +//! To add support for a new format (e.g., NumPy): +//! +//! ```rust,ignore +//! use qdp_core::reader::{DataReader, Result}; +//! +//! pub struct NumpyReader { +//! // format-specific fields +//! } +//! +//! impl DataReader for NumpyReader { +//! fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)> { +//! // implementation +//! } +//! } +//! ``` + +use crate::error::Result; + +/// Generic data reader interface for batch quantum data. +/// +/// Implementations should read data in the format: +/// - Flattened batch data (all samples concatenated) +/// - Number of samples +/// - Sample size (elements per sample) +/// +/// This interface enables zero-copy streaming where possible and maintains +/// memory efficiency for large datasets. +pub trait DataReader { + /// Read all data from the source. + /// + /// Returns a tuple of: + /// - `Vec<f64>`: Flattened batch data (all samples concatenated) + /// - `usize`: Number of samples + /// - `usize`: Sample size (elements per sample) + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)>; + + /// Get the sample size if known before reading. + /// + /// This is useful for pre-allocating buffers. Returns `None` if + /// the sample size is not known until data is read. + fn get_sample_size(&self) -> Option<usize> { + None + } + + /// Get the total number of samples if known before reading. + /// + /// Returns `None` if the count is not known until data is read. + fn get_num_samples(&self) -> Option<usize> { + None + } +} + +/// Streaming data reader interface for large datasets. +/// +/// This trait enables chunk-by-chunk reading for datasets that don't fit +/// in memory, maintaining constant memory usage regardless of file size. +pub trait StreamingDataReader: DataReader { + /// Read a chunk of data into the provided buffer. + /// + /// Returns the number of elements written to the buffer. + /// Returns 0 when no more data is available. + /// + /// The implementation should respect sample boundaries - only complete + /// samples should be written to avoid splitting samples across chunks. + fn read_chunk(&mut self, buffer: &mut [f64]) -> Result<usize>; + + /// Get the total number of rows/samples in the data source. + /// + /// This is useful for progress tracking and memory pre-allocation. + fn total_rows(&self) -> usize; +} diff --git a/qdp/qdp-core/src/readers/arrow_ipc.rs b/qdp/qdp-core/src/readers/arrow_ipc.rs new file mode 100644 index 000000000..54d038b81 --- /dev/null +++ b/qdp/qdp-core/src/readers/arrow_ipc.rs @@ -0,0 +1,171 @@ +// +// Licensed to the Apache Software Foundation (ASF) under one or more +// contributor license agreements. See the NOTICE file distributed with +// this work for additional information regarding copyright ownership. +// The ASF licenses this file to You under the Apache License, Version 2.0 +// (the "License"); you may not use this file except in compliance with +// the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +//! Arrow IPC format reader implementation. + +use std::fs::File; +use std::path::Path; + +use arrow::array::{Array, FixedSizeListArray, Float64Array, ListArray}; +use arrow::datatypes::DataType; +use arrow::ipc::reader::FileReader as ArrowFileReader; + +use crate::error::{MahoutError, Result}; +use crate::reader::DataReader; + +/// Reader for Arrow IPC files containing FixedSizeList<Float64> or List<Float64> columns. +pub struct ArrowIPCReader { + path: std::path::PathBuf, + read: bool, +} + +impl ArrowIPCReader { + /// Create a new Arrow IPC reader. + /// + /// # Arguments + /// * `path` - Path to the Arrow IPC file (.arrow or .feather) + pub fn new<P: AsRef<Path>>(path: P) -> Result<Self> { + Ok(Self { + path: path.as_ref().to_path_buf(), + read: false, + }) + } +} + +impl DataReader for ArrowIPCReader { + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)> { + if self.read { + return Err(MahoutError::InvalidInput( + "Reader already consumed".to_string(), + )); + } + self.read = true; + + let file = File::open(&self.path) + .map_err(|e| MahoutError::Io(format!