Change python doc push script to print the undocumented modules

.ci/docker/common/install_onnx.sh

@@ -19,7 +19,7 @@ pip_install \
   transformers==4.36.2
 
 pip_install coloredlogs packaging
-pip_install onnxruntime==1.23.1
+pip_install onnxruntime==1.23.0
 pip_install onnxscript==0.5.4
 
 # Cache the transformers model to be used later by ONNX tests. We need to run the transformers

.ci/docker/requirements-ci.txt

@@ -334,12 +334,12 @@ sympy==1.13.3
 #Pinned versions:
 #test that import:
 
-onnx==1.19.1
+onnx==1.18.0
 #Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:
 
-onnxscript==0.5.4
+onnxscript==0.5.3
 #Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:

.ci/docker/requirements-docs.txt

@@ -1,15 +1,11 @@
-sphinx==5.3.0
+sphinx==7.2.6
 #Description: This is used to generate PyTorch docs
-#Pinned versions: 5.3.0
+#Pinned versions: 7.2.6
 
-standard-imghdr==3.13.0; python_version >= "3.13"
-#Description: This is needed by Sphinx, so it needs to be added here.
-# The reasons are as follows:
-# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
-# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
-# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
+pytorch_sphinx_theme2==0.1.0
+#Description: This is needed to generate PyTorch docs
+#Pinned versions: 0.1.0
 
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
 # TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
 # but it doesn't seem to work and hangs around idly. The initial thought that it is probably
 # something related to Docker setup. We can investigate this later.
@@ -36,17 +32,17 @@ tensorboard==2.18.0 ; python_version >= "3.13"
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 2.13.0
 
-breathe==4.34.0
+breathe==4.36.0
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.34.0
+#Pinned versions: 4.36.0
 
-exhale==0.2.3
+exhale==0.3.7
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.2.3
+#Pinned versions: 0.3.7
 
-docutils==0.16
+docutils==0.20
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.16
+#Pinned versions: 0.20
 
 bs4==0.0.1
 #Description: This is used to generate PyTorch C++ docs
@@ -56,13 +52,13 @@ IPython==8.12.0
 #Description: This is used to generate PyTorch functorch docs
 #Pinned versions: 8.12.0
 
-myst-nb==0.17.2
+myst-nb==1.3.0
 #Description: This is used to generate PyTorch functorch and torch.compile docs.
-#Pinned versions: 0.17.2
+#Pinned versions: 1.3.0
 
 # The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
 python-etcd==0.4.5
 sphinx-copybutton==0.5.0
-sphinx-design==0.4.0
+sphinx-design==0.6.1
 sphinxcontrib-mermaid==1.0.0
-myst-parser==0.18.1
+myst-parser==4.0.1

.ci/lumen_cli/pyproject.toml

@@ -6,7 +6,7 @@ dependencies = [
     "GitPython==3.1.45",
     "docker==7.1.0",
     "pytest==7.3.2",
-    "uv==0.9.5"
+    "uv==0.8.6"
 ]
 
 [tool.setuptools]

.ci/pytorch/python_doc_push_script.sh

@@ -102,8 +102,18 @@ if [ "$is_main_doc" = true ]; then
     echo coverage output not found
     exit 1
   elif [ $undocumented -gt 0 ]; then
-    echo undocumented objects found:
+    echo "======================================"
+    echo "ERROR: $undocumented undocumented objects found!"
+    echo "======================================"
+    echo ""
+    echo "Full coverage report:"
     cat build/coverage/python.txt
+    echo ""
+    echo "======================================"
+    echo "Undocumented modules/objects (lines after TOTAL):"
+    tail -n +$((lines - undocumented + 1)) build/coverage/python.txt
+    echo "======================================"
+    echo ""
     echo "Make sure you've updated relevant .rsts in docs/source!"
     echo "You can reproduce locally by running 'cd docs && make coverage && cat build/coverage/python.txt'"
     exit 1

.claude/skills/docstring/SKILL.md

@@ -1,359 +0,0 @@
----
-name: docstring
-description: Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
----
-
-# PyTorch Docstring Writing Guide
-
-This skill describes how to write docstrings for functions and methods in the PyTorch project, following the conventions in `torch/_tensor_docs.py` and `torch/nn/functional.py`.
-
-## General Principles
-
-- Use **raw strings** (`r"""..."""`) for all docstrings to avoid issues with LaTeX/math backslashes
-- Follow **Sphinx/reStructuredText** (reST) format for documentation
-- Be **concise but complete** - include all essential information
-- Always include **examples** when possible
-- Use **cross-references** to related functions/classes
-
-## Docstring Structure
-
-### 1. Function Signature (First Line)
-
-Start with the function signature showing all parameters:
-
-```python
-r"""function_name(param1, param2, *, kwarg1=default1, kwarg2=default2) -> ReturnType
-```
-
-**Notes:**
-- Include the function name
-- Show positional and keyword-only arguments (use `*` separator)
-- Include default values
-- Show return type annotation
-- This line should NOT end with a period
-
-### 2. Brief Description
-
-Provide a one-line description of what the function does:
-
-```python
-r"""conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
-
-Applies a 2D convolution over an input image composed of several input
-planes.
-```
-
-### 3. Mathematical Formulas (if applicable)
-
-Use Sphinx math directives for mathematical expressions:
-
-```python
-.. math::
-    \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
-```
-
-Or inline math: `:math:\`x^2\``
-
-### 4. Cross-References
-
-Link to related classes and functions using Sphinx roles:
-
-- `:class:\`~torch.nn.ModuleName\`` - Link to a class
-- `:func:\`torch.function_name\`` - Link to a function
-- `:meth:\`~Tensor.method_name\`` - Link to a method
-- `:attr:\`attribute_name\`` - Reference an attribute
-- The `~` prefix shows only the last component (e.g., `Conv2d` instead of `torch.nn.Conv2d`)
-
-**Example:**
-```python
-See :class:`~torch.nn.Conv2d` for details and output shape.
-```
-
-### 5. Notes and Warnings
-
-Use admonitions for important information:
-
-```python
-.. note::
-    This function doesn't work directly with NLLLoss,
-    which expects the Log to be computed between the Softmax and itself.
-    Use log_softmax instead (it's faster and has better numerical properties).
-
-.. warning::
-    :func:`new_tensor` always copies :attr:`data`. If you have a Tensor
-    ``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_`
-    or :func:`torch.Tensor.detach`.
-```
-
-### 6. Args Section
-
-Document all parameters with type annotations and descriptions:
-
-```python
-Args:
-    input (Tensor): input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
-    weight (Tensor): filters of shape :math:`(\text{out\_channels} , kH , kW)`
-    bias (Tensor, optional): optional bias tensor of shape :math:`(\text{out\_channels})`. Default: ``None``
-    stride (int or tuple): the stride of the convolving kernel. Can be a single number or a
-      tuple `(sH, sW)`. Default: 1
-```
-
-**Formatting rules:**
-- Parameter name in **lowercase**
-- Type in parentheses: `(Type)`, `(Type, optional)` for optional parameters
-- Description follows the type
-- For optional parameters, include "Default: ``value``" at the end
-- Use double backticks for inline code: ``` ``None`` ```
-- Indent continuation lines by 2 spaces
-
-### 7. Keyword Args Section (if applicable)
-
-Sometimes keyword arguments are documented separately:
-
-```python
-Keyword args:
-    dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
-        Default: if None, same :class:`torch.dtype` as this tensor.
-    device (:class:`torch.device`, optional): the desired device of returned tensor.
-        Default: if None, same :class:`torch.device` as this tensor.
-    requires_grad (bool, optional): If autograd should record operations on the
-        returned tensor. Default: ``False``.
-```
-
-### 8. Returns Section (if needed)
-
-Document the return value:
-
-```python
-Returns:
-    Tensor: Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
-        If ``hard=True``, the returned samples will be one-hot, otherwise they will
-        be probability distributions that sum to 1 across `dim`.
-```
-
-Or simply include it in the function signature line if obvious from context.
-
-### 9. Examples Section
-
-Always include examples when possible:
-
-```python
-Examples::
-
-    >>> inputs = torch.randn(33, 16, 30)
-    >>> filters = torch.randn(20, 16, 5)
-    >>> F.conv1d(inputs, filters)
-
-    >>> # With square kernels and equal stride
-    >>> filters = torch.randn(8, 4, 3, 3)
-    >>> inputs = torch.randn(1, 4, 5, 5)
-    >>> F.conv2d(inputs, filters, padding=1)
-```
-
-**Formatting rules:**
-- Use `Examples::` with double colon
-- Use `>>>` prompt for Python code
-- Include comments with `#` when helpful
-- Show actual output when it helps understanding (indent without `>>>`)
-
-### 10. External References
-
-Link to papers or external documentation:
-
-```python
-.. _Link Name:
-    https://arxiv.org/abs/1611.00712
-```
-
-Reference them in text: ```See `Link Name`_```
-
-## Method Types
-
-### Native Python Functions
-
-For regular Python functions, use a standard docstring:
-
-```python
-def relu(input: Tensor, inplace: bool = False) -> Tensor:
-    r"""relu(input, inplace=False) -> Tensor
-
-    Applies the rectified linear unit function element-wise. See
-    :class:`~torch.nn.ReLU` for more details.
-    """
-    # implementation
-```
-
-### C-Bound Functions (using add_docstr)
-
-For C-bound functions, use `_add_docstr`:
-
-```python
-conv1d = _add_docstr(
-    torch.conv1d,
-    r"""
-conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
-
-Applies a 1D convolution over an input signal composed of several input
-planes.
-
-See :class:`~torch.nn.Conv1d` for details and output shape.
-
-Args:
-    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
-    weight: filters of shape :math:`(\text{out\_channels} , kW)`
-    ...
-""",
-)
-```
-
-### In-Place Variants
-
-For in-place operations (ending with `_`), reference the original:
-
-```python
-add_docstr_all(
-    "abs_",
-    r"""
-abs_() -> Tensor
-
-In-place version of :meth:`~Tensor.abs`
-""",
-)
-```
-
-### Alias Functions
-
-For aliases, simply reference the original:
-
-```python
-add_docstr_all(
-    "absolute",
-    r"""
-absolute() -> Tensor
-
-Alias for :func:`abs`
-""",
-)
-```
-
-## Common Patterns
-
-### Shape Documentation
-
-Use LaTeX math notation for tensor shapes:
-
-```python
-:math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
-```
-
-### Reusable Argument Definitions
-
-For commonly used arguments, define them once and reuse:
-
-```python
-common_args = parse_kwargs(
-    """
-    dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
-        Default: if None, same as this tensor.
-"""
-)
-
-# Then use with .format():
-r"""
-...
-
-Keyword args:
-    {dtype}
-    {device}
-""".format(**common_args)
-```
-
-### Template Insertion
-
-Insert reproducibility notes or other common text:
-
-```python
-r"""
-{tf32_note}
-
-{cudnn_reproducibility_note}
-""".format(**reproducibility_notes, **tf32_notes)
-```
-
-## Complete Example
-
-Here's a complete example showing all elements:
-
-```python
-def gumbel_softmax(
-    logits: Tensor,
-    tau: float = 1,
-    hard: bool = False,
-    eps: float = 1e-10,
-    dim: int = -1,
-) -> Tensor:
-    r"""
-    Sample from the Gumbel-Softmax distribution and optionally discretize.
-
-    Args:
-        logits (Tensor): `[..., num_features]` unnormalized log probabilities
-        tau (float): non-negative scalar temperature
-        hard (bool): if ``True``, the returned samples will be discretized as one-hot vectors,
-              but will be differentiated as if it is the soft sample in autograd. Default: ``False``
-        dim (int): A dimension along which softmax will be computed. Default: -1
-
-    Returns:
-        Tensor: Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
-            If ``hard=True``, the returned samples will be one-hot, otherwise they will
-            be probability distributions that sum to 1 across `dim`.
-
-    .. note::
-        This function is here for legacy reasons, may be removed from nn.Functional in the future.
-
-    Examples::
-        >>> logits = torch.randn(20, 32)
-        >>> # Sample soft categorical using reparametrization trick:
-        >>> F.gumbel_softmax(logits, tau=1, hard=False)
-        >>> # Sample hard categorical using "Straight-through" trick:
-        >>> F.gumbel_softmax(logits, tau=1, hard=True)
-
-    .. _Link 1:
-        https://arxiv.org/abs/1611.00712
-    """
-    # implementation
-```
-
-## Quick Checklist
-
-When writing a PyTorch docstring, ensure:
-
-- [ ] Use raw string (`r"""`)
-- [ ] Include function signature on first line
-- [ ] Provide brief description
-- [ ] Document all parameters in Args section with types
-- [ ] Include default values for optional parameters
-- [ ] Use Sphinx cross-references (`:func:`, `:class:`, `:meth:`)
-- [ ] Add mathematical formulas if applicable
-- [ ] Include at least one example in Examples section
-- [ ] Add warnings/notes for important caveats
-- [ ] Link to related module class with `:class:`
-- [ ] Use proper math notation for tensor shapes
-- [ ] Follow consistent formatting and indentation
-
-## Common Sphinx Roles Reference
-
-- `:class:\`~torch.nn.Module\`` - Class reference
-- `:func:\`torch.function\`` - Function reference
-- `:meth:\`~Tensor.method\`` - Method reference
-- `:attr:\`attribute\`` - Attribute reference
-- `:math:\`equation\`` - Inline math
-- `:ref:\`label\`` - Internal reference
-- ``` ``code`` ``` - Inline code (use double backticks)
-
-## Additional Notes
-
-- **Indentation**: Use 4 spaces for code, 2 spaces for continuation of parameter descriptions
-- **Line length**: Try to keep lines under 100 characters when possible
-- **Periods**: End sentences with periods, but not the signature line
-- **Backticks**: Use double backticks for code: ``` ``True`` ``None`` ``False`` ```
-- **Types**: Common types are `Tensor`, `int`, `float`, `bool`, `str`, `tuple`, `list`, etc.

.claude/skills/skill-writer/SKILL.md

@@ -1,385 +0,0 @@
----
-name: skill-writer
-description: Guide users through creating Agent Skills for Claude Code. Use when the user wants to create, write, author, or design a new Skill, or needs help with SKILL.md files, frontmatter, or skill structure.
----
-
-# Skill Writer
-
-This Skill helps you create well-structured Agent Skills for Claude Code that follow best practices and validation requirements.
-
-## When to use this Skill
-
-Use this Skill when:
-- Creating a new Agent Skill
-- Writing or updating SKILL.md files
-- Designing skill structure and frontmatter
-- Troubleshooting skill discovery issues
-- Converting existing prompts or workflows into Skills
-
-## Instructions
-
-### Step 1: Determine Skill scope
-
-First, understand what the Skill should do:
-
-1. **Ask clarifying questions**:
-   - What specific capability should this Skill provide?
-   - When should Claude use this Skill?
-   - What tools or resources does it need?
-   - Is this for personal use or team sharing?
-
-2. **Keep it focused**: One Skill = one capability
-   - Good: "PDF form filling", "Excel data analysis"
-   - Too broad: "Document processing", "Data tools"
-
-### Step 2: Choose Skill location
-
-Determine where to create the Skill:
-
-**Personal Skills** (`~/.claude/skills/`):
-- Individual workflows and preferences
-- Experimental Skills
-- Personal productivity tools
-
-**Project Skills** (`.claude/skills/`):
-- Team workflows and conventions
-- Project-specific expertise
-- Shared utilities (committed to git)
-
-### Step 3: Create Skill structure
-
-Create the directory and files:
-
-```bash
-# Personal
-mkdir -p ~/.claude/skills/skill-name
-
-# Project
-mkdir -p .claude/skills/skill-name
-```
-
-For multi-file Skills:
-```
-skill-name/
-├── SKILL.md (required)
-├── reference.md (optional)
-├── examples.md (optional)
-├── scripts/
-│   └── helper.py (optional)
-└── templates/
-    └── template.txt (optional)
-```
-
-### Step 4: Write SKILL.md frontmatter
-
-Create YAML frontmatter with required fields:
-
-```yaml
----
-name: skill-name
-description: Brief description of what this does and when to use it
----
-```
-
-**Field requirements**:
-
-- **name**:
-  - Lowercase letters, numbers, hyphens only
-  - Max 64 characters
-  - Must match directory name
-  - Good: `pdf-processor`, `git-commit-helper`
-  - Bad: `PDF_Processor`, `Git Commits!`
-
-- **description**:
-  - Max 1024 characters
-  - Include BOTH what it does AND when to use it
-  - Use specific trigger words users would say
-  - Mention file types, operations, and context
-
-**Optional frontmatter fields**:
-
-- **allowed-tools**: Restrict tool access (comma-separated list)
-  ```yaml
-  allowed-tools: Read, Grep, Glob
-  ```
-  Use for:
-  - Read-only Skills
-  - Security-sensitive workflows
-  - Limited-scope operations
-
-### Step 5: Write effective descriptions
-
-The description is critical for Claude to discover your Skill.
-
-**Formula**: `[What it does] + [When to use it] + [Key triggers]`
-
-**Examples**:
-
-✅ **Good**:
-```yaml
-description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
-```
-
-✅ **Good**:
-```yaml
-description: Analyze Excel spreadsheets, create pivot tables, and generate charts. Use when working with Excel files, spreadsheets, or analyzing tabular data in .xlsx format.
-```
-
-❌ **Too vague**:
-```yaml
-description: Helps with documents
-description: For data analysis
-```
-
-**Tips**:
-- Include specific file extensions (.pdf, .xlsx, .json)
-- Mention common user phrases ("analyze", "extract", "generate")
-- List concrete operations (not generic verbs)
-- Add context clues ("Use when...", "For...")
-
-### Step 6: Structure the Skill content
-
-Use clear Markdown sections:
-
-```markdown
-# Skill Name
-
-Brief overview of what this Skill does.
-
-## Quick start
-
-Provide a simple example to get started immediately.
-
-## Instructions
-
-Step-by-step guidance for Claude:
-1. First step with clear action
-2. Second step with expected outcome
-3. Handle edge cases
-
-## Examples
-
-Show concrete usage examples with code or commands.
-
-## Best practices
-
-- Key conventions to follow
-- Common pitfalls to avoid
-- When to use vs. not use
-
-## Requirements
-
-List any dependencies or prerequisites:
-```bash
-pip install package-name
-```
-
-## Advanced usage
-
-For complex scenarios, see [reference.md](reference.md).
-```
-
-### Step 7: Add supporting files (optional)
-
-Create additional files for progressive disclosure:
-
-**reference.md**: Detailed API docs, advanced options
-**examples.md**: Extended examples and use cases
-**scripts/**: Helper scripts and utilities
-**templates/**: File templates or boilerplate
-
-Reference them from SKILL.md:
-```markdown
-For advanced usage, see [reference.md](reference.md).
-
-Run the helper script:
-\`\`\`bash
-python scripts/helper.py input.txt
-\`\`\`
-```
-
-### Step 8: Validate the Skill
-
-Check these requirements:
-
-✅ **File structure**:
-- [ ] SKILL.md exists in correct location
-- [ ] Directory name matches frontmatter `name`
-
-✅ **YAML frontmatter**:
-- [ ] Opening `---` on line 1
-- [ ] Closing `---` before content
-- [ ] Valid YAML (no tabs, correct indentation)
-- [ ] `name` follows naming rules
-- [ ] `description` is specific and < 1024 chars
-
-✅ **Content quality**:
-- [ ] Clear instructions for Claude
-- [ ] Concrete examples provided
-- [ ] Edge cases handled
-- [ ] Dependencies listed (if any)
-
-✅ **Testing**:
-- [ ] Description matches user questions
-- [ ] Skill activates on relevant queries
-- [ ] Instructions are clear and actionable
-
-### Step 9: Test the Skill
-
-1. **Restart Claude Code** (if running) to load the Skill
-
-2. **Ask relevant questions** that match the description:
-   ```
-   Can you help me extract text from this PDF?
-   ```
-
-3. **Verify activation**: Claude should use the Skill automatically
-
-4. **Check behavior**: Confirm Claude follows the instructions correctly
-
-### Step 10: Debug if needed
-
-If Claude doesn't use the Skill:
-
-1. **Make description more specific**:
-   - Add trigger words
-   - Include file types
-   - Mention common user phrases
-
-2. **Check file location**:
-   ```bash
-   ls ~/.claude/skills/skill-name/SKILL.md
-   ls .claude/skills/skill-name/SKILL.md
-   ```
-
-3. **Validate YAML**:
-   ```bash
-   cat SKILL.md | head -n 10
-   ```
-
-4. **Run debug mode**:
-   ```bash
-   claude --debug
-   ```
-
-## Common patterns
-
-### Read-only Skill
-
-```yaml
----
-name: code-reader
-description: Read and analyze code without making changes. Use for code review, understanding codebases, or documentation.
-allowed-tools: Read, Grep, Glob
----
-```
-
-### Script-based Skill
-
-```yaml
----
-name: data-processor
-description: Process CSV and JSON data files with Python scripts. Use when analyzing data files or transforming datasets.
----
-
-# Data Processor
-
-## Instructions
-
-1. Use the processing script:
-\`\`\`bash
-python scripts/process.py input.csv --output results.json
-\`\`\`
-
-2. Validate output with:
-\`\`\`bash
-python scripts/validate.py results.json
-\`\`\`
-```
-
-### Multi-file Skill with progressive disclosure
-
-```yaml
----
-name: api-designer
-description: Design REST APIs following best practices. Use when creating API endpoints, designing routes, or planning API architecture.
----
-
-# API Designer
-
-Quick start: See [examples.md](examples.md)
-
-Detailed reference: See [reference.md](reference.md)
-
-## Instructions
-
-1. Gather requirements
-2. Design endpoints (see examples.md)
-3. Document with OpenAPI spec
-4. Review against best practices (see reference.md)
-```
-
-## Best practices for Skill authors
-
-1. **One Skill, one purpose**: Don't create mega-Skills
-2. **Specific descriptions**: Include trigger words users will say
-3. **Clear instructions**: Write for Claude, not humans
-4. **Concrete examples**: Show real code, not pseudocode
-5. **List dependencies**: Mention required packages in description
-6. **Test with teammates**: Verify activation and clarity
-7. **Version your Skills**: Document changes in content
-8. **Use progressive disclosure**: Put advanced details in separate files
-
-## Validation checklist
-
-Before finalizing a Skill, verify:
-
-- [ ] Name is lowercase, hyphens only, max 64 chars
-- [ ] Description is specific and < 1024 chars
-- [ ] Description includes "what" and "when"
-- [ ] YAML frontmatter is valid
-- [ ] Instructions are step-by-step
-- [ ] Examples are concrete and realistic
-- [ ] Dependencies are documented
-- [ ] File paths use forward slashes
-- [ ] Skill activates on relevant queries
-- [ ] Claude follows instructions correctly
-
-## Troubleshooting
-
-**Skill doesn't activate**:
-- Make description more specific with trigger words
-- Include file types and operations in description
-- Add "Use when..." clause with user phrases
-
-**Multiple Skills conflict**:
-- Make descriptions more distinct
-- Use different trigger words
-- Narrow the scope of each Skill
-
-**Skill has errors**:
-- Check YAML syntax (no tabs, proper indentation)
-- Verify file paths (use forward slashes)
-- Ensure scripts have execute permissions
-- List all dependencies
-
-## Examples
-
-See the documentation for complete examples:
-- Simple single-file Skill (commit-helper)
-- Skill with tool permissions (code-reviewer)
-- Multi-file Skill (pdf-processing)
-
-## Output format
-
-When creating a Skill, I will:
-
-1. Ask clarifying questions about scope and requirements
-2. Suggest a Skill name and location
-3. Create the SKILL.md file with proper frontmatter
-4. Include clear instructions and examples
-5. Add supporting files if needed
-6. Provide testing instructions
-7. Validate against all requirements
-
-The result will be a complete, working Skill that follows all best practices and validation rules.

.github/actions/setup-rocm/action.yml

@@ -124,10 +124,3 @@ runs:
       id: login-ecr
       continue-on-error: true
       uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
-
-    - name: Preserve github env variables for use in docker
-      shell: bash
-      run: |
-        env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
-        env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
-        env | grep '^RUNNER' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-1752fe6809b74921644866275ab80244b96e80bc
+faffd5cf673615583da6517275e361cb3dbc77e6

.github/ci_configs/vllm/Dockerfile

@@ -283,9 +283,6 @@ RUN --mount=type=bind,source=${TORCH_WHEELS_PATH},target=/dist \
         uv pip install --system $(cat torch_build_versions.txt | xargs) --index-url https://download.pytorch.org/whl/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
     fi
 
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system --pre apache-tvm-ffi==0.1.0b15
-
 # Install the vllm wheel from previous stage
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system /wheels/vllm/*.whl --verbose
@@ -298,8 +295,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 ARG torch_cuda_arch_list='8.0;8.9;9.0a;10.0a;12.0'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 
-# TODO(elainewy): remove this once vllm commit is updated, and install flashinfer from pip
-# see https://github.com/pytorch/pytorch/pull/165274#issuecomment-3408531784
 ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
 ARG FLASHINFER_GIT_REF="v0.2.14.post1"
 

.github/label_to_label.yml

@@ -15,11 +15,6 @@
   - "module: reinplacing"
   then:
   - "module: pt2-dispatcher"
-- any:
-  - "vllm-compile"
-  then:
-  - "module: vllm"
-  - "oncall: pt2"
 - any:
   - "module: vmap"
   then:
@@ -32,6 +27,10 @@
   - "module: pt2 optimizer"
   then:
   - "module: dynamo"
+- any:
+  - "module: flex attention"
+  then:
+  - "module: higher order operators"
 - any:
   - "module: aotinductor"
   then:

.github/workflows/inductor-periodic.yml

@@ -88,6 +88,7 @@ jobs:
     with:
       build-environment: linux-jammy-rocm-py3_10
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
+      sync-tag: rocm-build
       test-matrix: |
         { include: [
           { config: "dynamo_eager_torchbench", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },

.github/workflows/periodic.yml

@@ -147,16 +147,15 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-cuda12.8-py3.10-gcc9-debug
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9
-      cuda-arch-list: 8.9
       test-matrix: |
         { include: [
-          { config: "default", shard: 1, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 2, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 3, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 4, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 5, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 6, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 7, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 1, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 2, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 3, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 4, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 5, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 6, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 7, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
         ]}
     secrets: inherit
 

.github/workflows/pull.yml

@@ -347,8 +347,7 @@ jobs:
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
-      # This should sync with the build in xpu.yml but xpu uses a larger runner
-      # sync-tag: linux-xpu-n-build
+      sync-tag: linux-xpu-n-build
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
       build-environment: linux-jammy-xpu-n-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3

.github/workflows/rocm-mi300.yml

@@ -45,6 +45,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-noble-rocm-py3.12-mi300
       docker-image-name: ci-image:pytorch-linux-noble-rocm-n-py3
+      sync-tag: rocm-build
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1" },

.github/workflows/rocm-mi355.yml

@@ -42,6 +42,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-noble-rocm-py3.12-mi355
       docker-image-name: ci-image:pytorch-linux-noble-rocm-n-py3
+      sync-tag: rocm-build
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },

.github/workflows/rocm-navi31.yml

@@ -26,23 +26,11 @@ jobs:
       id-token: write
       contents: read
 
-  get-label-type:
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
   linux-jammy-rocm-py3_10-build:
     if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
     name: linux-jammy-rocm-py3.10
     uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
     with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-rocm-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
       sync-tag: rocm-build

.github/workflows/rocm.yml

@@ -26,23 +26,11 @@ jobs:
       id-token: write
       contents: read
 
-  get-label-type:
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
   linux-jammy-rocm-py3_10-build:
     if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
     name: linux-jammy-rocm-py3.10
     uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
     with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-rocm-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
       sync-tag: rocm-build

.github/workflows/trunk-tagging.yml

@@ -58,10 +58,8 @@ jobs:
           else
             COMMIT_SHA="${{ github.sha }}"
           fi
-          {
-            echo "sha=${COMMIT_SHA}"
-            echo "tag_name=trunk/${COMMIT_SHA}"
-          } >> "${GITHUB_OUTPUT}"
+          echo "sha=${COMMIT_SHA}" >> "${GITHUB_OUTPUT}"
+          echo "tag_name=trunk/${COMMIT_SHA}" >> "${GITHUB_OUTPUT}"
 
       - name: Validate commit SHA
         run: |
@@ -89,7 +87,7 @@ jobs:
             echo "✅ Commit ${COMMIT_SHA} is valid (automatic push trigger)"
           fi
 
-      - name: Create and push tag(s) with retry
+      - name: Create and push tag with retry
         id: check_tag
         env:
           TAG_NAME: ${{ steps.commit.outputs.tag_name }}
@@ -114,23 +112,14 @@ jobs:
             return 1
           }
 
-          # Counters for summary reporting
-          created_count=0
-          skipped_count=0
-          failed_count=0
+          # Exit early if tag already exists
+          if check_tag_exists; then
+            echo "✅ Tag already exists - no action needed"
+            echo "exists=true" >> "${GITHUB_OUTPUT}"
+            exit 0
+          fi
 
-          # Always write outputs once on exit
-          finish() {
-            set +e
-            if [ -n "${GITHUB_OUTPUT:-}" ]; then
-              {
-                echo "created_count=${created_count}"
-                echo "skipped_count=${skipped_count}"
-                echo "failed_count=${failed_count}"
-              } >> "${GITHUB_OUTPUT}"
-            fi
-          }
-          trap finish EXIT
+          echo "Tag ${TAG_NAME} does not exist, proceeding with creation"
 
           # Retry configuration
           MAX_RETRIES=5
@@ -205,111 +194,31 @@ jobs:
             }
           }
 
-          # New behavior for push events: enumerate commits in the push and tag each one.
-          # For workflow_dispatch, retain existing single-SHA behavior.
-
-          # Always fetch tags once up front to improve idempotency in loops
-          git fetch origin --tags --quiet || true
-
-          if [ "${{ github.event_name }}" = "push" ]; then
-            BEFORE_SHA="${{ github.event.before }}"
-            AFTER_SHA="${{ github.sha }}"  # same as event.after
-
-            # List commits introduced by this push (old..new), oldest first for stable ordering
-            commits_file="$(mktemp)"
-            git rev-list --reverse "${BEFORE_SHA}..${AFTER_SHA}" > "${commits_file}"
-
-            if [ ! -s "${commits_file}" ]; then
-              echo "No new commits found between ${BEFORE_SHA}..${AFTER_SHA}; nothing to tag."
-              rm -f "${commits_file}"
-              exit 0
-            fi
-
-            commit_count="$(wc -l < "${commits_file}" | tr -d ' ')"
-            echo "Found ${commit_count} commit(s) to tag for push:"
-            while IFS= read -r sha; do
-              printf '  %s\n' "${sha}"
-            done < "${commits_file}"
-
-            while IFS= read -r sha; do
-              TAG_NAME="trunk/${sha}"
-              COMMIT_SHA="${sha}"
-
-              # If tag already exists locally or remotely, skip (idempotent)
-              if check_tag_exists; then
-                echo "✅ Tag ${TAG_NAME} already exists - skipping"
-                skipped_count=$((skipped_count + 1))
-                continue
-              fi
-
-              echo "Tag ${TAG_NAME} does not exist, proceeding with creation"
-
-              if retry_with_backoff "tag_with_retry" "Creating tag ${TAG_NAME} for commit ${COMMIT_SHA}"; then
-                created_count=$((created_count + 1))
-              else
-                echo "Tag creation failed after all retry attempts for ${TAG_NAME}"
-                failed_count=$((failed_count + 1))
-              fi
-            done < "${commits_file}"
-
-            rm -f "${commits_file}"
-
-            if [ "${failed_count}" -gt 0 ]; then
-              exit 1
-            fi
+          # Execute with retry
+          if retry_with_backoff "tag_with_retry" "Creating tag ${TAG_NAME} for commit ${COMMIT_SHA}"; then
+            echo "exists=false" >> "${GITHUB_OUTPUT}"
             exit 0
           else
-            # workflow_dispatch path (single SHA tagging preserved)
-
-            # Exit early if tag already exists
-            if check_tag_exists; then
-              echo "✅ Tag already exists - no action needed"
-              skipped_count=1
-              exit 0
-            fi
-
-            echo "Tag ${TAG_NAME} does not exist, proceeding with creation"
-
-            if retry_with_backoff "tag_with_retry" "Creating tag ${TAG_NAME} for commit ${COMMIT_SHA}"; then
-              created_count=1
-              exit 0
-            else
-              echo "Tag creation failed after all retry attempts"
-              failed_count=1
-              exit 1
-            fi
+            echo "Tag creation failed after all retry attempts"
+            exit 1
           fi
 
       - name: Tag creation summary
         if: always()
         run: |
-          if [ "${{ github.event_name }}" = "push" ]; then
-            echo "Trigger: push on main"
-            echo "Created: ${{ steps.check_tag.outputs.created_count }}"
-            echo "Skipped (already existed): ${{ steps.check_tag.outputs.skipped_count }}"
-            echo "Failed: ${{ steps.check_tag.outputs.failed_count }}"
-            if [ "${{ steps.check_tag.outputs.failed_count }}" = "0" ]; then
-              echo "✅ Completed tagging for push range ${{ github.event.before }}..${{ github.sha }}"
-            else
-              echo "❌ Some tags failed to create for push range ${{ github.event.before }}..${{ github.sha }}"
-            fi
+          if [ "${{ steps.check_tag.outputs.exists }}" = "true" ]; then
+            echo "✅ Tag ${{ steps.commit.outputs.tag_name }} already existed - no action needed"
+          elif [ "${{ job.status }}" = "success" ]; then
+            echo "✅ Successfully created tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
           else
-            if [ "${{ steps.check_tag.outputs.failed_count }}" = "0" ]; then
-              if [ "${{ steps.check_tag.outputs.created_count }}" = "0" ]; then
-                echo "✅ Tag ${{ steps.commit.outputs.tag_name }} already existed - no action needed"
-              else
-                echo "✅ Successfully created tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
-              fi
-            else
-              echo "❌ Failed to create tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
-            fi
-
-            echo ""
-            echo "Tag details:"
-            echo "  Name: ${{ steps.commit.outputs.tag_name }}"
-            echo "  Commit: ${{ steps.commit.outputs.sha }}"
-            echo "  Trigger: ${{ github.event_name }}"
-            if [ -n "${{ github.event.inputs.commit_sha }}" ]; then
-              echo "  Manual commit: ${{ github.event.inputs.commit_sha }}"
-            fi
+            echo "❌ Failed to create tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
+          fi
+
+          echo ""
+          echo "Tag details:"
+          echo "  Name: ${{ steps.commit.outputs.tag_name }}"
+          echo "  Commit: ${{ steps.commit.outputs.sha }}"
+          echo "  Trigger: ${{ github.event_name }}"
+          if [ -n "${{ github.event.inputs.commit_sha }}" ]; then
+            echo "  Manual commit: ${{ github.event.inputs.commit_sha }}"
           fi

