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/

fp8-fbgemm

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kernels-bot commited on 17 days ago

Commit

67ee4ad

verified ·

1 Parent(s): 02170a0

Uploaded using `kernel-builder`.

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Files changed (6) hide show

README.md +10 -3
build/torch-cuda/__init__.py +1 -3
build/torch-cuda/_ops.py +3 -6
build/torch-cuda/metadata.json +8 -7
build/torch-cuda/metadata.json.sigstore +1 -1
build/torch-cuda/symbols.json +43 -0

README.md CHANGED Viewed

@@ -7,11 +7,18 @@ This is the repository card of kernels-community/fp8-fbgemm that has been pushed
 ## How to use
-Usage example not available.
-## Available functions
-Function list not available.
 ## Benchmarks

 ## How to use
+```python
+# make sure `kernels` is installed: `pip install -U kernels`
+from kernels import get_kernel
+kernel_module = get_kernel("kernels-community/fp8-fbgemm", version=1)
+quantize_fp8_per_row = kernel_module.quantize_fp8_per_row
+quantize_fp8_per_row(...)
+```
+## Available functions
+- `quantize_fp8_per_row`
 ## Benchmarks

build/torch-cuda/__init__.py CHANGED Viewed

@@ -1,5 +1,3 @@
 from .quantizer import quantize_fp8_per_row
-__all__ = [
-    quantize_fp8_per_row
-]


1	from .quantizer import quantize_fp8_per_row
2
3	+ __all__ = ["quantize_fp8_per_row"]

build/torch-cuda/_ops.py CHANGED Viewed

@@ -4,11 +4,8 @@ def get_backend() -> str:
     """Detect the backend by inspecting torch."""
     import torch
-    if hasattr(torch.backends, "tpu"):
-        # torch_tpu sets torch.backends.tpu when it is imported (via
-        # torch's device-backend autoload), regardless of whether TPU
-        # hardware is present — analogous to torch.version.cuda being
-        # set on CUDA builds without a GPU.
         return "tpu"
     elif hasattr(torch, "neuron"):
         # Needs to be sorted before specific Torch builds, since Neuron
@@ -28,7 +25,7 @@ def get_backend() -> str:
 def _find_ops_name() -> str:
     kernel_name = "fp8_fbgemm"
-    unique_id = "89d4054"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

     """Detect the backend by inspecting torch."""
     import torch
+    acc = torch.accelerator.current_accelerator() if hasattr(torch, "accelerator") else None
+    if acc is not None and acc.type == "tpu":
         return "tpu"
     elif hasattr(torch, "neuron"):
         # Needs to be sorted before specific Torch builds, since Neuron
 def _find_ops_name() -> str:
     kernel_name = "fp8_fbgemm"
+    unique_id = "59ac2f9"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

build/torch-cuda/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "fp8-fbgemm",
-  "id": "_fp8_fbgemm_cuda_89d4054",
   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "BSD-3-Clause",
@@ -12,19 +12,20 @@
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     "algorithm": "sha256",
     "files": {
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-      "_ops.py": "HTRfprwtXSXFucEgp4BvQm4iTRyAg9bSb0GASXf0JiU=",
-      "quantizer.py": "aRkTfkkpdnvZmSZUr1wolKa1hYPVpglJQJUGA4fGpIk="
     }
   },
   "provenance": {
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-      "commit": "a7f0afdb29a6a3372b1d47180cc0c182454c5e3b",
       "dirty": false
     },
     "kernel": {
-      "commit": "89d4054f3755039d5474cfed03f06de026ee2ce7",
       "dirty": false
     }
   }

 {
   "name": "fp8-fbgemm",
+  "id": "_fp8_fbgemm_cuda_59ac2f9",
   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "BSD-3-Clause",
   "digest": {
     "algorithm": "sha256",
     "files": {
+      "__init__.py": "cnbDTqOVtB0KgeyLHSSwZaOK2Hi9tpDszqyFNG8gabM=",
+      "_ops.py": "gTXqSByhEjr/ahPbBKId36EcM6HAOSHQ1g2FiiZSVcA=",
+      "quantizer.py": "aRkTfkkpdnvZmSZUr1wolKa1hYPVpglJQJUGA4fGpIk=",
+      "symbols.json": "IscbvuHF0yntpX9YREwxnFVX9EUD3wqc0kmiZSRqONM="
     }
   },
   "provenance": {
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+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
       "dirty": false
     },
     "kernel": {
+      "commit": "59ac2f9edd19416fbeeb88cb00a2b521c412280a",
       "dirty": false
     }
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build/torch-cuda/symbols.json ADDED Viewed

	@@ -0,0 +1,43 @@

+{
+  "schema_version": 1,
+  "module": "_fp8_fbgemm_cuda_59ac2f9",
+  "functions": [
+    {
+      "name": "quantize_fp8_per_row",
+      "qualname": "quantize_fp8_per_row",
+      "module": "_fp8_fbgemm_cuda_59ac2f9.quantizer",
+      "kind": "function",
+      "doc": "Call the triton quantize fp8 row kernel to quantize a tensor to fp8 with row-wise scalings.\n\nArgs:\n    a (Tensor): higher precision input tensor of 4 dimension.\n    scale_ub (Tensor): Maximum allowed value for scale.\n    zero_start_index_M (Tensor): Indicates number of nonzero elements in each row.\n    align_rows_to: Pad rows to align to this value. Useful for downstream kernels accepting specific sizes (e.g., multiple of 16)\nReturns:\n    torch.Tensor: fp8 scaled tensor.\n    torch.Tensor: reciprocal scale tensor per row.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "a",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "scale_ub",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "None",
+            "annotation": "torch.Tensor | None"
+          },
+          {
+            "name": "zero_start_index_M",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "None",
+            "annotation": "torch.Tensor | None"
+          },
+          {
+            "name": "align_rows_to",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "None",
+            "annotation": "int | None"
+          }
+        ],
+        "return_annotation": "tuple[torch.Tensor, torch.Tensor]"
+      }
+    }
+  ],
+  "layers": []
+}