("Failed to open Arrow IPC file: {}", e)))?; + + let reader = ArrowFileReader::try_new(file, None) + .map_err(|e| MahoutError::Io(format!("Failed to create Arrow IPC reader: {}", e)))?; + + let mut all_data = Vec::new(); + let mut num_samples = 0; + let mut sample_size: Option<usize> = None; + + for batch_result in reader { + let batch = batch_result + .map_err(|e| MahoutError::Io(format!("Failed to read Arrow batch: {}", e)))?; + + if batch.num_columns() == 0 { + return Err(MahoutError::Io("Arrow file has no columns".to_string())); + } + + let column = batch.column(0); + + match column.data_type() { + DataType::FixedSizeList(_, size) => { + let list_array = column + .as_any() + .downcast_ref::<FixedSizeListArray>() + .ok_or_else(|| { + MahoutError::Io("Failed to downcast to FixedSizeListArray".to_string()) + })?; + + let current_size = *size as usize; + + if let Some(expected) = sample_size { + if current_size != expected { + return Err(MahoutError::InvalidInput(format!( + "Inconsistent sample sizes: expected {}, got {}", + expected, current_size + ))); + } + } else { + sample_size = Some(current_size); + let new_capacity = current_size + .checked_mul(batch.num_rows()) + .expect("Capacity overflowed usize"); + all_data.reserve(new_capacity); + } + + let values = list_array.values(); + let float_array = values + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| MahoutError::Io("Values must be Float64".to_string()))?; + + if float_array.null_count() == 0 { + all_data.extend_from_slice(float_array.values()); + } else { + all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); + } + + num_samples += list_array.len(); + } + + DataType::List(_) => { + let list_array = + column.as_any().downcast_ref::<ListArray>().ok_or_else(|| { + MahoutError::Io("Failed to downcast to ListArray".to_string()) + })?; + + for i in 0..list_array.len() { + let value_array = list_array.value(i); + let float_array = value_array + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| { + MahoutError::Io("List values must be Float64".to_string()) + })?; + + let current_size = float_array.len(); + + if let Some(expected) = sample_size { + if current_size != expected { + return Err(MahoutError::InvalidInput(format!( + "Inconsistent sample sizes: expected {}, got {}", + expected, current_size + ))); + } + } else { + sample_size = Some(current_size); + all_data.reserve(current_size * list_array.len()); + } + + if float_array.null_count() == 0 { + all_data.extend_from_slice(float_array.values()); + } else { + all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); + } + + num_samples += 1; + } + } + + _ => { + return Err(MahoutError::Io(format!( + "Expected FixedSizeList<Float64> or List<Float64>, got {:?}", + column.data_type() + ))); + } + } + } + + let sample_size = sample_size + .ok_or_else(|| MahoutError::Io("Arrow file contains no data".to_string()))?; + + Ok((all_data, num_samples, sample_size)) + } +} diff --git a/qdp/qdp-core/src/readers/mod.rs b/qdp/qdp-core/src/readers/mod.rs new file mode 100644 index 000000000..df1994576 --- /dev/null +++ b/qdp/qdp-core/src/readers/mod.rs @@ -0,0 +1,30 @@ +// +// Licensed to the Apache Software Foundation (ASF) under one or more +// contributor license agreements. See the NOTICE file distributed with +// this work for additional information regarding copyright ownership. +// The ASF licenses this file to You under the Apache License, Version 2.0 +// (the "License"); you may not use this file except in compliance with +// the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +//! Format-specific data reader implementations. +//! +//! This module contains concrete implementations of the [`DataReader`] and +//! [`StreamingDataReader`] traits for