.lintrunner.toml

@@ -833,7 +833,8 @@ exclude_patterns = [
 command = [
     'python3',
     'tools/linter/adapters/grep_linter.py',
-    '--pattern=(cudaSetDevice|cudaGetDevice)\\(',
+    '--pattern=cudaSetDevice(',
+    '--pattern=cudaGetDevice(',
     '--linter-name=RAWCUDADEVICE',
     '--error-name=raw CUDA API usage',
     """--error-description=\
@@ -1137,8 +1138,11 @@ command = [
 [[linter]]
 code = 'WORKFLOWSYNC'
 include_patterns = [
-    '.github/workflows/*.yml',
-    '.github/workflows/*.yaml',
+    '.github/workflows/pull.yml',
+    '.github/workflows/trunk.yml',
+    '.github/workflows/periodic.yml',
+    '.github/workflows/mac-mps.yml',
+    '.github/workflows/slow.yml',
 ]
 command = [
     'python3',

aten/src/ATen/CMakeLists.txt

@@ -289,15 +289,14 @@ IF(USE_FBGEMM_GENAI)
 
     set_target_properties(fbgemm_genai PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
-    set(fbgemm_genai_cuh
+    set(fbgemm_genai_mx8mx8bf16_grouped
       "${FBGEMM_GENAI_SRCS}/cutlass_extensions/mx8mx8bf16_grouped/"
-      "${FBGEMM_GENAI_SRCS}/"
     )
 
     target_include_directories(fbgemm_genai PRIVATE
       ${FBGEMM_THIRD_PARTY}/cutlass/include
       ${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
-      ${fbgemm_genai_cuh}
+      ${fbgemm_genai_mx8mx8bf16_grouped}
       ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
       ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
     )

aten/src/ATen/Context.h

@@ -19,7 +19,6 @@
 #include <ATen/detail/MPSHooksInterface.h>
 #include <ATen/detail/MTIAHooksInterface.h>
 #include <ATen/detail/PrivateUse1HooksInterface.h>
-#include <ATen/detail/XLAHooksInterface.h>
 #include <ATen/detail/XPUHooksInterface.h>
 #include <c10/core/QEngine.h>
 #include <c10/core/impl/DeviceGuardImplInterface.h>
@@ -89,8 +88,6 @@ class TORCH_API Context {
       return at::detail::getHIPHooks();
     } else if (opt_device_type == at::kHPU) {
       return at::detail::getHPUHooks();
-    } else if (opt_device_type == at::kXLA) {
-      return at::detail::getXLAHooks();
     } else {
       TORCH_CHECK(
           false,
@@ -199,7 +196,7 @@ class TORCH_API Context {
     return c10::impl::hasDeviceGuardImpl(c10::DeviceType::IPU);
   }
   static bool hasXLA() {
-    return detail::getXLAHooks().hasXLA();
+    return c10::impl::hasDeviceGuardImpl(c10::DeviceType::XLA);
   }
   static bool hasXPU() {
     return detail::getXPUHooks().hasXPU();

aten/src/ATen/core/Generator.h

@@ -59,7 +59,9 @@ struct TORCH_API Generator {
 
   explicit Generator(c10::intrusive_ptr<c10::GeneratorImpl> gen_impl)
    : impl_(std::move(gen_impl)) {
-    TORCH_CHECK(impl_.get(), "GeneratorImpl with nullptr is not supported");
+    if (impl_.get() == nullptr) {
+      throw std::runtime_error("GeneratorImpl with nullptr is not supported");
+    }
   }
 
   bool operator==(const Generator& rhs) const {

aten/src/ATen/core/TensorBase.h

@@ -111,7 +111,9 @@ class TORCH_API TensorBase {
   explicit TensorBase(
       c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl> tensor_impl)
       : impl_(std::move(tensor_impl)) {
-    TORCH_CHECK(impl_.get(), "TensorImpl with nullptr is not supported");
+    if (impl_.get() == nullptr) {
+      throw std::runtime_error("TensorImpl with nullptr is not supported");
+    }
   }
   TensorBase(const TensorBase&) = default;
   TensorBase(TensorBase&&) noexcept = default;

aten/src/ATen/core/VariableFallbackKernel.cpp

@@ -109,10 +109,6 @@ TORCH_LIBRARY_IMPL(_, AutogradHPU, m) {
   m.fallback(AUTOGRAD_FALLBACK);
 }
 
-TORCH_LIBRARY_IMPL(_, AutogradPrivateUse1, m) {
-  m.fallback(AUTOGRAD_FALLBACK);
-}
-
 #undef AUTOGRAD_FALLBACK
 
 } // namespace

aten/src/ATen/core/dispatch/Dispatcher.cpp

@@ -442,17 +442,11 @@ RegistrationHandleRAII Dispatcher::registerFallback(DispatchKey dispatchKey, Ker
 
   auto idx = getDispatchTableIndexForDispatchKey(dispatchKey);
   TORCH_CHECK(idx >= 0 && static_cast<uint64_t>(idx) < backendFallbackKernels_.size(), "idx=", idx);
-  // NB: Perserve BC for registering fallback for AutogradPrivateUse1 multiple time,
-  // refer to https://github.com/pytorch/pytorch/issues/163979 for more informations.
   TORCH_CHECK(
-      dispatchKey == DispatchKey::AutogradPrivateUse1 ||
-          !backendFallbackKernels_[idx].kernel.isValid(),
-      "Tried to register multiple backend fallbacks for the same dispatch key ",
-      dispatchKey,
-      "; previous registration ",
-      backendFallbackKernels_[idx].debug,
-      ", new registration ",
-      debug);
+    !backendFallbackKernels_[idx].kernel.isValid(),
+    "Tried to register multiple backend fallbacks for the same dispatch key ", dispatchKey, "; previous registration ",
+    backendFallbackKernels_[idx].debug, ", new registration ", debug
+  );
   // NB: inferred function schema is always nullptr for fallbacks, as fallbacks
   // cannot be unboxed
   backendFallbackKernels_[idx] = impl::AnnotatedKernel(std::move(kernel), nullptr, std::move(debug));

aten/src/ATen/core/interned_strings.cpp

@@ -68,7 +68,11 @@ Symbol InternedStrings::_symbol(const std::string& s) {
     return it->second;
 
   auto pos = s.find("::");
-  TORCH_CHECK(pos != std::string::npos, "all symbols must have a namespace, <namespace>::<string>, but found: ", s);
+  if (pos == std::string::npos) {
+    std::stringstream ss;
+    ss << "all symbols must have a namespace, <namespace>::<string>, but found: " << s;
+    throw std::runtime_error(ss.str());
+  }
   Symbol ns = _symbol("namespaces::" + s.substr(0, pos));
 
   Symbol sym(sym_to_info_.size());
@@ -117,7 +121,12 @@ std::string Symbol::domainString() const {
 }
 
 Symbol Symbol::fromDomainAndUnqualString(const std::string & d, const std::string & s) {
-  TORCH_CHECK(d.compare(0, domain_prefix().size(), domain_prefix()) == 0, "Symbol: domain string is expected to be prefixed with '", domain_prefix(), "', e.g. 'org.pytorch.aten'");
+  if (d.compare(0, domain_prefix().size(), domain_prefix()) != 0) {
+    std::ostringstream ss;
+    ss << "Symbol: domain string is expected to be prefixed with '"
+       << domain_prefix() << "', e.g. 'org.pytorch.aten'";
+    throw std::runtime_error(ss.str());
+  }
   std::string qualString = d.substr(domain_prefix().size()) + "::" + s;
   return fromQualString(qualString);
 }

aten/src/ATen/core/ivalue.cpp

@@ -7,7 +7,6 @@
 #include <ATen/core/jit_type.h>
 #include <ATen/core/stack.h>
 #include <ATen/core/type_factory.h>
-#include <c10/util/Exception.h>
 #include <c10/util/StringUtil.h>
 #include <c10/util/hash.h>
 #include <c10/util/irange.h>
@@ -413,7 +412,7 @@ size_t IValue::hash(const IValue& v) {
     case Tag::Enum:
     case Tag::Stream:
     case Tag::Uninitialized:
-      TORCH_CHECK(false,
+      throw std::runtime_error(
           "unhashable type: '" + v.type()->repr_str() + "'");
   }
   // the above switch should be exhaustive

aten/src/ATen/core/jit_type.h

@@ -8,7 +8,6 @@
 #include <ATen/core/type_factory.h>
 #include <ATen/core/qualified_name.h>
 #include <c10/util/TypeList.h>
-#include <c10/util/Exception.h>
 #include <optional>
 #include <c10/core/SymFloat.h>
 #include <c10/core/SymBool.h>
@@ -117,8 +116,10 @@ struct SingleElementType : public SharedType {
 
  protected:
   SingleElementType(TypePtr elem) : SharedType(Kind), elem(std::move(elem)) {
-    TORCH_CHECK(this->elem, c10::str(
+    if (!this->elem) {
+      throw std::runtime_error(c10::str(
             "Can not create ", typeKindToString(Kind), " with None type"));
+    }
   }
 
  private:
@@ -415,12 +416,16 @@ struct TORCH_API SymbolicShape {
   }
 
   ShapeSymbol operator[](size_t i) const {
-    TORCH_CHECK(dims_, "Rank isn't fixed");
+    if (!dims_) {
+      throw std::runtime_error("Rank isn't fixed");
+    }
     return (*dims_).at(i);
   }
 
   ShapeSymbol at(size_t i) const {
-    TORCH_CHECK(dims_, "Rank isn't fixed");
+    if (!dims_) {
+      throw std::runtime_error("Rank isn't fixed");
+    }
     return (*dims_).at(i);
   }
 
@@ -515,7 +520,9 @@ struct VaryingShape {
   }
 
   const std::optional<T> &operator[](size_t i) const {
-    TORCH_CHECK(dims_, "Rank isn't fixed");
+    if (!dims_) {
+      throw std::runtime_error("Rank isn't fixed");
+    }
     return (*dims_).at(i);
   }
 
@@ -950,7 +957,9 @@ struct TORCH_API DictType : public SharedType {
 
   TypePtr createWithContained(
       std::vector<TypePtr> contained_types) const override {
-    TORCH_CHECK(contained_types.size() == 2, "Expected 2 contained types");
+    if (contained_types.size() != 2) {
+      throw std::runtime_error("Expected 2 contained types");
+    }
     return create(std::move(contained_types.at(0)), std::move(contained_types.at(1)));
   }
 

aten/src/ATen/core/jit_type_base.h

@@ -185,11 +185,11 @@ struct TORCH_API Type {
         : repr_(nullptr) {}
 
     /* implicit */ SingletonOrSharedTypePtr(SingletonTypePtr<T> p)
-        : repr_(makeSingletonSharedPtr(p.get())) {}
+        : repr_(p) {}
 
     template <typename U, std::enable_if_t<std::is_convertible_v<U*, T*>, bool> = true>
     /* implicit */ SingletonOrSharedTypePtr(SingletonTypePtr<U> p)
-        : repr_(makeSingletonSharedPtr(static_cast<T*>(p.get()))) {}
+        : repr_(SingletonTypePtr<T>(p.get())) {}
 
 
     // We need to support construction from T* for pybind. The problem
@@ -202,8 +202,8 @@ struct TORCH_API Type {
     // Case 2: if T is exactly Type, we need to do a dynamic_cast to
     // check if it's a SharedType and do the right thing.
     //
-    // Case 3: Otherwise, T is not a SharedType. Use a singleton
-    // pointer.
+    // Case 3: Otherwise, T is not a SharedType. (debug-check this
+    // assumption!) Use a singleton pointer.
 
     template <typename U = T, std::enable_if_t<std::is_base_of_v<SharedType, U>, bool> = true>
     /* implicit */ SingletonOrSharedTypePtr(T* p) : SingletonOrSharedTypePtr(static_cast<typename detail::as_shared_type<U>::type>(p)->shared_from_this()) {}
@@ -211,15 +211,15 @@ struct TORCH_API Type {
     template <typename U = T, std::enable_if_t<std::is_same_v<Type, U>, bool> = true>
     /* implicit */ SingletonOrSharedTypePtr(T* p) {
       if (auto* shared_p = dynamic_cast<typename detail::as_shared_type<U>::type>(p)) {
-        repr_ = shared_p->shared_from_this();
+        repr_ = Repr(shared_p->shared_from_this());
       } else {
-        repr_ = makeSingletonSharedPtr(p);
+        repr_ = Repr(p);
       }
     }
 
     template <typename U = T, std::enable_if_t<!std::is_same_v<Type, U> && !std::is_base_of_v<SharedType, U>, bool> = true>
     /* implicit */ SingletonOrSharedTypePtr(T* p)
-        : repr_(makeSingletonSharedPtr(p)) {
+        : repr_(p) {
       TORCH_INTERNAL_ASSERT_DEBUG_ONLY(dynamic_cast<typename detail::as_shared_type<U>::type>(p) == nullptr);
     }
 
@@ -230,19 +230,19 @@ struct TORCH_API Type {
     ~SingletonOrSharedTypePtr() = default;
 
     T* get() const {
-      return repr_.get();
+      return repr_.isSharedAndNonNull() ? repr_.shared_.repr_.get() : static_cast<T*>(repr_.rawRepr().first);
     }
 
     operator bool() const {
-      return repr_ != nullptr;
+      return repr_.isNonNull();
     }
 
     bool operator==(std::nullptr_t) const {
-      return repr_ == nullptr;
+      return !repr_.isNonNull();
     }
 
     bool operator!=(std::nullptr_t) const {
-      return repr_ != nullptr;
+      return repr_.isNonNull();
     }
 
     template <typename U = T, std::enable_if_t<!std::is_same_v<std::remove_const_t<U>, void>, bool> = true>
@@ -255,14 +255,138 @@ struct TORCH_API Type {
     }
 
   private:
-    // Use shared_ptr's aliasing constructor to create a non-owning pointer
-    // to a singleton. The lifetime is tied to the null shared_ptr, so there's
-    // no reference counting overhead for the singleton itself.
-    static std::shared_ptr<T> makeSingletonSharedPtr(T* ptr) {
-      return std::shared_ptr<T>(std::shared_ptr<T>(), ptr);
-    }
+    // NOTE: SharedPtrWrapper exists to work around a baffling bug in
+    // nvcc; see comment in destroy() below.
+    struct SharedPtrWrapper {
+      SharedPtrWrapper(std::shared_ptr<T> &&x)
+          : repr_(std::move(x)) {}
+      std::shared_ptr<T> repr_;
+    };
+    union Repr {
+      Repr() : Repr(nullptr) {}
 
-    std::shared_ptr<T> repr_;
+      explicit Repr(std::shared_ptr<T> x)
+          : shared_(std::move(x)) {}
+
+      explicit Repr(std::nullptr_t)
+          : singletonRepr_(nullptr) {}
+
+      explicit Repr(SingletonTypePtr<T> p)
+          : singletonRepr_(p.get()) {}
+
+      ~Repr() {
+        destroy();
+      }
+
+      // NOTE: the only non-UB way to access our null state is through
+      // rawRepr(), because our copy operation doesn't preserve which
+      // union member is active for null pointers.
+      Repr(const Repr& rhs) {
+        if (rhs.isSharedAndNonNull()) {
+          new (&shared_) SharedPtrWrapper(rhs.shared_);
+        } else {
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.singletonRepr_.unused_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+      }
+
+      Repr(Repr&& rhs) noexcept {
+        if (rhs.isSharedAndNonNull()) {
+          new (&shared_) SharedPtrWrapper(std::move(rhs.shared_));
+        } else {
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.singletonRepr_.unused_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+      }
+
+      Repr& operator=(const Repr& rhs) {
+        if (&rhs == this) {
+          return *this;
+        }
+        if (rhs.isSharedAndNonNull()) {
+          if (isSharedAndNonNull()) {
+            shared_ = rhs.shared_;
+          } else {
+            new (&shared_) SharedPtrWrapper(rhs.shared_);
+          }
+        } else {
+          if (isSharedAndNonNull()) {
+            destroy();
+          }
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.rawRepr().nullIfSingleton_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+        return *this;
+      }
+
+      Repr& operator=(Repr&& rhs) noexcept {
+        if (&rhs == this) {
+          return *this;
+        }
+        if (rhs.isSharedAndNonNull()) {
+          if (isSharedAndNonNull()) {
+            shared_ = std::move(rhs.shared_);
+          } else {
+            new (&shared_) SharedPtrWrapper(std::move(rhs.shared_));
+          }
+        } else {
+          if (isSharedAndNonNull()) {
+            destroy();
+          }
+          singletonRepr_.singleton_ = static_cast<T*>(rhs.rawRepr().first);
+          TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.rawRepr().nullIfSingleton_ == nullptr);
+          singletonRepr_.unused_ = nullptr;
+        }
+        return *this;
+      }
+
+      SharedPtrWrapper shared_;
+
+      struct SingletonRepr {
+        explicit SingletonRepr(T* s) : singleton_(s) {}
+        T* singleton_;
+        void* unused_ = nullptr;
+      } singletonRepr_;
+      struct RawRepr {
+        void* first;
+        void* nullIfSingleton_;
+      };
+
+      // It is UB to read the singleton part of Repr if it was
+      // constructed as a shared_ptr and vice versa, but memcpying out
+      // the representation is always OK, so here's an accessor to obey
+      // the letter of the law.
+      RawRepr rawRepr() const {
+        RawRepr repr{};
+        memcpy(&repr, reinterpret_cast<const char *>(this), sizeof(RawRepr));
+        return repr;
+      }
+
+      bool isNonNull() const {
+        auto repr = rawRepr();
+        TORCH_INTERNAL_ASSERT_DEBUG_ONLY(repr.nullIfSingleton_ == nullptr || repr.first != nullptr);
+        return repr.first != nullptr;
+      }
+
+      bool isSharedAndNonNull() const {
+        return rawRepr().nullIfSingleton_ != nullptr;
+      }
+
+     private:
+      void destroy() {
+        if (isSharedAndNonNull()) {
+          // Without SharedPtrWrapper, this line would read
+          // `shared_.~shared_ptr()` and nvcc would complain with
+          // "error: expected primary-expression before '>' token"
+          // referring to the "t" in "shared_ptr". SharedPtrWrapper
+          // exists to work around this compiler bug.
+          shared_.~SharedPtrWrapper();
+        }
+      }
+    } repr_;
   };
 
   using TypePtr = SingletonOrSharedTypePtr<Type>;

aten/src/ATen/core/type.cpp

@@ -8,7 +8,6 @@
 #include <ATen/core/jit_type.h>
 #include <c10/macros/Macros.h>
 #include <c10/util/env.h>
-#include <c10/util/Exception.h>
 #include <c10/util/flat_hash_map.h>
 #include <c10/util/irange.h>
 #include <array>
@@ -827,7 +826,9 @@ TupleType::TupleType(
     : NamedType(TypeKind::TupleType, std::move(name)),
       elements_(std::move(elements)),
       has_free_variables_(std::any_of(elements_.begin(), elements_.end(), [](const TypePtr& v) {
-        TORCH_CHECK(v, "Can not create tuple with None type");
+        if (!v) {
+          throw std::runtime_error("Can not create tuple with None type");
+        }
         return v->hasFreeVariables();
       })), schema_(std::move(schema)) {
 

aten/src/ATen/cpu/vec/sve/vec_float.h

@@ -104,6 +104,71 @@ class Vectorized<float> {
     }
     return b;
   }
+  // Implementation is picked from
+  // https://github.com/ARM-software/ComputeLibrary/blob/v25.01/src/core/NEON/SVEMath.inl#L105
+  inline svfloat32_t svexp_f32_z(svbool_t pg, svfloat32_t x) const {
+    const auto c1 =
+        svreinterpret_f32_u32(svdup_n_u32(0x3f7ffff6)); // x^1: 0x1.ffffecp-1f
+    const auto c2 =
+        svreinterpret_f32_u32(svdup_n_u32(0x3efffedb)); // x^2: 0x1.fffdb6p-2f
+    const auto c3 =
+        svreinterpret_f32_u32(svdup_n_u32(0x3e2aaf33)); // x^3: 0x1.555e66p-3f
+    const auto c4 =
+        svreinterpret_f32_u32(svdup_n_u32(0x3d2b9f17)); // x^4: 0x1.573e2ep-5f
+    const auto c5 =
+        svreinterpret_f32_u32(svdup_n_u32(0x3c072010)); // x^5: 0x1.0e4020p-7f
+    const auto shift = svreinterpret_f32_u32(
+        svdup_n_u32(0x4b00007f)); // 2^23 + 127 = 0x1.0000fep23f
+    const auto inv_ln2 = svreinterpret_f32_u32(
+        svdup_n_u32(0x3fb8aa3b)); // 1 / ln(2) = 0x1.715476p+0f
+    const auto neg_ln2_hi = svreinterpret_f32_u32(svdup_n_u32(
+        0xbf317200)); // -ln(2) from bits  -1 to -19: -0x1.62e400p-1f
+    const auto neg_ln2_lo = svreinterpret_f32_u32(svdup_n_u32(
+        0xb5bfbe8e)); // -ln(2) from bits -20 to -42: -0x1.7f7d1cp-20f
+    const auto inf = svdup_n_f32(std::numeric_limits<float>::infinity());
+    const auto max_input = svdup_n_f32(88.37f); // Approximately ln(2^127.5)
+    const auto zero = svdup_n_f32(0.f);
+    const auto min_input = svdup_n_f32(-86.64f); // Approximately ln(2^-125)
+    // Range reduction:
+    //   e^x = 2^n * e^r
+    // where:
+    //   n = floor(x / ln(2))
+    //   r = x - n * ln(2)
+    //
+    // By adding x / ln(2) with 2^23 + 127 (shift):
+    //   * As FP32 fraction part only has 23-bits, the addition of 2^23 + 127
+    //   forces decimal part
+    //     of x / ln(2) out of the result. The integer part of x / ln(2) (i.e.
+    //     n) + 127 will occupy the whole fraction part of z in FP32 format.
+    //     Subtracting 2^23 + 127 (shift) from z will result in the integer part
+    //     of x / ln(2) (i.e. n) because the decimal part has been pushed out
+    //     and lost.
+    //   * The addition of 127 makes the FP32 fraction part of z ready to be
+    //   used as the exponent
+    //     in FP32 format. Left shifting z by 23 bits will result in 2^n.
+    const auto z = svmla_f32_z(pg, shift, x, inv_ln2);
+    const auto n = svsub_f32_z(pg, z, shift);
+    const auto scale = svreinterpret_f32_u32(
+        svlsl_n_u32_z(pg, svreinterpret_u32_f32(z), 23)); // 2^n
+    // The calculation of n * ln(2) is done using 2 steps to achieve accuracy
+    // beyond FP32. This outperforms longer Taylor series (3-4 tabs) both in
+    // term of accuracy and performance.
+    const auto r_hi = svmla_f32_z(pg, x, n, neg_ln2_hi);
+    const auto r = svmla_f32_z(pg, r_hi, n, neg_ln2_lo);
+    // Compute the truncated Taylor series of e^r.
+    //   poly = scale * (1 + c1 * r + c2 * r^2 + c3 * r^3 + c4 * r^4 + c5 * r^5)
+    const auto r2 = svmul_f32_z(pg, r, r);
+    const auto p1 = svmul_f32_z(pg, c1, r);
+    const auto p23 = svmla_f32_z(pg, c2, c3, r);
+    const auto p45 = svmla_f32_z(pg, c4, c5, r);
+    const auto p2345 = svmla_f32_z(pg, p23, p45, r2);
+    const auto p12345 = svmla_f32_z(pg, p1, p2345, r2);
+    auto poly = svmla_f32_z(pg, scale, p12345, scale);
+    // Handle underflow and overflow.
+    poly = svsel_f32(svcmplt_f32(pg, x, min_input), zero, poly);
+    poly = svsel_f32(svcmpgt_f32(pg, x, max_input), inf, poly);
+    return poly;
+  }
   static Vectorized<float> loadu(const void* ptr, int64_t count = size()) {
     if (count == size())
       return svld1_f32(ptrue, reinterpret_cast<const float*>(ptr));
@@ -248,41 +313,11 @@ class Vectorized<float> {
     return USE_SLEEF(
         Vectorized<float>(Sleef_expm1fx_u10sve(values)), map(std::expm1));
   }
-  // Implementation copied from Arm Optimized Routines:
-  // https://github.com/ARM-software/optimized-routines/blob/master/math/aarch64/sve/expf.c
   Vectorized<float> exp_u20() const {
-    // special case to handle special inputs that are too large or too small
-    // i.e. where there's at least one element x, s.t. |x| >= 87.3...
-    svbool_t is_special_case = svacgt(svptrue_b32(), values, 0x1.5d5e2ap+6f);
-    if (svptest_any(svptrue_b32(), is_special_case)) {
-      return exp();
-    }
-    const svfloat32_t ln2_hi = svdup_n_f32(0x1.62e4p-1f);
-    const svfloat32_t ln2_lo = svdup_n_f32(0x1.7f7d1cp-20f);
-    const svfloat32_t c1 = svdup_n_f32(0.5f);
-    const svfloat32_t inv_ln2 = svdup_n_f32(0x1.715476p+0f);
-
-    const float shift = 0x1.803f8p17f;
-
-    /* n = round(x/(ln2/N)).  */
-    svfloat32_t z = svmad_x(svptrue_b32(), inv_ln2, values, shift);
-    svfloat32_t n = svsub_x(svptrue_b32(), z, shift);
-
-    /* r = x - n*ln2/N.  */
-    svfloat32_t r = values;
-    r = svmls_x(svptrue_b32(), r, n, ln2_hi);
-    r = svmls_x(svptrue_b32(), r, n, ln2_lo);
-
-    /* scale = 2^(n/N).  */
-    svfloat32_t scale = svexpa(svreinterpret_u32(z));
-
-    /* poly(r) = exp(r) - 1 ~= r + 0.5 r^2.  */
-    svfloat32_t r2 = svmul_x(svptrue_b32(), r, r);
-    svfloat32_t poly = svmla_x(svptrue_b32(), r, r2, c1);
-    return svmla_x(svptrue_b32(), scale, scale, poly);
+    return exp();
   }
   Vectorized<float> fexp_u20() const {
-    return exp_u20();
+    return exp();
   }
   Vectorized<float> fmod(const Vectorized<float>& q) const {USE_SLEEF(
       { return Vectorized<float>(Sleef_fmodfx_sve(values, q)); },
@@ -418,11 +453,9 @@ class Vectorized<float> {
         ptrue, svmax_f32_z(ptrue, values, CONST_MIN_TANH), CONST_MAX_TANH);
 
     // Step 2: Calculate exp(2 * x), where x is the clamped value.
-    // svmul_f32_z computes 2 * x, and exp_u20() computes the exponential of
-    // the result (via Vectorized<float>, then auto-converts back to
-    // svfloat32_t).
-    svfloat32_t exp2x =
-        Vectorized<float>(svmul_f32_z(ptrue, CONST_2, x)).exp_u20();
+    // svmul_f32_z computes 2 * x, and svexp_f32_z computes the exponential of
+    // the result.
+    svfloat32_t exp2x = svexp_f32_z(ptrue, svmul_f32_z(ptrue, CONST_2, x));
 
     // Step 3: Calculate the numerator of the tanh function, which is exp(2x)
     // - 1.

aten/src/ATen/cpu/vec/vec128/vec128.h

@@ -6,11 +6,9 @@
 #ifdef __aarch64__
 #if !defined(CPU_CAPABILITY_SVE)
 #include <ATen/cpu/vec/vec128/vec128_bfloat16_neon.h>
-#include <ATen/cpu/vec/vec128/vec128_double_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_float_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_half_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_int_aarch64.h>
-#include <ATen/cpu/vec/vec128/vec128_uint_aarch64.h>
 #endif
 
 #include <ATen/cpu/vec/vec128/vec128_convert.h>

aten/src/ATen/cpu/vec/vec128/vec128_bfloat16_neon.h

@@ -354,47 +354,9 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
 
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(abs)
   Vectorized frac() const;
+  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(trunc)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(sqrt)
-
-#ifdef __ARM_FEATURE_BF16
-  Vectorized<c10::BFloat16> neg() const {
-    return -values;
-  }
-  Vectorized<c10::BFloat16> reciprocal() const {
-    return 1.0f / values;
-  }
-  Vectorized<c10::BFloat16> operator==(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values == other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator!=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values != other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator<(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values < other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator<=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values <= other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator>(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values > other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator>=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values >= other.values;
-  }
-#else
-  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(reciprocal)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator==)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator!=)
@@ -402,7 +364,6 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator<=)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>=)
-#endif
 
 #undef DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
 #undef DEFINE_BINARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
@@ -451,52 +412,28 @@ template <>
 Vectorized<c10::BFloat16> inline operator+(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x + y;
-#else
   return binary_operator_via_float(std::plus<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator-(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x - y;
-#else
   return binary_operator_via_float(std::minus<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator*(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x * y;
-#else
   return binary_operator_via_float(std::multiplies<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator/(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x / y;
-#else
   return binary_operator_via_float(std::divides<Vectorized<float>>(), a, b);
-#endif
 }
 
 // frac. Implement this here so we can use subtraction
@@ -607,19 +544,12 @@ Vectorized<c10::BFloat16> inline fmadd(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return x * y + z;
-#else
   // NOTE [BF16 FMA]: There isn't an FMA that accumulates into BF16!  Also,
   // vbfmlalbq_f32 and vbfmlaltq_f32 take the even and odd-numbered
   // elements, not the bottom and top half, so they don't seem
   // particularly useful here. Ideally we would include dot product in
   // the Vectorized interface...
   return a * b + c;
-#endif
 }
 
 template <>
@@ -627,15 +557,8 @@ Vectorized<c10::BFloat16> inline fnmadd(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return (-x) * y + z;
-#else
   // See NOTE [BF16 FMA] above.
   return -a * b + c;
-#endif
 }
 
 template <>
@@ -643,15 +566,8 @@ Vectorized<c10::BFloat16> inline fmsub(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return x * y - z;
-#else
   // See NOTE [BF16 FMA] above.
   return a * b - c;
-#endif
 }
 
 template <>
@@ -659,15 +575,8 @@ Vectorized<c10::BFloat16> inline fnmsub(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return (-x) * y - z;
-#else
   // See NOTE [BF16 FMA] above.
   return -a * b - c;
-#endif
 }
 
 #endif // !defined(C10_MOBILE) && defined(__aarch64__)

aten/src/ATen/cpu/vec/vec128/vec128_convert.h

@@ -5,114 +5,6 @@
 namespace at::vec {
 inline namespace CPU_CAPABILITY {
 #if (defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256))
-
-// Enable auto-vectorization for GCC-13+ and clang-17+
-// GCC-12 has a bug: gcc.gnu.org/bugzilla/show_bug.cgi?id=117001
-#if __GNUC__ > 12 || (defined(__clang__) && (__clang_major__ >= 17))
-
-template <typename from_type, typename to_type>
-inline void convertImpl(
-    const from_type* __restrict src,
-    to_type* __restrict dst,
-    int64_t n) {
-  uint64_t len = static_cast<uint64_t>(n);
-  for (uint64_t i = 0; i < len; i++) {
-    dst[i] = static_cast<to_type>(src[i]);
-  }
-}
-
-#define CONVERT_TEMPLATE(from_type, to_type)                           \
-  template <>                                                          \
-  inline void convert(const from_type* src, to_type* dst, int64_t n) { \
-    return convertImpl<from_type, to_type>(src, dst, n);               \
-  }
-
-CONVERT_TEMPLATE(uint8_t, uint8_t)
-CONVERT_TEMPLATE(uint8_t, int8_t)
-CONVERT_TEMPLATE(uint8_t, int16_t)
-CONVERT_TEMPLATE(uint8_t, int32_t)
-CONVERT_TEMPLATE(uint8_t, int64_t)
-CONVERT_TEMPLATE(uint8_t, float)
-CONVERT_TEMPLATE(uint8_t, double)
-CONVERT_TEMPLATE(int8_t, uint8_t)
-CONVERT_TEMPLATE(int8_t, int8_t)
-CONVERT_TEMPLATE(int8_t, int16_t)
-CONVERT_TEMPLATE(int8_t, int32_t)
-CONVERT_TEMPLATE(int8_t, int64_t)
-CONVERT_TEMPLATE(int8_t, float)
-CONVERT_TEMPLATE(int8_t, double)
-CONVERT_TEMPLATE(int16_t, uint8_t)
-CONVERT_TEMPLATE(int16_t, int8_t)
-CONVERT_TEMPLATE(int16_t, int16_t)
-CONVERT_TEMPLATE(int16_t, int32_t)
-CONVERT_TEMPLATE(int16_t, int64_t)
-CONVERT_TEMPLATE(int16_t, float)
-CONVERT_TEMPLATE(int16_t, double)
-CONVERT_TEMPLATE(int32_t, uint8_t)
-CONVERT_TEMPLATE(int32_t, int8_t)
-CONVERT_TEMPLATE(int32_t, int16_t)
-CONVERT_TEMPLATE(int32_t, int32_t)
-CONVERT_TEMPLATE(int32_t, int64_t)
-CONVERT_TEMPLATE(int32_t, float)
-CONVERT_TEMPLATE(int32_t, double)
-CONVERT_TEMPLATE(int64_t, uint8_t)
-CONVERT_TEMPLATE(int64_t, int8_t)
-CONVERT_TEMPLATE(int64_t, int16_t)
-CONVERT_TEMPLATE(int64_t, int32_t)
-CONVERT_TEMPLATE(int64_t, int64_t)
-CONVERT_TEMPLATE(int64_t, float)
-CONVERT_TEMPLATE(int64_t, double)
-CONVERT_TEMPLATE(float, uint8_t)
-CONVERT_TEMPLATE(float, int8_t)
-CONVERT_TEMPLATE(float, int16_t)
-CONVERT_TEMPLATE(float, int32_t)
-CONVERT_TEMPLATE(float, int64_t)
-CONVERT_TEMPLATE(float, float)
-CONVERT_TEMPLATE(float, double)
-CONVERT_TEMPLATE(double, uint8_t)
-CONVERT_TEMPLATE(double, int8_t)
-CONVERT_TEMPLATE(double, int16_t)
-CONVERT_TEMPLATE(double, int32_t)
-CONVERT_TEMPLATE(double, int64_t)
-CONVERT_TEMPLATE(double, float)
-CONVERT_TEMPLATE(double, double)
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-CONVERT_TEMPLATE(float16_t, uint8_t)
-CONVERT_TEMPLATE(float16_t, int8_t)
-CONVERT_TEMPLATE(float16_t, int16_t)
-CONVERT_TEMPLATE(float16_t, int32_t)
-CONVERT_TEMPLATE(float16_t, int64_t)
-CONVERT_TEMPLATE(float16_t, float16_t)
-CONVERT_TEMPLATE(float16_t, float)
-CONVERT_TEMPLATE(float16_t, double)
-CONVERT_TEMPLATE(uint8_t, float16_t)
-CONVERT_TEMPLATE(int8_t, float16_t)
-CONVERT_TEMPLATE(int16_t, float16_t)
-CONVERT_TEMPLATE(int32_t, float16_t)
-CONVERT_TEMPLATE(int64_t, float16_t)
-CONVERT_TEMPLATE(float, float16_t)
-CONVERT_TEMPLATE(double, float16_t)
-#endif
-#ifdef __ARM_FEATURE_BF16
-CONVERT_TEMPLATE(bfloat16_t, uint8_t)
-CONVERT_TEMPLATE(bfloat16_t, int8_t)
-CONVERT_TEMPLATE(bfloat16_t, int16_t)
-CONVERT_TEMPLATE(bfloat16_t, int32_t)
-CONVERT_TEMPLATE(bfloat16_t, int64_t)
-CONVERT_TEMPLATE(bfloat16_t, bfloat16_t)
-CONVERT_TEMPLATE(bfloat16_t, float)
-CONVERT_TEMPLATE(bfloat16_t, double)
-CONVERT_TEMPLATE(uint8_t, bfloat16_t)
-CONVERT_TEMPLATE(int8_t, bfloat16_t)
-CONVERT_TEMPLATE(int16_t, bfloat16_t)
-CONVERT_TEMPLATE(int32_t, bfloat16_t)
-CONVERT_TEMPLATE(int64_t, bfloat16_t)
-CONVERT_TEMPLATE(float, bfloat16_t)
-CONVERT_TEMPLATE(double, bfloat16_t)
-#endif
-
-#endif
-
 template <typename src_t>
 struct VecConvert<
     float,

