[
https://issues.apache.org/jira/browse/MAHOUT-878?focusedWorklogId=1001186&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-1001186
]
ASF GitHub Bot logged work on MAHOUT-878:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 21/Jan/26 14:32
Start Date: 21/Jan/26 14:32
Worklog Time Spent: 10m
Work Description: ryankert01 commented on code in PR #881:
URL: https://github.com/apache/mahout/pull/881#discussion_r2712826273
##########
qdp/qdp-python/src/lib.rs:
##########
@@ -171,6 +171,145 @@ fn validate_tensor(tensor: &Bound<'_, PyAny>) ->
PyResult<()> {
Ok(())
}
+/// Check if a PyTorch tensor is on a CUDA device
+fn is_cuda_tensor(tensor: &Bound<'_, PyAny>) -> PyResult<bool> {
+ let device = tensor.getattr("device")?;
+ let device_type: String = device.getattr("type")?.extract()?;
+ Ok(device_type == "cuda")
+}
+
+/// Get the CUDA device index from a PyTorch tensor
+fn get_tensor_device_id(tensor: &Bound<'_, PyAny>) -> PyResult<i32> {
+ let device = tensor.getattr("device")?;
+ let device_index: i32 = device.getattr("index")?.extract()?;
+ Ok(device_index)
+}
+
+/// Validate a CUDA tensor for direct GPU encoding
+/// Checks: dtype=float64, contiguous, non-empty, device_id matches engine
+fn validate_cuda_tensor_for_encoding(
+ tensor: &Bound<'_, PyAny>,
+ expected_device_id: usize,
+ encoding_method: &str,
+) -> PyResult<()> {
+ // Check encoding method support (currently only amplitude is supported
for CUDA tensors)
+ if encoding_method != "amplitude" {
+ return Err(PyRuntimeError::new_err(format!(
+ "CUDA tensor encoding currently only supports 'amplitude' method,
got '{}'. \
+ Use tensor.cpu() to convert to CPU tensor for other encoding
methods.",
+ encoding_method
+ )));
+ }
+
+ // Check dtype is float64
+ let dtype = tensor.getattr("dtype")?;
+ let dtype_str: String = dtype.str()?.extract()?;
+ if !dtype_str.contains("float64") {
+ return Err(PyRuntimeError::new_err(format!(
+ "CUDA tensor must have dtype float64, got {}. Use
tensor.to(torch.float64)",
+ dtype_str
+ )));
+ }
+
+ // Check contiguous
+ let is_contiguous: bool = tensor.call_method0("is_contiguous")?.extract()?;
+ if !is_contiguous {
+ return Err(PyRuntimeError::new_err(
+ "CUDA tensor must be contiguous. Use tensor.contiguous()",
+ ));
+ }
+
+ // Check non-empty
+ let numel: usize = tensor.call_method0("numel")?.extract()?;
+ if numel == 0 {
+ return Err(PyRuntimeError::new_err("CUDA tensor cannot be empty"));
+ }
+
+ // Check device matches engine
+ let tensor_device_id = get_tensor_device_id(tensor)?;
+ if tensor_device_id as usize != expected_device_id {
+ return Err(PyRuntimeError::new_err(format!(
+ "Device mismatch: tensor is on cuda:{}, but engine is on cuda:{}. \
+ Move tensor with tensor.to('cuda:{}')",
+ tensor_device_id, expected_device_id, expected_device_id
+ )));
+ }
+
+ Ok(())
+}
+
+/// DLPack tensor information extracted from a PyCapsule
+struct DLPackTensorInfo {
+ data_ptr: *const f64,
+ shape: Vec<i64>,
+ /// CUDA device ID from DLPack metadata.
+ /// Currently unused but kept for potential future device validation or
multi-GPU support.
+ #[allow(dead_code)]
+ device_id: i32,
+}
+
+/// Extract GPU pointer from PyTorch tensor's __dlpack__() capsule
+///
+/// # Safety
+/// The returned `data_ptr` points to GPU memory owned by the source tensor.
+/// The caller must ensure the source tensor remains alive and unmodified
+/// for the entire duration that `data_ptr` is in use. Python's GIL ensures
+/// the tensor won't be garbage collected during `encode()`, but the caller
+/// must not deallocate or resize the tensor while encoding is in progress.
+fn extract_dlpack_tensor(_py: Python<'_>, tensor: &Bound<'_, PyAny>) ->
PyResult<DLPackTensorInfo> {
Review Comment:
yes, but I'm not sure if I've solved it all, I only solve related ones.
Issue Time Tracking
-------------------
Worklog Id: (was: 1001186)
Time Spent: 4h 20m (was: 4h 10m)
> Provide better examples for the parallel ALS recommender code
> -------------------------------------------------------------
>
> Key: MAHOUT-878
> URL: https://issues.apache.org/jira/browse/MAHOUT-878
> Project: Mahout
> Issue Type: Task
> Affects Versions: 1.0.0
> Reporter: Sebastian Schelter
> Assignee: Sebastian Schelter
> Priority: Major
> Fix For: 0.6
>
> Attachments: MAHOUT-878.patch
>
> Time Spent: 4h 20m
> Remaining Estimate: 0h
>
> We should provide examples that show how to apply the parallel ALS
> recommender to the Netflix or KDD2011 datasets.
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