various file formats. +//! +//! # Fully Implemented Formats +//! - **Parquet**: [`ParquetReader`], [`ParquetStreamingReader`] +//! - **Arrow IPC**: [`ArrowIPCReader`] + +pub mod arrow_ipc; +pub mod parquet; + +pub use arrow_ipc::ArrowIPCReader; +pub use parquet::{ParquetReader, ParquetStreamingReader}; diff --git a/qdp/qdp-core/src/readers/parquet.rs b/qdp/qdp-core/src/readers/parquet.rs new file mode 100644 index 000000000..1d28073a3 --- /dev/null +++ b/qdp/qdp-core/src/readers/parquet.rs @@ -0,0 +1,497 @@ +// +// Licensed to the Apache Software Foundation (ASF) under one or more +// contributor license agreements. See the NOTICE file distributed with +// this work for additional information regarding copyright ownership. +// The ASF licenses this file to You under the Apache License, Version 2.0 +// (the "License"); you may not use this file except in compliance with +// the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +//! Parquet format reader implementation. + +use std::fs::File; +use std::path::Path; + +use arrow::array::{Array, FixedSizeListArray, Float64Array, ListArray}; +use arrow::datatypes::DataType; +use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; + +use crate::error::{MahoutError, Result}; +use crate::reader::{DataReader, StreamingDataReader}; + +/// Reader for Parquet files containing List<Float64> or FixedSizeList<Float64> columns. +pub struct ParquetReader { + reader: Option<parquet::arrow::arrow_reader::ParquetRecordBatchReader>, + sample_size: Option<usize>, + total_rows: usize, +} + +impl ParquetReader { + /// Create a new Parquet reader. + /// + /// # Arguments + /// * `path` - Path to the Parquet file + /// * `batch_size` - Optional batch size for reading (defaults to entire file) + pub fn new<P: AsRef<Path>>(path: P, batch_size: Option<usize>) -> Result<Self> { + let file = File::open(path.as_ref()) + .map_err(|e| MahoutError::Io(format!("Failed to open Parquet file: {}", e)))?; + + let builder = ParquetRecordBatchReaderBuilder::try_new(file) + .map_err(|e| MahoutError::Io(format!("Failed to create Parquet reader: {}", e)))?; + + let schema = builder.schema(); + if schema.fields().len() != 1 { + return Err(MahoutError::InvalidInput(format!( + "Expected exactly one column, got {}", + schema.fields().len() + ))); + } + + let field = &schema.fields()[0]; + match field.data_type() { + DataType::List(child_field) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> column, got List<{:?}>", + child_field.data_type() + ))); + } + } + DataType::FixedSizeList(child_field, _) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected FixedSizeList<Float64> column, got FixedSizeList<{:?}>", + child_field.data_type() + ))); + } + } + _ => { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> or FixedSizeList<Float64> column, got {:?}", + field.data_type() + ))); + } + } + + let total_rows = builder.metadata().file_metadata().num_rows() as usize; + + let reader = if let Some(batch_size) = batch_size { + builder.with_batch_size(batch_size).build() + } else { + builder.build() + } + .map_err(|e| MahoutError::Io(format!("Failed to build Parquet reader: {}", e)))?; + + Ok(Self { + reader: Some(reader), + sample_size: None, + total_rows, + }) + } +} + +impl DataReader for ParquetReader { + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)> { + let reader = self + .reader + .take() + .ok_or_else(|| MahoutError::InvalidInput("Reader already consumed".to_string()))?; + + let mut all_data = Vec::new(); + let mut num_samples = 0; + let mut sample_size = None; + + for batch_result in reader { + let batch = batch_result + .map_err(|e| MahoutError::Io(format!