aten/src/ATen/cpu/vec/vec128/vec128_double_neon.h

@@ -1,586 +0,0 @@
-#pragma once
-
-#include <ATen/cpu/vec/intrinsics.h>
-#include <ATen/cpu/vec/vec_base.h>
-#include <c10/macros/Macros.h>
-#include <c10/util/irange.h>
-#include <cmath>
-
-namespace at::vec {
-// Note [CPU_CAPABILITY namespace]
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// This header, and all of its subheaders, will be compiled with
-// different architecture flags for each supported set of vector
-// intrinsics. So we need to make sure they aren't inadvertently
-// linked together. We do this by declaring objects in an `inline
-// namespace` which changes the name mangling, but can still be
-// accessed as `at::vec`.
-inline namespace CPU_CAPABILITY {
-
-template <>
-struct is_vec_specialized_for<double> : std::bool_constant<true> {};
-
-template <>
-class Vectorized<double> {
- private:
-  float64x2_t values;
-
- public:
-  using value_type = double;
-  using size_type = int;
-  static constexpr size_type size() {
-    return 2;
-  }
-  Vectorized() {
-    values = vdupq_n_f64(0.0);
-  }
-  Vectorized(float64x2_t v) : values(v) {}
-  Vectorized(double val) {
-    values = vdupq_n_f64(val);
-  }
-  template <
-      typename... Args,
-      typename = std::enable_if_t<(sizeof...(Args) == size())>>
-  Vectorized(Args... vals) {
-    __at_align__ double buffer[size()] = {vals...};
-    values = vld1q_f64(buffer);
-  }
-  operator float64x2_t() const {
-    return values;
-  }
-  template <int64_t mask>
-  static Vectorized<double> blend(
-      const Vectorized<double>& a,
-      const Vectorized<double>& b) {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint64x2_t maskArray = {
-        (mask & 1ULL) ? 0xFFFFFFFFFFFFFFFF : 0,
-        (mask & 2ULL) ? 0xFFFFFFFFFFFFFFFF : 0};
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_f64(maskArray, b.values, a.values);
-  }
-  static Vectorized<double> blendv(
-      const Vectorized<double>& a,
-      const Vectorized<double>& b,
-      const Vectorized<double>& mask_) {
-    return vbslq_f64(vreinterpretq_u64_f64(mask_.values), b.values, a.values);
-  }
-  template <typename step_t>
-  static Vectorized<double> arange(
-      double base = 0.,
-      step_t step = static_cast<step_t>(1)) {
-    return {base, base + static_cast<double>(step)};
-  }
-  static inline Vectorized<double> set(
-      const Vectorized<double>& a,
-      const Vectorized<double>& b,
-      int64_t count = size()) {
-    if (count == 0) {
-      return a;
-    } else if (count >= 2) {
-      return b;
-    } else {
-      float64x2_t c = {b.values[0], a.values[1]};
-      return c;
-    }
-  }
-  static Vectorized<double> loadu(const void* ptr, int64_t count = size()) {
-    if (count == size()) {
-      return vld1q_f64(reinterpret_cast<const double*>(ptr));
-    } else if (count == 1) {
-      float64x1_t x = vld1_f64(reinterpret_cast<const double*>(ptr));
-      float64x1_t z = {0.0};
-      return vcombine_f64(x, z);
-    } else {
-      return vdupq_n_f64(0.0);
-    }
-  }
-  void store(void* ptr, int64_t count = size()) const {
-    if (count == size()) {
-      vst1q_f64(reinterpret_cast<double*>(ptr), values);
-    } else if (count == 1) {
-      vst1_f64(reinterpret_cast<double*>(ptr), vget_low_f64(values));
-    }
-  }
-  const double& operator[](int idx) const = delete;
-  double& operator[](int idx) = delete;
-  int64_t zero_mask() const {
-    // returns an integer mask where all zero elements are translated to 1-bit
-    // and others are translated to 0-bit
-    uint64x2_t cmpReg = vceqzq_f64(values);
-    uint64x2_t mask = {1, 2};
-    uint64x2_t res = vandq_u64(cmpReg, mask);
-    return res[0] | res[1];
-  }
-  Vectorized<double> isnan() const {
-    // NaN check
-    return vreinterpretq_f64_u32(
-        vmvnq_u32(vreinterpretq_u32_u64(vceqq_f64(values, values))));
-  }
-  bool has_inf_nan() const {
-    Vectorized<double> x = vsubq_f64(values, values);
-    float64x2_t r = x.isnan();
-    uint64x2_t u = vreinterpretq_u64_f64(r);
-    return u[0] | u[1];
-  }
-  Vectorized<double> map(double (*f)(double)) const {
-    float64x2_t result;
-    result[0] = f(values[0]);
-    result[1] = f(values[1]);
-    return result;
-  }
-  Vectorized<double> map2(
-      const Vectorized<double>& second,
-      double (*const f)(double, double)) const {
-    float64x2_t result;
-    result[0] = f(values[0], second.values[0]);
-    result[1] = f(values[1], second.values[1]);
-    return result;
-  }
-  Vectorized<double> abs() const {
-    return vabsq_f64(values);
-  }
-  Vectorized<double> angle() const {
-    auto zero = Vectorized<double>(0.0);
-    auto pi = Vectorized<double>(c10::pi<double>);
-    auto tmp = blendv(zero, pi, vreinterpretq_f64_u64(vcltzq_f64(values)));
-    return blendv(tmp, *this, isnan());
-  }
-  Vectorized<double> real() const {
-    return *this;
-  }
-  Vectorized<double> imag() const {
-    return Vectorized<double>(0.0);
-  }
-  Vectorized<double> conj() const {
-    return *this;
-  }
-  Vectorized<double> acos() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_acosd2_u10(values)), map(std::acos));
-  }
-  Vectorized<double> acosh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_acoshd2_u10(values)), map(std::acosh));
-  }
-  Vectorized<double> asin() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_asind2_u10(values)), map(std::asin));
-  }
-  Vectorized<double> asinh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_asinhd2_u10(values)), map(std::asinh));
-  }
-  Vectorized<double> atan() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_atand2_u10(values)), map(std::atan));
-  }
-  Vectorized<double> atanh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_atanhd2_u10(values)), map(std::atanh));
-  }
-  Vectorized<double> atan2(const Vectorized<double>& b) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_atan2d2_u10(values, b)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_b[size()];
-        store(tmp);
-        b.store(tmp_b);
-        for (int64_t i = 0; i < size(); i++) {
-          tmp[i] = std::atan2(tmp[i], tmp_b[i]);
-        }
-        return loadu(tmp);
-      })} Vectorized<double> copysign(const Vectorized<double>& sign) const {
-      USE_SLEEF(
-          { return Vectorized<double>(Sleef_copysignd2(values, sign)); },
-          {
-            __at_align__ double tmp[size()];
-            __at_align__ double tmp_sign[size()];
-            store(tmp);
-            sign.store(tmp_sign);
-            for (int64_t i = 0; i < size(); i++) {
-              tmp[i] = std::copysign(tmp[i], tmp_sign[i]);
-            }
-            return loadu(tmp);
-          })} Vectorized<double> erf() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_erfd2_u10(values)), map(std::erf));
-  }
-  Vectorized<double> erfc() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_erfcd2_u15(values)), map(std::erfc));
-  }
-  Vectorized<double> exp() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_expd2_u10(values)), map(std::exp));
-  }
-  Vectorized<double> exp2() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_exp2d2_u10(values)), map(std::exp2));
-  }
-  Vectorized<double> expm1() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_expm1d2_u10(values)), map(std::expm1));
-  }
-  Vectorized<double> fmod(const Vectorized<double>& q) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_fmodd2(values, q)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_q[size()];
-        store(tmp);
-        q.store(tmp_q);
-        for (int64_t i = 0; i < size(); i++) {
-          tmp[i] = std::fmod(tmp[i], tmp_q[i]);
-        }
-        return loadu(tmp);
-      })} Vectorized<double> hypot(const Vectorized<double>& b) const {
-      USE_SLEEF(
-          { return Vectorized<double>(Sleef_hypotd2_u05(values, b)); },
-          {
-            __at_align__ double tmp[size()];
-            __at_align__ double tmp_b[size()];
-            store(tmp);
-            b.store(tmp_b);
-            for (int64_t i = 0; i < size(); i++) {
-              tmp[i] = std::hypot(tmp[i], tmp_b[i]);
-            }
-            return loadu(tmp);
-          })} Vectorized<double> i0() const {
-    return map(calc_i0);
-  }
-  Vectorized<double> nextafter(const Vectorized<double>& b) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_nextafterd2(values, b)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_b[size()];
-        store(tmp);
-        b.store(tmp_b);
-        for (int64_t i = 0; i < size(); ++i) {
-          tmp[i] = std::nextafter(tmp[i], tmp_b[i]);
-        }
-        return loadu(tmp);
-      })} Vectorized<double> log() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_logd2_u10(values)), map(std::log));
-  }
-  Vectorized<double> log2() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_log2d2_u10(values)), map(std::log2));
-  }
-  Vectorized<double> log10() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_log10d2_u10(values)), map(std::log10));
-  }
-  Vectorized<double> log1p() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_log1pd2_u10(values)), map(std::log1p));
-  }
-  Vectorized<double> frac() const;
-  Vectorized<double> sin() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_sind2_u10(values)), map(std::sin));
-  }
-  Vectorized<double> sinh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_sinhd2_u10(values)), map(std::sinh));
-  }
-  Vectorized<double> cos() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_cosd2_u10(values)), map(std::cos));
-  }
-  Vectorized<double> cosh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_coshd2_u10(values)), map(std::cosh));
-  }
-  Vectorized<double> pow(const Vectorized<double>& b) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_powd2_u10(values, b)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_b[size()];
-        store(tmp);
-        b.store(tmp_b);
-        for (int64_t i = 0; i < size(); i++) {
-          tmp[i] = std::pow(tmp[i], tmp_b[i]);
-        }
-        return loadu(tmp);
-      })} // Comparison using the _CMP_**_OQ predicate.
-          //   `O`: get false if an operand is NaN
-          //   `Q`: do not raise if an operand is NaN
-  Vectorized<double> tan() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_tand2_u10(values)), map(std::tan));
-  }
-  Vectorized<double> tanh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_tanhd2_u10(values)), map(std::tanh));
-  }
-  Vectorized<double> lgamma() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_lgammad2_u10(values)), map(std::lgamma));
-  }
-  Vectorized<double> erfinv() const {
-    return map(calc_erfinv);
-  }
-  Vectorized<double> exp_u20() const {
-    return exp();
-  }
-  Vectorized<double> fexp_u20() const {
-    return exp();
-  }
-  Vectorized<double> i0e() const {
-    return map(calc_i0e);
-  }
-  Vectorized<double> digamma() const {
-    return map(calc_digamma);
-  }
-  Vectorized<double> igamma(const Vectorized<double>& x) const {
-    __at_align__ double tmp[size()];
-    __at_align__ double tmp_x[size()];
-    store(tmp);
-    x.store(tmp_x);
-    for (int64_t i = 0; i < size(); i++) {
-      tmp[i] = calc_igamma(tmp[i], tmp_x[i]);
-    }
-    return loadu(tmp);
-  }
-  Vectorized<double> igammac(const Vectorized<double>& x) const {
-    __at_align__ double tmp[size()];
-    __at_align__ double tmp_x[size()];
-    store(tmp);
-    x.store(tmp_x);
-    for (int64_t i = 0; i < size(); i++) {
-      tmp[i] = calc_igammac(tmp[i], tmp_x[i]);
-    }
-    return loadu(tmp);
-  }
-  Vectorized<double> ceil() const {
-    return vrndpq_f64(values);
-  }
-  Vectorized<double> floor() const {
-    return vrndmq_f64(values);
-  }
-  Vectorized<double> neg() const {
-    return vnegq_f64(values);
-  }
-  Vectorized<double> round() const {
-    return vrndiq_f64(values);
-  }
-  Vectorized<double> trunc() const {
-    return vrndq_f64(values);
-  }
-  Vectorized<double> sqrt() const {
-    return vsqrtq_f64(values);
-  }
-  Vectorized<double> reciprocal() const {
-    return vdivq_f64(vdupq_n_f64(1.0), values);
-  }
-  Vectorized<double> rsqrt() const {
-    return vdivq_f64(vdupq_n_f64(1.0), vsqrtq_f64(values));
-  }
-  double reduce_add() const {
-    return vaddvq_f64(values);
-  }
-  double reduce_max() const {
-    return vmaxvq_f64(values);
-  }
-  Vectorized<double> operator==(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vceqq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator!=(const Vectorized<double>& other) const {
-    float64x2_t r0 = vreinterpretq_f64_u32(
-        vmvnq_u32(vreinterpretq_u32_u64(vceqq_f64(values, other.values))));
-    return Vectorized<double>(r0);
-  }
-
-  Vectorized<double> operator<(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcltq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator<=(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcleq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator>(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcgtq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator>=(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcgeq_f64(values, other.values)));
-  }
-
-  Vectorized<double> eq(const Vectorized<double>& other) const;
-  Vectorized<double> ne(const Vectorized<double>& other) const;
-  Vectorized<double> gt(const Vectorized<double>& other) const;
-  Vectorized<double> ge(const Vectorized<double>& other) const;
-  Vectorized<double> lt(const Vectorized<double>& other) const;
-  Vectorized<double> le(const Vectorized<double>& other) const;
-};
-
-template <>
-Vectorized<double> inline operator+(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vaddq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline operator-(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vsubq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline operator*(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vmulq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline operator/(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vdivq_f64(a, b);
-}
-
-// frac. Implement this here so we can use subtraction
-Vectorized<double> inline Vectorized<double>::frac() const {
-  return *this - this->trunc();
-}
-
-// Implements the IEEE 754 201X `maximum` operation, which propagates NaN if
-// either input is a NaN.
-template <>
-Vectorized<double> inline maximum(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vmaxq_f64(a, b);
-}
-
-// Implements the IEEE 754 201X `minimum` operation, which propagates NaN if
-// either input is a NaN.
-template <>
-Vectorized<double> inline minimum(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vminq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline clamp(
-    const Vectorized<double>& a,
-    const Vectorized<double>& min,
-    const Vectorized<double>& max) {
-  return vminq_f64(max, vmaxq_f64(min, a));
-}
-
-template <>
-Vectorized<double> inline clamp_max(
-    const Vectorized<double>& a,
-    const Vectorized<double>& max) {
-  return vminq_f64(max, a);
-}
-
-template <>
-Vectorized<double> inline clamp_min(
-    const Vectorized<double>& a,
-    const Vectorized<double>& min) {
-  return vmaxq_f64(min, a);
-}
-
-template <>
-Vectorized<double> inline operator&(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vreinterpretq_f64_u64(
-      vandq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
-}
-
-template <>
-Vectorized<double> inline operator|(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vreinterpretq_f64_u64(
-      vorrq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
-}
-
-template <>
-Vectorized<double> inline operator^(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vreinterpretq_f64_u64(
-      veorq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
-}
-
-inline Vectorized<double> Vectorized<double>::eq(
-    const Vectorized<double>& other) const {
-  return (*this == other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::ne(
-    const Vectorized<double>& other) const {
-  return (*this != other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::gt(
-    const Vectorized<double>& other) const {
-  return (*this > other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::ge(
-    const Vectorized<double>& other) const {
-  return (*this >= other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::lt(
-    const Vectorized<double>& other) const {
-  return (*this < other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::le(
-    const Vectorized<double>& other) const {
-  return (*this <= other) & Vectorized<double>(1.0);
-}
-
-template <>
-Vectorized<double> inline fmadd(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmaq_f64(c, a, b);
-}
-
-template <>
-Vectorized<double> inline fnmadd(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmsq_f64(c, a, b);
-}
-
-template <>
-Vectorized<double> inline fmsub(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmaq_f64(vnegq_f64(c), a, b);
-}
-
-template <>
-Vectorized<double> inline fnmsub(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmsq_f64(vnegq_f64(c), a, b);
-}
-
-} // namespace CPU_CAPABILITY
-} // namespace at::vec

aten/src/ATen/cpu/vec/vec128/vec128_float_neon.h

@@ -307,49 +307,11 @@ class Vectorized<float> {
   DEFINE_SLEEF_COMPATIBLE_UNARY_ELEMENTWISE_FUNC(exp)
   DEFINE_SLEEF_COMPATIBLE_UNARY_ELEMENTWISE_FUNC(exp2)
   DEFINE_SLEEF_COMPATIBLE_UNARY_ELEMENTWISE_FUNC(expm1)
-  // Implementation copied from Arm Optimized Routine
-  // https://github.com/ARM-software/optimized-routines/blob/master/math/aarch64/advsimd/expf.c
   Vectorized<float> exp_u20() const {
-    // bail out to sleef if it's a special case:
-    // i.e. there's an input s.t. |input| > 87.3....
-    const float32x4_t special_bound = vdupq_n_f32(0x1.5d5e2ap+6f);
-    uint32x4_t cmp = vcagtq_f32(values, special_bound);
-    if (vpaddd_u64(vreinterpretq_u64_u32(cmp)) != 0) {
-      return exp();
-    }
-
-    const float32x4_t inv_ln2 = vdupq_n_f32(0x1.715476p+0f);
-    const float ln2_hi = 0x1.62e4p-1f;
-    const float ln2_lo = 0x1.7f7d1cp-20f;
-    const float c0 = 0x1.0e4020p-7f;
-    const float c2 = 0x1.555e66p-3f;
-    const float32x4_t ln2_c02 = {ln2_hi, ln2_lo, c0, c2};
-
-    const uint32x4_t exponent_bias = vdupq_n_u32(0x3f800000);
-    const float32x4_t c1 = vdupq_n_f32(0x1.573e2ep-5f);
-    const float32x4_t c3 = vdupq_n_f32(0x1.fffdb6p-2f);
-    const float32x4_t c4 = vdupq_n_f32(0x1.ffffecp-1f);
-
-    /* exp(x) = 2^n (1 + poly(r)), with 1 + poly(r) in [1/sqrt(2),sqrt(2)]
-      x = ln2*n + r, with r in [-ln2/2, ln2/2].  */
-
-    float32x4_t n = vrndaq_f32(vmulq_f32(values, inv_ln2));
-    float32x4_t r = vfmsq_laneq_f32(values, n, ln2_c02, 0);
-    r = vfmsq_laneq_f32(r, n, ln2_c02, 1);
-    uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_s32(vcvtq_s32_f32(n)), 23);
-    float32x4_t scale = vreinterpretq_f32_u32(vaddq_u32(e, exponent_bias));
-
-    float32x4_t r2 = vmulq_f32(r, r);
-    float32x4_t p = vfmaq_laneq_f32(c1, r, ln2_c02, 2);
-    float32x4_t q = vfmaq_laneq_f32(c3, r, ln2_c02, 3);
-    q = vfmaq_f32(q, p, r2);
-    p = vmulq_f32(c4, r);
-    float32x4_t poly = vfmaq_f32(p, q, r2);
-
-    return vfmaq_f32(scale, poly, scale);
+    return exp();
   }
   Vectorized<float> fexp_u20() const {
-    return exp_u20();
+    return exp();
   }
   DEFINE_SLEEF_COMPATIBLE_BINARY_ELEMENTWISE_FUNC_WITH_SLEEF_NAME(
       fmod,
@@ -578,6 +540,42 @@ inline Vectorized<float> Vectorized<float>::le(
   return (*this <= other) & Vectorized<float>(1.0f);
 }
 
+template <>
+inline void convert(const float* src, int32_t* dst, int64_t n) {
+  int64_t i;
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (i = 0; i <= (n - Vectorized<float>::size());
+       i += Vectorized<float>::size()) {
+    vst1q_s32(dst + i, vcvtq_s32_f32(vld1q_f32(src + i)));
+  }
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (; i < n; i++) {
+    dst[i] = static_cast<int32_t>(src[i]);
+  }
+}
+
+template <>
+inline void convert(const int32_t* src, float* dst, int64_t n) {
+  int64_t i;
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (i = 0; i <= (n - Vectorized<float>::size());
+       i += Vectorized<float>::size()) {
+    vst1q_f32(dst + i, vcvtq_f32_s32(vld1q_s32(src + i)));
+  }
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (; i < n; i++) {
+    dst[i] = static_cast<float>(src[i]);
+  }
+}
+
 template <>
 Vectorized<float> inline fmadd(
     const Vectorized<float>& a,

aten/src/ATen/cpu/vec/vec128/vec128_half_neon.h

@@ -569,6 +569,46 @@ inline Vectorized<c10::Half> Vectorized<c10::Half>::le(
   return (*this <= other) & Vectorized<c10::Half>(1);
 }
 
+// These are global functions, so the defaults in vec_base.h should
+// work fine if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC is not available.
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <>
+inline void convert(const float16_t* src, int16_t* dst, int64_t n) {
+  int64_t i;
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (i = 0; i <= (n - Vectorized<c10::Half>::size());
+       i += Vectorized<c10::Half>::size()) {
+    vst1q_s16(dst + i, vcvtq_s16_f16(vld1q_f16(src + i)));
+  }
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (; i < n; i++) {
+    dst[i] = static_cast<int16_t>(src[i]);
+  }
+}
+
+template <>
+inline void convert(const int16_t* src, float16_t* dst, int64_t n) {
+  int64_t i;
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (i = 0; i <= (n - Vectorized<c10::Half>::size());
+       i += Vectorized<c10::Half>::size()) {
+    vst1q_f16(dst + i, vcvtq_f16_s16(vld1q_s16(src + i)));
+  }
+#ifndef __msvc_cl__
+#pragma unroll
+#endif
+  for (; i < n; i++) {
+    dst[i] = static_cast<float16_t>(src[i]);
+  }
+}
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
 template <>
 Vectorized<c10::Half> inline fmadd(
     const Vectorized<c10::Half>& a,

aten/src/ATen/cpu/vec/vec128/vec128_uint_aarch64.h

@@ -1,378 +0,0 @@
-#pragma once
-
-#include <ATen/cpu/vec/intrinsics.h>
-#include <ATen/cpu/vec/vec_base.h>
-#include <c10/macros/Macros.h>
-#include <c10/util/irange.h>
-
-namespace at::vec {
-// Note [CPU_CAPABILITY namespace]
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// This header, and all of its subheaders, will be compiled with
-// different architecture flags for each supported set of vector
-// intrinsics. So we need to make sure they aren't inadvertently
-// linked together. We do this by declaring objects in an `inline
-// namespace` which changes the name mangling, but can still be
-// accessed as `at::vec`.
-inline namespace CPU_CAPABILITY {
-
-#define VEC_UINT_NEON_TEMPLATE(vl, bit)                                       \
-  template <>                                                                 \
-  struct is_vec_specialized_for<uint##bit##_t> : std::bool_constant<true> {}; \
-                                                                              \
-  template <>                                                                 \
-  class Vectorized<uint##bit##_t> {                                           \
-    using neon_type = uint##bit##x##vl##_t;                                   \
-                                                                              \
-   private:                                                                   \
-    neon_type values;                                                         \
-                                                                              \
-   public:                                                                    \
-    using value_type = uint##bit##_t;                                         \
-    using size_type = int;                                                    \
-    static constexpr size_type size() {                                       \
-      return vl;                                                              \
-    }                                                                         \
-    Vectorized() {                                                            \
-      values = vdupq_n_u##bit(0);                                             \
-    }                                                                         \
-    Vectorized(neon_type v) : values(v) {}                                    \
-    Vectorized(uint##bit##_t val);                                            \
-    template <                                                                \
-        typename... Args,                                                     \
-        typename = std::enable_if_t<(sizeof...(Args) == size())>>             \
-    Vectorized(Args... vals) {                                                \
-      __at_align__ uint##bit##_t buffer[size()] = {vals...};                  \
-      values = vld1q_u##bit(buffer);                                          \
-    }                                                                         \
-    operator neon_type() const {                                              \
-      return values;                                                          \
-    }                                                                         \
-    static Vectorized<uint##bit##_t> loadu(                                   \
-        const void* ptr,                                                      \
-        uint64_t count = size());                                             \
-    void store(void* ptr, uint64_t count = size()) const;                     \
-    template <uint64_t mask>                                                  \
-    static Vectorized<uint##bit##_t> blend(                                   \
-        const Vectorized<uint##bit##_t>& a,                                   \
-        const Vectorized<uint##bit##_t>& b);                                  \
-    static Vectorized<uint##bit##_t> blendv(                                  \
-        const Vectorized<uint##bit##_t>& a,                                   \
-        const Vectorized<uint##bit##_t>& b,                                   \
-        const Vectorized<uint##bit##_t>& mask_) {                             \
-      return vbslq_u##bit(mask_.values, b, a);                                \
-    }                                                                         \
-    template <typename step_t>                                                \
-    static Vectorized<uint##bit##_t> arange(                                  \
-        value_type base = 0,                                                  \
-        step_t step = static_cast<step_t>(1));                                \
-    static Vectorized<uint##bit##_t> set(                                     \
-        const Vectorized<uint##bit##_t>& a,                                   \
-        const Vectorized<uint##bit##_t>& b,                                   \
-        uint64_t count = size());                                             \
-    const uint##bit##_t& operator[](uint idx) const = delete;                 \
-    uint##bit##_t& operator[](uint idx) = delete;                             \
-    Vectorized<uint##bit##_t> abs() const {                                   \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<uint##bit##_t> real() const {                                  \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<uint##bit##_t> imag() const {                                  \
-      return vdupq_n_u##bit(0);                                               \
-    }                                                                         \
-    Vectorized<uint##bit##_t> conj() const {                                  \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<uint##bit##_t> neg() const {                                   \
-      return vreinterpretq_u##bit##_s##bit(                                   \
-          vnegq_s##bit(vreinterpretq_s##bit##_u##bit(values)));               \
-    }                                                                         \
-    uint##bit##_t reduce_add() const {                                        \
-      return vaddvq_u##bit(values);                                           \
-    }                                                                         \
-    uint##bit##_t reduce_max() const;                                         \
-    Vectorized<uint##bit##_t> operator==(                                     \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vceqq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator!=(                                     \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> operator<(                                      \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcltq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator<=(                                     \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcleq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator>(                                      \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcgtq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator>=(                                     \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcgeq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> eq(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> ne(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> gt(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> ge(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> lt(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> le(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-  };                                                                          \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator+(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vaddq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator-(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vsubq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator&(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vandq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator|(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vorrq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator^(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return veorq_u##bit(a, b);                                                \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::eq(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this == other) & Vectorized<uint##bit##_t>(1);                   \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::ne(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this != other) & Vectorized<uint##bit##_t>(1);                   \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::gt(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this > other) & Vectorized<uint##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::ge(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this >= other) & Vectorized<uint##bit##_t>(1);                   \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::lt(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this < other) & Vectorized<uint##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::le(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this <= other) & Vectorized<uint##bit##_t>(1);                   \
-  }
-
-VEC_UINT_NEON_TEMPLATE(16, 8)
-
-inline uint8_t Vectorized<uint8_t>::reduce_max() const {
-  return vmaxvq_u8(values);
-}
-
-template <>
-Vectorized<uint8_t> inline operator*(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  return vmulq_u8(a, b);
-}
-
-template <>
-inline Vectorized<uint8_t> operator~(const Vectorized<uint8_t>& a) {
-  return vmvnq_u8(a);
-}
-
-inline Vectorized<uint8_t> Vectorized<uint8_t>::operator!=(
-    const Vectorized<uint8_t>& other) const {
-  return ~(*this == other);
-}
-
-template <>
-Vectorized<uint8_t> inline minimum(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  return vminq_u8(a, b);
-}
-
-template <>
-Vectorized<uint8_t> inline maximum(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  return vmaxq_u8(a, b);
-}
-
-template <uint64_t mask>
-Vectorized<uint8_t> Vectorized<uint8_t>::blend(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  // Build an array of flags: each bit of element is 1 if the corresponding bit
-  // in 'mask' is set, 0 otherwise.
-  uint8x16_t maskArray = {
-      (mask & 1LL) ? 0xFF : 0,
-      (mask & 2LL) ? 0xFF : 0,
-      (mask & 4LL) ? 0xFF : 0,
-      (mask & 8LL) ? 0xFF : 0,
-      (mask & 16LL) ? 0xFF : 0,
-      (mask & 32LL) ? 0xFF : 0,
-      (mask & 64LL) ? 0xFF : 0,
-      (mask & 128LL) ? 0xFF : 0,
-      (mask & 256LL) ? 0xFF : 0,
-      (mask & 512LL) ? 0xFF : 0,
-      (mask & 1024LL) ? 0xFF : 0,
-      (mask & 2048LL) ? 0xFF : 0,
-      (mask & 4096LL) ? 0xFF : 0,
-      (mask & 8192LL) ? 0xFF : 0,
-      (mask & 16384LL) ? 0xFF : 0,
-      (mask & 32768LL) ? 0xFF : 0};
-  // Use BSL to select elements from b where the mask is 1, else from a
-  return vbslq_u8(maskArray, b.values, a.values);
-}
-
-#define VEC_UINT_NEON_OPS(vl, bit)                                             \
-  inline Vectorized<uint##bit##_t>::Vectorized(uint##bit##_t val) {            \
-    values = vdupq_n_u##bit(val);                                              \
-  }                                                                            \
-  inline Vectorized<uint##bit##_t> Vectorized<uint##bit##_t>::loadu(           \
-      const void* ptr, uint64_t count) {                                       \
-    if (count == size()) {                                                     \
-      return vld1q_u##bit(reinterpret_cast<const uint##bit##_t*>(ptr));        \
-    } else {                                                                   \
-      __at_align__ uint##bit##_t tmp_values[size()];                           \
-      for (const auto i : c10::irange(size())) {                               \
-        tmp_values[i] = 0;                                                     \
-      }                                                                        \
-      std::memcpy(                                                             \
-          tmp_values,                                                          \
-          reinterpret_cast<const uint##bit##_t*>(ptr),                         \
-          count * sizeof(uint##bit##_t));                                      \
-      return vld1q_u##bit(reinterpret_cast<const uint##bit##_t*>(tmp_values)); \
-    }                                                                          \
-  }                                                                            \
-  inline void Vectorized<uint##bit##_t>::store(void* ptr, uint64_t count)      \
-      const {                                                                  \
-    if (count == size()) {                                                     \
-      vst1q_u##bit(reinterpret_cast<uint##bit##_t*>(ptr), values);             \
-    } else {                                                                   \
-      uint##bit##_t tmp_values[size()];                                        \
-      vst1q_u##bit(reinterpret_cast<uint##bit##_t*>(tmp_values), values);      \
-      std::memcpy(ptr, tmp_values, count * sizeof(uint##bit##_t));             \
-    }                                                                          \
-  }
-
-VEC_UINT_NEON_OPS(16, 8)
-
-template <typename step_t>
-inline Vectorized<uint8_t> Vectorized<uint8_t>::arange(
-    uint8_t base,
-    step_t step) {
-  const Vectorized<uint8_t> base_vec(base);
-  const Vectorized<uint8_t> step_vec(step);
-  const uint8x16_t step_sizes = {
-      0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
-  return vmlaq_u8(base_vec, step_sizes, step_vec);
-}
-
-template <>
-Vectorized<uint8_t> inline operator>>(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  uint8x16_t x = a;
-  uint8x16_t bound = vdupq_n_u8(8);
-  uint8x16_t z = vminq_u8(b, bound);
-  return x >> z;
-}
-
-template <>
-Vectorized<uint8_t> inline operator<<(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  uint8x16_t bound = vdupq_n_u8(8);
-  uint8x16_t z = vminq_u8(b, bound);
-  return vshlq_u8(a, vreinterpretq_s8_u8(z));
-}
-
-inline Vectorized<uint8_t> Vectorized<uint8_t>::set(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b,
-    uint64_t count) {
-  if (count == 0) {
-    return a;
-  } else if (count >= 16) {
-    return b;
-  } else {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint8x16_t maskArray = {
-        static_cast<uint8_t>((count >= 1LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 2LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 3LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 4LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 5LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 6LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 7LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 8LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 9LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 10LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 11LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 12LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 13LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 14LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 15LL) ? 0xFF : 0),
-        0};
-
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_u8(maskArray, b.values, a.values);
-  }
-}
-
-template <>
-Vectorized<uint8_t> inline operator/(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  uint8x16_t x = a;
-  uint8x16_t y = b;
-  return x / y;
-}
-
-template <>
-Vectorized<uint8_t> inline clamp(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& min,
-    const Vectorized<uint8_t>& max) {
-  return minimum(max, maximum(min, a));
-}
-
-template <>
-Vectorized<uint8_t> inline clamp_max(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& max) {
-  return minimum(max, a);
-}
-
-template <>
-Vectorized<uint8_t> inline clamp_min(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& min) {
-  return maximum(min, a);
-}
-
-} // namespace CPU_CAPABILITY
-} // namespace at::vec

aten/src/ATen/cpu/vec/vec256/vec256_qint.h

@@ -1390,7 +1390,7 @@ std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
 
 std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
     at::vec::Vectorized<uint8_t> src) {
-  auto u8x8 = vget_low_u8(src);
+  auto u8x8 = vld1_u8(src.operator const uint8_t*());
   auto u16x8 = vmovl_u8(u8x8);
   auto u32x4_hi = vmovl_u16(vget_high_u16(u16x8));
   auto u32x4_lo = vmovl_u16(vget_low_u16(u16x8));
@@ -1412,7 +1412,7 @@ Vectorized<float> inline convert_int8_half_register_to_float(
 
