55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
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import pytest
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import torch
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import vllm._custom_ops as ops
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from tests.kernels.quant_utils import (ref_dynamic_per_tensor_fp8_quant,
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ref_dynamic_per_token_quant)
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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HIDDEN_SIZES = [1, 2, 3, 4, 16, 67, 768, 2048, 5120, 5137, 8192,
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8193] # Arbitrary values for testing
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HIDDEN_SIZES += list(range(1024, 1033)) # vectorized conversion edge cases
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NUM_TOKENS = [1, 7, 83, 4096] # Arbitrary values for testing
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SEEDS = [0]
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@torch.inference_mode()
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def test_dynamic_per_token_fp8_quant(num_tokens: int, hidden_size: int,
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dtype: torch.dtype, seed: int) -> None:
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torch.random.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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x = torch.rand(num_tokens, hidden_size, dtype=dtype,
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device="cuda") + 1e-6 # avoid nans
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ref_out, ref_scales = ref_dynamic_per_token_quant(x, torch.float8_e4m3fn)
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ops_out, ops_scales = ops.dynamic_per_token_scaled_fp8_quant(x)
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assert torch.allclose(ref_scales, ops_scales)
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assert torch.allclose(ref_out.to(dtype=torch.float32),
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ops_out.to(dtype=torch.float32))
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@torch.inference_mode()
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def test_dynamic_per_tensor_fp8_quant(num_tokens: int, hidden_size: int,
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dtype: torch.dtype, seed: int) -> None:
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torch.random.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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x = torch.rand(num_tokens, hidden_size, dtype=dtype, device="cuda")
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ref_out, ref_scale = ref_dynamic_per_tensor_fp8_quant(x)
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ops_out, ops_scale = ops.scaled_fp8_quant(x)
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assert torch.allclose(ref_scale, ops_scale)
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assert torch.allclose(ref_out.to(dtype=torch.float32),
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ops_out.to(dtype=torch.float32))
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