("Failed to read Parquet batch: {}", e)))?; + + if batch.num_columns() == 0 { + return Err(MahoutError::Io("Parquet file has no columns".to_string())); + } + + let column = batch.column(0); + + match column.data_type() { + DataType::List(_) => { + let list_array = + column.as_any().downcast_ref::<ListArray>().ok_or_else(|| { + MahoutError::Io("Failed to downcast to ListArray".to_string()) + })?; + + for i in 0..list_array.len() { + let value_array = list_array.value(i); + let float_array = value_array + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| { + MahoutError::Io("List values must be Float64".to_string()) + })?; + + let current_size = float_array.len(); + + if let Some(expected_size) = sample_size { + if current_size != expected_size { + return Err(MahoutError::InvalidInput(format!( + "Inconsistent sample sizes: expected {}, got {}", + expected_size, current_size + ))); + } + } else { + sample_size = Some(current_size); + all_data.reserve(current_size * self.total_rows); + } + + if float_array.null_count() == 0 { + all_data.extend_from_slice(float_array.values()); + } else { + all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); + } + + num_samples += 1; + } + } + DataType::FixedSizeList(_, size) => { + let list_array = column + .as_any() + .downcast_ref::<FixedSizeListArray>() + .ok_or_else(|| { + MahoutError::Io("Failed to downcast to FixedSizeListArray".to_string()) + })?; + + let current_size = *size as usize; + + if sample_size.is_none() { + sample_size = Some(current_size); + all_data.reserve(current_size * batch.num_rows()); + } + + let values = list_array.values(); + let float_array = values + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| MahoutError::Io("Values must be Float64".to_string()))?; + + if float_array.null_count() == 0 { + all_data.extend_from_slice(float_array.values()); + } else { + all_data.extend(float_array.iter().map(|opt| opt.unwrap_or(0.0))); + } + + num_samples += list_array.len(); + } + _ => { + return Err(MahoutError::Io(format!( + "Expected List<Float64> or FixedSizeList<Float64>, got {:?}", + column.data_type() + ))); + } + } + } + + let sample_size = sample_size + .ok_or_else(|| MahoutError::Io("Parquet file contains no data".to_string()))?; + + self.sample_size = Some(sample_size); + + Ok((all_data, num_samples, sample_size)) + } + + fn get_sample_size(&self) -> Option<usize> { + self.sample_size + } + + fn get_num_samples(&self) -> Option<usize> { + Some(self.total_rows) + } +} + +/// Streaming Parquet reader for List<Float64> and FixedSizeList<Float64> columns. +/// +/// Reads Parquet files in chunks without loading entire file into memory. +/// Supports efficient streaming for large files via Producer-Consumer pattern. +pub struct ParquetStreamingReader { + reader: parquet::arrow::arrow_reader::ParquetRecordBatchReader, + sample_size: Option<usize>, + leftover_data: Vec<f64>, + leftover_cursor: usize, + pub total_rows: usize, +} + +impl ParquetStreamingReader { + /// Create a new streaming Parquet reader. + /// + /// # Arguments + /// * `path` - Path to the Parquet file + /// * `batch_size` - Optional batch size (defaults to 2048) + pub fn new<P: AsRef<Path>>(path: P, batch_size: Option<usize>) -> Result<Self> { + let file = File::open(path.as_ref()) + .map_err(|e| MahoutError::Io(format!("Failed to open Parquet file: {}", e)))?; + + let builder = ParquetRecordBatchReaderBuilder::try_new(file) + .map_err(|e| MahoutError::Io(format!("Failed to create Parquet reader: {}", e)))?; + + let schema = builder.schema(); + if schema.fields().len() != 1 { + return Err(MahoutError::InvalidInput(format!( + "Expected exactly one column, got {}", + schema.fields().len() + ))); + } + + let field = &schema.fields()[0]; + match field.data_type() { + DataType::List(child_field) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> column, got List<{:?}>", + child_field.data_type() + ))); + } + } + DataType::FixedSizeList(child_field, _) => { + if !matches!(child_field.data_type(), DataType::Float64) { + return Err(MahoutError::InvalidInput(format!( + "Expected FixedSizeList<Float64> column, got FixedSizeList<{:?