 Vectorized<float> inline convert_int8_half_register_to_float(
     at::vec::Vectorized<uint8_t> src) {
-  auto u8x8 = vget_low_u8(src);
+  auto u8x8 = vld1_u8(src.operator const uint8_t*());
   auto u16x8 = vmovl_u8(u8x8);
   auto u32x4_lo = vmovl_u16(vget_low_u16(u16x8));
 

aten/src/ATen/cuda/CUDAGreenContext.cpp

@@ -1,192 +0,0 @@
-#include <ATen/cuda/CUDAGreenContext.h>
-
-namespace at::cuda {
-  GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
-#if CUDA_HAS_GREEN_CONTEXT
-    int driver_version;
-    C10_CUDA_CHECK(cudaDriverGetVersion(&driver_version));
-    TORCH_CHECK(
-        driver_version >= 12080, "cuda driver too old to use green context!");
-    CUcontext pctx = nullptr;
-    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&pctx));
-    if (C10_UNLIKELY(!pctx)) {
-      TORCH_WARN(
-          "Attempted to create a green context but"
-          " there was no primary context! Creating a primary context...");
-
-      cudaFree(0);
-    }
-
-    CUdevice device;
-    device_id_ = device_id;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuDeviceGet_(&device, device_id));
-
-    // Get device resources
-    CUdevResource device_resource;
-    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuDeviceGetDevResource_(
-        device, &device_resource, CU_DEV_RESOURCE_TYPE_SM));
-
-    // Split resources
-    std::vector<CUdevResource> result(1);
-    auto result_data = result.data();
-    unsigned int nb_groups = 1;
-    CUdevResource remaining;
-
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuDevSmResourceSplitByCount_(
-            result_data,
-            &nb_groups,
-            &device_resource,
-            &remaining,
-            0, // default flags
-            num_sms));
-
-    TORCH_CHECK(nb_groups == 1, "Failed to create single resource group");
-
-    // Generate resource descriptor
-    CUdevResourceDesc desc;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuDevResourceGenerateDesc_(
-            &desc, result_data, 1));
-
-    // Create green context
-    // CU_GREEN_CTX_DEFAULT_STREAM is required per docs:
-    // https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html
-    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxCreate_(
-        &green_ctx_, desc, device, CU_GREEN_CTX_DEFAULT_STREAM));
-
-    // Convert to regular context
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuCtxFromGreenCtx_(&context_, green_ctx_));
-    TORCH_CHECK(context_, "Green ctx conversion to regular ctx failed!");
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  std::unique_ptr<GreenContext> GreenContext::create(
-      uint32_t num_sms,
-      std::optional<uint32_t> device_id) {
-#if CUDA_HAS_GREEN_CONTEXT
-    if (!device_id.has_value()) {
-      device_id = at::cuda::current_device();
-    }
-    return std::make_unique<GreenContext>(device_id.value(), num_sms);
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  // Implement move operations
-  GreenContext::GreenContext(GreenContext&& other) noexcept{
-#if CUDA_HAS_GREEN_CONTEXT
-    device_id_ = std::exchange(other.device_id_, -1);
-    green_ctx_ = std::exchange(other.green_ctx_, nullptr);
-    context_ = std::exchange(other.context_, nullptr);
-    parent_stream_ = std::exchange(other.parent_stream_, nullptr);
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  GreenContext& GreenContext::operator=(GreenContext&& other) noexcept{
-#if CUDA_HAS_GREEN_CONTEXT
-    if (this != &other) {
-      // Clean up current resources
-      if (green_ctx_) {
-        CUcontext current = nullptr;
-        C10_CUDA_DRIVER_CHECK(
-            c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&current));
-        if (current == context_) {
-          TORCH_CHECK(
-              false,
-              "attempting to overwrite current green ctx "
-              "when it is active!");
-        }
-        C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxDestroy_(green_ctx_));
-      }
-
-      // Take ownership of other's resources
-      device_id_ = std::exchange(other.device_id_, -1);
-      green_ctx_ = std::exchange(other.green_ctx_, nullptr);
-      context_ = std::exchange(other.context_, nullptr);
-      parent_stream_ = std::exchange(other.parent_stream_, nullptr);
-    }
-    return *this;
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  GreenContext::~GreenContext() noexcept{
-#if CUDA_HAS_GREEN_CONTEXT
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuGreenCtxDestroy_(green_ctx_));
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  // Get the underlying CUDA context
-  CUcontext GreenContext::getContext() const {
-#if CUDA_HAS_GREEN_CONTEXT
-    return context_;
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  // Get the underlying green context
-#if CUDA_HAS_GREEN_CONTEXT
-  CUgreenCtx GreenContext::getGreenContext() const {
-    return green_ctx_;
-  }
-#endif
-
-  // Make this context current
-  void GreenContext::setContext() {
-#if CUDA_HAS_GREEN_CONTEXT
-    auto current_stream = c10::cuda::getCurrentCUDAStream();
-    parent_stream_ = current_stream.stream();
-
-    at::cuda::CUDAEvent ev;
-    ev.record(current_stream);
-
-    CUcontext current = nullptr;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&current));
-    if (!current) {
-      C10_CUDA_DRIVER_CHECK(
-          c10::cuda::DriverAPI::get()->cuCtxSetCurrent_(context_));
-    } else {
-      C10_CUDA_DRIVER_CHECK(
-          c10::cuda::DriverAPI::get()->cuCtxPushCurrent_(context_));
-    }
-    // currently hardcodes the new green context to use the default stream
-    // TODO(eqy): consider creating a new stream if e.g., it allows interop
-    // with CUDA Graph captures etc.
-    auto default_stream = c10::cuda::getDefaultCUDAStream();
-    ev.block(default_stream);
-    c10::cuda::setCurrentCUDAStream(default_stream);
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  void GreenContext::popContext() {
-#if CUDA_HAS_GREEN_CONTEXT
-    // see above note about stream being hardcoded to the default stream
-    at::cuda::CUDAEvent ev;
-    ev.record(c10::cuda::getCurrentCUDAStream());
-    CUcontext popped;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuCtxPopCurrent_(&popped));
-    TORCH_INTERNAL_ASSERT(
-        popped == context_, "expected popped context to be the current ctx");
-    ev.block(c10::cuda::getStreamFromExternal(parent_stream_, device_id_));
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-} // namespace at::cuda

aten/src/ATen/cuda/CUDAGreenContext.h

@@ -1,53 +0,0 @@
-#pragma once
-#include <ATen/cuda/CUDAEvent.h>
-
-#if defined(CUDA_VERSION) && !defined(USE_ROCM) && defined(PYTORCH_C10_DRIVER_API_SUPPORTED)
-#include <c10/cuda/driver_api.h>
-#include <cuda.h>
-#include <memory>
-#include <stdexcept>
-#include <vector>
-#define CUDA_HAS_GREEN_CONTEXT 1
-#else
-#define CUDA_HAS_GREEN_CONTEXT 0
-#endif
-
-namespace at::cuda {
-
-class TORCH_CUDA_CPP_API GreenContext {
- public:
-  GreenContext(uint32_t device_id, uint32_t num_sms);
-
-  static std::unique_ptr<GreenContext> create(uint32_t num_sms, std::optional<uint32_t> device_id);
-
-  // Delete copy constructor and assignment
-  GreenContext(const GreenContext&) = delete;
-  GreenContext& operator=(const GreenContext&) = delete;
-
-  // Implement move operations
-  GreenContext(GreenContext&& other) noexcept;
-  GreenContext& operator=(GreenContext&& other) noexcept;
-  ~GreenContext() noexcept;
-
-  // Get the underlying CUDA context
-  CUcontext getContext() const;
-
-  // Get the underlying green context
-#if CUDA_HAS_GREEN_CONTEXT
-  CUgreenCtx getGreenContext() const;
-#endif
-
-  // Make this context current
-  void setContext();
-
-  void popContext();
-
- private:
-#if CUDA_HAS_GREEN_CONTEXT
-  int32_t device_id_ = -1;
-  CUgreenCtx green_ctx_ = nullptr;
-  CUcontext context_ = nullptr;
-  cudaStream_t parent_stream_ = nullptr;
-#endif
-};
-} // namespace at::cuda

aten/src/ATen/cuda/CUDAScaledBlas.cpp

@@ -1,270 +0,0 @@
-#include <cstdint>
-#include <c10/util/typeid.h>
-#include <c10/util/Exception.h>
-#include <c10/util/SmallVector.h>
-#include <c10/core/Scalar.h>
-#include <c10/core/ScalarType.h>
-#include <c10/util/Exception.h>
-#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
-#include <ATen/core/Tensor.h>
-#include <ATen/core/NamedTensor.h>
-#include <ATen/Dispatch.h>
-#include <ATen/ExpandUtils.h>
-#include <ATen/OpMathType.h>
-#include <ATen/TensorUtils.h>
-#include <ATen/cuda/CUDABlas.h>
-#include <ATen/cuda/tunable/Tunable.h>
-#include <ATen/cuda/tunable/TunableGemm.h>
-#include <ATen/native/Resize.h>
-#include <c10/util/MaybeOwned.h>
-#include <ATen/native/GroupedMMUtils.h>
-#include <ATen/native/cuda/RowwiseScaledMM.h>
-#include <ATen/native/cuda/ScaledGroupMM.h>
-#include <ATen/native/cuda/GroupMM.h>
-#include <ATen/ceil_div.h>
-
-#ifdef USE_FBGEMM_GENAI
-#include <fbgemm_gpu/torch_ops.h>
-#endif
-
-#ifndef AT_PER_OPERATOR_HEADERS
-#include <ATen/Functions.h>
-#include <ATen/NativeFunctions.h>
-#else
-#include <ATen/ops/_addmm_activation_native.h>
-#include <ATen/ops/_efficientzerotensor.h>
-#include <ATen/ops/_scaled_mm_native.h>
-#include <ATen/ops/_unsafe_view_native.h>
-#include <ATen/ops/abs.h>
-#include <ATen/ops/addmm_native.h>
-#include <ATen/ops/addmv_native.h>
-#include <ATen/ops/baddbmm_native.h>
-#include <ATen/ops/bmm_native.h>
-#include <ATen/ops/copy_native.h>
-#include <ATen/ops/dot_native.h>
-#include <ATen/ops/empty.h>
-#include <ATen/ops/empty_strided.h>
-#include <ATen/ops/gelu.h>
-#include <ATen/ops/max.h>
-#include <ATen/ops/mm_native.h>
-#include <ATen/ops/mul.h>
-#include <ATen/ops/relu.h>
-#include <ATen/ops/ones.h>
-#include <ATen/ops/scalar_tensor_native.h>
-#include <ATen/ops/vdot_native.h>
-#endif
-
-using at::blas::ScalingType;
-using at::blas::SwizzleType;
-
-namespace at::cuda::scaled {
-
-/**
- * Both inputs must be fp8,
- * Each needs a single scale, {Tensorwise (float)}
- */
-bool check_tensorwise_recipe(c10::ScalarType type_a,
-                             std::vector<ScalingType>& recipe_a,
-                             ArrayRef<Tensor>& scales_a,
-                             c10::ScalarType type_b,
-                             std::vector<ScalingType>& recipe_b,
-                             ArrayRef<Tensor>& scales_b) {
-  // both types must be fp8
-  if (!isFloat8Type(type_a) || !isFloat8Type(type_b)) {
-    return false;
-  }
-
-  // 1 scale each, {Tensorwise, float}
-  if (scales_a.size() != 1 || recipe_a.size() != 1 || scales_b.size() != 1 || recipe_b.size() != 1) {
-    return false;
-  }
-  // Need {Blockwise_1x32, e8m0} for A & B
-  if (recipe_a[0] != ScalingType::TensorWise) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float) return false;
-  if (recipe_b[0] != ScalingType::TensorWise) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float) return false;
-
-  return true;
-}
-
-/**
- * Both inputs must be fp8,
- * Each needs scales, {Rowwise (float)}
- */
-bool check_rowwise_recipe(c10::ScalarType type_a,
-                             std::vector<ScalingType>& recipe_a,
-                             ArrayRef<Tensor>& scales_a,
-                             c10::ScalarType type_b,
-                             std::vector<ScalingType>& recipe_b,
-                             ArrayRef<Tensor>& scales_b) {
-  // both types must be fp8
-  if (!isFloat8Type(type_a) || !isFloat8Type(type_b)) {
-    return false;
-  }
-
-  // 1 scale each, {Tensorwise, float}
-  if (scales_a.size() != 1 || recipe_a.size() != 1 || scales_b.size() != 1 || recipe_b.size() != 1) {
-    return false;
-  }
-
-  // Need {RowWise, dp32} for A & B
-  if (recipe_a[0] != ScalingType::RowWise) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float) return false;
-  if (recipe_b[0] != ScalingType::RowWise) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float) return false;
-
-  return true;
-}
-
-
-/**
- * Two-level scaling, canonical NVFP4
- * Both inputs must be fp4
- * A, B need 2 scales, {Blockwise_1x16 (e4m3), Tensorwise (fp32)}
- */
-bool check_nvfp4_recipe(c10::ScalarType type_a,
-                        std::vector<ScalingType>& recipe_a,
-                        ArrayRef<Tensor>& scales_a,
-                        c10::ScalarType type_b,
-                        std::vector<ScalingType>& recipe_b,
-                        ArrayRef<Tensor>& scales_b) {
-  // both types must be fp4
-  if (type_a != ScalarType::Float4_e2m1fn_x2 || type_b != ScalarType::Float4_e2m1fn_x2) {
-    return false;
-  }
-
-  // 2 scales, 2 recipes for each input
-  if (scales_a.size() != 2 || recipe_a.size() != 2 || scales_b.size() != 2 || recipe_b.size() != 2) {
-    return false;
-  }
-
-  // Need {Blockwise_1x16, e4m3 for scale[0], Tensorwise, fp32 for scale[1]}
-  if (recipe_a[0] != ScalingType::BlockWise1x16 || recipe_a[1] != ScalingType::TensorWise) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float8_e4m3fn || scales_a[1].scalar_type() != ScalarType::Float) return false;
-  if (recipe_b[0] != ScalingType::BlockWise1x16 || recipe_b[1] != ScalingType::TensorWise) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float8_e4m3fn || scales_b[1].scalar_type() != ScalarType::Float) return false;
-
-  return true;
-}
-
-/**
- * Single-level scaling, what PyT currently understands
- * Both inputs must be fp4
- * A, B need 1 scale, {Blockwise_1x16 (e4m3)}
- */
-bool check_nvfp4_recipe_single_scale
-                       (c10::ScalarType type_a,
-                        std::vector<ScalingType>& recipe_a,
-                        ArrayRef<Tensor>& scales_a,
-                        c10::ScalarType type_b,
-                        std::vector<ScalingType>& recipe_b,
-                        ArrayRef<Tensor>& scales_b) {
-  // both types must be fp4
-  if (type_a != ScalarType::Float4_e2m1fn_x2 || type_b != ScalarType::Float4_e2m1fn_x2) {
-    return false;
-  }
-
-  // 2 scales, 2 recipes for each input
-  if (scales_a.size() != 1 || recipe_a.size() != 1 || scales_b.size() != 1 || recipe_b.size() != 1) {
-    return false;
-  }
-
-  // Need {Blockwise_1x16, e4m3 for scale[0], Tensorwise, fp32 for scale[1]}
-  if (recipe_a[0] != ScalingType::BlockWise1x16) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float8_e4m3fn) return false;
-  if (recipe_b[0] != ScalingType::BlockWise1x16) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float8_e4m3fn) return false;
-
-  return true;
-}
-
-/**
- * Both inputs must be fp8
- * A, B must only have 1 scale each, A: {Blockwise_1x128 (float), B: {Blockwise_128x128 (float)
- */
-bool check_deepseek_recipe(ScalingType expected_recipe_a,
-                           ScalingType expected_recipe_b,
-                           c10::ScalarType type_a,
-                           std::vector<ScalingType>& recipe_a,
-                           ArrayRef<Tensor>& scales_a,
-                           c10::ScalarType type_b,
-                           std::vector<ScalingType>& recipe_b,
-                           ArrayRef<Tensor>& scales_b) {
-  // both types must be fp8
-  if (type_a != ScalarType::Float8_e4m3fn || type_b != ScalarType::Float8_e4m3fn) {
-    return false;
-  }
-
-  // 1 scales, 1 recipes for each input
-  if (scales_a.size() != 1 || recipe_a.size() != 1 || scales_b.size() != 1 || recipe_b.size() != 1) {
-    return false;
-  }
-
-  // Need {Blockwise_1x128, float} for A, {Blockwise_128x128, float} for B
-  if (recipe_a[0] != expected_recipe_a) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float) return false;
-  if (recipe_b[0] != expected_recipe_b) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float) return false;
-
-  return true;
-}
-
-/**
- * Both inputs must be fp8
- * A, B must have 1 scale each, {Blockwise_1x32, e8m0}
- */
-bool check_mxfp8_recipe(c10::ScalarType type_a,
-                        std::vector<ScalingType>& recipe_a,
-                        ArrayRef<Tensor>& scales_a,
-                        c10::ScalarType type_b,
-                        std::vector<ScalingType>& recipe_b,
-                        ArrayRef<Tensor>& scales_b) {
-  // both types must be fp8
-  if (type_a != ScalarType::Float8_e4m3fn || type_b != ScalarType::Float8_e4m3fn) {
-    return false;
-  }
-
-  // 1 scales, 1 recipes for each input
-  if (scales_a.size() != 1 || recipe_a.size() != 1 || scales_b.size() != 1 || recipe_b.size() != 1) {
-    return false;
-  }
-
-  // Need {Blockwise_1x32, e8m0} for A & B
-  if (recipe_a[0] != ScalingType::BlockWise1x32) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float8_e8m0fnu) return false;
-  if (recipe_b[0] != ScalingType::BlockWise1x32) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float8_e8m0fnu) return false;
-
-  return true;
-}
-
-/**
- * Both inputs must be fp4
- * A, B must have 1 scale each, {Blockwise_1x32, e8m0}
- */
-bool check_mxfp4_recipe(c10::ScalarType type_a,
-                        std::vector<ScalingType>& recipe_a,
-                        ArrayRef<Tensor>& scales_a,
-                        c10::ScalarType type_b,
-                        std::vector<ScalingType>& recipe_b,
-                        ArrayRef<Tensor>& scales_b) {
-  // both types must be fp4
-  if (type_a != ScalarType::Float4_e2m1fn_x2 || type_b != ScalarType::Float4_e2m1fn_x2) {
-    return false;
-  }
-
-  // 1 scales, 1 recipes for each input
-  if (scales_a.size() != 1 || recipe_a.size() != 1 || scales_b.size() != 1 || recipe_b.size() != 1) {
-    return false;
-  }
-
-  // Need {Blockwise_1x32, e8m0} for A & B
-  if (recipe_a[0] != ScalingType::BlockWise1x32) return false;
-  if (scales_a[0].scalar_type() != ScalarType::Float8_e8m0fnu) return false;
-  if (recipe_b[0] != ScalingType::BlockWise1x32) return false;
-  if (scales_b[0].scalar_type() != ScalarType::Float8_e8m0fnu) return false;
-
-  return true;
-}
-
-} // namespace at::native::cuda::blas::scaled

aten/src/ATen/cuda/CUDAScaledBlas.h

@@ -1,174 +0,0 @@
-#include <cstdint>
-#include <c10/util/typeid.h>
-#include <c10/util/Exception.h>
-#include <c10/util/SmallVector.h>
-#include <c10/core/Scalar.h>
-#include <c10/core/ScalarType.h>
-#include <c10/util/Exception.h>
-#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
-#include <ATen/core/Tensor.h>
-#include <ATen/core/NamedTensor.h>
-#include <ATen/Dispatch.h>
-#include <ATen/ExpandUtils.h>
-#include <ATen/OpMathType.h>
-#include <ATen/TensorUtils.h>
-#include <ATen/cuda/CUDABlas.h>
-#include <ATen/cuda/tunable/Tunable.h>
-#include <ATen/cuda/tunable/TunableGemm.h>
-#include <ATen/native/Resize.h>
-#include <c10/util/MaybeOwned.h>
-#include <ATen/native/GroupedMMUtils.h>
-#include <ATen/native/cuda/RowwiseScaledMM.h>
-#include <ATen/native/cuda/ScaledGroupMM.h>
-#include <ATen/native/cuda/GroupMM.h>
-#include <ATen/ceil_div.h>
-
-#ifdef USE_FBGEMM_GENAI
-#include <fbgemm_gpu/torch_ops.h>
-#endif
-
-#ifndef AT_PER_OPERATOR_HEADERS
-#include <ATen/Functions.h>
-#include <ATen/NativeFunctions.h>
-#else
-#include <ATen/ops/_addmm_activation_native.h>
-#include <ATen/ops/_efficientzerotensor.h>
-#include <ATen/ops/_scaled_mm_native.h>
-#include <ATen/ops/_unsafe_view_native.h>
-#include <ATen/ops/abs.h>
-#include <ATen/ops/addmm_native.h>
-#include <ATen/ops/addmv_native.h>
-#include <ATen/ops/baddbmm_native.h>
-#include <ATen/ops/bmm_native.h>
-#include <ATen/ops/copy_native.h>
-#include <ATen/ops/dot_native.h>
-#include <ATen/ops/empty.h>
-#include <ATen/ops/empty_strided.h>
-#include <ATen/ops/gelu.h>
-#include <ATen/ops/max.h>
-#include <ATen/ops/mm_native.h>
-#include <ATen/ops/mul.h>
-#include <ATen/ops/relu.h>
-#include <ATen/ops/ones.h>
-#include <ATen/ops/scalar_tensor_native.h>
-#include <ATen/ops/vdot_native.h>
-#endif
-
-using at::blas::ScalingType;
-using at::blas::SwizzleType;
-
-namespace at::cuda::scaled {
-
-static bool _scaled_mm_allowed_device(bool sm90_only=false, bool sm100_only=false) {
-#ifdef USE_ROCM
-    static const std::vector<std::string> archs = {
-        "gfx942",
-#if ROCM_VERSION >= 60300
-        "gfx1200", "gfx1201",
-#endif
-#if ROCM_VERSION >= 60500
-        "gfx950"
-#endif
-    };
-    return at::detail::getCUDAHooks().isGPUArch(archs);
-#else
-    auto dprops = at::cuda::getCurrentDeviceProperties();
-
-    if (sm90_only || sm100_only) {
-      return (sm90_only && dprops->major == 9) || (sm100_only && dprops->major == 10);
-    } else {
-      return dprops->major >= 9 || (dprops->major == 8 && dprops->minor == 9);
-    }
-#endif
-}
-
-#ifdef USE_ROCM
-static bool _scaled_mm_is_fnuz() {
-    return at::detail::getCUDAHooks().isGPUArch({"gfx942"});
-}
-#endif
-/**
- * Track concrete implementations available
- */
-enum class ScaledGemmImplementation {
-  NONE = 0,
-  TENSORWISE_TENSORWISE = 1,
-  ROWWISE_ROWWISE = 2,
-  BLOCK_128x128_1x128 = 3,
-  BLOCK_1x128_128x128 = 4,
-  BLOCK_1x128_1x128 = 5,
-  MXFP8_MXFP8 = 6,
-  NVFP4_NVFP4 = 7,
-  NVFP4_NVFP4_SINGLE_SCALE = 8,
-  MXFP4_MXFP4 = 9,
-};
-
-/**
- * Convert passed int (enum) from python back into a
- * strictly-typed enum
- */
-template <class EnumType, class ArrayType>
-std::vector<EnumType> convert_int_to_enum(ArrayType& v) {
-  std::vector<EnumType> converted;
-  converted.reserve(v.size());
-
-  for (auto vi : v) {
-    converted.push_back(static_cast<EnumType>(vi));
-  }
-  return converted;
-}
-
-bool check_tensorwise_recipe(c10::ScalarType,
-                             std::vector<ScalingType>&,
-                             ArrayRef<Tensor>&,
-                             c10::ScalarType,
-                             std::vector<ScalingType>&,
-                             ArrayRef<Tensor>&);
-
-
-bool check_rowwise_recipe(c10::ScalarType,
-                             std::vector<ScalingType>&,
-                             ArrayRef<Tensor>&,
-                             c10::ScalarType,
-                             std::vector<ScalingType>&,
-                             ArrayRef<Tensor>&);
-
-bool check_nvfp4_recipe(c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&,
-                        c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&);
-
-bool check_nvfp4_recipe_single_scale
-                       (c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&,
-                        c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&);
-
-bool check_deepseek_recipe(ScalingType,
-                           ScalingType,
-                           c10::ScalarType,
-                           std::vector<ScalingType>&,
-                           ArrayRef<Tensor>&,
-                           c10::ScalarType,
-                           std::vector<ScalingType>&,
-                           ArrayRef<Tensor>&);
-
-bool check_mxfp8_recipe(c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&,
-                        c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&);
-
-bool check_mxfp4_recipe(c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&,
-                        c10::ScalarType,
-                        std::vector<ScalingType>&,
-                        ArrayRef<Tensor>&);
-
-} // namespace at::native::cuda::blas::scaled

aten/src/ATen/cuda/cub.cuh

@@ -70,7 +70,11 @@
 #define ATEN_CUB_MAXIMUM() NO_ROCM(at_cuda_detail)ROCM_HIPCUB(::cub)::Max()
 #endif
 
-#if defined(USE_ROCM)
+#if (!defined(USE_ROCM) && !CUB_SUPPORTS_NV_BFLOAT16()) || defined(USE_ROCM)
+
+#if !defined(USE_ROCM)
+namespace at_cuda_detail {
+#endif
 
 // backport https://github.com/NVIDIA/cub/pull/306 for c10::BFloat16
 
@@ -92,6 +96,10 @@ template <>
 struct ROCM_HIPCUB(cub)::NumericTraits<c10::BFloat16>:
        ROCM_HIPCUB(cub)::BaseTraits<ROCM_HIPCUB(cub)::FLOATING_POINT, true, false, unsigned short, c10::BFloat16> {};
 
+#if !defined(USE_ROCM)
+} // namespace at_cuda_detail
+#endif
+
 #endif
 
 #if !defined(USE_ROCM)
@@ -113,7 +121,7 @@ struct cuda_type<c10::Half> {
   using type = __half;
 };
 
-#if !defined(USE_ROCM)
+#if !defined(USE_ROCM) && CUB_SUPPORTS_NV_BFLOAT16()
 
 template<>
 struct cuda_type<c10::BFloat16> {
@@ -195,6 +203,36 @@ __global__ void transform_vals(InputIteratorT1 a, InputIteratorT2 b, OutputItera
   *out = scan_op(static_cast<acc_t>(*a), static_cast<acc_t>(*b));
 }
 
+#if !CUB_SUPPORTS_FUTURE_VALUE()
+template<typename ValueT, typename InputIteratorT>
+struct chained_iterator {
+  using iterator_category = std::random_access_iterator_tag;
+  using difference_type   = std::ptrdiff_t;
+  using value_type        = ValueT;
+  using pointer           = ValueT*;
+  using reference         = ValueT&;
+
+  InputIteratorT iter;
+  ValueT *first;
+  difference_type offset = 0;
+
+  __device__ ValueT operator[](difference_type i) {
+    i +=  offset;
+    if (i == 0) {
+      return *first;
+    } else {
+      return ValueT(iter[i - 1]);
+    }
+  }
+  __device__ chained_iterator operator+(difference_type i) {
+    return chained_iterator{iter, first, i};
+  }
+  __device__ ValueT operator*() {
+    return (*this)[0];
+  }
+};
+#endif
+
 // even though cub is supposed to support tensors with int_max elements, in reality it doesn't,
 // so split at int_max/2
 constexpr int max_cub_size = std::numeric_limits<int>::max() / 2 + 1; // 2**30
@@ -239,6 +277,25 @@ inline void inclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         first_elem_ptr,
         scan_op);
     C10_CUDA_KERNEL_LAUNCH_CHECK();
+#if !CUB_SUPPORTS_FUTURE_VALUE()
+    using ArgIndexInputIterator = NO_ROCM(at_cuda_detail)::cub::ArgIndexInputIterator<InputIteratorT>;
+    using tuple = typename ArgIndexInputIterator::value_type;
+    auto input_iter_transform = [=] __device__ (const tuple &x)->input_t  {
+      if (x.key == 0) {
+        return *first_elem_ptr;
+      } else {
+        return x.value;
+      }
+    };
+    auto input_ = ATEN_CUB_TRANSFORM_ITERATOR(input_t, decltype(input_iter_transform), ArgIndexInputIterator)(
+      ArgIndexInputIterator(input + i), input_iter_transform);
+    CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::InclusiveScan,
+        input_,
+        output + i,
+        scan_op,
+        size_cub,
+        at::cuda::getCurrentCUDAStream());
+#else
     CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::ExclusiveScan,
         input + i + 1,
         output + i,
@@ -246,6 +303,7 @@ inline void inclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         ::at_cuda_detail::cub::FutureValue<input_t>(first_elem_ptr),
         size_cub,
         at::cuda::getCurrentCUDAStream());
+#endif
   }
 #endif
 }
@@ -497,6 +555,16 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         first_elem_ptr,
         scan_op);
     C10_CUDA_KERNEL_LAUNCH_CHECK();
+#if !CUB_SUPPORTS_FUTURE_VALUE()
+    auto input_ = impl::chained_iterator<InitValueT, InputIteratorT>{
+      input + i, first_elem_ptr};
+    CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::InclusiveScan,
+        input_,
+        output + i,
+        scan_op,
+        size_cub,
+        at::cuda::getCurrentCUDAStream());
+#else
     CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::ExclusiveScan,
         input + i,
         output + i,
@@ -504,6 +572,7 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         ::at_cuda_detail::cub::FutureValue<InitValueT>(first_elem_ptr),
         size_cub,
         at::cuda::getCurrentCUDAStream());
+#endif
   }
 #endif
 }

aten/src/ATen/cuda/cub_definitions.cuh

@@ -10,6 +10,14 @@
 #define CUB_VERSION 200001
 #endif
 
+// cub sort support for __nv_bfloat16 is added to cub 1.13 in:
+// https://github.com/NVIDIA/cub/pull/306
+#if CUB_VERSION >= 101300
+#define CUB_SUPPORTS_NV_BFLOAT16() true
+#else
+#define CUB_SUPPORTS_NV_BFLOAT16() false
+#endif
+
 // cub support for CUB_WRAPPED_NAMESPACE is added to cub 1.13.1 in:
 // https://github.com/NVIDIA/cub/pull/326
 // CUB_WRAPPED_NAMESPACE is defined globally in cmake/Dependencies.cmake
@@ -20,6 +28,14 @@
 #define USE_GLOBAL_CUB_WRAPPED_NAMESPACE() false
 #endif
 
+// cub support for cub::FutureValue is added to cub 1.15 in:
+// https://github.com/NVIDIA/cub/pull/305
+#if CUB_VERSION >= 101500
+#define CUB_SUPPORTS_FUTURE_VALUE() true
+#else
+#define CUB_SUPPORTS_FUTURE_VALUE() false
+#endif
+
 // There were many bc-breaking changes in major version release of CCCL v3.0.0
 // Please see https://nvidia.github.io/cccl/cccl/3.0_migration_guide.html
 #if CUB_VERSION >= 200800

aten/src/ATen/detail/XLAHooksInterface.cpp

@@ -1,23 +0,0 @@
-#include <ATen/detail/XLAHooksInterface.h>
-
-namespace at {
-namespace detail {
-
-const XLAHooksInterface& getXLAHooks() {
-  auto create_impl = [] {
-    // Create XLA hooks using the registry
-    auto hooks = XLAHooksRegistry()->Create("torch_xla::detail::XLAHooks", XLAHooksArgs{});
-    if (hooks) {
-      return hooks;
-    }
-    // If hooks creation fails, fall back to default implementation
-    return std::make_unique<XLAHooksInterface>();
-  };
-  static auto hooks = create_impl();
-  return *hooks;
-}
-} // namespace detail
-
-C10_DEFINE_REGISTRY(XLAHooksRegistry, XLAHooksInterface, XLAHooksArgs)
-
-} // namespace at

aten/src/ATen/detail/XLAHooksInterface.h

@@ -1,79 +0,0 @@
-#pragma once
-
-#include <c10/core/Device.h>
-#include <c10/util/Exception.h>
-#include <c10/util/Registry.h>
-
-#include <ATen/detail/AcceleratorHooksInterface.h>
-
-C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-parameter")
-
-namespace at {
-
-constexpr const char* XLA_HELP =
-  "This error has occurred because you are trying "
-  "to use some XLA functionality, but the XLA library has not been "
-  "loaded by the dynamic linker. You must load xla libraries by `import torch_xla`";
-
-struct TORCH_API XLAHooksInterface : AcceleratorHooksInterface {
-  ~XLAHooksInterface() override = default;
-
-  void init() const override {
-    TORCH_CHECK(false, "Cannot initialize XLA without torch_xla library. ", XLA_HELP);
-  }
-
-  virtual bool hasXLA() const {
-    return false;
-  }
-
-  virtual std::string showConfig() const {
-    TORCH_CHECK(
-        false,
-        "Cannot query detailed XLA version without torch_xla library. ",
-        XLA_HELP);
-  }
-
-  const Generator& getDefaultGenerator(
-      [[maybe_unused]] DeviceIndex device_index = -1) const override {
-    TORCH_CHECK(
-        false, "Cannot get default XLA generator without torch_xla library. ", XLA_HELP);
-  }
-
-  Generator getNewGenerator(
-      [[maybe_unused]] DeviceIndex device_index = -1) const override {
-    TORCH_CHECK(false, "Cannot get XLA generator without torch_xla library. ", XLA_HELP);
-  }
-
-  virtual DeviceIndex getCurrentDevice() const override {
-    TORCH_CHECK(false, "Cannot get current XLA device without torch_xla library. ", XLA_HELP);
-  }
-
-  Device getDeviceFromPtr(void* /*data*/) const override {
-    TORCH_CHECK(false, "Cannot get device of pointer on XLA without torch_xla library. ", XLA_HELP);
-  }
-
-  Allocator* getPinnedMemoryAllocator() const override {
-    TORCH_CHECK(false, "Cannot get XLA pinned memory allocator without torch_xla library. ", XLA_HELP);
-  }
-
-  bool isPinnedPtr(const void* data) const override {
-    return false;
-  }
-
-  bool hasPrimaryContext(DeviceIndex device_index) const override {
-    TORCH_CHECK(false, "Cannot query primary context without torch_xla library. ", XLA_HELP);
-  }
-
-};
-
-struct TORCH_API XLAHooksArgs {};
-
-TORCH_DECLARE_REGISTRY(XLAHooksRegistry, XLAHooksInterface, XLAHooksArgs);
-#define REGISTER_XLA_HOOKS(clsname) \
-  C10_REGISTER_CLASS(XLAHooksRegistry, clsname, clsname)
-
-namespace detail {
-TORCH_API const XLAHooksInterface& getXLAHooks();
-} // namespace detail
-} // namespace at
-C10_DIAGNOSTIC_POP()

aten/src/ATen/native/PixelShuffle.h

@@ -11,8 +11,6 @@ inline void check_pixel_shuffle_shapes(const Tensor& self, int64_t upscale_facto
               "pixel_shuffle expects a positive upscale_factor, but got ",
               upscale_factor);
   int64_t c = self.size(-3);
-  TORCH_CHECK_VALUE(upscale_factor <= std::numeric_limits<decltype(upscale_factor)>::max() / upscale_factor,
-        "upscale factor is too large, (upscale_factor)^2 overflowed: upscale_factor=", upscale_factor);
   int64_t upscale_factor_squared = upscale_factor * upscale_factor;
   TORCH_CHECK(c % upscale_factor_squared == 0,
               "pixel_shuffle expects its input's 'channel' dimension to be divisible by the square of "

aten/src/ATen/native/cpu/DepthwiseConvKernel.cpp

@@ -259,20 +259,11 @@ inline void winograd_f2k3_input_transform_inplace__rvv(
   const vfloat32m1_t wd1 = __riscv_vfadd_vv_f32m1(d1, d2, 4);
   const vfloat32m1_t wd2 = __riscv_vfsub_vv_f32m1(d2, d1, 4);
   const vfloat32m1_t wd3 = __riscv_vfsub_vv_f32m1(d1, d3, 4);
-  /* GCC 14.2 (RISC-V RVV) ICE workaround:
-   * Avoid single-statement read-modify-write on MEM_REF like:
-   *   *input_tile_val =
-   *     __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, idx, val);
-   * This triggers an ICE during GIMPLE lower (gsi_replace / riscv_gimple_fold_builtin)
-   * with -march=rv64gcv. Use a temporary then write back.
-   * Do NOT refactor into the single-statement form. Clang is unaffected.
-   */
-  vfloat32m1x4_t tmp_input_tile_val = *input_tile_val;
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 0, wd0);
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 1, wd1);
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 2, wd2);
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 3, wd3);
-  *input_tile_val = tmp_input_tile_val;
+
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 0, wd0);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 1, wd1);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 2, wd2);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 3, wd3);
 }
 
 inline void winograd_f2k3_output_transform_inplace__rvv(
@@ -286,15 +277,9 @@ inline void winograd_f2k3_output_transform_inplace__rvv(
   const vfloat32m1_t wm0 = __riscv_vfadd_vv_f32m1(m0_plus_m1, m2, 4);
   const vfloat32m1_t m1_sub_m2 = __riscv_vfsub_vv_f32m1(m1, m2, 4);
   const vfloat32m1_t wm1 = __riscv_vfsub_vv_f32m1(m1_sub_m2, m3, 4);
-  /* GCC 14.2 (RISC-V RVV) ICE workaround — see note above.
-   * Keep the temporary + write-back pattern to avoid ICE.
-   * Do NOT rewrite into:
-   *   *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, idx, val);
-   */
-  vfloat32m1x4_t tmp_output_tile_val = *input_tile_val;
-  tmp_output_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_output_tile_val, 0, wm0);
-  tmp_output_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_output_tile_val, 1, wm1);
-  *input_tile_val = tmp_output_tile_val;
+
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 0, wm0);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 1, wm1);
 }
 
