Move scaling logic to input generation #338
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Summary: Pull Request resolved: meta-pytorch#338 Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Pull Request resolved: meta-pytorch#338 Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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This pull request was exported from Phabricator. Differential Revision: D80571223 |
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Summary: Pull Request resolved: meta-pytorch#338 Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Reviewed By: NikhilAPatel Differential Revision: D80571223
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Test Plan: Imported from GitHub, without a `Test Plan:` line. Rollback Plan: Differential Revision: D80571223 Pulled By: jananisriram
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Test Plan: Imported from GitHub, without a `Test Plan:` line. Rollback Plan: Differential Revision: D80571223 Pulled By: jananisriram
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Test Plan: Imported from GitHub, without a `Test Plan:` line. Rollback Plan: Differential Revision: D80571223 Pulled By: jananisriram
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Test Plan: Imported from GitHub, without a `Test Plan:` line. Rollback Plan: Differential Revision: D80571223 Pulled By: jananisriram
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Summary: Move scaling logic for FP8 benchmarks to `get_input_iter()`. This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`). This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types. Pull Request resolved: meta-pytorch#338 Test Plan: Imported from GitHub, without a `Test Plan:` line. Rollback Plan: Reviewed By: xuzhao9 Differential Revision: D80571223 Pulled By: jananisriram
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Summary:
Move scaling logic for FP8 benchmarks to
get_input_iter().This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (
bfloat16,float16), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (float8_e4m3fn).This diff also circumvents performing unsupported operations, like
torch.maxandtorch.abs, on low-precision data types.Reviewed By: NikhilAPatel
Differential Revision: D80571223