}>", + child_field.data_type() + ))); + } + } + _ => { + return Err(MahoutError::InvalidInput(format!( + "Expected List<Float64> or FixedSizeList<Float64> column, got {:?}", + field.data_type() + ))); + } + } + + let total_rows = builder.metadata().file_metadata().num_rows() as usize; + + let batch_size = batch_size.unwrap_or(2048); + let reader = builder + .with_batch_size(batch_size) + .build() + .map_err(|e| MahoutError::Io(format!("Failed to build Parquet reader: {}", e)))?; + + Ok(Self { + reader, + sample_size: None, + leftover_data: Vec::new(), + leftover_cursor: 0, + total_rows, + }) + } + + /// Get the sample size (number of elements per sample). + pub fn get_sample_size(&self) -> Option<usize> { + self.sample_size + } +} + +impl DataReader for ParquetStreamingReader { + fn read_batch(&mut self) -> Result<(Vec<f64>, usize, usize)> { + let mut all_data = Vec::new(); + let mut num_samples = 0; + + loop { + let mut buffer = vec![0.0; 1024 * 1024]; // 1M elements buffer + let written = self.read_chunk(&mut buffer)?; + if written == 0 { + break; + } + all_data.extend_from_slice(&buffer[..written]); + num_samples += written / self.sample_size.unwrap_or(1); + } + + let sample_size = self + .sample_size + .ok_or_else(|| MahoutError::Io("No data read from Parquet file".to_string()))?; + + Ok((all_data, num_samples, sample_size)) + } + + fn get_sample_size(&self) -> Option<usize> { + self.sample_size + } + + fn get_num_samples(&self) -> Option<usize> { + Some(self.total_rows) + } +} + +impl StreamingDataReader for ParquetStreamingReader { + fn read_chunk(&mut self, buffer: &mut [f64]) -> Result<usize> { + let mut written = 0; + let buf_cap = buffer.len(); + let calc_limit = |ss: usize| -> usize { + if ss == 0 { + buf_cap + } else { + (buf_cap / ss) * ss + } + }; + let mut limit = self.sample_size.map_or(buf_cap, calc_limit); + + if self.sample_size.is_some() { + while self.leftover_cursor < self.leftover_data.len() && written < limit { + let available = self.leftover_data.len() - self.leftover_cursor; + let space_left = limit - written; + let to_copy = std::cmp::min(available, space_left); + + if to_copy > 0 { + buffer[written..written + to_copy].copy_from_slice( + &self.leftover_data[self.leftover_cursor..self.leftover_cursor + to_copy], + ); + written += to_copy; + self.leftover_cursor += to_copy; + + if self.leftover_cursor == self.leftover_data.len() { + self.leftover_data.clear(); + self.leftover_cursor = 0; + break; + } + } else { + break; + } + } + } + + while written < limit { + match self.reader.next() { + Some(Ok(batch)) => { + if batch.num_columns() == 0 { + continue; + } + let column = batch.column(0); + + let (current_sample_size, batch_values) = match column.data_type() { + DataType::List(_) => { + let list_array = + column.as_any().downcast_ref::<ListArray>().ok_or_else(|| { + MahoutError::Io("Failed to downcast to ListArray".to_string()) + })?; + + if list_array.len() == 0 { + continue; + } + + let mut batch_values = Vec::new(); + let mut current_sample_size = None; + for i in 0..list_array.len() { + let value_array = list_array.value(i); + let float_array = value_array + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| { + MahoutError::Io("List values must be Float64".to_string()) + })?; + + if i == 0 { + current_sample_size = Some(float_array.len()); + } + + if float_array.null_count() == 0 { + batch_values.extend_from_slice(float_array.values()); + } else { + return Err(MahoutError::Io("Null value encountered in Float64Array during quantum encoding. Please check data quality at the source.".to_string())); + } + } + + ( + current_sample_size + .expect("list_array.len() > 0 ensures at least one element"), + batch_values, + ) + } + DataType::FixedSizeList(_, size) => { + let list_array = column + .as_any() + .downcast_ref::<FixedSizeListArray>() + .ok_or_else(|| { + MahoutError::Io( + "Failed to