 inline vfloat32m1_t
@@ -315,17 +300,11 @@ inline void winograd_f2k3_kernel_transform__rvv(
   const vfloat32m1_t const_half = __riscv_vfmv_v_f_f32m1(0.5f, 4);
   const vfloat32m1_t g0_plus_g2 = __riscv_vfadd_vv_f32m1(g0, g2, 4);
   vfloat32m1_t half_g0_plus_g2 =  __riscv_vfmul_vv_f32m1(const_half, g0_plus_g2, 4);
-  /* GCC 14.2 (RISC-V RVV) ICE workaround — see note above.
-   * Keep the temporary + write-back pattern to avoid ICE.
-   * Do NOT rewrite into:
-   *   *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, idx, val);
-   */
-  vfloat32m1x4_t tmp_transform = *transform;
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 0, g0);
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 1, vmuladdq_f32(half_g0_plus_g2, const_half, g1));
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 2, vmulsubq_f32(half_g0_plus_g2, const_half, g1));
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 3, g2);
-  *transform = tmp_transform;
+
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 0, g0);
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 1, vmuladdq_f32(half_g0_plus_g2, const_half, g1));
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 2, vmulsubq_f32(half_g0_plus_g2, const_half, g1));
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 3, g2);
 }
 
 inline vfloat32m1x4_t v4f_transpose4x4__rvv(const vfloat32m1x4_t m) {

aten/src/ATen/native/cuda/GroupedBlas.cpp

@@ -1,574 +0,0 @@
-#include <cstdint>
-#include <c10/util/typeid.h>
-#include <c10/util/Exception.h>
-#include <c10/util/SmallVector.h>
-#include <c10/core/Scalar.h>
-#include <c10/core/ScalarType.h>
-#include <c10/util/Exception.h>
-#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
-#include <ATen/core/Tensor.h>
-#include <ATen/core/NamedTensor.h>
-#include <ATen/Dispatch.h>
-#include <ATen/ExpandUtils.h>
-#include <ATen/OpMathType.h>
-#include <ATen/TensorUtils.h>
-#include <ATen/cuda/CUDABlas.h>
-#include <ATen/cuda/CUDAScaledBlas.h>
-#include <ATen/cuda/tunable/Tunable.h>
-#include <ATen/cuda/tunable/TunableGemm.h>
-#include <ATen/native/Resize.h>
-#include <c10/util/MaybeOwned.h>
-#include <ATen/native/GroupedMMUtils.h>
-#include <ATen/native/cuda/RowwiseScaledMM.h>
-#include <ATen/native/cuda/ScaledGroupMM.h>
-#include <ATen/native/cuda/GroupMM.h>
-#include <ATen/ceil_div.h>
-
-#ifdef USE_FBGEMM_GENAI
-#include <fbgemm_gpu/torch_ops.h>
-#endif
-
-#ifndef AT_PER_OPERATOR_HEADERS
-#include <ATen/Functions.h>
-#include <ATen/NativeFunctions.h>
-#else
-#include <ATen/ops/_addmm_activation_native.h>
-#include <ATen/ops/_efficientzerotensor.h>
-#include <ATen/ops/_scaled_mm_native.h>
-#include <ATen/ops/_unsafe_view_native.h>
-#include <ATen/ops/abs.h>
-#include <ATen/ops/addmm_native.h>
-#include <ATen/ops/addmv_native.h>
-#include <ATen/ops/baddbmm_native.h>
-#include <ATen/ops/bmm_native.h>
-#include <ATen/ops/copy_native.h>
-#include <ATen/ops/dot_native.h>
-#include <ATen/ops/empty.h>
-#include <ATen/ops/empty_strided.h>
-#include <ATen/ops/gelu.h>
-#include <ATen/ops/max.h>
-#include <ATen/ops/mm_native.h>
-#include <ATen/ops/mul.h>
-#include <ATen/ops/relu.h>
-#include <ATen/ops/ones.h>
-#include <ATen/ops/scalar_tensor_native.h>
-#include <ATen/ops/vdot_native.h>
-#endif
-
-using at::blas::ScalingType;
-using at::blas::SwizzleType;
-
-namespace scaled_blas = at::cuda::scaled;
-using scaled_blas::ScaledGemmImplementation;
-using scaled_blas::convert_int_to_enum;
-using scaled_blas::_scaled_mm_allowed_device;
-
-namespace at::native {
-
-namespace {
-
-// 2d-2d and 2d-3d
-// scaling=MXFP8
-// CUDA-only
-Tensor&
-_mx8_mx8_bf16_grouped_mm_fbgemm(
-        const Tensor& mat_a,
-        const Tensor& mat_b,
-        const Tensor& scale_a,
-        const SwizzleType& swizzle_a,
-        const Tensor& scale_b,
-        const SwizzleType& swizzle_b,
-        const std::optional<at::Tensor>& offs,
-        Tensor& out) {
-    const bool a_is_2d = mat_a.dim() == 2;
-    const bool b_is_2d = mat_b.dim() == 2;
-    bool b_is_3d = mat_b.dim() == 3;
-    bool is_2d_2d = a_is_2d && b_is_2d;
-    bool is_2d_3d = a_is_2d && b_is_3d;
-    TORCH_CHECK_VALUE(is_2d_2d || is_2d_3d, "MXFP8 grouped GEMM currently only supports 2d-2d and 2d-3d cases");
-    TORCH_CHECK_VALUE(offs.has_value(), "MXFP8 2d-2d and 2d-3d grouped GEMMs requires offsets");
-    TORCH_CHECK_VALUE(out.scalar_type() == at::kBFloat16, "Only bf16 out_dtype is supported for MXFP8 grouped gemm");
-    // MXFP8 expects float8_e8m0fnu scales.
-    TORCH_CHECK_VALUE(scale_a.scalar_type() == at::kFloat8_e8m0fnu && scale_b.scalar_type() == at::kFloat8_e8m0fnu,
-        "For MXFP8 grouped gemm, both scales must be float8_e8m0fnu tensors.");
-#ifdef USE_ROCM
-    TORCH_CHECK_VALUE(swizzle_a == SwizzleType::NO_SWIZZLE && swizzle_b == SwizzleType::NO_SWIZZLE,
-        "For ROCM MXFP8 grouped gemm, both scale swizzle types must be SWIZZLE_NONE");
-#else
-    TORCH_CHECK_VALUE(swizzle_a == SwizzleType::SWIZZLE_32_4_4 && swizzle_b == SwizzleType::SWIZZLE_32_4_4,
-        "For CUDA MXFP8 grouped gemm, both scale swizzle types must be SWIZZLE_32_4_4");
-#endif
-
-#if defined(USE_FBGEMM_GENAI) and !defined(USE_ROCM)
-    fbgemm_gpu::mx8mx8bf16_grouped_mm(
-        mat_a,
-        mat_b,
-        scale_a,
-        scale_b,
-        offs.value(),
-        out);
-#else
-    TORCH_CHECK_NOT_IMPLEMENTED(false, "mxfp8_mxfp8 grouped gemm requires compile with USE_FBGEMM_GENAI");
-#endif
-    return out;
-}
-
-// 2d-2d and 2d-3d cases
-// scaling=rowwise
-// CUDA-only
-Tensor&
-_f8_f8_bf16_rowwise_grouped_mm_cuda(
-          const Tensor& mat_a,
-          const Tensor& mat_b,
-          const Tensor& scale_a,
-          const Tensor& scale_b,
-          const std::optional<Tensor>& offs,
-          const std::optional<Tensor>& bias,
-          const bool use_fast_accum,
-          Tensor& out) {
-  TORCH_CHECK_VALUE(mat_a.dtype() == at::kFloat8_e4m3fn, "Expected mat_a to be Float8_e4m3 matrix got ", mat_a.scalar_type());
-  TORCH_CHECK_VALUE(mat_b.dtype() == at::kFloat8_e4m3fn, "Expected mat_a to be Float8_e4m3 matrix got ", mat_b.scalar_type());
-
-  at::cuda::detail::f8f8bf16_grouped_mm(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      offs,
-      bias,
-      use_fast_accum,
-      out);
-    return out;
-}
-
-// 2d-2d and 2d-3d cases
-// scaling=rowwise
-// only being called for rocm
-Tensor&
-_f8_f8_bf16_rowwise_grouped_mm_rocm(
-      const Tensor& mat_a,
-      const Tensor& mat_b,
-      const Tensor& scale_a,
-      const Tensor& scale_b,
-      const std::optional<Tensor>& offs,
-      Tensor& out) {
-  TORCH_CHECK_VALUE(mat_a.dtype() == at::kFloat8_e4m3fnuz, "Expected mat_a to be Float8_e4m3fnuz matrix got ", mat_a.scalar_type());
-  TORCH_CHECK_VALUE(mat_b.dtype() == at::kFloat8_e4m3fnuz, "Expected mat_a to be Float8_e4m3fnuz matrix got ", mat_b.scalar_type());
-
-#if defined(USE_FBGEMM_GENAI) && defined(USE_ROCM)
-  fbgemm_gpu::f8f8bf16_rowwise_grouped_mm(
-      mat_a,
-      // FBGEMM expects B matrix shape to be (.., N, K)
-      mat_b.transpose(-2, -1),
-      scale_a,
-      scale_b,
-      offs,
-      out);
-#else
-  TORCH_CHECK_NOT_IMPLEMENTED(false, "grouped gemm is not supported without USE_FBGEMM_GENAI on ROCM")
-#endif
-  return out;
-
-}
-
-// Dispatch f8 x f8 -> bf16 row-wise scaled to rocm/cuda
-Tensor&
-_f8_f8_bf16_rowwise_grouped_mm(
-      const Tensor& mat_a,
-      const Tensor& mat_b,
-      const Tensor& scale_a,
-      const Tensor& scale_b,
-      const std::optional<Tensor>& offs,
-      const std::optional<Tensor>& bias,
-      bool use_fast_accum,
-      Tensor& out) {
-  // FP8 per-tensor and per-row scaling expect fp32 scales.
-  TORCH_CHECK_VALUE(scale_a.scalar_type() == kFloat && scale_b.scalar_type() == kFloat,
-      "For grouped FP8 rowwise, both scales must be float32 tensors");
-#ifndef USE_ROCM
-  return _f8_f8_bf16_rowwise_grouped_mm_cuda(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      offs,
-      bias,
-      use_fast_accum,
-      out);
-#else
-  // NOTE: ignore use_fast_accum
-  TORCH_CHECK_VALUE(!bias.has_value(), "ROCM grouped gemm does not support bias")
-  return _f8_f8_bf16_rowwise_grouped_mm_rocm(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      offs,
-      out);
-#endif
-}
-
-void _check_scales_fp8_rowwise(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx, const int scale_multiplier=1) {
-  // Checks scales for 2d or 3d target tensors (`mat`).
-  if (mat.dim() == 2) {
-    TORCH_CHECK(
-        scale.dim() == 1,
-        "scale must be a 1D tensor, but got ",
-        scale.dim(),
-        "D, arg ",
-        arg_idx);
-    TORCH_CHECK(
-        scale.is_contiguous(), "scale must be contiguous for arg ", arg_idx);
-    TORCH_CHECK(
-        scale.size(0) == mat.size(dim) * scale_multiplier,
-        "scale must have the same length as mat for arg ",
-        arg_idx);
-  } else {
-    TORCH_CHECK(
-        scale.dim() == 2,
-        "scale must be a 2D tensor, but got ",
-        scale.dim(),
-        "D for arg ",
-        arg_idx);
-    TORCH_CHECK(
-        scale.stride(1) == 1,
-        "scale must be contiguous in the last dimension for arg ",
-        arg_idx);
-    TORCH_CHECK(
-        scale.size(0) == mat.size(0),
-        "scale must have the same batch dimension as mat for arg ",
-        arg_idx);
-    TORCH_CHECK(
-        scale.size(1) == mat.size(1 + dim),
-        "scale must have the same first dimension as mat for arg ",
-        arg_idx);
-  }
-}
-
-void _check_scales_mxfp8(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx) {
-  // Checks scales for 2d or 3d target tensors (`mat`).
-  if (mat.dim() == 2) {
-    // For MXFP8, 2d tensors have variable size groups represented as subtensors,
-    // that are converted to blocked padded format individually,
-    // so we can't check the scale sizes without doing a d2h sync to get the group sizes here.
-    TORCH_CHECK(
-      scale.dim() == mat.dim(),
-      "for mxfp8, scale must have same number of dimensions as parent tensor, but got mat.dim() = ", mat.dim(), " and scale.dim() = ", scale.dim(), " for arg ", arg_idx);
-
-    // LHS mat shape (M, total_K) -> scale shape (rounded_up(M, 128), rounded_up_per_group(K/32, 4))
-    // RHS mat shape (total_K, N) -> scale shape (rounded_up(N, 128), rounded_up_per_group(K/32, 4))
-    //   * weight is transposed prior to the call, scale stays non-transposed.
-    bool LHS = arg_idx == 0;
-    int scale_dim_to_check = 0;
-    int mat_dim_to_check = LHS ? 0 : 1;
-    TORCH_CHECK(
-        scale.size(scale_dim_to_check) >= mat.size(mat_dim_to_check),
-        "for mxfp8, arg ", arg_idx, " tensor shape (", mat.size(0), ", ", mat.size(1), ") ",
-        "must have scale.shape[", scale_dim_to_check, "] >= ", mat.size(mat_dim_to_check), " but got scale.shape=(", scale.size(0), ", ", scale.size(1), ")");
-  } else {
-    // For MXFP8, 3d tensors have static group sizes (stack of 2d tensors),
-    // so we can check the exact expected scale sizes here without a d2h sync.
-    auto round_up = [](auto x, auto y) {
-        return ((x + y - 1) / y) * y;
-    };
-
-    // TODO: this is for 3d tensor in 2d-3d case specifically.
-    // We'll need to support 3d-3d and 3d-2d cases once mxfp8 grouped gemm supports them.
-    int64_t G = mat.size(0);
-    int64_t K = mat.size(1);
-    int64_t N = mat.size(2);
-    int64_t blocked_scale_K = round_up(K/32, 4);
-    int64_t blocked_scale_N = round_up(N, 128);
-
-    // fbgemm expects stack of flattened blocked scales for 3d tensor, shape (G, blocked_scale_K * blocked_scale_N).
-    TORCH_CHECK(
-      scale.dim() == mat.dim() - 1,
-      "for mxfp8 2d-3d grouped GEMM, the 3d tensor of shape (G,K,N) must have a 2d scale of shape (G, blocked_scale_K * blocked_scale_N), but scale is ", scale.dim(), "D for arg ", arg_idx
-    );
-    TORCH_CHECK(
-      scale.size(0) == G && scale.size(1) == blocked_scale_K * blocked_scale_N,
-      "for mxfp8, the tensor shape (", G, ", ", K, ", ", N, ") must have scale shape (", G, ",", blocked_scale_K, ",", blocked_scale_N, ") for arg ", arg_idx
-    );
-  }
-}
-
-void check_scale(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx, const int scale_multiplier=1) {
-  bool using_fp8_rowwise = scale.scalar_type() == kFloat;
-  bool using_mxfp8 = scale.scalar_type() == at::kFloat8_e8m0fnu;
-  if (using_fp8_rowwise) {
-    _check_scales_fp8_rowwise(mat, scale, dim, arg_idx, scale_multiplier);
-  } else if (using_mxfp8) {
-    _check_scales_mxfp8(mat, scale, dim, arg_idx);
-  } else {
-    TORCH_CHECK(false, "scale must be float32 or float8_e8m0fnu, but got ", scale.dtype());
-  }
-}
-
-} // namespace
-
-Tensor
-_scaled_grouped_mm_cuda(
-        const Tensor& mat_a,
-        const Tensor& mat_b,
-        const Tensor& scale_a,
-        const Tensor& scale_b,
-        const std::optional<at::Tensor>& offs,
-        const std::optional<at::Tensor>& bias,
-        const std::optional<at::Tensor>& scale_result,
-        std::optional<c10::ScalarType> out_dtype,
-        bool use_fast_accum) {
-  bool allowed_device = _scaled_mm_allowed_device(/*sm90_only*/true, /*sm100_only*/true);
-  TORCH_CHECK_VALUE(allowed_device, "torch._scaled_grouped_mm is only supported on CUDA devices with compute capability = [9.0, 10.0], or ROCm MI300+");
-
-  TORCH_CHECK_VALUE(!check_valid_strides_and_return_transposed(mat_a), "Expected mat1 to not be transposed");
-  TORCH_CHECK_VALUE(check_valid_strides_and_return_transposed(mat_b), "Expected mat2 to be transposed");
-  TORCH_CHECK_VALUE(mat_a.dim() == 2 || mat_a.dim() == 3, "mat_a has to be 2 or 3d");
-  TORCH_CHECK_VALUE(mat_b.dim() == 2 || mat_b.dim() == 3, "mat_b has to be 2 or 3d");
-  const bool a_is_2d = mat_a.dim() == 2;
-  const bool b_is_2d = mat_b.dim() == 2;
-
-  // NOTE(slayton): For sub-1B formats want contraction_dim argument?
-  if (!a_is_2d || !b_is_2d) {
-    TORCH_CHECK_VALUE(mat_a.size(-1) == mat_b.size(-2), "contraction dimension of mat_a and mat_b must match");
-  }
-  TORCH_CHECK_VALUE(
-    mat_a.size(-1) % 16 == 0,
-    "Expected trailing dimension of mat_a to be divisible by 16 ",
-    "but got mat1 shape: (",
-    mat_a.sizes(),
-    ").");
-  TORCH_CHECK_VALUE(mat_b.size(-2) % 16 == 0 && mat_b.size(-1) % 16 == 0,
-    "Expected mat_b shape to be divisible by 16 ",
-    "but got mat_b shape: (",
-    mat_b.sizes(),
-    ").");
-
-
-  TORCH_CHECK_VALUE(!bias.has_value(), "Bias not supported yet");
-  TORCH_CHECK_VALUE(!scale_result.has_value(), "Scale result not supported yet");
-  TORCH_CHECK_VALUE(offs.has_value() ==  (a_is_2d || b_is_2d), "Have to provide offsets if there is a 2d matrix");
-
-  // NOTE: mxfp8 x mxfp8 requires (and asserts later) that offsets is present.
-  //       for rowwise, no offsets implies 3d-3d and is handled by lower-level
-  //       routines
-  if (offs.has_value()) {
-    TORCH_CHECK_VALUE(offs->dim() == 1, "offs has to be 1D");
-    TORCH_CHECK_VALUE(offs->dtype() == at::kInt, "Offsets have to be int32");
-  }
-  // FP8 per-tensor and per-row scaling expect fp32 scales.
-  // MXFP8 expects float8_e8m0fnu scales.
-  TORCH_CHECK_VALUE(
-      (scale_a.scalar_type() == kFloat && scale_b.scalar_type() == kFloat) ||
-      (scale_a.scalar_type() == at::kFloat8_e8m0fnu && scale_b.scalar_type() == at::kFloat8_e8m0fnu),
-      "For FP8 tensorwise and rowwise, both scales must both be float32 tensors. For MXFP8, scales must both be float8_e8m0fnu tensors.");
-
-  const int scale_multiplier = (mat_a.dim() == 2 && mat_b.dim() == 2) ? offs->size(0) : 1;
-  check_scale(mat_a, scale_a, 0 ,0, scale_multiplier);
-  check_scale(mat_b, scale_b, 1, 1, scale_multiplier);
-
-  const auto out_dtype_ = out_dtype.value_or(kBFloat16);
-  TORCH_CHECK_VALUE(out_dtype_ == kBFloat16, "Only bf16 high precision output types are supported for grouped gemm");
-
-  Tensor out = create_grouped_gemm_output_tensor(mat_a, mat_b, offs, out_dtype_);
-
-#if defined(USE_FBGEMM_GENAI) && defined(USE_CUDA) && !defined(USE_ROCM)
-  // MXFP8 grouped GEMM dispatching
-  bool is_mx8mx8bf16 = (
-    mat_a.scalar_type() == at::kFloat8_e4m3fn && mat_b.scalar_type() == at::kFloat8_e4m3fn &&
-    scale_a.scalar_type() == at::kFloat8_e8m0fnu && scale_b.scalar_type() == at::kFloat8_e8m0fnu
-  );
-#else
-  bool is_mx8mx8bf16 = false;
-#endif
-
-  if (is_mx8mx8bf16) {
-    // Note: Passing implied SwizzleType here, correctness of scale previously checked
-    //       in `check_scale` call
-    return _mx8_mx8_bf16_grouped_mm_fbgemm(
-        mat_a,
-        mat_b,
-        scale_a,
-        SwizzleType::SWIZZLE_32_4_4,
-        scale_b,
-        SwizzleType::SWIZZLE_32_4_4,
-        offs.value(),
-        out);
-  }
-
-  // If we're not MXFP8, then we're row-wise scaling.
-  return _f8_f8_bf16_rowwise_grouped_mm(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      offs,
-      bias,
-      use_fast_accum,
-      out);
-}
-
-namespace {
-
-using acceptance_fn = std::function<bool(c10::ScalarType, std::vector<ScalingType>&, ArrayRef<Tensor>&, c10::ScalarType, std::vector<ScalingType>&, ArrayRef<Tensor>&)>;
-
-std::array<std::tuple<std::string, acceptance_fn, ScaledGemmImplementation>, 2> scale_grouped_kernel_dispatch = {{
-  { "rowwise_rowwise", scaled_blas::check_rowwise_recipe, ScaledGemmImplementation::ROWWISE_ROWWISE},
-  { "mxfp8_mxfp8", scaled_blas::check_mxfp8_recipe, ScaledGemmImplementation::MXFP8_MXFP8}}};
-
-} // anonymous namespace
-
-Tensor
-_scaled_grouped_mm_cuda_v2(
-          const Tensor& mat_a, const Tensor& mat_b,
-          ArrayRef<Tensor> scale_a,
-          IntArrayRef scale_recipe_a,
-          IntArrayRef swizzle_a,
-          ArrayRef<Tensor> scale_b,
-          IntArrayRef scale_recipe_b,
-          IntArrayRef swizzle_b,
-          const std::optional<Tensor>& offs,
-          const std::optional<Tensor>& bias,
-          const std::optional<c10::ScalarType> out_dtype,
-          IntArrayRef contraction_dim,
-          bool use_fast_accum) {
-  bool allowed_device = _scaled_mm_allowed_device(/*sm90_only*/true, /*sm100_only*/true);
-  TORCH_CHECK_VALUE(allowed_device, "torch._scaled_grouped_mm is only supported on CUDA devices with compute capability = [9.0, 10.0], or ROCm MI300+");
-
-  TORCH_CHECK_VALUE(!check_valid_strides_and_return_transposed(mat_a), "Expected mat1 to not be transposed");
-  TORCH_CHECK_VALUE(check_valid_strides_and_return_transposed(mat_b), "Expected mat2 to be transposed");
-  TORCH_CHECK_VALUE(mat_a.dim() == 2 || mat_a.dim() == 3, "mat_a has to be 2 or 3d");
-  TORCH_CHECK_VALUE(mat_b.dim() == 2 || mat_b.dim() == 3, "mat_b has to be 2 or 3d");
-  const bool a_is_2d = mat_a.dim() == 2;
-  const bool b_is_2d = mat_b.dim() == 2;
-
-  // NOTE(slayton): For sub-1B formats want contraction_dim argument?
-  if (!a_is_2d || !b_is_2d) {
-    if (contraction_dim.size() > 0) {
-      const int dim_a = contraction_dim[0], dim_b = mat_b.size(contraction_dim[1]);
-      TORCH_CHECK_VALUE(mat_a.size(dim_a) == mat_b.size(dim_b),
-          "Contraction dimensions (", dim_a, ",", dim_b, ") of mat_a and mat_b must match, got: ", mat_a.size(dim_a), " and ",
-          mat_b.size(dim_b));
-      // Note: only (-1, -2) is currently supported
-      TORCH_CHECK_VALUE(dim_a == -1 && dim_b == -2, "Curently contraction dims must be (-1, -2) only");
-    } else {
-      TORCH_CHECK_VALUE(mat_a.size(-1) == mat_b.size(-2), "contraction dimension of mat_a and mat_b must match");
-    }
-  }
-  TORCH_CHECK_VALUE(
-    mat_a.size(-1) % 16 == 0,
-    "Expected trailing dimension of mat_a to be divisible by 16 ",
-    "but got mat1 shape: (",
-    mat_a.sizes(),
-    ").");
-  TORCH_CHECK_VALUE(mat_b.size(-2) % 16 == 0 && mat_b.size(-1) % 16 == 0,
-    "Expected mat_b shape to be divisible by 16 ",
-    "but got mat_b shape: (",
-    mat_b.sizes(),
-    ").");
-
-  TORCH_CHECK_VALUE(!bias.has_value(), "Bias not supported yet");
-  TORCH_CHECK_VALUE(offs.has_value() ==  (a_is_2d || b_is_2d), "Have to provide offsets if there is a 2d matrix");
-
-  // NOTE: mxfp8 x mxfp8 requires (and asserts later) that offsets is present.
-  //       for rowwise, no offsets implies 3d-3d and is handled by lower-level
-  //       routines
-  if (offs.has_value()) {
-    TORCH_CHECK_VALUE(offs->dim() == 1, "offs has to be 1D");
-    TORCH_CHECK_VALUE(offs->dtype() == at::kInt, "Offsets have to be int32");
-  }
-
-  const auto out_dtype_ = out_dtype.value_or(kBFloat16);
-  TORCH_CHECK_VALUE(out_dtype_ == kBFloat16, "Only bf16 high precision output types are supported for grouped gemm");
-
-  Tensor out = create_grouped_gemm_output_tensor(mat_a, mat_b, offs, out_dtype_);
-
-  // Conversion of implicitly-defined enums to explicit
-  auto scale_recipe_a_enum = convert_int_to_enum<ScalingType>(scale_recipe_a);
-  auto swizzle_a_enum = convert_int_to_enum<SwizzleType>(swizzle_a);
-  auto scale_recipe_b_enum = convert_int_to_enum<ScalingType>(scale_recipe_b);
-  auto swizzle_b_enum = convert_int_to_enum<SwizzleType>(swizzle_b);
-
-  // at this point we can start working out what we want to be doing
-  // Try to do as few steps as possible.
-  // NOTE: support is deliberately sparse, can explicitly enumerate all combinations allowed.
-  // Do this via a list of defined (name, acceptance, concrete_impl) tuples.
-  ScaledGemmImplementation gemm_impl = ScaledGemmImplementation::NONE;
-  for (const auto& fn_entry : scale_grouped_kernel_dispatch) {
-    const auto [name, accept_fn, scaled_gemm_impl] = fn_entry;
-    bool ok = accept_fn(mat_a.scalar_type(),
-                        scale_recipe_a_enum,
-                        scale_a,
-                        mat_b.scalar_type(),
-                        scale_recipe_b_enum,
-                        scale_b);
-    if (ok) {
-      gemm_impl = scaled_gemm_impl;
-      break;
-    }
-  }
-  TORCH_CHECK_VALUE(gemm_impl != ScaledGemmImplementation::NONE,
-      "No gemm implementation was found");
-
-  switch (gemm_impl) {
-    case ScaledGemmImplementation::ROWWISE_ROWWISE: {
-      const int scale_multiplier = (mat_a.dim() == 2 && mat_b.dim() == 2) ? offs->size(0) : 1;
-      _check_scales_fp8_rowwise(mat_a, scale_a[0], 0 /* dim */ , 0 /* arg_idx */, scale_multiplier);
-      _check_scales_fp8_rowwise(mat_b, scale_b[0], 1 /* dim */ , 1 /* arg_idx */, scale_multiplier);
-      return _f8_f8_bf16_rowwise_grouped_mm(
-          mat_a,
-          mat_b,
-          scale_a[0],
-          scale_b[0],
-          offs,
-          bias,
-          use_fast_accum,
-          out);
-    }
-    case ScaledGemmImplementation::MXFP8_MXFP8: {
-      _check_scales_mxfp8(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
-      _check_scales_mxfp8(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
-      return _mx8_mx8_bf16_grouped_mm_fbgemm(
-          mat_a,
-          mat_b,
-          scale_a[0],
-          swizzle_a_enum[0],
-          scale_b[0],
-          swizzle_b_enum[0],
-          offs.value(),
-          out);
-    }
-    default:
-      TORCH_CHECK_NOT_IMPLEMENTED(false,
-          "_scaled_grouped_mm_cuda_v2 is in an inconsistent state - should never reach here");
-  }
-}
-
-Tensor _grouped_mm_cuda(const Tensor& mat_a, const Tensor& mat_b,
-const std::optional<at::Tensor>& offs,
-const std::optional<at::Tensor>& bias,
-std::optional<c10::ScalarType> out_dtype) {
-  _grouped_mm_validate_inputs(mat_a, mat_b, offs, bias, out_dtype);
-  bool a_b_and_out_are_bf16 = (
-    mat_a.dtype() == at::kBFloat16 &&
-    mat_b.dtype() == at::kBFloat16 &&
-    out_dtype.value_or(at::kBFloat16) == at::kBFloat16
-  );
-#ifndef USE_ROCM
-  bool use_fast_path = _scaled_mm_allowed_device(/*sm90_only*/true, /*sm100_only*/true) && a_b_and_out_are_bf16;
-#else
-  // _scaled_mm_allowed_device is used here within _grouped_mm_cuda which seems incorrect since scale is not used.
-  // the _grouped_mm_fallback should be safe for any ROCm GPU since it's just calling typical mm/bmm
-  bool use_fast_path = false;
-#endif
-  const auto out_dtype_ = _resolve_grouped_mm_out_dtype(mat_a, mat_b, out_dtype);
-  Tensor out = create_grouped_gemm_output_tensor(mat_a, mat_b, offs, out_dtype_);
-  if (use_fast_path) {
-    // fast path, no d2h sync needed
-    at::cuda::detail::bf16bf16_grouped_mm(mat_a, mat_b, offs, bias, out);
-  } else {
-    _grouped_mm_fallback(mat_a, mat_b, offs, bias, out_dtype, out);
-  }
-  return out;
-}
-
-} // namespace at::native

aten/src/ATen/native/cuda/Loops.cuh

@@ -1,17 +1,18 @@
 #pragma once
 
-#include <ATen/OpMathType.h>
-#include <ATen/cuda/detail/OffsetCalculator.cuh>
 #include <ATen/detail/FunctionTraits.h>
 #include <ATen/native/TensorIterator.h>
 #include <ATen/native/TensorIteratorDynamicCasting.h>
+#include <ATen/cuda/detail/OffsetCalculator.cuh>
+#include <ATen/OpMathType.h>
 #include <ATen/native/cuda/thread_constants.h>
+
+#include <thrust/tuple.h>
+
 #include <ATen/native/cuda/MemoryAccess.cuh>
 
 #include <tuple>
 
-
-
 namespace at::native {
 
 template<int N>
@@ -61,11 +62,7 @@ __device__ inline void elementwise_kernel_helper(func_t f, policy_t policy) {
   #pragma unroll
   for (int i = 0; i < elems_per_thread; i++) {
     if (policy.check_inbounds(i)) {
-#if defined(__HIP__)
       results[i] = c10::guts::apply(f, args[i]);
-#else
-      results[i] = std::apply(f, args[i]);
-#endif
     }
   }
 

aten/src/ATen/native/cuda/Normalization.cuh

@@ -23,7 +23,7 @@ namespace at::native {
 
 // The maximum number of threads in a block
 #if defined(USE_ROCM)
-constexpr int MAX_BLOCK_SIZE = 1024;
+constexpr int MAX_BLOCK_SIZE = 256;
 #else
 constexpr int MAX_BLOCK_SIZE = 512;
 #endif
@@ -33,7 +33,7 @@ constexpr unsigned MAX_GRID_SIZE = 65535u;
 // Number of threads in a block given an input size up to MAX_BLOCK_SIZE
 static int getNumThreads(int nElem) {
 #if defined(USE_ROCM)
-  int threadSizes[5] = { 64, 128, 256, 512, MAX_BLOCK_SIZE };
+  int threadSizes[5] = { 16, 32, 64, 128, MAX_BLOCK_SIZE };
 #else
   int threadSizes[5] = { 32, 64, 128, 256, MAX_BLOCK_SIZE };
 #endif
@@ -115,23 +115,9 @@ __device__ scalar_t reduce(Op op, PTA tensor, int plane) {
   // first the reductions each thread does separately
   scalar_t sum = static_cast<scalar_t>(0);
   for (int batch = threadIdx.y; batch < tensor.size(0); batch += blockDim.y) {
-#if defined(USE_ROCM)
-    constexpr int UNRL = 4; // load deserilize factor
-    scalar_t tmp[UNRL];
-    for (int x = threadIdx.x; x < tensor.size(2); x += blockDim.x*UNRL) {
-#pragma unroll
-      for (int u = 0; u < UNRL; u++)
-        tmp[u] = op(batch, plane, std::min((int)tensor.size(2)-1, (int)(x+u*blockDim.x)));
-#pragma unroll
-      for (int u = 0; u < UNRL; u++)
-        if (x+u*blockDim.x < tensor.size(2))
-          sum += tmp[u];
-    }
-#else
     for (int x = threadIdx.x; x < tensor.size(2); x += blockDim.x) {
       sum += op(batch, plane, x);
     }
-#endif
   }
   __shared__ scalar_t shared[C10_WARP_SIZE];
   SumReduceOp<scalar_t> reduce_op;
@@ -306,22 +292,6 @@ __global__ void batch_norm_collect_statistics_kernel(
   stat_accscalar_t var_n = 0;
   int n = 0;
   for (int batch = threadIdx.y; batch < input.size(0); batch += blockDim.y) {
-#if defined(USE_ROCM)
-    constexpr int UNRL = 4;
-    stat_accscalar_t v_[UNRL];
-    for (int x = threadIdx.x; x < input.size(2); x += blockDim.x*UNRL) {
-      for (int u = 0; u < UNRL; u++)
-        v_[u] = input[batch][plane][min(x+u*blockDim.x, input.size(2)-1)];
-      for (int u = 0; u < UNRL; u++) {
-        if (x+u*blockDim.x < input.size(2)) {
-          stat_accscalar_t d1 = v_[u] - avg;
-          n++;
-          avg += d1 / n;
-          var_n += d1 * (v_[u] - avg);
-        }
-      }
-    }
-#else
     for (int x = threadIdx.x; x < input.size(2); x += blockDim.x) {
       stat_accscalar_t v = input[batch][plane][x];
       stat_accscalar_t d1 = v - avg;
@@ -329,7 +299,6 @@ __global__ void batch_norm_collect_statistics_kernel(
       avg += d1 / n;
       var_n += d1 * (v - avg);
     }
-#endif
   }
 
   // first warpSum to get one value per thread to

aten/src/ATen/native/cuda/ReflectionPad.cu

@@ -92,16 +92,6 @@ inline thrust::pair<int64_t, int64_t>  get_index_mapping2d(
     output_offset + output_y * output_dim_x + output_x);
 }
 
-__device__ __forceinline__ int64_t reflect_index(int64_t x, int64_t len) {
-  const int64_t two = (len - 1) * 2;
-  if (two <= 0) {
-    return 0;
-  }
-  int64_t m = x % two;
-  if (m < 0) m += two;
-  return (m < len) ? m : (two - m);
-}
-
 template<typename scalar_t>
 __global__ void reflection_pad1d_out_kernel(
     const scalar_t * input, scalar_t * output,
@@ -116,28 +106,6 @@ __global__ void reflection_pad1d_out_kernel(
   }
 }
 
-template <typename scalar_t>
-__global__ void reflection_pad1d_flat(
-    const scalar_t* __restrict__ input,
-    scalar_t* __restrict__ output,
-    int64_t input_w, int64_t pad_l, int64_t pad_r,
-    int64_t out_w, int64_t plane_count) {
-
-  const int64_t bx = blockDim.x;
-  const int64_t tx = threadIdx.x;
-
-  const int64_t total = plane_count * out_w;
-  const int64_t grid_stride = static_cast<int64_t>(bx) * gridDim.x;
-  int64_t linear = static_cast<int64_t>(blockIdx.x) * bx + tx;
-
-  for (; linear < total; linear += grid_stride) {
-    const int64_t plane = linear / out_w;
-    const int64_t x = linear - plane * out_w;
-    const int64_t j = reflect_index(x - pad_l, input_w);
-    output[plane * out_w + x] = input[plane * input_w + j];
-  }
-}
-
 template <typename scalar_t>
 __global__ void reflection_pad1d_backward_out_kernel(
     scalar_t * grad_input, const scalar_t * grad_output,
@@ -742,44 +710,25 @@ TORCH_IMPL_FUNC(reflection_pad1d_out_cuda)
   int64_t input_w = input_.size(dim_w);
   int64_t output_w = input_w + pad_l + pad_r;
 
+  dim3 block_size(output_w > 256 ? 256 : output_w);
+  dim3 grid_size((int)::ceil(output_w / 256.0), nplane, nbatch);
 
   Tensor input = input_.contiguous();
 
-  const int block_x = static_cast<int>(std::min<int64_t>(256, std::max<int64_t>(1, output_w)));
-  const cudaDeviceProp* prop = at::cuda::getCurrentDeviceProperties();
-  const int max_x = prop->maxGridSize[0];
-  const int max_y = prop->maxGridSize[1];
-  const int max_z = prop->maxGridSize[2];
-
-  AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kHalf, kBFloat16, input.scalar_type(), "reflection_pad1d_out", [&] {
-    auto stream = at::cuda::getCurrentCUDAStream();
-
-    const int64_t gx = at::ceil_div(output_w, static_cast<int64_t>(block_x));
-
-    const bool fits3d = (nplane <= max_y) && (nbatch <= max_z) && (gx <= max_x);
-
-    if (fits3d) {
-      dim3 block(block_x, 1, 1);
-      dim3 grid(gx, static_cast<unsigned>(nplane), static_cast<unsigned>(nbatch));
-      reflection_pad1d_out_kernel<scalar_t><<<grid, block, 0, stream>>>(
-          input.const_data_ptr<scalar_t>(),
-          output.mutable_data_ptr<scalar_t>(),
-          input_w, pad_l, pad_r);
-    } else {
-      dim3 block(block_x, 1, 1);
-      const int64_t plane_count = nplane * nbatch;
-      const int64_t total_blocks = at::ceil_div(plane_count * output_w, static_cast<int64_t>(block_x));
-      const int grid_x = static_cast<int>(std::min<int64_t>(max_x, std::max<int64_t>(1, total_blocks)));
-      dim3 grid(grid_x, 1, 1);
-
-      reflection_pad1d_flat<scalar_t><<<grid, block, 0, stream>>>(
-          input.const_data_ptr<scalar_t>(),
-          output.mutable_data_ptr<scalar_t>(),
-          input_w, pad_l, pad_r, output_w, plane_count);
-    }
-
-    C10_CUDA_KERNEL_LAUNCH_CHECK();
-  });
+  AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
+      kHalf, kBFloat16, input.scalar_type(), "reflection_pad1d_out_template", [&] {
+        reflection_pad1d_out_kernel<<<
+            grid_size,
+            block_size,
+            0,
+            at::cuda::getCurrentCUDAStream()>>>(
+            input.const_data_ptr<scalar_t>(),
+            output.mutable_data_ptr<scalar_t>(),
+            input_w,
+            pad_l,
+            pad_r);
+        C10_CUDA_KERNEL_LAUNCH_CHECK();
+      });
 }
 
 TORCH_IMPL_FUNC(reflection_pad1d_backward_out_cuda)(const Tensor& grad_output_,

aten/src/ATen/native/cuda/Sorting.cpp

@@ -43,12 +43,6 @@ std::tuple<Tensor&, Tensor&> kthvalue_out_impl_cuda(
   TORCH_CHECK(k >= 1 && k <= slicesize,
               "kthvalue(): selected number k out of range for dimension ", dim);
 
-  TORCH_CHECK(
-      slicesize <= std::numeric_limits<int32_t>::max(),
-      "kthvalue(): dimension ", dim, " is too large (", slicesize,
-      "). The current CUDA implementation supports dimension sizes up to ",
-      std::numeric_limits<int32_t>::max());
-
   at::assert_no_overlap(self, values);
 
   _reduction_with_indices_allocate_or_resize_output(
@@ -169,6 +163,10 @@ std::tuple<Tensor&, Tensor&> kthvalue_out_cuda(
     bool keepdim,
     Tensor& values,
     Tensor& indices) {
+  // See note [Writing Nondeterministic Operations]
+  // If there are duplicate elements of the kth value, the procedure for choosing which
+  // of the duplicates to use for the indices output is nondeterministic.
+  at::globalContext().alertNotDeterministic("kthvalue CUDA");
   auto result = [&]() {
     NoNamesGuard guard;
     // `kthvalue_out_impl_cuda` expects contiguous in input `self`.

aten/src/ATen/native/cuda/Sorting.cu

@@ -65,34 +65,25 @@ __global__ void gatherKthValue(
       &kValue);
 
   // Find the index of the k-th highest element
-  __shared__ int32_t minIndexFound;
-
-  if (threadIdx.x == 0) {
-      minIndexFound = static_cast<int32_t>(inputSliceSize);
-  }
-  __syncthreads();
+  index_t kValueIndex = 0;
+  bool foundKValue = false;
 
   for (index_t i = threadIdx.x; i < inputSliceSize; i += blockDim.x) {
-      // Early exit based on best-so-far
-      if (i >= minIndexFound) {
-          break;
-      }
-
-      scalar_t v = doLdg(&inputSliceStart[i * inputWithinSliceStride]);
-      bool isKValue =
-          ((v == kValue) || (at::_isnan(v) && at::_isnan(kValue)));
-
-      if (isKValue) {
-          atomicMin(&minIndexFound, static_cast<int32_t>(i));
-          break;
-      }
+    bool inRange = (i < inputSliceSize);
+    scalar_t v = inRange ? doLdg(&inputSliceStart[i * inputWithinSliceStride])
+                         : static_cast<scalar_t>(0);
+    bool isKValue = inRange &&
+        ((v == kValue) || (at::_isnan(v) && at::_isnan(kValue)));
+    if (isKValue) {
+      kValueIndex = i;
+      foundKValue = true;
+      break;
+    }
   }
 
-  __syncthreads();
-
-  if (threadIdx.x == 0) {
-      indicesSliceStart[0] = static_cast<index_t>(minIndexFound);
-      kthValueSliceStart[0] = kValue;
+  if (foundKValue) {
+    kthValueSliceStart[0] = kValue;
+    indicesSliceStart[0] = kValueIndex;
   }
 }
 

aten/src/ATen/native/cuda/UpSampleBilinear2d.cu

@@ -127,29 +127,6 @@ __global__ void upsample_bilinear2d_nhwc_out_frame(
   }
 }
 
-#ifdef USE_ROCM
-// Helper function to compute output pixel range that can contribute to input pixel
-template <typename accscalar_t>
-__device__ __forceinline__ void compute_output_range(
-    int input_pos,
-    accscalar_t scale,
-    int output_size,
-    bool align_corners,
-    int& min_output,
-    int& max_output) {
-  accscalar_t lo, hi;
-  if (align_corners) {
-      lo = static_cast<accscalar_t>(input_pos - 1) / scale;
-      hi = static_cast<accscalar_t>(input_pos + 1) / scale;
-  } else {
-      lo = (input_pos - static_cast<accscalar_t>(0.5)) / scale - static_cast<accscalar_t>(0.5);
-      hi = (input_pos + static_cast<accscalar_t>(1.5)) / scale - static_cast<accscalar_t>(0.5);
-  }
-  min_output = max(0, static_cast<int>(std::ceil(lo)));
-  max_output = min(output_size - 1, static_cast<int>(std::floor(hi)));
-}
-#endif
-
 // Backward (adjoint) operation 1 <- 2 (accumulates)
 template <typename scalar_t, typename accscalar_t>
 C10_LAUNCH_BOUNDS_1(1024)
@@ -164,74 +141,8 @@ __global__ void upsample_bilinear2d_backward_out_frame(
     const bool align_corners,
     scalar_t* __restrict__ idata,
     const scalar_t* __restrict__ odata) {
-  // In C++, integer multiplication, like in standard arithmetic, is generally commutative.
-  const size_t i_numel = nc * width1 * height1;
-#ifdef USE_ROCM
-  for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < i_numel;
-       index += blockDim.x * gridDim.x) {
-    // Decode input pixel coordinates
-    size_t index_temp = index;
-    const int w1 = index_temp % width1;
-    index_temp /= width1;
-    const int h1 = index_temp % height1;
-    const size_t nc_idx = index_temp / height1;
-
-    accscalar_t grad_sum = 0;
-
-    // Find range of output pixels that could interpolate from this input pixel
-    int h2_min, h2_max, w2_min, w2_max;
-    compute_output_range<accscalar_t>(h1, rheight, height2, align_corners, h2_min, h2_max);
-    compute_output_range<accscalar_t>(w1, rwidth, width2, align_corners, w2_min, w2_max);
-
-    // Iterate over potential output pixels
-    for (int h2 = h2_min; h2 <= h2_max; h2++) {
-      for (int w2 = w2_min; w2 <= w2_max; w2++) {
-        // Compute source coordinates for this output pixel
-        const accscalar_t h1r = area_pixel_compute_source_index<accscalar_t>(
-            rheight, h2, align_corners, /*cubic=*/false);
-        const int h1_base = (int)h1r;
-        const int h1p = (h1_base < height1 - 1) ? 1 : 0;
-        const accscalar_t h1lambda = h1r - h1_base;
-        const accscalar_t h0lambda = static_cast<accscalar_t>(1) - h1lambda;
-
-        const accscalar_t w1r = area_pixel_compute_source_index<accscalar_t>(
-            rwidth, w2, align_corners, /*cubic=*/false);
-        const int w1_base = (int)w1r;
-        const int w1p = (w1_base < width1 - 1) ? 1 : 0;
-        const accscalar_t w1lambda = w1r - w1_base;
-        const accscalar_t w0lambda = static_cast<accscalar_t>(1) - w1lambda;
-
-        // Check if our input pixel participates in this interpolation and accumulate all weights
-        // At boundaries, h1p=0 or w1p=0 causes some sampling positions to collapse
-        // to the same pixel, so we need to accumulate weights from all matching positions
-        accscalar_t weight = 0;
-
-        // Check all four interpolation positions and accumulate weights
-        if (h1 == h1_base && w1 == w1_base) {
-          weight += h0lambda * w0lambda;  // top-left
-        }
-        if (h1 == h1_base && w1 == w1_base + w1p) {
-          weight += h0lambda * w1lambda;  // top-right (may be same as top-left if w1p=0)
-        }
-        if (h1 == h1_base + h1p && w1 == w1_base) {
-          weight += h1lambda * w0lambda;  // bottom-left (may be same as top-left if h1p=0)
-        }
-        if (h1 == h1_base + h1p && w1 == w1_base + w1p) {
-          weight += h1lambda * w1lambda;  // bottom-right (may collapse to other positions)
-        }
-
-        if (weight > 0) {
-          const size_t output_idx = nc_idx * height2 * width2 + h2 * width2 + w2;
-          grad_sum += weight * static_cast<accscalar_t>(odata[output_idx]);
-        }
-      }
-    }
-
-    // Write accumulated gradient (no atomics needed)
-    idata[index] = static_cast<scalar_t>(grad_sum);
-  }
-#else
   const size_t o_numel = nc * width2 * height2;
+  const size_t i_numel = nc * width1 * height1;
   for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < o_numel;
        index += blockDim.x * gridDim.x) {
     size_t index_temp = index;
@@ -280,7 +191,6 @@ __global__ void upsample_bilinear2d_backward_out_frame(
         static_cast<scalar_t>(h1lambda * w1lambda * d2val),
         true);
   }
-#endif
 }
 
 template <typename scalar_t, typename accscalar_t>
@@ -477,6 +387,7 @@ static void upsample_bilinear2d_backward_out_cuda_template(
   // threads are not covering the whole input tensor.
   grad_input.zero_();
 
+  const size_t num_kernels = nbatch * channels * output_height * output_width;
   const int num_threads = std::min(
       at::cuda::getCurrentDeviceProperties()->maxThreadsPerBlock, 1024);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -486,12 +397,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
     return;
   }
 
-#ifdef USE_ROCM
-  constexpr bool use_input = true;
-#else
-  constexpr bool use_input = false;
-#endif
-
   AT_DISPATCH_FLOATING_TYPES_AND2(
       at::ScalarType::Half, at::ScalarType::BFloat16,
       grad_output_.scalar_type(), "upsample_bilinear2d_backward_out_frame", [&] {
@@ -509,8 +414,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
       const accscalar_t rwidth = area_pixel_compute_scale<accscalar_t>(
           input_width, output_width, align_corners, scales_w);
 
-      const size_t num_kernels = nbatch * channels * output_height * output_width;
-
       upsample_bilinear2d_backward_nhwc_out_frame<scalar_t, accscalar_t>
           <<<ceil_div(num_kernels, static_cast<size_t>(num_threads)), num_threads, 0, stream>>>(
               input_height,
@@ -541,8 +444,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
       const accscalar_t rwidth = area_pixel_compute_scale<accscalar_t>(
           input_width, output_width, align_corners, scales_w);
 
-      const size_t num_kernels = nbatch * channels * (use_input ? input_height * input_width : output_height * output_width);
-
       upsample_bilinear2d_backward_out_frame<scalar_t, accscalar_t>
           <<<ceil_div(num_kernels, static_cast<size_t>(num_threads)),
              num_threads,

aten/src/ATen/native/cuda/fused_adagrad_utils.cuh

@@ -52,7 +52,7 @@ struct FusedAdagradMathFunctor {
   using opmath_t = at::opmath_type<scalar_t>;
 
   C10_DEVICE __forceinline__ void operator()(
-      int64_t chunk_size,
+      int chunk_size,
       FusedOptimizerTensorListMetadata<3>& tl,
       const float* lr_ptr,
       const double& lr,
@@ -133,4 +133,4 @@ struct FusedAdagradMathFunctor {
 
 } // namespace
 
-} // namespace at::native
+} // namespace at::native

aten/src/ATen/native/mkldnn/xpu/Blas.cpp

@@ -2,7 +2,6 @@
 #include <ATen/WrapDimUtilsMulti.h>
 #include <ATen/native/Resize.h>
 #include <ATen/native/mkldnn/xpu/detail/oneDNN.h>
-#include <ATen/native/xpu/Blas.h>
 #include <torch/library.h>
 #ifndef AT_PER_OPERATOR_HEADERS
 
@@ -51,13 +50,9 @@ Tensor& addmm_out(
       mat1.dtype(),
       " != ",
       mat2.dtype())
-
   // complex case
-  if (self.is_complex()) {
-    at::native::addmm_complex_out_xpu(self, mat1, mat2, beta, alpha, result);
-
-    return result;
-  }
+  TORCH_CHECK(
+      !mat1.is_complex(), "Complex datatype matmul is not supported in oneDNN");
 
   std::vector<int64_t> result_shape = {mat1.size(0), mat2.size(1)};
   result.resize_(result_shape);
@@ -172,11 +167,8 @@ Tensor& mm_out(const Tensor& self, const Tensor& mat2, Tensor& result) {
     return result;
   }
 
-  if (self.is_complex()) {
-    at::native::mm_complex_out_xpu(self, mat2, result);
-
-    return result;
-  }
+  TORCH_CHECK(
+      !self.is_complex(), "Complex datatype matmul is not supported in oneDNN");
 
   onednn::matmul(result, self, mat2, Tensor(), true, onednn::Attr());
   return result;
@@ -216,12 +208,9 @@ Tensor& baddbmm_out(
       input.sizes());
 
   // complex case
-  if (input.is_complex()) {
-    at::native::baddbmm_complex_out_xpu(
-        input, batch1, batch2, beta, alpha, result);
-
-    return result;
-  }
+  TORCH_CHECK(
+      !batch1.is_complex(),
+      "Complex datatype matmul is not supported in oneDNN");
 
   // general case
   onednn::Attr attr;
@@ -268,13 +257,8 @@ Tensor& bmm_out(const Tensor& self, const Tensor& batch2, Tensor& result) {
     return result;
   }
 
-  // complex case
-  if (self.is_complex()) {
-    at::native::bmm_complex_out_xpu(self, batch2, result);
-
-    return result;
-  }
-
+  TORCH_CHECK(
+      !self.is_complex(), "Complex datatype matmul is not supported in oneDNN");
   onednn::matmul(result, self, batch2, at::Tensor(), true, onednn::Attr());
   return result;
 }

aten/src/ATen/native/mps/kernels/BinaryKernel.metal

@@ -222,13 +222,6 @@ struct nextafter_functor {
   }
 };
 
-struct hypot_functor {
-  template <typename T>
-  inline T operator()(const T a, const T b) {
-    return static_cast<T>(precise::sqrt(float(a) * a + float(b) * b));
-  }
-};
-
 // Complex binary functors
 struct polar_functor {
   template <typename U>
@@ -369,7 +362,6 @@ struct igammac_functor {
   REGISTER_OPMATH_BINARY_OP(NAME, half, half);   \
   REGISTER_OPMATH_BINARY_OP(NAME, bfloat, bfloat)
 
-REGISTER_FLOAT_BINARY_OP(hypot);
 REGISTER_FLOAT_BINARY_OP(copysign);
 REGISTER_INT2FLOAT_BINARY_OP(copysign);
 REGISTER_FLOAT_BINARY_OP(fmax);

aten/src/ATen/native/mps/kernels/LinearAlgebra.h

@@ -1,16 +0,0 @@
-#pragma onces
-#include <c10/metal/common.h>
-
-template <unsigned N = c10::metal::max_ndim>
-struct OrgqrParams {
-  int32_t num_batch_dims;
-
-  uint32_t m;
-  uint32_t n;
-  uint32_t k;
-
-  ::c10::metal::array<uint32_t, N> A_strides;
-  ::c10::metal::array<uint32_t, N> tau_strides;
-  ::c10::metal::array<uint32_t, N> H_strides;
-  ::c10::metal::array<uint32_t, N> H_sizes;
-};

aten/src/ATen/native/mps/kernels/LinearAlgebra.metal

@@ -1,4 +1,3 @@
-#include <ATen/native/mps/kernels/LinearAlgebra.h>
 #include <c10/metal/utils.h>
 #include <metal_array>
 #include <metal_simdgroup>
@@ -641,164 +640,6 @@ kernel void applyPivots(
   }
 }
 
-template <typename T>
-static T bool_to_float(bool b) {
-  return static_cast<T>(b);
-}
-
-template <>
-half2 bool_to_float(bool b) {
-  return half2(b ? 1 : 0, 0);
-}
-
-template <>
-float2 bool_to_float(bool b) {
-  return float2(b ? 1 : 0, 0);
-}
-
-template <typename T>
-static T calc_H_irc(
-    device T* A,
-    uint32_t A_stride_r,
-    uint32_t A_stride_c,
-    constant T* tau,
-    uint32_t tau_stride,
-    uint32_t r,
-    uint32_t c,
-    uint32_t i) {
-  T I_val = bool_to_float<T>(r == c);
-  T tau_val = tau[i * tau_stride];
-
-  T A_ci = c10::metal::conj(A[c * A_stride_r + i * A_stride_c]);
-  T A_ri = A[r * A_stride_r + i * A_stride_c];
-
-  T c_eq_i = bool_to_float<T>(c == i);
-  T r_eq_i = bool_to_float<T>(r == i);
-
-  T A_ci_ = (c > i) ? A_ci : c_eq_i;
-  T A_ri_ = (r > i) ? A_ri : r_eq_i;
-
-  return I_val - c10::metal::mul(tau_val, c10::metal::mul(A_ci_, A_ri_));
-}
-
-// Calculate (A @ B)[r, c], the element in the r-th row and c-th column of the
-// result of matrix multiplying A and B together. A and B must be size m-by-m
-// and have the same strides. The formula for this operation, written in Python
-// syntax, is:
-//   (A @ B)[r, c] = A[r, :].dot(B[:, c])
-template <typename T>
-static T calc_matmul_rc(
-    device T* A,
-    device T* B,
-    uint32_t stride_r,
-    uint32_t stride_c,
-    uint32_t m,
-    uint32_t r,
-    uint32_t c) {
-  T AB_rc = 0;
-  auto A_row_offset = r * stride_r;
-  auto B_col_offset = c * stride_c;
-
-  uint32_t A_col_offset = 0;
-  uint32_t B_row_offset = 0;
-
-  for (uint32_t j = 0; j < m;
-       j++, A_col_offset += stride_c, B_row_offset += stride_r) {
-    AB_rc += c10::metal::mul(
-        A[A_row_offset + A_col_offset], B[B_row_offset + B_col_offset]);
-  }
-  return AB_rc;
-}
-
-template <typename T>
-kernel void orgqr(
-    device T* A [[buffer(0)]],
-    constant T* tau [[buffer(1)]],
-    device T* H [[buffer(2)]],
-    device T* H_prod [[buffer(3)]],
-    constant OrgqrParams<>& params [[buffer(4)]],
-    uint tid [[thread_position_in_grid]]) {
-  constant auto& A_strides = params.A_strides;
-  constant auto& tau_strides = params.tau_strides;
-  constant auto& H_strides = params.H_strides;
-  constant auto& H_sizes = params.H_sizes;
-
-  auto num_batch_dims = params.num_batch_dims;
-  auto m = params.m;
-  auto n = params.n;
-  auto k = params.k;
-
-  auto m2 = m * m;
-  auto batch_idx = tid / m2;
-
-  // Find the matrices for this thread's batch index
-  uint32_t A_offset = 0;
-  uint32_t tau_offset = 0;
-  uint32_t H_offset = 0;
-
-  for (auto dim = num_batch_dims - 1; dim >= 0; dim--) {
-    auto dim_size = H_sizes[dim];
-    auto dim_idx = batch_idx % dim_size;
-
-    A_offset += dim_idx * A_strides[dim];
-    tau_offset += dim_idx * tau_strides[dim];
-    H_offset += dim_idx * H_strides[dim];
-
-    batch_idx /= dim_size;
-  }
-
-  A += A_offset;
-  tau += tau_offset;
-  H += H_offset;
-  H_prod += H_offset;
-
-  auto matrix_idx = tid % m2;
-  auto r = matrix_idx / m;
-  auto c = matrix_idx % m;
-  auto A_stride_r = A_strides[num_batch_dims];
-  auto A_stride_c = A_strides[num_batch_dims + 1];
-  auto tau_stride = tau_strides[num_batch_dims];
-  auto H_stride_r = H_strides[num_batch_dims];
-  auto H_stride_c = H_strides[num_batch_dims + 1];
-
-  // Find the element of H and H_prod that this thread will calculate
-  device T* H_elem_ptr = H + (r * H_stride_r + c * H_stride_c);
-  device T* H_prod_elem_ptr = H_prod + (r * H_stride_r + c * H_stride_c);
-
-  for (uint32_t i = 0; i < k; i++) {
-    // Calculate and write H_i
-
-    T H_irc = calc_H_irc(A, A_stride_r, A_stride_c, tau, tau_stride, r, c, i);
-
-    // Calculate element [r, c] of prod(H_0, ..., H_i)
-    if (i == 0) {
-      *H_prod_elem_ptr = H_irc;
-    } else {
-      *H_elem_ptr = H_irc;
-
-      // Need this sync because the below matmul requires all threads to finish
-      // writing their entries to `H_prod` and `H`.
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      T H_prod_0_to_i_rc =
-          calc_matmul_rc(H_prod, H, H_stride_r, H_stride_c, m, r, c);
-
-      // Need this sync because the above matmul uses the current values in
-      // `H_prod`, and we don't want to overwrite those until all threads are
-      // finished using them.
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      *H_prod_elem_ptr = H_prod_0_to_i_rc;
-    }
-  }
-
-  device T* A_elem_ptr = A + (r * A_stride_r + c * A_stride_c);
-
-  if (c < n) {
-    *A_elem_ptr = *H_prod_elem_ptr;
-  }
-}
-
 #define INSTANTIATE_MM_OPS(DTYPE)                                           \
   template [[host_name("matmul_" #DTYPE)]] kernel void matmul<DTYPE>(       \
       constant DTYPE * mat1Data [[buffer(0)]],                              \
@@ -838,19 +679,3 @@ INSTANTIATE_MM_OPS(int);
 INSTANTIATE_MM_OPS(short);
 INSTANTIATE_MM_OPS(char);
 INSTANTIATE_MM_OPS(uchar);
-
-#define REGISTER_ORGQR(T)                            \
-  template [[host_name("orgqr_" #T)]]                \
-  kernel void orgqr<T>(                              \
-      device T * A [[buffer(0)]],                    \
-      constant T * tau [[buffer(1)]],                \
-      device T * H [[buffer(2)]],                    \
-      device T * H_prod [[buffer(3)]],               \
-      constant OrgqrParams<> & params [[buffer(4)]], \
-      uint tid [[thread_position_in_grid]]);
-
-REGISTER_ORGQR(float);
-REGISTER_ORGQR(half);
-REGISTER_ORGQR(bfloat);
-REGISTER_ORGQR(float2);
-REGISTER_ORGQR(half2);

aten/src/ATen/native/mps/kernels/UnaryKernel.metal

@@ -5,21 +5,6 @@
 using namespace metal;
 using namespace c10::metal;
 
-struct angle_functor {
-  template <typename T, enable_if_t<is_complex_v<T>, bool> = true>
-  inline T operator()(const T x) {
-    return T(atan2(x.y, x.x), 0);
-  }
-  template <typename T, enable_if_t<is_scalar_floating_point_v<T>, bool> = true>
-  inline T operator()(const T x) {
-    return T(isnan(x) ? x : x < 0 ? M_PI_F : 0.0);
-  }
-  template <typename T, enable_if_t<is_scalar_integral_v<T>, bool> = true>
-  inline float operator()(const T x) {
-    return x < 0 ? M_PI_F : 0.0;
-  }
-};
-
 // Implement exp wrapper for both real and complex types
 template <typename T, enable_if_t<is_scalar_floating_point_v<T>, bool> = true>
 inline T exp_(const T x) {
@@ -560,7 +545,6 @@ REGISTER_UNARY_OP(abs, float, float);
 REGISTER_UNARY_OP(abs, half, half);
 
 #define INSTANTIATE_UNARY_KERNELS2(DTYPE0, DTYPE1) \
-  REGISTER_UNARY_OP(angle, DTYPE1, DTYPE0);        \
   REGISTER_UNARY_OP(erf, DTYPE1, DTYPE0);          \
   REGISTER_UNARY_OP(erfc, DTYPE1, DTYPE0);         \
   REGISTER_UNARY_OP(erfinv, DTYPE1, DTYPE0);       \
@@ -599,7 +583,6 @@ INSTANTIATE_UNARY_KERNELS2(float, int);
 INSTANTIATE_UNARY_KERNELS2(float, long);
 
 #define INSTANTIATE_UNARY_KERNELS_VEC2(DTYPE)     \
-  REGISTER_UNARY_OP(angle, DTYPE##2, DTYPE##2);   \
   REGISTER_UNARY_OP(neg, DTYPE##2, DTYPE##2);     \
   REGISTER_UNARY_OP(exp, DTYPE##2, DTYPE##2);     \
   REGISTER_UNARY_OP(expm1, DTYPE##2, DTYPE##2);   \

aten/src/ATen/native/mps/operations/Attention.mm

@@ -92,8 +92,13 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
           }
 
           // upcasting to float32 if needed to improve precision when multiplying by the scale factor
-          maskedMM = castMPSTensor(mpsGraph, maskedMM, MPSDataTypeFloat32);
+          if ([maskedMM dataType] != MPSDataTypeFloat32) {
+            maskedMM = [mpsGraph castTensor:maskedMM toType:MPSDataTypeFloat32 name:nil];
+          }
           maskedMM = [mpsGraph multiplicationWithPrimaryTensor:maskedMM secondaryTensor:scaleTensor name:nil];
+          if ([maskedMM dataType] != qTensor.dataType) {
+            maskedMM = [mpsGraph castTensor:maskedMM toType:qTensor.dataType name:nil];
+          }
 
           if (is_causal) {
             auto causalMask = [mpsGraph constantWithScalar:1.0f
@@ -107,9 +112,7 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
                                                       name:nil];
           } else if (attn_mask) {
             graph->maskTensor = mpsGraphRankedPlaceHolder(mpsGraph, *attn_mask);
-            maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM
-                                           secondaryTensor:castMPSTensor(mpsGraph, graph->maskTensor, maskedMM.dataType)
-                                                      name:nil];
+            maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM secondaryTensor:graph->maskTensor name:nil];
           }
 
           // Account for case where all values were masked causing division by 0 in softmax (issue:#156707)
@@ -130,8 +133,8 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
           graph->qTensor = qTensor;
           graph->kTensor = kTensor;
           graph->vTensor = vTensor;
-          graph->outputTensor = castMPSTensor(mpsGraph, output, qTensor.dataType);
-          graph->attnTensor = castMPSTensor(mpsGraph, sm, qTensor.dataType);
+          graph->outputTensor = output;
+          graph->attnTensor = sm;
         });
     auto qPlaceholder = Placeholder(cachedGraph->qTensor, query);
     auto kPlaceholder = Placeholder(cachedGraph->kTensor, key);

aten/src/ATen/native/mps/operations/BinaryKernel.mm

@@ -202,10 +202,6 @@ static void igammac_mps_kernel(TensorIteratorBase& iter) {
   lib.exec_binary_kernel(iter, "igammac");
 }
 
-static void hypot_mps_kernel(TensorIteratorBase& iter) {
-  lib.exec_binary_kernel(iter, "hypot");
-}
-
 REGISTER_DISPATCH(fmax_stub, &fmax_mps_kernel)
 REGISTER_DISPATCH(fmin_stub, &fmin_mps_kernel)
 REGISTER_DISPATCH(copysign_stub, &copysign_mps_kernel)
@@ -233,5 +229,4 @@ REGISTER_DISPATCH(fmod_stub, &fmod_mps_kernel)
 REGISTER_DISPATCH(remainder_stub, &remainder_mps_kernel)
 REGISTER_DISPATCH(igamma_stub, &igamma_mps_kernel)
 REGISTER_DISPATCH(igammac_stub, &igammac_mps_kernel)
-REGISTER_DISPATCH(hypot_stub, &hypot_mps_kernel)
 } // namespace at::native

aten/src/ATen/native/mps/operations/BinaryOps.mm

@@ -16,6 +16,7 @@
 #include <ATen/ops/eq_native.h>
 #include <ATen/ops/ge_native.h>
 #include <ATen/ops/gt_native.h>
+#include <ATen/ops/hypot_native.h>
 #include <ATen/ops/le_native.h>
 #include <ATen/ops/logaddexp2_native.h>
 #include <ATen/ops/logaddexp_native.h>
@@ -277,6 +278,22 @@ TORCH_IMPL_FUNC(pow_Scalar_out_mps)(const Scalar& base, const Tensor& exp, const
   }
 }
 
+TORCH_IMPL_FUNC(hypot_out_mps)(const Tensor& self, const Tensor& other, const Tensor& output) {
+  mps::BinaryOpBlock hypot_op_block = ^BinaryOpFn(cachedGraph, primaryCastTensor, secondaryCastTensor) {
+    MPSGraph* mpsGraph = cachedGraph->graph();
+    MPSGraphTensor* twoTensor = [mpsGraph constantWithScalar:2.0 shape:@[ @1 ] dataType:primaryCastTensor.dataType];
+    MPSGraphTensor* sumTensor = [mpsGraph additionWithPrimaryTensor:[mpsGraph powerWithPrimaryTensor:primaryCastTensor
+                                                                                     secondaryTensor:twoTensor
+                                                                                                name:nil]
+                                                    secondaryTensor:[mpsGraph powerWithPrimaryTensor:secondaryCastTensor
+                                                                                     secondaryTensor:twoTensor
+                                                                                                name:nil]
+                                                               name:nil];
+    return [mpsGraph squareRootWithTensor:sumTensor name:nil];
+  };
+  mps::binaryOpTensor(self, other, output, "hypot_out_mps", hypot_op_block);
+}
+
 TORCH_IMPL_FUNC(logaddexp_out_mps)(const Tensor& self, const Tensor& other, const Tensor& output) {
   mps::BinaryOpBlock logaddexp_op_block = ^BinaryOpFn(cachedGraph, primaryCastTensor, secondaryCastTensor) {
     MPSGraph* mpsGraph = cachedGraph->graph();

aten/src/ATen/native/mps/operations/LinearAlgebra.mm

@@ -8,9 +8,6 @@
 #include <ATen/native/Resize.h>
 #include <ATen/native/mps/MPSGraphSequoiaOps.h>
 #include <ATen/native/mps/OperationUtils.h>
-#include <ATen/native/mps/kernels/LinearAlgebra.h>
-
-#include <fmt/format.h>
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/Functions.h>
@@ -31,7 +28,6 @@
 #include <ATen/ops/linalg_solve_triangular_native.h>
 #include <ATen/ops/lu_unpack_native.h>
 #include <ATen/ops/mm_native.h>
-#include <ATen/ops/orgqr_native.h>
 #include <ATen/ops/slice.h>
 #include <ATen/ops/stack.h>
 #include <ATen/ops/triangular_solve_native.h>
@@ -342,8 +338,6 @@ static void linalg_lu_factor_ex_out_mps_impl(const Tensor& A,
           ". See https://developer.apple.com/documentation/metalperformanceshaders/mpsmatrixdecompositionstatus for details.");
     }
   }
-
-  map_mps_decomposition_error_code_to_blas(info);
 }
 
 static void linalg_solve_out_mps_impl(const Tensor& A,
@@ -1239,69 +1233,6 @@ static void cholesky_stub_impl(const Tensor& out, const Tensor& info, bool upper
   }
 }
 
-static Tensor& orgqr_stub_impl(Tensor& self, const Tensor& tau) {
-  if (self.numel() == 0) {
-    return self;
-  }
-
-  auto m = self.size(-2);
-  auto n = self.size(-1);
-  auto k = tau.size(-1);
-
-  if (tau.numel() == 0) {
-    auto I = eye(m, self.scalar_type(), std::nullopt, self.device());
-    return self.copy_(I.slice(-1, 0, n));
-  }
-
-  auto num_batch_dims = self.dim() - 2;
-  auto batch_sizes = self.sizes().slice(0, num_batch_dims);
-
-  std::vector<int64_t> H_sizes(num_batch_dims + 2);
-  for (auto dim : c10::irange(num_batch_dims)) {
-    H_sizes[dim] = self.size(dim);
-  }
-  H_sizes[num_batch_dims] = m;
-  H_sizes[num_batch_dims + 1] = m;
-
-  auto H = at::empty(H_sizes, self.options().memory_format(MemoryFormat::Contiguous));
-  auto H_prod = at::empty_like(H);
-
-  OrgqrParams params;
-
-  params.num_batch_dims = num_batch_dims;
-  params.m = m;
-  params.n = n;
-  params.k = k;
-
-  for (const auto dim : c10::irange(self.dim())) {
-    params.A_strides[dim] = self.stride(dim);
-
-    if (dim < tau.dim()) {
-      params.tau_strides[dim] = tau.stride(dim);
-    }
-
-    params.H_strides[dim] = H.stride(dim);
-    params.H_sizes[dim] = H.size(dim);
-  }
-
-  auto num_threads = H.numel();
-  MPSStream* stream = getCurrentMPSStream();
-
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
-    @autoreleasepool {
-      id<MTLComputeCommandEncoder> compute_encoder = stream->commandEncoder();
-      auto pipeline_state = lib.getPipelineStateForFunc(fmt::format("orgqr_{}", scalarToMetalTypeString(self)));
-      getMPSProfiler().beginProfileKernel(pipeline_state, "orgqr", {self, tau});
-      [compute_encoder setComputePipelineState:pipeline_state];
-      mtl_setArgs(compute_encoder, self, tau, H, H_prod, params);
-      mtl_dispatch1DJob(compute_encoder, pipeline_state, num_threads);
-      getMPSProfiler().endProfileKernel(pipeline_state);
-    }
-  });
-
-  return self;
-}
-
 } // namespace mps
 
 Tensor addr_mps(const Tensor& self, const Tensor& vec1, const Tensor& vec2, const Scalar& beta, const Scalar& alpha) {
@@ -1517,6 +1448,20 @@ TORCH_IMPL_FUNC(_linalg_solve_ex_out_mps)
   mps::linalg_solve_out_mps_impl(A, B, left, check_errors, result, LU, pivots, info);
 }
 
+std::tuple<Tensor&, Tensor&> linalg_lu_factor_out_mps(const Tensor& A, bool pivot, Tensor& LU, Tensor& pivots) {
+  Tensor info = at::empty({}, A.options().dtype(kInt));
+  mps::linalg_lu_factor_ex_out_mps_impl(A, pivot, LU, pivots, info, false);
+  return std::tie(LU, pivots);
+}
+
+std::tuple<Tensor, Tensor> linalg_lu_factor_mps(const Tensor& A, bool pivot) {
+  Tensor LU = at::empty({0}, A.options());
+  Tensor pivots = at::empty({0}, A.options().dtype(kInt));
+  Tensor info = at::empty({}, A.options().dtype(kInt));
+  mps::linalg_lu_factor_ex_out_mps_impl(A, pivot, LU, pivots, info, false);
+  return std::make_tuple(std::move(LU), std::move(pivots));
+}
+
 TORCH_IMPL_FUNC(lu_unpack_out_mps)
 (const Tensor& LU_data,
  const Tensor& LU_pivots,
@@ -1538,6 +1483,4 @@ TORCH_IMPL_FUNC(linalg_inv_ex_out_mps)(const Tensor& A, bool check_errors, const
 }
 
 REGISTER_DISPATCH(cholesky_stub, mps::cholesky_stub_impl)
-REGISTER_DISPATCH(orgqr_stub, mps::orgqr_stub_impl);
-
 } // namespace at::native

aten/src/ATen/native/mps/operations/UnaryKernel.mm

@@ -34,7 +34,6 @@ REGISTER_UNARY_TI_DISPATCH(sinc);
 REGISTER_UNARY_TI_DISPATCH(sinh);
 REGISTER_UNARY_TI_DISPATCH(cosh);
 REGISTER_UNARY_TI_DISPATCH(tanh);
-REGISTER_UNARY_TI_DISPATCH(angle);
 REGISTER_UNARY_TI_DISPATCH(abs);
 REGISTER_UNARY_TI_DISPATCH(sin);
 REGISTER_UNARY_TI_DISPATCH(cos);

aten/src/ATen/native/mps/operations/UnaryOps.mm

@@ -12,6 +12,7 @@
 #include <ATen/ops/_copy_from_and_resize.h>
 #include <ATen/ops/acos_native.h>
 #include <ATen/ops/acosh_native.h>
+#include <ATen/ops/angle_native.h>
 #include <ATen/ops/asin_native.h>
 #include <ATen/ops/asinh_native.h>
 #include <ATen/ops/atan_native.h>
@@ -203,6 +204,23 @@ Tensor& logical_not_out_mps(const Tensor& self, Tensor& output) {
   return output;
 }
 
+Tensor& angle_out_mps(const Tensor& self, Tensor& output) {
+  mps::unary_op(self, output, "angle_out_mps", ^MPSGraphTensor*(MPSGraph* mpsGraph, MPSGraphTensor* inputTensor) {
+    auto realPart = [mpsGraph realPartOfTensor:inputTensor name:nil];
+    auto imagPart = [mpsGraph imaginaryPartOfTensor:inputTensor name:nil];
+    return [mpsGraph atan2WithPrimaryTensor:imagPart secondaryTensor:realPart name:nil];
+  });
+  return output;
+}
+
+Tensor angle_mps(const Tensor& self) {
+  const auto float_type = c10::isIntegralType(self.scalar_type(), /*includeBool=*/true)
+      ? c10::typeMetaToScalarType(c10::get_default_dtype())
+      : c10::toRealValueType(self.scalar_type());
+  Tensor result = at::empty({0}, self.options().dtype(float_type));
+  return angle_out_mps(self, result);
+}
+
 TORCH_IMPL_FUNC(frac_out_mps)(const Tensor& self, const Tensor& output) {
   TORCH_CHECK(isFloatingType(self.scalar_type()), "frac_out_mps is only implemented for floating types");
   mps::unary_op(self, output, "frac_out_mps", ^MPSGraphTensor*(MPSGraph* mpsGraph, MPSGraphTensor* inputTensor) {

aten/src/ATen/native/native_functions.yaml

@@ -403,14 +403,16 @@
   device_check: NoCheck   # TensorIterator
   variants: function, method
   dispatch:
-    CPU, CUDA, MPS: angle
+    CPU, CUDA: angle
+    MPS: angle_mps
     SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: angle_sparse_csr
   tags: pointwise
 
 - func: angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
   device_check: NoCheck   # TensorIterator
   dispatch:
-    CPU, CUDA, MPS: angle_out
+    CPU, CUDA: angle_out
+    MPS: angle_out_mps
     SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: angle_sparse_csr_out
   tags: pointwise
 
@@ -10040,7 +10042,8 @@
   structured: True
   structured_inherits: TensorIteratorBase
   dispatch:
-    CPU, CUDA, MPS: hypot_out
+    CPU, CUDA: hypot_out
+    MPS: hypot_out_mps
   tags: pointwise
 
 - func: hypot(Tensor self, Tensor other) -> Tensor
@@ -14154,10 +14157,16 @@
 - func: linalg_lu_factor(Tensor A, *, bool pivot=True) -> (Tensor LU, Tensor pivots)
   python_module: linalg
   variants: function
+  dispatch:
+    CompositeImplicitAutograd: linalg_lu_factor
+    MPS: linalg_lu_factor_mps
 
 - func: linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots)
   python_module: linalg
   variants: function
+  dispatch:
+    CompositeImplicitAutograd: linalg_lu_factor_out
+    MPS: linalg_lu_factor_out_mps
 
 - func: linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info)
   python_module: linalg
@@ -14359,12 +14368,12 @@
   python_module: linalg
   variants: function
   dispatch:
-    CPU, CUDA, MPS: linalg_householder_product
+    CPU, CUDA: linalg_householder_product
 
 - func: linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!)
   python_module: linalg
   dispatch:
-    CPU, CUDA, MPS: linalg_householder_product_out
+    CPU, CUDA: linalg_householder_product_out
 
 - func: linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info)
   python_module: linalg

aten/src/ATen/native/sparse/cuda/SparseMatMul.cu

@@ -40,7 +40,15 @@
 #include <thrust/iterator/discard_iterator.h>
 
 
+#if defined(__CUDACC__) && (defined(CUSPARSE_VERSION) || (defined(USE_ROCM) && ROCM_VERSION >= 60300))
+#define IS_CUSPARSE11_AVAILABLE() 1
+#else
+#define IS_CUSPARSE11_AVAILABLE() 0
+#endif
+
+#if IS_CUSPARSE11_AVAILABLE()
 #include <library_types.h>
+#endif
 
 namespace at::native {
 
@@ -95,9 +103,17 @@ struct csrMatrixRef {
   int nnz_{0};
   std::vector<int> size_{};
 
-  cusparseSpMatDescr_t description_{0};
+  #if IS_CUSPARSE11_AVAILABLE()
+    cusparseSpMatDescr_t description_{0};
+  #else
+    cusparseMatDescr_t description_{0};
+  #endif
 
-  csrMatrixRef() = default;
+  csrMatrixRef() {
+    #if !IS_CUSPARSE11_AVAILABLE()
+      create_general_description_(description_);
+    #endif
+  }
 
   csrMatrixRef(
       int* csr_indices,
@@ -110,6 +126,7 @@ struct csrMatrixRef {
         csr_values_{csr_values},
         nnz_{nnz},
         size_{size} {
+    #if IS_CUSPARSE11_AVAILABLE()
       cudaDataType cuda_data_type = at::cuda::getCudaDataType<scalar_t>();
       TORCH_CUDASPARSE_CHECK(cusparseCreateCsr(
         &description_,
@@ -123,10 +140,17 @@ struct csrMatrixRef {
         CUSPARSE_INDEX_32I,
         CUSPARSE_INDEX_BASE_ZERO,
         cuda_data_type));
+    #else
+      create_general_description_(description_);
+    #endif
   }
 
   ~csrMatrixRef() {
-    cusparseDestroySpMat(description_);
+    #if IS_CUSPARSE11_AVAILABLE()
+      cusparseDestroySpMat(description_);
+    #else
+      cusparseDestroyMatDescr(description_);
+    #endif
   }
 
   int size(int index) const {
@@ -172,6 +196,8 @@ struct csrOutput {
   }
 };
 
+#if IS_CUSPARSE11_AVAILABLE()
+
 // RAII guard helps to support cuSparse 11 API for `A @ B` operation
 // This generic template exists because with cuSparse the `scalar_t` type could be a double or float
 template <class scalar_t>
@@ -370,6 +396,284 @@ template struct CusparseMatrixMultiplyOp<float>;
 
 template struct CusparseMatrixMultiplyOp<double>;
 
+#else // if not IS_CUSPARSE11_AVAILABLE()
+
+using DcsrMatrixRef = csrMatrixRef<double>;
+using ScsrMatrixRef = csrMatrixRef<float>;
+
+// RAII guard helps to support cuSparse 10 API for `A @ B` operation
+// This generic template exists because with cuSparse the `scalar_t` type could be a double or float
+template <class scalar_t>
+struct CusparseMatrixMultiplyOp {
+  csrOutput operator()(
+      const csrMatrixRef<scalar_t>& lhs,
+      const csrMatrixRef<scalar_t>& rhs,
+      Tensor &output_values,
+      Tensor &output_indices)
+  {
+    static_assert(false&&sizeof(scalar_t), "cusparse csr sparse-sparse MM only supports data type of float and double.");
+  }
+};
+
+// Specializacion for `A @ B` operation for double values with cuSparse
+template<> struct CusparseMatrixMultiplyOp<double> {
+  csrgemm2Info_t gemm2Info_;
+
+  CusparseMatrixMultiplyOp() {
+    TORCH_CUDASPARSE_CHECK(cusparseCreateCsrgemm2Info(&gemm2Info_));
+  }
+  ~CusparseMatrixMultiplyOp() {
+    cusparseDestroyCsrgemm2Info(gemm2Info_);
+  }
+
+  csrOutput operator ()(
+      const DcsrMatrixRef& lhs,
+      const DcsrMatrixRef& rhs,
+      Tensor &output_values,
+      Tensor &output_indices) {
+    double alpha = 1.0;
+    DcsrMatrixRef empty;
+    return Dgemm2(lhs, rhs, empty, &alpha, nullptr, output_values, output_indices);
+  }
+
+  csrOutput Dgemm2(
+      const DcsrMatrixRef& A,
+      const DcsrMatrixRef& B,
+      const DcsrMatrixRef& C,
+      const double* alpha,
+      const double* beta,
+      Tensor &output_values,
+      Tensor &output_indices) {
+    void* buffer_{nullptr};
+    cusparseHandle_t cusparseHandle_ = at::cuda::getCurrentCUDASparseHandle();
+    TORCH_CUDASPARSE_CHECK(cusparseSetPointerMode(cusparseHandle_, CUSPARSE_POINTER_MODE_HOST));
+
+    csrOutput out({A.size(0), B.size(1)});
+    int innerSize = confirm_mult_size(A.size_, B.size_);
+    out.csr_pointers_ = at::empty({out.size(0) + 1}, output_indices.options().dtype(kInt));
+
+    // Compute needed buffer size
+    size_t new_bubber_sz;
+    TORCH_CUDASPARSE_CHECK(cusparseDcsrgemm2_bufferSizeExt(
+        cusparseHandle_,
+        out.size(0),
+        out.size(1),
+        innerSize,
+        alpha,
+        A.description_,
+        A.nnz_,
+        A.csr_pointers_,
+        A.csr_indices_,
+        B.description_,
+        B.nnz_,
+        B.csr_pointers_,
+        B.csr_indices_,
+        beta,
+        C.description_,
+        C.nnz_,
+        C.csr_pointers_,
+        C.csr_indices_,
+        gemm2Info_,
+        &new_bubber_sz));
+
+    // (Re)allocate buffer if needed
+    auto& allocator = *::c10::cuda::CUDACachingAllocator::get();
+    at::DataPtr data_ptr = allocator.allocate(new_bubber_sz);
+    buffer_ = data_ptr.get();
+
+    // Find the resulting non-zero pattern.
+    TORCH_CUDASPARSE_CHECK(cusparseXcsrgemm2Nnz(
+        cusparseHandle_,
+        out.size(0),
+        out.size(1),
+        innerSize,
+        A.description_,
+        A.nnz_,
+        A.csr_pointers_,
+        A.csr_indices_,
+        B.description_,
+        B.nnz_,
+        B.csr_pointers_,
+        B.csr_indices_,
+        C.description_,
+        C.nnz_,
+        C.csr_pointers_,
+        C.csr_indices_,
+        out.description_,
+        out.csr_pointers_.data_ptr<int>(),
+        &out.nnz_,
+        gemm2Info_,
+        buffer_));
+
+    out.csr_indices_ = at::empty({out.nnz_}, output_indices.options().dtype(kInt));
+    out.csr_values_ = at::empty({out.nnz_}, output_values.options());
+
+    // Perform the gemm2 operation for doubles
+    // out = alpha ∗ A ∗ B + beta ∗ C
+    TORCH_CUDASPARSE_CHECK(cusparseDcsrgemm2(
+        cusparseHandle_,
+        out.size(0),
+        out.size(1),
+        innerSize,
+        alpha,
+        A.description_,
+        A.nnz_,
+        A.csr_values_,
+        A.csr_pointers_,
+        A.csr_indices_,
+        B.description_,
+        B.nnz_,
+        B.csr_values_,
+        B.csr_pointers_,
+        B.csr_indices_,
+        beta,
+        C.description_,
+        C.nnz_,
+        C.csr_values_,
+        C.csr_pointers_,
+        C.csr_indices_,
+        out.description_,
+        out.csr_values_.data_ptr<double>(),
+        out.csr_pointers_.data_ptr<int>(),
+        out.csr_indices_.data_ptr<int>(),
+        gemm2Info_,
+        buffer_));
+    return out;
+  }
+};
+
+// Specializacion for `A @ B` operation for float values with cuSparse
+template<> struct CusparseMatrixMultiplyOp<float> {
+  csrgemm2Info_t gemm2Info_;
+
+  CusparseMatrixMultiplyOp() {
+    TORCH_CUDASPARSE_CHECK(cusparseCreateCsrgemm2Info(&gemm2Info_));
+
+  }
+  ~CusparseMatrixMultiplyOp() {
+    cusparseDestroyCsrgemm2Info(gemm2Info_);
+  }
+  csrOutput operator()(
+      const ScsrMatrixRef& lhs,
+      const ScsrMatrixRef& rhs,
+      Tensor &output_values,
+      Tensor &output_indices) {
+    float alpha = 1.0;
+    ScsrMatrixRef empty;
+    return Sgemm2(lhs, rhs, empty, &alpha, nullptr, output_values, output_indices);
+  }
+
+  csrOutput Sgemm2(
+      const ScsrMatrixRef& A,
+      const ScsrMatrixRef& B,
+      const ScsrMatrixRef& C,
+      const float* alpha,
+      const float* beta,
+      Tensor &output_values,
+      Tensor &output_indices) {
+    void* buffer_{nullptr};
+    cusparseHandle_t cusparseHandle_ = at::cuda::getCurrentCUDASparseHandle();
+    TORCH_CUDASPARSE_CHECK(cusparseSetPointerMode(cusparseHandle_, CUSPARSE_POINTER_MODE_HOST));
+
+    csrOutput out({A.size(0), B.size(1)});
+
+    int innerSize = confirm_mult_size(A.size_, B.size_);
+
+    out.csr_pointers_ = at::empty({out.size(0) + 1}, output_indices.options().dtype(kInt));
+
+    // Compute needed buffer size
+    size_t new_bubber_sz;
+    TORCH_CUDASPARSE_CHECK(cusparseScsrgemm2_bufferSizeExt(
+        cusparseHandle_,
+        out.size(0),
+        out.size(1),
+        innerSize,
+        alpha,
+        A.description_,
+        A.nnz_,
+        A.csr_pointers_,
+        A.csr_indices_,
+        B.description_,
+        B.nnz_,
+        B.csr_pointers_,
+        B.csr_indices_,
+        beta,
+        C.description_,
+        C.nnz_,
+        C.csr_pointers_,
+        C.csr_indices_,
+        gemm2Info_,
+        &new_bubber_sz));
+
+    auto& allocator = *::c10::cuda::CUDACachingAllocator::get();
+    at::DataPtr data_ptr = allocator.allocate(new_bubber_sz);
+    buffer_ = data_ptr.get();
+
+    // Find the resulting non-zero pattern.
+    TORCH_CUDASPARSE_CHECK(cusparseXcsrgemm2Nnz(
+        cusparseHandle_,
+        out.size(0),
+        out.size(1),
+        innerSize,
+        A.description_,
+        A.nnz_,
+        A.csr_pointers_,
+        A.csr_indices_,
+        B.description_,
+        B.nnz_,
+        B.csr_pointers_,
+        B.csr_indices_,
+        C.description_,
+        C.nnz_,
+        C.csr_pointers_,
+        C.csr_indices_,
+        out.description_,
+        out.csr_pointers_.data_ptr<int>(),
+        &out.nnz_,
+        gemm2Info_,
+        buffer_));
+
+    out.csr_indices_ = at::empty({out.nnz_}, output_indices.options().dtype(kInt));
+    out.csr_values_ = at::empty({out.nnz_}, output_values.options());
+
+    // Perform the gemm2 operation for doubles
+    // out = alpha ∗ A ∗ B + beta ∗ C
+    TORCH_CUDASPARSE_CHECK(cusparseScsrgemm2(
+        cusparseHandle_,
+        out.size(0),
+        out.size(1),
+        innerSize,
+        alpha,
+        A.description_,
+        A.nnz_,
+        A.csr_values_,
+        A.csr_pointers_,
+        A.csr_indices_,
+        B.description_,
+        B.nnz_,
+        B.csr_values_,
+        B.csr_pointers_,
+        B.csr_indices_,
+        beta,
+        C.description_,
+        C.nnz_,
+        C.csr_values_,
+        C.csr_pointers_,
+        C.csr_indices_,
+        out.description_,
+        out.csr_values_.data_ptr<float>(),
+        out.csr_pointers_.data_ptr<int>(),
+        out.csr_indices_.data_ptr<int>(),
+        gemm2Info_,
+        buffer_));
+    return out;
+  }
+};
+
+
+
+#endif // IS_CUSPARSE11_AVAILABLE()
+
 template <typename scalar_t>
 void sparse_sparse_matmul_cuda_kernel(
     Tensor& result,
@@ -511,15 +815,19 @@ Tensor sparse_sparse_matmul_cuda(const Tensor& mat1_, const Tensor& mat2_) {
   auto output = at::native::empty_like(mat1_);
   output.sparse_resize_and_clear_({mat1_.size(0), mat2_.size(1)}, mat1_.sparse_dim(), 0);
 
-#if !defined(USE_ROCM)
+#if IS_CUSPARSE11_AVAILABLE() && !defined(USE_ROCM)
   AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(kHalf, kBFloat16, mat1_.scalar_type(), "sparse_matmul", [&] {
       sparse_sparse_matmul_cuda_kernel<scalar_t>(output, mat1_.coalesce(), mat2_.coalesce());
   });
-#else
+#elif IS_CUSPARSE11_AVAILABLE() && defined(USE_ROCM)
   // ROCm does not support half and bfloat16 types for sparse_matmul
   AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES(mat1_.scalar_type(), "sparse_matmul", [&] {
       sparse_sparse_matmul_cuda_kernel<scalar_t>(output, mat1_.coalesce(), mat2_.coalesce());
   });
+#else
+  AT_DISPATCH_FLOATING_TYPES(mat1_.scalar_type(), "sparse_matmul", [&] {
+    sparse_sparse_matmul_cuda_kernel<scalar_t>(output, mat1_.coalesce(), mat2_.coalesce());
+  });
 #endif
   return output;
 }

aten/src/ATen/native/sparse/mps/SparseMPSTensorMath.mm

@@ -33,7 +33,7 @@ using namespace mps;
 #ifndef PYTORCH_JIT_COMPILE_SHADERS
 static auto& lib = MetalShaderLibrary::getBundledLibrary();
 #else
-#include <ATen/native/mps/SparseTensorMath_metallib.h>
+#include <ATen/native/mps/Mul_metallib.h>
 #endif
 
 static Tensor& s_addmm_out_sparse_dense_mps(
@@ -369,7 +369,12 @@ static SparseTensor& mul_out_dense_sparse_mps(
   }
 
   if (scalar_like) {
-    auto out_vals = values.mul(dense.to(values.options()));
+    auto scalar = dense;
+    if (dense.numel() == 1 && dense.dim() > 0) {
+      scalar = dense.view({});
+    }
+    scalar = scalar.to(values.options());
+    auto out_vals = values.mul(scalar);
     if (out.scalar_type() != commonDtype) {
       out_vals = out_vals.to(out.scalar_type());
     }
@@ -503,14 +508,14 @@ SparseTensor& mul_out_sparse_mps(const Tensor& t_, const Tensor& src_, SparseTen
   const auto device = r_.device();
   auto stream = getCurrentMPSStream();
 
-  auto lhs_indices = lhs._indices().contiguous();
-  auto rhs_indices = rhs._indices().contiguous();
-  auto lhs_values  = lhs._values().to(commonDtype).contiguous();
-  auto rhs_values  = rhs._values().to(commonDtype).contiguous();
+  auto lhs_indices = lhs._indices();
+  auto rhs_indices = rhs._indices();
+  auto lhs_values  = lhs._values().to(commonDtype);
+  auto rhs_values  = rhs._values().to(commonDtype);
 
   // Flatten sparse indices to keys
-  auto lhs_keys = flatten_indices(lhs_indices, lhs.sizes().slice(0, ndim_i));
-  auto rhs_keys = flatten_indices(rhs_indices, rhs.sizes().slice(0, ndim_i));
+  auto lhs_keys = flatten_indices(lhs_indices, lhs.sizes());
+  auto rhs_keys = flatten_indices(rhs_indices, rhs.sizes());
 
   // Intersect sorted keys (search the shorter in the longer)
   const bool A_is_lhs = (lhs_nnz <= rhs_nnz);
@@ -541,54 +546,35 @@ SparseTensor& mul_out_sparse_mps(const Tensor& t_, const Tensor& src_, SparseTen
   auto out_indices = at::empty({ndim_i, static_cast<int64_t>(M)}, at::device(device).dtype(at::kLong));
   auto lhs_match = outA_idx.narrow(0, 0, M);
   auto rhs_match = outB_idx.narrow(0, 0, M);
-  auto dense_sizes_vec = lhs.sizes().slice(ndim_i).vec();
-  int64_t cols64 = 1;
-  for (auto s : dense_sizes_vec) cols64 *= s;
-  const uint32_t cols = static_cast<uint32_t>(std::max<int64_t>(cols64, 1));
-
-  auto to2d = [&](Tensor t, int64_t nnz) -> Tensor {
-    const int64_t t_cols = t.numel() / nnz;
-    if (t_cols == cols64) {
-      return t.view({nnz, cols64});
-    }
-    return t.view({nnz, 1}).expand({nnz, cols64}).contiguous();
-  };
-
-  // make both sides 2d [nnz, cols] buffers so the kernel can index it
-  auto lhs_vals2d = to2d(lhs_values, lhs_nnz);
-  auto rhs_vals2d = to2d(rhs_values, rhs_nnz);
-
-  std::vector<int64_t> out_val_sizes;
-  out_val_sizes.reserve(1 + dense_sizes_vec.size());
-  out_val_sizes.push_back(static_cast<int64_t>(M));
-  out_val_sizes.insert(out_val_sizes.end(), dense_sizes_vec.begin(), dense_sizes_vec.end());
+  auto out_val_sizes = lhs_values.sizes().vec();
+  out_val_sizes[0] = static_cast<int64_t>(M);
   auto out_values = at::empty(out_val_sizes, lhs_values.options());
 
-  if (M > 0) {
-    dispatch_sync_with_rethrow(stream->queue(), ^() {
-      @autoreleasepool {
-        auto pso = lib.getPipelineStateForFunc(
-            "fused_gather_mul_kernel_" + mps::scalarToMetalTypeString(lhs_values));
-        auto enc = stream->commandEncoder();
-        [enc setComputePipelineState:pso];
+  const uint32_t cols = static_cast<uint32_t>(
+      lhs_values.numel() / std::max<int64_t>(1, lhs_nnz));
 
-        const uint32_t tew = pso.threadExecutionWidth;
-        const uint32_t gridW = std::max<uint32_t>(cols, 1u);
-        const uint32_t tgW = std::min(gridW, tew);
-        MTLSize grid = MTLSizeMake(gridW, 1, M);
-        MTLSize tgs  = MTLSizeMake(tgW, 1, 1);
+  dispatch_sync_with_rethrow(stream->queue(), ^() {
+    @autoreleasepool {
+      auto pso = lib.getPipelineStateForFunc(
+          "fused_gather_mul_kernel_" + mps::scalarToMetalTypeString(lhs_values));
+      auto enc = stream->commandEncoder();
+      [enc setComputePipelineState:pso];
 
-        mtl_setArgs(enc,
-                    lhs_vals2d, rhs_vals2d,
-                    lhs_match, rhs_match,
-                    lhs_indices, out_indices,
-                    out_values,
-                    std::array<uint32_t, 2>{static_cast<uint32_t>(ndim_i), static_cast<uint32_t>(lhs_nnz)},
-                    std::array<uint32_t, 2>{M, cols});
-        [enc dispatchThreads:grid threadsPerThreadgroup:tgs];
-      }
-    });
-  }
+      const uint32_t tew  = pso.threadExecutionWidth;
+      uint32_t tgW = std::min(cols, tew);
+      MTLSize grid = MTLSizeMake(cols, 1, M);
+      MTLSize tgs  = MTLSizeMake(tgW, 1, 1);
+
+      mtl_setArgs(enc,
+                  lhs_values, rhs_values,
+                  lhs_match, rhs_match,
+                  lhs_indices, out_indices,
+                  out_values,
+                  std::array<uint32_t, 2>{static_cast<uint32_t>(ndim_i), static_cast<uint32_t>(lhs_nnz)},
+                  std::array<uint32_t, 2>{M, cols});
+      [enc dispatchThreads:grid threadsPerThreadgroup:tgs];
+    }
+  });
 
   if (r_.scalar_type() != commonDtype) {
     out_values = out_values.to(r_.scalar_type());

aten/src/ATen/native/sparse/mps/kernels/Mul.metal

@@ -62,6 +62,7 @@ kernel void build_row_ptr_from_sorted_rows_by_batch(
 
 template <typename T>
 kernel void spmm_bmm_coo_rows_grouped(
+    device const long*   rows      [[buffer(0)]],
     device const long*   cols      [[buffer(1)]],
     device const T*      vals      [[buffer(2)]],
     device const T*      dense     [[buffer(3)]],
@@ -72,6 +73,7 @@ kernel void spmm_bmm_coo_rows_grouped(
     uint3                ltid      [[thread_position_in_threadgroup]],
     uint3                tptg      [[threads_per_threadgroup]])
 {
+  const uint B = dims.x;
   const uint I = dims.y;
   const uint J = dims.z;
   const uint K = dims.w;
@@ -195,9 +197,9 @@ kernel void fused_gather_mul_kernel(
     const ulong offR = (ulong)iR * (ulong)view_cols + (ulong)col;
     const ulong offO = (ulong)k  * (ulong)view_cols + (ulong)col;
 
-    const auto a = static_cast<accum_t<T>>(lhs_vals[offL]);
-    const auto b = static_cast<accum_t<T>>(rhs_vals[offR]);
-    out_vals[offO] = static_cast<T>(mul(a, b));
+    const float a = (float)lhs_vals[offL];
+    const float b = (float)rhs_vals[offR];
+    out_vals[offO] = (T)(a * b);
   }
 
   // One thread per match copies the indices column
@@ -319,6 +321,7 @@ INSTANTIATE_FOR_FLOAT_TYPES(INSTANTIATE_FUSED_GATHER_MUL);
 #define INSTANTIATE_SPMM_BMM_COO_ROWS_GROUPED(DTYPE)                         \
   template [[host_name("spmm_bmm_coo_rows_grouped_" #DTYPE)]] kernel void    \
   spmm_bmm_coo_rows_grouped<DTYPE>(                                          \
+      device const long*   rows      [[buffer(0)]],                          \
       device const long*   cols      [[buffer(1)]],                          \
       device const DTYPE*  vals      [[buffer(2)]],                          \
       device const DTYPE*  dense     [[buffer(3)]],                          \

aten/src/ATen/native/transformers/cuda/sdp_utils.cpp

@@ -76,21 +76,14 @@ bool priority_order_init_ = false;
 // TODO(eqy): more benchmarking to determine whether this should include sm86/89
 // Needs to be kept in-sync with test_fused_chocie in test_transformers.py
 bool check_prefer_cudnn_attention() {
-  static const bool prefer_cudnn = c10::utils::check_env("TORCH_CUDNN_SDPA_DEPRIORITIZED") != true;
+  static const bool prefer_cudnn = c10::utils::check_env("TORCH_CUDNN_SDPA_PREFERRED") != false;
   if (!prefer_cudnn) {
     return false;
   }
 #if (defined(CUDNN_VERSION) && (CUDNN_VERSION >= 90900))
-  try {
-    auto dprops = at::cuda::getCurrentDeviceProperties();
-    auto major = dprops->major;
-    return (major == 9 || major == 10) && !dprops->minor;
-  } catch (c10::Error const& e) {
-#ifdef DEBUG
-    TORCH_WARN("check_prefer_cudnn_attention() caught exception ", e.what());
-#endif
-    return false;
-  }
+  auto dprops = at::cuda::getCurrentDeviceProperties();
+  auto major = dprops->major;
+  return (major == 9 || major == 10) && !dprops->minor;
 #else
   return false;
 #endif

aten/src/ATen/test/type_ptr_test.cpp

@@ -37,10 +37,6 @@ TEST(SingletonOrSharedTypePtr, Comparison) {
 
   EXPECT_NE(empty, p);
   EXPECT_NE(p, p2);
-
-  EXPECT_EQ(empty, empty);
-  EXPECT_EQ(p, p);
-  EXPECT_EQ(p2, p2);
 }
 
 TEST(SingletonOrSharedTypePtr, SingletonComparison) {
@@ -51,8 +47,6 @@ TEST(SingletonOrSharedTypePtr, SingletonComparison) {
   c10::TypePtr type = c10::NoneType::get();
   EXPECT_NE(type, c10::StringType::get());
   EXPECT_NE(type, c10::DeviceObjType::get());
-  EXPECT_EQ(type, type);
-  EXPECT_EQ(type, c10::NoneType::get());
 }
 
 

aten/src/ATen/test/vec_test_all_types.cpp

@@ -526,41 +526,6 @@ namespace {
             [](const vec& v) { return v.expm1(); },
             createDefaultUnaryTestCase<vec>(TestSeed(), false, true));
     }
-    TYPED_TEST(Exponents, ExpU20) {
-        using vec = TypeParam;
-        using VT = ValueType<TypeParam>;
-        using UVT = UvalueType<TypeParam>;
-
-        // Explicit edge values
-        VT v_too_small = VT(-100.0); // much less than -87.3
-        VT exp_too_small = std::exp(v_too_small);
-        VT v_neg_edge = VT(-0x1.5d5e2ap+6f);   // just at the edge
-        VT exp_neg_edge = std::exp(v_neg_edge);
-        VT v_zero = VT(0.0);         // middle, normal case
-        VT exp_zero = std::exp(v_zero);
-        VT v_pos_edge = VT(0x1.5d5e2ap+6f);    // just at the edge
-        VT exp_pos_edge = std::exp(v_pos_edge);
-        VT v_too_large = VT(100.0);  // much more than 87.3
-        VT exp_too_large = std::exp(v_too_large);
-
-        auto test_case = TestingCase<vec>::getBuilder()
-            // Randoms in normal range, but the .addCustom() below guarantees we hit the special/fallback cases
-            .addDomain(CheckWithinDomains<UVT>{{{-100, 100}}, false, getDefaultTolerance<UVT>()})
-            .addCustom({ {v_too_small}, exp_too_small })
-            .addCustom({ {v_neg_edge}, exp_neg_edge })
-            .addCustom({ {v_zero}, exp_zero })
-            .addCustom({ {v_pos_edge}, exp_pos_edge })
-            .addCustom({ {v_too_large}, exp_too_large })
-            .setTrialCount(65536)
-            .setTestSeed(TestSeed());
-
-        test_unary<vec>(
-            NAME_INFO(exp_u20_edge_cases),
-            RESOLVE_OVERLOAD(std::exp),
-            [](const vec& v) { return v.exp_u20(); },
-            test_case
-        );
-    }
     TYPED_TEST(ErrorFunctions, Erf) {
         using vec = TypeParam;
         test_unary<vec>(

benchmarks/dynamo/genai_layers/benchmark.py

@@ -58,7 +58,8 @@ def list_benchmarks():
 
 def run_benchmark(
     benchmark_name: str,
-    script_args,
+    should_visualize: bool = False,
+    compile_mode: str = "max-autotune-no-cudagraphs",
 ):
     """Run a specific benchmark."""
     if benchmark_name not in BENCHMARK_REGISTRY:
@@ -67,29 +68,29 @@ def run_benchmark(
         return False
 
     print(f"Running benchmark: {benchmark_name}")
-    print(f"Torch compile mode: {script_args.compile_mode}")
+    print(f"Torch compile mode: {compile_mode}")
     print("=" * 60)
 
     benchmark_class = BENCHMARK_REGISTRY[benchmark_name]
-    benchmark = benchmark_class(script_args)
+    benchmark = benchmark_class(compile_mode)
     benchmark.benchmark()
-    if script_args.visualize:
+    if should_visualize:
         benchmark.visualize()
 
     return True
 
 
-def run_all_benchmarks(script_args):
+def run_all_benchmarks(should_visualize: bool = False, compile_mode: str = "default"):
     """Run all available benchmarks."""
     print("Running all benchmarks...")
-    print(f"Torch compile mode: {script_args.compile_mode}")
+    print(f"Torch compile mode: {compile_mode}")
     print("=" * 60)
 
     for name, cls in BENCHMARK_REGISTRY.items():
         print(f"\n{'=' * 20} {name.upper()} {'=' * 20}")
-        benchmark = cls(script_args)
+        benchmark = cls(compile_mode)
         benchmark.benchmark()
-        if script_args.visualize:
+        if should_visualize:
             benchmark.visualize()
         print()
 
@@ -136,19 +137,6 @@ Examples:
         help="Torch compile mode to use (default: default)",
     )
 
-    parser.add_argument(
-        "--tolerance",
-        type=float,
-        default=None,
-        help="Tolerance for the accuracy check",
-    )
-
-    parser.add_argument(
-        "--exit-on-accuracy-failure",
-        action="store_true",
-        help="Whether to exit with an error message for accuracy failure",
-    )
-
     args = parser.parse_args()
 
     # Handle list option
@@ -158,7 +146,7 @@ Examples:
 
     # Handle all option
     if args.all:
-        run_all_benchmarks(args)
+        run_all_benchmarks(args.visualize, args.compile_mode)
         return
 
     # Handle specific benchmarks
@@ -169,7 +157,7 @@ Examples:
         sys.exit(1)
 
     for benchmark_name in args.benchmarks:
-        run_benchmark(benchmark_name, args)
+        run_benchmark(benchmark_name, args.visualize, args.compile_mode)
         print()  # Add spacing between benchmarks
 
 

benchmarks/dynamo/genai_layers/kernels.py

@@ -9,8 +9,8 @@ import torch.nn.functional as F
 
 
 class CrossEntropyForward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -106,8 +106,8 @@ class CrossEntropyForward(BenchmarkKernel):
 
 
 class CrossEntropyBackward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -194,8 +194,8 @@ class CrossEntropyBackward(BenchmarkKernel):
 
 
 class SoftmaxForward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -259,8 +259,8 @@ class SoftmaxForward(BenchmarkKernel):
 
 
 class SoftmaxBackward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -329,8 +329,8 @@ class SoftmaxBackward(BenchmarkKernel):
 
 
 class RMSNormForward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -383,22 +383,7 @@ class RMSNormForward(BenchmarkKernel):
         from quack.rmsnorm import _rmsnorm_fwd
 
         x, w = args
-        y = torch.empty_like(x)
-
-        def quack_fwd():
-            _rmsnorm_fwd(
-                x,
-                w,
-                out=y,
-                bias=None,
-                rstd=None,
-                residual=None,
-                residual_out=None,
-                eps=1e-6,
-            )
-            return y
-
-        return quack_fwd
+        return lambda: _rmsnorm_fwd(x, w, eps=1e-6)
 
     def liger(self, args, kwargs) -> Any:
         from liger_kernel.transformers.rms_norm import LigerRMSNorm
@@ -419,14 +404,9 @@ class RMSNormForward(BenchmarkKernel):
 
 
 class RMSNormBackward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
-        self.available_backends = [
-            "eager",
-            "compiled",
-            "quack",
-            "liger",
-        ]
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
+        self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
         # TODO: OOM for (32768, 65536) on h100
@@ -474,11 +454,8 @@ class RMSNormBackward(BenchmarkKernel):
             y, [x, w], grad_outputs=dy, retain_graph=True
         )
 
-    def compute_rstd(self, x, eps):
-        return torch.rsqrt(torch.mean(x.float().square(), dim=-1, keepdim=True) + eps)
-
     def quack(self, args, kwargs=None) -> Any:
-        from quack.rmsnorm import _get_sm_count, _rmsnorm_bwd
+        from quack.rmsnorm import _rmsnorm_backward
 
         (
             x,
@@ -486,40 +463,15 @@ class RMSNormBackward(BenchmarkKernel):
             dy,
         ) = args
         M, N = x.shape
-
-        rstd = self.compute_rstd(x, eps=1e-6)
-        dx = torch.empty_like(x)
-        sm_count = _get_sm_count(x.size(1), x.device)
-        dw_partial = torch.empty(
-            sm_count, x.size(1), device=x.device, dtype=torch.float32
-        )
-
-        def quack_bwd():
-            _rmsnorm_bwd(
-                x,
-                w,
-                dy,
-                rstd,
-                dx,
-                dw_partial,
-                db_partial=None,
-                dresidual_out=None,
-                dresidual=None,
-                sm_count=sm_count,
-            )
-            dw = dw_partial.sum(dim=0).to(w.dtype)
-            return dx, dw
-
-        return quack_bwd
+        rstd = torch.randn(M, device="cuda", dtype=torch.float32)
+        return lambda: _rmsnorm_backward(x, w, dy, rstd)
 
     def liger(self, args, kwargs=None) -> Any:
         from liger_kernel.transformers.rms_norm import LigerRMSNorm
 
         x, w, dy = args
         M, N = x.shape
-        liger_rmsnorm = LigerRMSNorm(
-            hidden_size=N, eps=1e-6, casting_mode="gemma"
-        ).cuda()
+        liger_rmsnorm = LigerRMSNorm(hidden_size=N, eps=1e-6).cuda()
         liger_rmsnorm.weight.data.copy_(w)
         y = liger_rmsnorm(x)
         return lambda: torch.autograd.grad(
@@ -537,8 +489,8 @@ class RMSNormBackward(BenchmarkKernel):
 
 
 class LayerNormForward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "quack", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -611,8 +563,8 @@ class LayerNormForward(BenchmarkKernel):
 
 
 class LayerNormBackward(BenchmarkKernel):
-    def __init__(self, script_args):
-        super().__init__(script_args)
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
+        super().__init__(compile_mode)
         self.available_backends = ["eager", "compiled", "liger"]
 
     def get_shapes(self) -> tuple[tuple[int, ...], ...]:
@@ -662,31 +614,20 @@ class LayerNormBackward(BenchmarkKernel):
             y, [x, w], grad_outputs=dy, retain_graph=True
         )
 
-    def compute_mean_rstd(self, x, eps):
-        x = x.float()
-
-        var, mean = torch.var_mean(x, dim=-1, keepdim=True, correction=0)
-        rstd = torch.rsqrt(var + eps)
-        return mean, rstd
-
     def liger(self, args, kwargs) -> Any:
-        """
-        Call layer_norm_backward directly rather than calling
-        liger_kernel.transformers.layer_norm.LigerLayerNorm and
-        torch.autograd.grad.
-
-        The latter fashion saves mean/rstd in x.dtype which can fail
-        accuracy test. We call layer_norm_backward with fp32 mean and
-        rstd.
-        """
-        from liger_kernel.ops.layer_norm import layer_norm_backward
+        from liger_kernel.transformers.layer_norm import LigerLayerNorm
 
         x, w, dy = args
-        eps = 1e-6
-        mean, rstd = self.compute_mean_rstd(x, eps)
         M, N = x.shape
-
-        return lambda: layer_norm_backward(dy, x, w, None, mean, rstd)[0:2]
+        liger_layernorm = LigerLayerNorm(hidden_size=N, eps=1e-6).cuda()
+        liger_layernorm.weight.data.copy_(w)
+        liger_layernorm.bias.data.copy_(
+            torch.zeros(N, device="cuda", dtype=torch.float32)
+        )
+        y = liger_layernorm(x)
+        return lambda: torch.autograd.grad(
+            y, [x, liger_layernorm.weight], grad_outputs=dy, retain_graph=True
+        )
 
     def benchmark(self):
         for M, N in self.get_shapes():

benchmarks/dynamo/genai_layers/utils.py

@@ -1,5 +1,4 @@
 import os
-import sys
 from collections import defaultdict
 from collections.abc import Callable
 from dataclasses import dataclass
@@ -44,11 +43,10 @@ class Performance:
 
 
 class BenchmarkKernel:
-    def __init__(self, script_args):
-        self.script_args = script_args
+    def __init__(self, compile_mode: str = "max-autotune-no-cudagraphs"):
         self.name = self.__class__.__name__
         self.available_backends: list[str] = []
-        self.compile_mode: str = script_args.compile_mode
+        self.compile_mode: str = compile_mode
 
         # mapping from backend to list of performance results
         self.profiling_results: defaultdict[str, list[Performance]] = defaultdict(list)
@@ -108,21 +106,14 @@ class BenchmarkKernel:
             args_ref, kwargs_ref = self.clone_inputs(args, kwargs)
             res[backend] = getattr(self, backend)(args_ref, kwargs_ref)()
         gold = res["eager"]
-
-        tol = {}
-        if self.script_args.tolerance:
-            tol = {
-                "atol": self.script_args.tolerance,
-                "rtol": self.script_args.tolerance,
-            }
         for backend in self.available_backends:
             if backend == "eager":
                 continue
             try:
-                torch.testing.assert_close(res[backend], gold, **tol)
+                torch.testing.assert_close(res[backend], gold)
                 for t, gold_t in zip(res[backend], gold):
                     if t.requires_grad:
-                        torch.testing.assert_close(t.grad, gold_t.grad, **tol)
+                        torch.testing.assert_close(t.grad, gold_t.grad)
                 print(
                     f"Accuracy check \033[92m✓ succeed\033[0m for {backend} backend on {self.name} kernel"
                 )
@@ -130,9 +121,6 @@ class BenchmarkKernel:
                 print(
                     f"Accuracy check \033[91m✗ failed\033[0m for {backend} backend on {self.name} kernel. Error {e}"
                 )
-                if self.script_args.exit_on_accuracy_failure:
-                    print("Exit right away since --exit-on-accuracy-failure is set")
-                    sys.exit(1)
 
     def benchmark_single_shape(
         self, args, kwargs=None, should_check_accuracy=True, setting: str = ""

benchmarks/dynamo/pr_time_benchmarks/expected_results.csv

@@ -1,8 +1,8 @@
-add_loop_eager,compile_time_instruction_count,3184000000,0.1
+add_loop_eager,compile_time_instruction_count,3070000000,0.1
 
 
 
-add_loop_eager_dynamic,compile_time_instruction_count,4595000000,0.1
+add_loop_eager_dynamic,compile_time_instruction_count,4432000000,0.1
 
 
 
@@ -18,7 +18,7 @@ add_loop_inductor_gpu,compile_time_instruction_count,26800000000,0.1
 
 
 
-basic_modules_ListOfLinears_eager,compile_time_instruction_count,1096000000,0.1
+basic_modules_ListOfLinears_eager,compile_time_instruction_count,1048000000,0.1
 
 
 
@@ -26,7 +26,7 @@ basic_modules_ListOfLinears_inductor,compile_time_instruction_count,15240000000,
 
 
 
-basic_modules_ListOfLinears_inductor_gpu_force_shape_pad,compile_time_instruction_count,17720000000,0.1
+basic_modules_ListOfLinears_inductor_gpu_force_shape_pad,compile_time_instruction_count,17020000000,0.1
 
 
 
@@ -34,11 +34,11 @@ basic_modules_ListOfLinears_inductor_gpu,compile_time_instruction_count,11090000
 
 
 
-update_hint_regression,compile_time_instruction_count,1645000000,0.1
+update_hint_regression,compile_time_instruction_count,1719000000,0.1
 
 
 
-sum_floordiv_regression,compile_time_instruction_count,3813000000,0.1
+sum_floordiv_regression,compile_time_instruction_count,3686995725,0.1
 
 
 
@@ -50,31 +50,31 @@ symint_sum_loop,compile_time_instruction_count,4299000000,0.1
 
 
 
-aotdispatcher_inference_nosubclass_cpu,compile_time_instruction_count,1793000000,0.1
+aotdispatcher_inference_nosubclass_cpu,compile_time_instruction_count,1869000000,0.1
 
 
 
-aotdispatcher_inference_subclass_cpu,compile_time_instruction_count,5120000000,0.1
+aotdispatcher_inference_subclass_cpu,compile_time_instruction_count,5281000000,0.1
 
 
 
-aotdispatcher_partitioner_cpu,compile_time_instruction_count,7936000000,0.1
+aotdispatcher_partitioner_cpu,compile_time_instruction_count,8333000000,0.1
 
 
 
-aotdispatcher_partitioner_cpu2,compile_time_instruction_count,1848000000,0.1
+aotdispatcher_partitioner_cpu2,compile_time_instruction_count,1909000000,0.1
 
 
 
-aotdispatcher_training_nosubclass_cpu,compile_time_instruction_count,3152000000,0.1
+aotdispatcher_training_nosubclass_cpu,compile_time_instruction_count,3442000000,0.1
 
 
 
-aotdispatcher_training_subclass_cpu,compile_time_instruction_count,8301000000,0.1
+aotdispatcher_training_subclass_cpu,compile_time_instruction_count,9239000000,0.1
 
 
 
-mm_loop_inductor_gpu,compile_time_instruction_count,4958000000,0.1
+mm_loop_inductor_gpu,compile_time_instruction_count,4820968837,0.1
 
 
 
@@ -82,8 +82,8 @@ mm_loop_inductor_dynamic_gpu,compile_time_instruction_count,9051000000,0.1
 
 
 
-basic_NestedModule_eager,compile_time_instruction_count,9990000000,0.1
+basic_NestedModule_eager,compile_time_instruction_count,9554000000,0.1
 
 
 
-basic_InlineMod_eager,compile_time_instruction_count,8126000000,0.1
+basic_InlineMod_eager,compile_time_instruction_count,7618000000,0.1

benchmarks/dynamo/torchbench.yaml

@@ -43,7 +43,6 @@ tolerance:
     - doctr_reco_predictor
     - drq
     - phlippe_resnet
-    - pytorch_CycleGAN_and_pix2pix
 
   higher_bf16:
     - doctr_reco_predictor

benchmarks/operator_benchmark/aarch64_expected_ci_operator_benchmark_eager_float32_cpu.csv

@@ -44,101 +44,21 @@ PyTorch,div_,div__M1_N1_K1_cpu_dtype_onetorch.float32_dtype_twotorch.float32,sho
 PyTorch,div_,div__M64_N64_K64_cpu_dtype_onetorch.float32_dtype_twotorch.float32,short,False,59.241161,0.000000
 PyTorch,div_,div__M64_N64_K128_cpu_dtype_onetorch.float32_dtype_twotorch.float32,short,False,59.852816,0.000000
 PyTorch,add,"add_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float32",short,False,57.006677,0.000000
-PyTorch,add,"add_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.bfloat16",short,False,88.167000,0.000000
-PyTorch,add,"add_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float64",short,False,57.519000,0.000000
 PyTorch,sub,"sub_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float32",short,False,55.606088,0.000000
-PyTorch,sub,"sub_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.bfloat16",short,False,86.551000,0.000000
-PyTorch,sub,"sub_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float64",short,False,57.864088,0.000000
 PyTorch,div,"div_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float32",short,False,58.529255,0.000000
-PyTorch,div,"div_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.bfloat16",short,False,71.641000,0.000000
-PyTorch,div,"div_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float64",short,False,83.073000,0.000000
 PyTorch,mul,"mul_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float32",short,False,54.645077,0.000000
-PyTorch,mul,"mul_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.bfloat16",short,False,67.570000,0.000000
-PyTorch,mul,"mul_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.float64",short,False,57.895000,0.000000
 PyTorch,add,add_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,4.397014,0.000000
-PyTorch,add,add_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,7.739000,0.000000
-PyTorch,add,add_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,7.786000,0.000000
-PyTorch,add,add_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,1.911000,0.000000
 PyTorch,add,add_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,59.243500,0.000000
-PyTorch,add,add_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,105.066000,0.000000
-PyTorch,add,add_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,106.076000,0.000000
-PyTorch,add,add_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,47.225000,0.000000
 PyTorch,add,add_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,57.947691,0.000000
-PyTorch,add,add_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,107.291000,0.000000
-PyTorch,add,add_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,107.224000,0.000000
-PyTorch,add,add_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,47.912000,0.000000
 PyTorch,sub,sub_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,1.925851,0.000000
-PyTorch,sub,sub_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,8.0240000,0.000000
-PyTorch,sub,sub_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,8.069000,0.000000
-PyTorch,sub,sub_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,1.938000,0.000000
 PyTorch,sub,sub_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,57.308320,0.000000
-PyTorch,sub,sub_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,107.091000,0.000000
-PyTorch,sub,sub_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,108.710000,0.000000
-PyTorch,sub,sub_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,47.502000,0.000000
 PyTorch,sub,sub_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,57.787743,0.000000
-PyTorch,sub,sub_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,108.863000,0.000000
-PyTorch,sub,sub_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,108.939000,0.000000
-PyTorch,sub,sub_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,47.603000,0.000000
 PyTorch,div,div_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,7.978539,0.000000
-PyTorch,div,div_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,8.741000,0.000000
-PyTorch,div,div_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,8.757000,0.000000
-PyTorch,div,div_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,8.774000,0.000000
 PyTorch,div,div_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,159.754860,0.000000
-PyTorch,div,div_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,165.552000,0.000000
-PyTorch,div,div_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,165.755000,0.000000
-PyTorch,div,div_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,165.714000,0.000000
 PyTorch,div,div_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,165.360235,0.000000
-PyTorch,div,div_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,168.376000,0.000000
-PyTorch,div,div_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,169.604000,0.000000
-PyTorch,div,div_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,168.428000,0.000000
 PyTorch,mul,mul_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,3.928136,0.000000
-PyTorch,mul,mul_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,7.402000,0.000000
-PyTorch,mul,mul_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,7.567000,0.000000
-PyTorch,mul,mul_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,4.020000,0.000000
 PyTorch,mul,mul_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,56.413499,0.000000
-PyTorch,mul,mul_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,104.638000,0.000000
-PyTorch,mul,mul_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,104.335000,0.000000
-PyTorch,mul,mul_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,48.612000,0.000000
 PyTorch,mul,mul_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,55.925090,0.000000
-PyTorch,mul,mul_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,106.110000,0.000000
-PyTorch,mul,mul_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,106.389000,0.000000
-PyTorch,mul,mul_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,48.195000,0.000000
-PyTorch,asr,asr_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,1.989000,0.000000
-PyTorch,asr,asr_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,7.999000,0.000000
-PyTorch,asr,asr_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,7.939000,0.000000
-PyTorch,asr,asr_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,1.980000,0.000000
-PyTorch,asr,asr_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,54.408000,0.000000
-PyTorch,asr,asr_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,105.647000,0.000000
-PyTorch,asr,asr_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,106.476000,0.000000
-PyTorch,asr,asr_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,48.784000,0.000000
-PyTorch,asr,asr_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,55.583000,0.000000
-PyTorch,asr,asr_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,108.083000,0.000000
-PyTorch,asr,asr_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,107.663000,0.000000
-PyTorch,asr,asr_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,48.283000,0.000000
-PyTorch,lsl,lsl_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,1.986000,0.000000
-PyTorch,lsl,lsl_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,7.676000,0.000000
-PyTorch,lsl,lsl_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,7.618000,0.000000
-PyTorch,lsl,lsl_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,1.982000,0.000000
-PyTorch,lsl,lsl_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,54.698000,0.000000
-PyTorch,lsl,lsl_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,105.899000,0.000000
-PyTorch,lsl,lsl_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,106.741000,0.000000
-PyTorch,lsl,lsl_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,51.182000,0.000000
-PyTorch,lsl,lsl_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,55.290000,0.000000
-PyTorch,lsl,lsl_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,107.744000,0.000000
-PyTorch,lsl,lsl_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,107.820000,0.000000
-PyTorch,lsl,lsl_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,51.298000,0.000000
-PyTorch,xor,xor_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,1.988000,0.000000
-PyTorch,xor,xor_M1_N1_K1_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,7.689000,0.000000
-PyTorch,xor,xor_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,7.695000,0.000000
-PyTorch,xor,xor_M1_N1_K1_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,1.978000,0.000000
-PyTorch,xor,xor_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,54.934000,0.000000
-PyTorch,xor,xor_M64_N64_K64_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,105.217000,0.000000
-PyTorch,xor,xor_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,104.215000,0.000000
-PyTorch,xor,xor_M64_N64_K64_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,47.115000,0.000000
-PyTorch,xor,xor_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,55.974000,0.000000
-PyTorch,xor,xor_M64_N64_K128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,106.828000,0.000000
-PyTorch,xor,xor_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,106.879000,0.000000
-PyTorch,xor,xor_M64_N64_K128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,48.197000,0.000000
 PyTorch,logical_and,"logical_and_in_one[64,1,64]_in_two[1,64,1]_cpu_dtypetorch.bool",short,False,78.404254,0.000000
 PyTorch,logical_and,logical_and_M1_N1_K1_cpu_dtype_onetorch.bool_dtype_twotorch.bool,short,False,5.354032,0.000000
 PyTorch,logical_and,logical_and_M64_N64_K64_cpu_dtype_onetorch.bool_dtype_twotorch.bool,short,False,54.072783,0.000000
@@ -151,9 +71,6 @@ PyTorch,baddbmm,baddbmm_B2_M1_N8_K2_cpu_dtypetorch.float32,short,False,6.631313,
 PyTorch,baddbmm,baddbmm_B2_M1_N8_K2_cpu_dtypetorch.bfloat16,short,False,6.476986,0.000000
 PyTorch,baddbmm,baddbmm_B128_M64_N32_K64_cpu_dtypetorch.float32,short,False,266.065131,0.000000
 PyTorch,baddbmm,baddbmm_B128_M64_N32_K64_cpu_dtypetorch.bfloat16,short,False,295.503063,0.000000
-PyTorch,all,all_M1_N1_K1_cpu,short,False,5.773000,0.000000
-PyTorch,all,all_M64_N64_K64_cpu,short,False,89.427000,0.000000
-PyTorch,all,all_M64_N64_K128_cpu,short,False,120.119000,0.000000
 PyTorch,cat,"cat_sizes(1,1,1)_N2_dim0_cpu",short,False,4.301950,0.000000
 PyTorch,cat,"cat_sizes(512,512,2)_N2_dim1_cpu",short,False,99.093415,0.000000
 PyTorch,cat,"cat_sizes(128,1024,2)_N2_dim1_cpu",short,False,96.771578,0.000000

benchmarks/operator_benchmark/benchmark_core.py

@@ -580,9 +580,6 @@ class BenchmarkRunner:
                 else "unknown"
             )
 
-            # Extract operator name from test_name
-            operator_name = test_name.split("_")[0]
-
             # Create the record
             @dataclass
             class BenchmarkInfo:
@@ -596,7 +593,6 @@ class BenchmarkRunner:
                 name: str
                 type: str
                 origins: list[str]
-                extra_info: dict[str, Any]
 
             @dataclass
             class MetricInfo:
@@ -622,14 +618,10 @@ class BenchmarkRunner:
                         "device": device,
                         "arch": device_arch,
                         "use_compile": use_compile,
-                        "operator_name": operator_name,
                     },
                 ),
                 model=ModelInfo(
-                    name=test_name,
-                    type="micro-benchmark",
-                    origins=["pytorch"],
-                    extra_info={"operator_name": operator_name},
+                    name=test_name, type="micro-benchmark", origins=["pytorch"]
                 ),
                 metric=MetricInfo(
                     name="latency",

benchmarks/operator_benchmark/pt/binary_test.py

@@ -25,7 +25,7 @@ binary_configs_broadcast = op_bench.config_list(
     ],
     cross_product_configs={
         "device": ["cpu"],
-        "dtype": [torch.float, torch.bfloat16, torch.float64],
+        "dtype": [torch.float],
     },
     tags=["short"],
 )
@@ -71,8 +71,8 @@ binary_short_configs = op_bench.config_list(
     ],
     cross_product_configs={
         "device": ["cpu", "cuda"],
-        "dtype_one": [torch.int32, torch.uint8],
-        "dtype_two": [torch.int32, torch.uint8],
+        "dtype_one": [torch.int32],
+        "dtype_two": [torch.int32],
     },
     tags=["short"],
 )
@@ -82,8 +82,8 @@ binary_long_configs = op_bench.cross_product_configs(
     N=[32, 64],
     K=[256, 512],
     device=["cpu", "cuda"],
-    dtype_one=[torch.int8, torch.int32, torch.uint8],
-    dtype_two=[torch.int8, torch.int32, torch.uint8],
+    dtype_one=[torch.int8, torch.int32],
+    dtype_two=[torch.int8, torch.int32],
     tags=["long"],
 )
 

buckbuild.bzl

@@ -176,8 +176,8 @@ THIRD_PARTY_LIBS = {
     "omp": ["//xplat/third-party/linker_lib:omp", "//third_party:no-op"],
     "pocketfft": ["//third-party/pocket_fft:pocketfft", "//third_party:pocketfft_header"],
     "psimd": ["//xplat/third-party/psimd:psimd", "//third_party:psimd"],
-    "pthreadpool": ["fbsource//xplat/third-party/pthreadpool:pthreadpool", "//third_party:pthreadpool"],
-    "pthreadpool_header": ["fbsource//xplat/third-party/pthreadpool:pthreadpool_header", "//third_party:pthreadpool_header"],
+    "pthreadpool": ["//xplat/third-party/pthreadpool:pthreadpool", "//third_party:pthreadpool"],
+    "pthreadpool_header": ["//xplat/third-party/pthreadpool:pthreadpool_header", "//third_party:pthreadpool_header"],
     "moodycamel": ["//third-party/moodycamel:moodycamel", "//third_party:moodycamel"],
     "pyyaml": ["//third-party/pypi/pyyaml:pyyaml", "//third_party:pyyaml"],
     "rt": ["//xplat/third-party/linker_lib:rt", "//third_party:rt"],
@@ -1729,10 +1729,8 @@ def define_buck_targets(
             "torch/csrc/jit/backends/backend_debug_info.cpp",
             "torch/csrc/jit/backends/backend_interface.cpp",
         ],
-        compiler_flags = get_pt_compiler_flags() + select({
-            "DEFAULT": [],
-            "ovr_config//os:android": c2_fbandroid_xplat_compiler_flags
-        }),
+        compiler_flags = get_pt_compiler_flags(),
+        fbandroid_compiler_flags = c2_fbandroid_xplat_compiler_flags,
         # @lint-ignore BUCKLINT link_whole
         link_whole = True,
         linker_flags = get_no_as_needed_linker_flag(),
@@ -2025,9 +2023,6 @@ def define_buck_targets(
                 "ovr_config//os:android-x86_64": [
                     "-mssse3",
                 ],
-            }) + select({
-                "DEFAULT": [],
-                "ovr_config//os:android": c2_fbandroid_xplat_compiler_flags,
             }),
             exported_preprocessor_flags = get_aten_preprocessor_flags(),
             exported_deps = [

build_variables.bzl

@@ -855,7 +855,6 @@ libtorch_python_cuda_core_sources = [
     "torch/csrc/cuda/Stream.cpp",
     "torch/csrc/cuda/Graph.cpp",
     "torch/csrc/cuda/MemPool.cpp",
-    "torch/csrc/cuda/GreenContext.cpp",
     "torch/csrc/cuda/shared/cudart.cpp",
     "torch/csrc/cuda/shared/nvtx.cpp",
     "torch/csrc/cuda/utils.cpp",

c10/core/AllocatorConfig.h

@@ -13,17 +13,7 @@
 namespace c10::CachingAllocator {
 
 // "large" allocations may be packed in 20 MiB blocks
-constexpr size_t kLargeBuffer = 20971520;
-// "small" allocations are packed in 2 MiB blocks
-constexpr size_t kSmallBuffer = 2097152;
-// all sizes are rounded to at least 512 bytes
-constexpr size_t kMinBlockSize = 512;
-// largest "small" allocation is 1 MiB
-constexpr size_t kSmallSize = 1048576;
-// allocations between 1 and 10 MiB may use kLargeBuffer
-constexpr size_t kMinLargeAlloc = 10485760;
-// round up large allocations to 2 MiB
-constexpr size_t kRoundLarge = 2097152;
+const size_t kLargeBuffer = 20971520;
 
 // A utility class for tokenizing allocator configuration strings into discrete
 // parts. For example, the config string:

c10/core/Backend.h

@@ -223,7 +223,7 @@ inline DispatchKey backendToDispatchKey(Backend b) {
     case Backend::PrivateUse1:
       return DispatchKey::PrivateUse1;
     default:
-      TORCH_CHECK(false, "Unknown backend");
+      throw std::runtime_error("Unknown backend");
   }
 }
 

c10/core/Scalar.h

@@ -336,7 +336,7 @@ class C10_API Scalar {
     } else if (isBoolean()) {
       return ScalarType::Bool;
     } else {
-      TORCH_CHECK(false, "Unknown scalar type.");
+      throw std::runtime_error("Unknown scalar type.");
     }
   }
 

c10/core/ScalarType.cpp

@@ -228,7 +228,7 @@ std::pair<std::string, std::string> getDtypeNames(c10::ScalarType scalarType) {
     case c10::ScalarType::Float4_e2m1fn_x2:
       return std::make_pair("float4_e2m1fn_x2", "");
     default:
-      TORCH_CHECK(false, "Unimplemented scalar type");
+      throw std::runtime_error("Unimplemented scalar type");
   }
 }
 

c10/core/ScalarType.h

@@ -52,6 +52,19 @@ AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
 AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CONSTANT)
 #undef DEFINE_CONSTANT
 
+inline const char* toString(ScalarType t) {
+#define DEFINE_CASE(_, name) \
+  case ScalarType::name:     \
+    return #name;
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE)
+    default:
+      return "UNKNOWN_SCALAR";
+  }
+#undef DEFINE_CASE
+}
+
 inline size_t elementSize(ScalarType t) {
 #define CASE_ELEMENTSIZE_CASE(ctype, name) \
   case ScalarType::name:                   \
@@ -137,6 +150,22 @@ inline ScalarType toQIntType(ScalarType t) {
   }
 }
 
+inline ScalarType toUnderlying(ScalarType t) {
+  switch (t) {
+    case ScalarType::QUInt8:
+    case ScalarType::QUInt4x2:
+      [[fallthrough]];
+    case ScalarType::QUInt2x4:
+      return ScalarType::Byte;
+    case ScalarType::QInt8:
+      return ScalarType::Char;
+    case ScalarType::QInt32:
+      return ScalarType::Int;
+    default:
+      return t;
+  }
+}
+
 inline bool isSignedType(ScalarType t) {
 #define CASE_ISSIGNED(name)     \
   case ScalarType::name:        \
@@ -279,6 +308,12 @@ inline bool canCast(const ScalarType from, const ScalarType to) {
 
 C10_API ScalarType promoteTypes(ScalarType a, ScalarType b);
 
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::ScalarType scalar_type) {
+  return stream << toString(scalar_type);
+}
+
 // Returns a pair of strings representing the names for each dtype.
 // The returned pair is (name, legacy_name_if_applicable)
 C10_API std::pair<std::string, std::string> getDtypeNames(

c10/core/thread_pool.cpp

@@ -87,7 +87,9 @@ bool ThreadPool::inThreadPool() const {
 }
 
 void ThreadPool::run(std::function<void()> func) {
-  TORCH_CHECK(threads_.size() > 0, "No threads to run a task");
+  if (threads_.empty()) {
+    throw std::runtime_error("No threads to run a task");
+  }
   std::unique_lock<std::mutex> lock(mutex_);
 
   // Set task and signal condition variable so that a worker thread will

c10/cuda/CUDACachingAllocator.cpp

@@ -131,6 +131,15 @@ namespace Native {
  *                  notifyCaptureDestroy.
  */
 
+constexpr size_t kMinBlockSize =
+    512; // all sizes are rounded to at least 512 bytes
+constexpr size_t kSmallSize = 1048576; // largest "small" allocation is 1 MiB
+constexpr size_t kSmallBuffer =
+    2097152; // "small" allocations are packed in 2 MiB blocks
+constexpr size_t kMinLargeAlloc =
+    10485760; // allocations between 1 and 10 MiB may use kLargeBuffer
+constexpr size_t kRoundLarge = 2097152; // round up large allocations to 2 MiB
+
 static char SHAREABLE_HANDLE_VERSION = 2;
 enum ShareableHandleType : char {
   SHAREABLE_CUDA_MALLOC = 'c',
@@ -4469,10 +4478,7 @@ struct BackendStaticInitializer {
         if (key == "backend") {
           tokenizer.checkToken(++i, ":");
           i++; // Move to the value after the colon
-          // break up token to trick hipify
-          if (tokenizer[i] ==
-                  "c"
-                  "udaMallocAsync"
+          if (tokenizer[i] == "cudaMallocAsync"
 #ifdef USE_ROCM
               // convenience for ROCm users to allow either CUDA or HIP env var
               || tokenizer[i] == "hipMallocAsync"

c10/cuda/CUDAMallocAsyncAllocator.cpp

@@ -913,9 +913,7 @@ struct CudaMallocAsyncAllocator : public CUDAAllocator {
     }
   }
   std::string name() override {
-    // break up token to trick hipify
-    return "c"
-           "udaMallocAsync";
+    return "cudaMallocAsync";
   }
   void copy_data(void* dest, const void* src, std::size_t count) const final {
     C10_CUDA_CHECK(

c10/cuda/driver_api.h

@@ -51,17 +51,6 @@
 
 #if defined(CUDA_VERSION) && (CUDA_VERSION >= 12030)
 #define C10_LIBCUDA_DRIVER_API_OPTIONAL(_) \
-  _(cuCtxFromGreenCtx, 12080)              \
-  _(cuCtxGetCurrent, 12080)                \
-  _(cuCtxPopCurrent, 12080)                \
-  _(cuCtxPushCurrent, 12080)               \
-  _(cuCtxSetCurrent, 12080)                \
-  _(cuGreenCtxCreate, 12080)               \
-  _(cuGreenCtxDestroy, 12080)              \
-  _(cuDevSmResourceSplitByCount, 12080)    \
-  _(cuDeviceGet, 12080)                    \
-  _(cuDeviceGetDevResource, 12080)         \
-  _(cuDevResourceGenerateDesc, 12080)      \
   _(cuMulticastAddDevice, 12030)           \
   _(cuMulticastBindMem, 12030)             \
   _(cuMulticastCreate, 12030)              \

c10/metal/utils.h

@@ -328,21 +328,6 @@ struct pair {
   T2 second;
 };
 
-template <typename T>
-static T conj(T a) {
-  return a;
-}
-
-template <>
-half2 conj(half2 a) {
-  return half2(a.x, -a.y);
-}
-
-template <>
-float2 conj(float2 a) {
-  return float2(a.x, -a.y);
-}
-
 #define INSTANTIATE_FOR_ALL_TYPES(MACRO) \
   MACRO(float);                          \
   MACRO(half);                           \

c10/util/C++17.h

@@ -45,7 +45,14 @@ constexpr bool is_pod_v = is_pod<T>::value;
 
 namespace guts {
 
-#if defined(__HIP__)
+#if defined(__cpp_lib_apply) && !defined(__CUDA_ARCH__) && !defined(__HIP__)
+
+template <class F, class Tuple>
+C10_HOST_DEVICE inline constexpr decltype(auto) apply(F&& f, Tuple&& t) {
+  return std::apply(std::forward<F>(f), std::forward<Tuple>(t));
+}
+
+#else
 
 // Implementation from http://en.cppreference.com/w/cpp/utility/apply (but
 // modified)

c10/xpu/XPUCachingAllocator.cpp

@@ -14,6 +14,16 @@ using namespace c10::CachingDeviceAllocator;
 
 // newly allocated memory with 512-byte alignment.
 constexpr size_t kDeviceAlignment = 512;
+// all sizes are rounded to at least 512 bytes
+constexpr size_t kMinBlockSize = 512;
+// largest "small" allocation is 1 MiB
+constexpr size_t kSmallSize = 1048576;
+// "small" allocations are packed in 2 MiB blocks
+constexpr size_t kSmallBuffer = 2097152;
+// allocations between 1 and 10 MiB may use kLargeBuffer
+constexpr size_t kMinLargeAlloc = 10485760;
+// round up large allocations to 2 MiB
+constexpr size_t kRoundLarge = 2097152;
 
 namespace {
 using stream_set = ska::flat_hash_set<xpu::XPUStream>;