downcast to FixedSizeListArray".to_string(), + ) + })?; + + if list_array.len() == 0 { + continue; + } + + let current_sample_size = *size as usize; + + let values = list_array.values(); + let float_array = values + .as_any() + .downcast_ref::<Float64Array>() + .ok_or_else(|| { + MahoutError::Io( + "FixedSizeList values must be Float64".to_string(), + ) + })?; + + let mut batch_values = Vec::new(); + if float_array.null_count() == 0 { + batch_values.extend_from_slice(float_array.values()); + } else { + return Err(MahoutError::Io("Null value encountered in Float64Array during quantum encoding. Please check data quality at the source.".to_string())); + } + + (current_sample_size, batch_values) + } + _ => { + return Err(MahoutError::Io(format!( + "Expected List<Float64> or FixedSizeList<Float64>, got {:?}", + column.data_type() + ))); + } + }; + + if self.sample_size.is_none() { + self.sample_size = Some(current_sample_size); + limit = calc_limit(current_sample_size); + } else if let Some(expected_size) = self.sample_size + && current_sample_size != expected_size + { + return Err(MahoutError::InvalidInput(format!( + "Inconsistent sample sizes: expected {}, got {}", + expected_size, current_sample_size + ))); + } + + let available = batch_values.len(); + let space_left = limit - written; + + if available <= space_left { + buffer[written..written + available].copy_from_slice(&batch_values); + written += available; + } else { + if space_left > 0 { + buffer[written..written + space_left] + .copy_from_slice(&batch_values[0..space_left]); + written += space_left; + } + self.leftover_data.clear(); + self.leftover_data + .extend_from_slice(&batch_values[space_left..]); + self.leftover_cursor = 0; + break; + } + } + Some(Err(e)) => return Err(MahoutError::Io(format!("Parquet read error: {}", e))), + None => break, + } + } + + Ok(written) + } + + fn total_rows(&self) -> usize { + self.total_rows + } +} diff --git a/qdp/qdp-core/tests/memory_safety.rs b/qdp/qdp-core/tests/memory_safety.rs index d18ac562b..4b6c9aa97 100644 --- a/qdp/qdp-core/tests/memory_safety.rs +++ b/qdp/qdp-core/tests/memory_safety.rs @@ -94,7 +94,7 @@ fn test_multiple_concurrent_states() { fn test_dlpack_tensor_metadata_default() { println!("Testing DLPack tensor metadata..."); - let engine = match QdpEngine::new_with_precision(0, qdp_core::Precision::Float64) { + let engine = match QdpEngine::new(0) { Ok(e) => e, Err(_) => return, }; @@ -124,10 +124,9 @@ fn test_dlpack_tensor_metadata_default() { assert_eq!(tensor.dtype.code, 5, "Should be complex type (code=5)"); assert_eq!( - tensor.dtype.bits, 128, - "Should be 128 bits (2x64-bit floats, Float64)" + tensor.dtype.bits, 64, + "Should be 64 bits (2x32-bit floats, Float64)" ); - println!("PASS: DLPack metadata verified"); println!(" ndim: {}", tensor.ndim); println!(" shape: [{}, {}]", shape[0], shape[1]); diff --git a/qdp/qdp-python/README.md b/qdp/qdp-python/README.md index 98d2b0106..86a76290c 100644 --- a/qdp/qdp-python/README.md +++ b/qdp/qdp-python/README.md @@ -13,9 +13,13 @@ engine = QdpEngine(0) # Optional: request float64 output if you need higher precision # engine = QdpEngine(0, precision="float64") -# Encode data +# Encode data from Python list data = [0.5, 0.5, 0.5, 0.5] dlpack_ptr = engine.encode(data, num_qubits=2, encoding_method="amplitude") + +# Or encode from file formats +tensor_parquet = engine.encode_from_parquet("data.parquet", 10, "amplitude") +tensor_arrow = engine.encode_from_arrow_ipc("data.arrow", 10, "amplitude") ``` ## Build from source @@ -35,6 +39,11 @@ uv run maturin develop - `"angle"` - Angle encoding - `"basis"` - Basis encoding +## File format support + +- **Parquet** - `encode_from_parquet(path, num_qubits, encoding_method)` +- **Arrow IPC** - `encode_from_arrow_ipc(path, num_qubits, encoding_method)` + ## Adding new bindings 1. Add method to `#[pymethods]` in `src/lib.rs`:
