[weight checker] refactor: add precision branch; allow ULP quant err; used chunked compare (#28974)
This commit is contained in:
Yueming Yuan
@@ -14,27 +14,33 @@
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"""Unit tests for sglang/srt/utils/weight_checker.py."""
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import unittest
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from typing import Iterable, List, Tuple
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from typing import Iterable, List
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from unittest.mock import patch
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import torch
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from torch import nn
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from sglang.srt.layers.quantization.fp8_utils import (
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block_quant_dequant,
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quant_weight_ue8m0,
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transform_scale_ue8m0,
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)
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from sglang.srt.utils.weight_checker import (
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CheckEntry,
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ChecksumInfo,
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ParallelismInfo,
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QuantizedWeight,
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WeightChecker,
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_build_check_entries,
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_build_quantized_set,
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_check_tensors,
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_hash_tensor,
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_is_non_persistent_buffer_name,
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_postprocess_tensors,
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_random_like,
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)
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from sglang.srt.utils.weight_checker_comparator import (
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Fp8BlockComparable,
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RawComparable,
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)
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.test_utils import CustomTestCase
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@@ -46,31 +52,39 @@ register_cuda_ci(est_time=30, stage="base-b", runner_config="1-gpu-small")
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# ---------------------------------------------------------------------------
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Triple = Tuple[str, bool, torch.Tensor]
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def _assert_triples_close(actual: Iterable[Triple], expected: Iterable[Triple]) -> None:
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"""Compare two streams of (name, should_compare, tensor); element-wise tensor close."""
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actual_list: List[Triple] = list(actual)
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expected_list: List[Triple] = list(expected)
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def _assert_entries_close(
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actual: Iterable[CheckEntry], expected: Iterable[CheckEntry]
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) -> None:
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"""Compare two streams of (name, should_compare, ComparableWeight)."""
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actual_list: List[CheckEntry] = list(actual)
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expected_list: List[CheckEntry] = list(expected)
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assert len(actual_list) == len(
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expected_list
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), f"length mismatch: actual={len(actual_list)} expected={len(expected_list)}"
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for i, ((a_name, a_flag, a_t), (e_name, e_flag, e_t)) in enumerate(
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for i, ((a_name, a_flag, a_ref), (e_name, e_flag, e_ref)) in enumerate(
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zip(actual_list, expected_list)
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):
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assert a_name == e_name, f"[{i}] name: {a_name!r} != {e_name!r}"
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assert a_flag == e_flag, f"[{i}] should_compare: {a_flag} != {e_flag}"
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torch.testing.assert_close(
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a_t, e_t, msg=f"[{i}] tensor mismatch for {a_name!r}"
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)
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assert type(a_ref) is type(e_ref), f"[{i}] kind mismatch for {a_name!r}"
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if isinstance(a_ref, Fp8BlockComparable):
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torch.testing.assert_close(
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a_ref.w_q, e_ref.w_q, msg=f"[{i}] w_q {a_name!r}"
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)
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torch.testing.assert_close(
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a_ref.w_s, e_ref.w_s, msg=f"[{i}] w_s {a_name!r}"
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)
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else:
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torch.testing.assert_close(
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a_ref.tensor, e_ref.tensor, msg=f"[{i}] tensor {a_name!r}"
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)
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def _build_fp8_quant_pair(device: str = "cuda"):
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"""Construct a real fp8-quantized weight + matching fp32 + ue8m0-packed scales.
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Returns (qweight, sf_fp32, sf_packed_int32) so callers can pick which scale dtype
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drives the _postprocess_tensors branch under test.
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drives the _build_check_entries branch under test.
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"""
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weight_bf16 = torch.randn((256, 128), dtype=torch.bfloat16, device=device)
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block_size = [128, 128]
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@@ -87,7 +101,7 @@ def _build_fp8_quant_pair(device: str = "cuda"):
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class _TinyModel(nn.Module):
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"""Mimics the buffer naming patterns _reset_tensors / _postprocess_tensors care about."""
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"""Mimics the buffer naming patterns _reset_tensors / _build_check_entries care about."""
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def __init__(self):
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super().__init__()
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@@ -173,9 +187,19 @@ class TestRandomLike(CustomTestCase):
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_random_like(t)
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torch.testing.assert_close(t, before)
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def test_floating_point_chunked_generation(self):
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with patch("sglang.srt.utils.weight_checker.CHUNK_NUMEL", 8):
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out = _random_like(torch.zeros(64, dtype=torch.bfloat16))
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self.assertEqual(out.dtype, torch.bfloat16)
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self.assertEqual(out.shape, (64,))
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self.assertGreater(out.unique().numel(), 8)
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self.assertGreaterEqual(out.float().min().item(), 0.0)
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# bf16 rounding may carry values just below 1.0 up to exactly 1.0
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self.assertLessEqual(out.float().max().item(), 1.0)
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# ---------------------------------------------------------------------------
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# _postprocess_tensors
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# _build_check_entries
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# ---------------------------------------------------------------------------
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@@ -187,15 +211,17 @@ class TestPostprocessTensors(CustomTestCase):
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a = torch.randn(4)
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b = torch.randn(4)
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raw = {"a.weight": a, "b.bias": b}
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_assert_triples_close(
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_postprocess_tensors(raw, set()),
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[("a.weight", True, a), ("b.bias", True, b)],
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_assert_entries_close(
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_build_check_entries(raw, set()),
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[("a.weight", True, RawComparable(a)), ("b.bias", True, RawComparable(b))],
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)
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def test_weight_alone_without_scale_inv_does_not_trigger_dequant(self):
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w = torch.randn(4)
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raw = {"x.weight": w}
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_assert_triples_close(_postprocess_tensors(raw, set()), [("x.weight", True, w)])
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_assert_entries_close(
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_build_check_entries(raw, set()), [("x.weight", True, RawComparable(w))]
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)
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# --- non-persistent buffer skip ---
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@@ -206,70 +232,49 @@ class TestPostprocessTensors(CustomTestCase):
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"model.rotary_emb.cos_sin_cache": cache,
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"model.layers.0.weight": plain,
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}
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_assert_triples_close(
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_postprocess_tensors(raw, set()),
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_assert_entries_close(
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_build_check_entries(raw, set()),
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[
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("model.rotary_emb.cos_sin_cache", False, cache),
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("model.layers.0.weight", True, plain),
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("model.rotary_emb.cos_sin_cache", False, RawComparable(cache)),
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("model.layers.0.weight", True, RawComparable(plain)),
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],
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)
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def test_skips_inv_freq_substring(self):
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t = torch.randn(4)
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_assert_triples_close(
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_postprocess_tensors({"model.rotary_emb.inv_freq": t}, set()),
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[("model.rotary_emb.inv_freq", False, t)],
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_assert_entries_close(
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_build_check_entries({"model.rotary_emb.inv_freq": t}, set()),
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[("model.rotary_emb.inv_freq", False, RawComparable(t))],
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)
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def test_skips_weight_fp32_substring(self):
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t = torch.randn(4)
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_assert_triples_close(
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_postprocess_tensors({"model.layers.0.mlp.gate._weight_fp32": t}, set()),
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[("model.layers.0.mlp.gate._weight_fp32", False, t)],
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_assert_entries_close(
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_build_check_entries({"model.layers.0.mlp.gate._weight_fp32": t}, set()),
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[("model.layers.0.mlp.gate._weight_fp32", False, RawComparable(t))],
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)
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def test_substring_match_not_endswith(self):
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# Pattern can appear anywhere in the name, not just at the end.
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t = torch.randn(4)
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_assert_triples_close(
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_postprocess_tensors({"weird.cos_sin_cache.foo.bar": t}, set()),
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[("weird.cos_sin_cache.foo.bar", False, t)],
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_assert_entries_close(
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_build_check_entries({"weird.cos_sin_cache.foo.bar": t}, set()),
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[("weird.cos_sin_cache.foo.bar", False, RawComparable(t))],
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)
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# --- fp8 quant pair (real dequant on real fp8 tensors) ---
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def test_fp8_quant_pair_with_int32_scale_dequants_via_ue8m0(self):
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def test_fp8_quant_pair_yields_lazy_pair(self):
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qweight, sf_fp32, sf_packed_int32 = _build_fp8_quant_pair()
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raw = {"x.weight": qweight, "x.weight_scale_inv": sf_packed_int32}
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# Reference: ue8m0 path inside _postprocess_tensors should eventually
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# call block_quant_dequant with the unpacked fp32 scale.
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expected_dequant = block_quant_dequant(
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qweight, sf_fp32, block_size=[128, 128], dtype=torch.bfloat16
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)
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_assert_triples_close(
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_postprocess_tensors(raw, set()),
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[
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("x.weight", True, expected_dequant),
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("x.weight", False, qweight),
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("x.weight_scale_inv", False, sf_packed_int32),
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],
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)
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def test_fp8_quant_pair_with_fp32_scale_dequants_directly(self):
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qweight, sf_fp32, _ = _build_fp8_quant_pair()
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raw = {"x.weight": qweight, "x.weight_scale_inv": sf_fp32}
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expected_dequant = block_quant_dequant(
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qweight, sf_fp32, block_size=[128, 128], dtype=torch.bfloat16
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)
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_assert_triples_close(
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_postprocess_tensors(raw, set()),
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[
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("x.weight", True, expected_dequant),
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("x.weight", False, qweight),
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("x.weight_scale_inv", False, sf_fp32),
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],
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ref = Fp8BlockComparable(qweight, sf_packed_int32)
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quantized_set = {
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"x.weight": QuantizedWeight(Fp8BlockComparable, "x.weight_scale_inv")
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}
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_assert_entries_close(
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_build_check_entries(raw, set(), quantized_set),
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[("x.weight", True, ref)],
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)
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def test_fp8_quant_pair_yield_order_alongside_other_entries(self):
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@@ -280,17 +285,16 @@ class TestPostprocessTensors(CustomTestCase):
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"x.weight_scale_inv": sf_fp32,
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"y.bias": bias,
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}
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expected_dequant = block_quant_dequant(
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qweight, sf_fp32, block_size=[128, 128], dtype=torch.bfloat16
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)
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# All dequant entries come first, then a raw pass over every key.
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_assert_triples_close(
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_postprocess_tensors(raw, set()),
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# scale_inv is consumed by its weight's comparable; y.bias stays raw.
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ref = Fp8BlockComparable(qweight, sf_fp32)
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quantized_set = {
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"x.weight": QuantizedWeight(Fp8BlockComparable, "x.weight_scale_inv")
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}
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_assert_entries_close(
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_build_check_entries(raw, set(), quantized_set),
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[
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("x.weight", True, expected_dequant),
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("x.weight", False, qweight),
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("x.weight_scale_inv", False, sf_fp32),
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("y.bias", True, bias),
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("x.weight", True, ref),
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("y.bias", True, RawComparable(bias)),
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],
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)
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@@ -298,9 +302,9 @@ class TestPostprocessTensors(CustomTestCase):
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# Without the matching `.weight`, no quant pair forms; the scale_inv flows
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# through as a normal entry with should_compare=True.
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s = torch.zeros(1, 1, dtype=torch.int32)
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_assert_triples_close(
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_postprocess_tensors({"x.weight_scale_inv": s}, set()),
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[("x.weight_scale_inv", True, s)],
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_assert_entries_close(
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_build_check_entries({"x.weight_scale_inv": s}, set()),
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[("x.weight_scale_inv", True, RawComparable(s))],
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)
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@@ -313,13 +317,19 @@ class TestCheckTensors(CustomTestCase):
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def test_passes_when_all_equal(self):
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t = torch.ones(2, 2)
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expect = [("a", True, t.clone()), ("b", True, t.clone())]
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actual = [("a", True, t.clone()), ("b", True, t.clone())]
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expect = [
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("a", True, RawComparable(t.clone())),
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("b", True, RawComparable(t.clone())),
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]
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actual = [
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("a", True, RawComparable(t.clone())),
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("b", True, RawComparable(t.clone())),
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]
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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def test_raises_when_should_compare_true_and_diff(self):
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expect = [("a", True, torch.ones(2, 2))]
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actual = [("a", True, torch.zeros(2, 2))]
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expect = [("a", True, RawComparable(torch.ones(2, 2)))]
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actual = [("a", True, RawComparable(torch.zeros(2, 2)))]
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with self.assertRaises(Exception) as ctx:
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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msg = str(ctx.exception)
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@@ -328,30 +338,141 @@ class TestCheckTensors(CustomTestCase):
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def test_passes_when_should_compare_false_even_if_diff(self):
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# should_compare=False -> diff is logged, not raised.
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expect = [("a", False, torch.ones(2, 2))]
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actual = [("a", False, torch.zeros(2, 2))]
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expect = [("a", False, RawComparable(torch.ones(2, 2)))]
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actual = [("a", False, RawComparable(torch.zeros(2, 2)))]
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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def test_asserts_on_name_mismatch(self):
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expect = [("a", True, torch.ones(2, 2))]
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actual = [("b", True, torch.ones(2, 2))]
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expect = [("a", True, RawComparable(torch.ones(2, 2)))]
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actual = [("b", True, RawComparable(torch.ones(2, 2)))]
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with self.assertRaises(AssertionError):
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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def test_asserts_on_should_compare_mismatch(self):
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expect = [("a", True, torch.ones(2, 2))]
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actual = [("a", False, torch.ones(2, 2))]
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expect = [("a", True, RawComparable(torch.ones(2, 2)))]
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actual = [("a", False, RawComparable(torch.ones(2, 2)))]
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with self.assertRaises(AssertionError):
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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def test_chunked_raw_stats_match_unchunked(self):
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expect = [("a", True, RawComparable(torch.zeros(10)))]
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actual = [("a", True, RawComparable(torch.arange(10.0)))]
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with patch("sglang.srt.utils.weight_checker_comparator.CHUNK_NUMEL", 3):
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with self.assertRaises(Exception) as ctx:
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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self.assertIn("max_abs_err=9.0", str(ctx.exception))
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self.assertIn("mean_abs_err=4.5", str(ctx.exception))
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def test_zip_strict_raises_on_length_mismatch(self):
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t = torch.ones(2, 2)
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expect = [("a", True, t.clone()), ("b", True, t.clone())]
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actual = [("a", True, t.clone())]
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expect = [
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("a", True, RawComparable(t.clone())),
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("b", True, RawComparable(t.clone())),
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]
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actual = [("a", True, RawComparable(t.clone()))]
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with self.assertRaises(ValueError):
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_check_tensors(expect_tensors=expect, actual_tensors=actual)
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# ---------------------------------------------------------------------------
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# _check_tensors + allow_quant_error
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# ---------------------------------------------------------------------------
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def _quantize_block_fp8(weight: torch.Tensor, scale_margin: float):
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"""Blockwise 128x128 fp8 quantization with a tweakable scale convention."""
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n, k = weight.shape
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blocks = weight.float().view(n // 128, 128, k // 128, 128).permute(0, 2, 1, 3)
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scale = blocks.abs().amax(dim=(-1, -2)) / 448.0 * scale_margin
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q = (blocks / scale[:, :, None, None]).to(torch.float8_e4m3fn)
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q = q.permute(0, 2, 1, 3).reshape(n, k)
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return q, scale
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class TestCheckTensorsAllowQuantError(CustomTestCase):
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def setUp(self):
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torch.manual_seed(0)
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weight = torch.randn(256, 256, device="cuda") * 0.02
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self.e_raw = self._as_raw(*_quantize_block_fp8(weight, 1.0))
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self.a_raw = self._as_raw(*_quantize_block_fp8(weight, 1.001))
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@staticmethod
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def _as_raw(q, s):
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return {"x.weight": q, "x.weight_scale_inv": s}
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def _check(self, expect_raw, actual_raw, **kwargs):
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quantized_set = {
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"x.weight": QuantizedWeight(Fp8BlockComparable, "x.weight_scale_inv")
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}
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_check_tensors(
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expect_tensors=_build_check_entries(expect_raw, set(), quantized_set),
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actual_tensors=_build_check_entries(actual_raw, set(), quantized_set),
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**kwargs,
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)
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def test_within_tolerance_passes_with_flag(self):
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self._check(self.e_raw, self.a_raw, allow_quant_error=True)
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def test_within_tolerance_fails_without_flag(self):
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with self.assertRaises(Exception) as ctx:
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self._check(self.e_raw, self.a_raw)
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self.assertIn("name=x.weight", str(ctx.exception))
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def test_exceeding_tolerance_fails_with_flag(self):
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bad_q = self.a_raw["x.weight"].clone().view(torch.uint8)
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bad_q[::50] += 8
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bad = self._as_raw(
|
||||
bad_q.view(torch.float8_e4m3fn), self.a_raw["x.weight_scale_inv"]
|
||||
)
|
||||
with self.assertRaises(Exception) as ctx:
|
||||
self._check(self.e_raw, bad, allow_quant_error=True)
|
||||
self.assertIn("num_exceed", str(ctx.exception))
|
||||
|
||||
def test_flag_does_not_relax_non_quant_tensors(self):
|
||||
expect = [("a", True, RawComparable(torch.ones(2, 2)))]
|
||||
actual = [("a", True, RawComparable(torch.ones(2, 2) + 0.5))]
|
||||
with self.assertRaises(Exception):
|
||||
_check_tensors(
|
||||
expect_tensors=expect, actual_tensors=actual, allow_quant_error=True
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# _build_quantized_set
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestBuildQuantizedSet(CustomTestCase):
|
||||
|
||||
def test_fp8_block_module_pairs_weight_and_scale(self):
|
||||
from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
|
||||
|
||||
method = Fp8LinearMethod.__new__(Fp8LinearMethod)
|
||||
method.block_quant = True
|
||||
method.use_mxfp8 = False
|
||||
model = nn.Module()
|
||||
model.proj = nn.Module()
|
||||
model.proj.quant_method = method
|
||||
model.proj.register_parameter(
|
||||
"weight", nn.Parameter(torch.zeros(4, 4), requires_grad=False)
|
||||
)
|
||||
model.proj.register_parameter(
|
||||
"weight_scale_inv", nn.Parameter(torch.zeros(1, 1), requires_grad=False)
|
||||
)
|
||||
self.assertEqual(
|
||||
_build_quantized_set(model),
|
||||
{
|
||||
"proj.weight": QuantizedWeight(
|
||||
Fp8BlockComparable, "proj.weight_scale_inv"
|
||||
)
|
||||
},
|
||||
)
|
||||
|
||||
def test_no_quant_method_yields_empty_plan(self):
|
||||
self.assertEqual(_build_quantized_set(_TinyModel()), {})
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# WeightChecker class
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@@ -0,0 +1,206 @@
|
||||
# Copyright 2023-2024 SGLang Team
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""Unit tests for sglang/srt/utils/weight_checker_comparator.py."""
|
||||
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
import torch
|
||||
|
||||
from sglang.srt.layers.quantization.fp8_utils import (
|
||||
quant_weight_ue8m0,
|
||||
transform_scale_ue8m0,
|
||||
)
|
||||
from sglang.srt.utils.weight_checker_comparator import (
|
||||
ComparableWeight,
|
||||
Fp8BlockComparable,
|
||||
compare_weights,
|
||||
select_comparable_weight,
|
||||
)
|
||||
from sglang.test.ci.ci_register import register_cuda_ci
|
||||
from sglang.test.test_utils import CustomTestCase
|
||||
|
||||
register_cuda_ci(est_time=15, stage="base-b", runner_config="1-gpu-small")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _compare_quant_pair(expect_q, expect_s, actual_q, actual_s):
|
||||
return compare_weights(
|
||||
Fp8BlockComparable(expect_q, expect_s), Fp8BlockComparable(actual_q, actual_s)
|
||||
)
|
||||
|
||||
|
||||
def _build_fp8_quant_pair(device: str = "cuda"):
|
||||
"""Returns (qweight, fp32 scale, ue8m0-packed int32 scale) for one random weight."""
|
||||
weight_bf16 = torch.randn((256, 128), dtype=torch.bfloat16, device=device)
|
||||
block_size = [128, 128]
|
||||
qweight, sf_fp32 = quant_weight_ue8m0(
|
||||
weight_dequant=weight_bf16, weight_block_size=block_size
|
||||
)
|
||||
sf_packed_int32 = transform_scale_ue8m0(sf_fp32, mn=qweight.shape[-2])
|
||||
return qweight, sf_fp32, sf_packed_int32
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# _quant_ulp
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestQuantUlp(CustomTestCase):
|
||||
|
||||
def test_matches_bruteforce_spacing_for_fp8(self):
|
||||
for dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
|
||||
all_bits = torch.arange(256, dtype=torch.uint8).view(dtype)
|
||||
vals = all_bits.to(torch.float32)
|
||||
magnitudes = torch.unique(vals[torch.isfinite(vals) & (vals >= 0)])
|
||||
# Brute-force ULP: spacing to the next representable magnitude
|
||||
# (the largest magnitude reuses the spacing below it).
|
||||
spacing = magnitudes[1:] - magnitudes[:-1]
|
||||
expected = torch.cat([spacing, spacing[-1:]])
|
||||
got = ComparableWeight._quant_ulp(magnitudes.to(dtype))
|
||||
torch.testing.assert_close(got, expected, rtol=0, atol=0)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# compare_weights
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestCompareQuantPair(CustomTestCase):
|
||||
"""Chunked dequantized-space comparison of block-quantized pairs."""
|
||||
|
||||
@staticmethod
|
||||
def _quantize(weight: torch.Tensor, scale_margin: float):
|
||||
"""Blockwise 128x128 fp8 quantization with a tweakable scale convention."""
|
||||
n, k = weight.shape
|
||||
blocks = weight.float().view(n // 128, 128, k // 128, 128).permute(0, 2, 1, 3)
|
||||
scale = blocks.abs().amax(dim=(-1, -2)) / 448.0 * scale_margin
|
||||
q = (blocks / scale[:, :, None, None]).to(torch.float8_e4m3fn)
|
||||
q = q.permute(0, 2, 1, 3).reshape(n, k)
|
||||
return q, scale
|
||||
|
||||
def setUp(self):
|
||||
torch.manual_seed(0)
|
||||
self.weight = torch.randn(256, 256, device="cuda") * 0.02
|
||||
self.e_q, self.e_s = self._quantize(self.weight, 1.0)
|
||||
self.a_q, self.a_s = self._quantize(self.weight, 1.001)
|
||||
|
||||
def test_identical_pair_is_equal(self):
|
||||
equal, max_err, mean_err, num_exceed = _compare_quant_pair(
|
||||
self.e_q, self.e_s, self.e_q.clone(), self.e_s.clone()
|
||||
)
|
||||
self.assertTrue(equal)
|
||||
self.assertEqual((max_err, mean_err, num_exceed), (0.0, 0.0, 0))
|
||||
|
||||
def test_ue8m0_packed_scale_equals_unpacked_scale(self):
|
||||
qweight, sf_fp32, sf_packed_int32 = _build_fp8_quant_pair()
|
||||
equal, *_ = _compare_quant_pair(qweight, sf_packed_int32, qweight, sf_fp32)
|
||||
self.assertTrue(equal)
|
||||
|
||||
def test_two_quantizations_stay_within_ulp_tolerance(self):
|
||||
equal, max_err, mean_err, num_exceed = _compare_quant_pair(
|
||||
self.e_q, self.e_s, self.a_q, self.a_s
|
||||
)
|
||||
self.assertFalse(equal)
|
||||
self.assertGreater(max_err, 0.0)
|
||||
self.assertEqual(num_exceed, 0)
|
||||
|
||||
def test_corruption_and_fp8_nan_exceed_tolerance(self):
|
||||
bad_q = self.a_q.clone().view(torch.uint8)
|
||||
bad_q[::50] += 8 # jumps a full binade; some bytes become fp8 NaN
|
||||
equal, max_err, mean_err, num_exceed = _compare_quant_pair(
|
||||
self.e_q, self.e_s, bad_q.view(torch.float8_e4m3fn), self.a_s
|
||||
)
|
||||
self.assertFalse(equal)
|
||||
self.assertGreater(num_exceed, 0)
|
||||
|
||||
def test_chunked_result_matches_unchunked(self):
|
||||
reference = _compare_quant_pair(self.e_q, self.e_s, self.a_q, self.a_s)
|
||||
with patch("sglang.srt.utils.weight_checker_comparator.CHUNK_NUMEL", 128 * 128):
|
||||
chunked = _compare_quant_pair(self.e_q, self.e_s, self.a_q, self.a_s)
|
||||
self.assertEqual(chunked, reference)
|
||||
|
||||
@staticmethod
|
||||
def _quantize_partial(weight: torch.Tensor, scale_margin: float):
|
||||
"""128x128 block quant where the last block per dim may be partial."""
|
||||
n, k = weight.shape
|
||||
s_n, s_k = -(-n // 128), -(-k // 128)
|
||||
q = torch.empty(n, k, dtype=torch.float8_e4m3fn, device=weight.device)
|
||||
scale = torch.empty(s_n, s_k, device=weight.device)
|
||||
for i in range(s_n):
|
||||
for j in range(s_k):
|
||||
blk = weight[i * 128 : (i + 1) * 128, j * 128 : (j + 1) * 128].float()
|
||||
s = blk.abs().amax() / 448.0 * scale_margin
|
||||
s = s if s > 0 else weight.new_ones(())
|
||||
scale[i, j] = s
|
||||
q[i * 128 : (i + 1) * 128, j * 128 : (j + 1) * 128] = (blk / s).to(
|
||||
torch.float8_e4m3fn
|
||||
)
|
||||
return q, scale
|
||||
|
||||
def test_partial_last_block_infers_true_block_size(self):
|
||||
# fused_qkv_a_proj_with_mqa out-dim is not a multiple of 128 (e.g. 2112 =
|
||||
# 16*128 + 64), so the last row-block is partial. ceil(dim/num_blocks)
|
||||
# would infer 125, misaligning scales; the true block size is 128.
|
||||
n, k = 3 * 128 + 64, 256
|
||||
weight = torch.randn(n, k, device="cuda") * 0.02
|
||||
e_q, e_s = self._quantize_partial(weight, 1.0)
|
||||
a_q, a_s = self._quantize_partial(weight, 1.001)
|
||||
self.assertEqual(list(e_s.shape), [4, 2]) # ceil(448/128)=4, 256/128=2
|
||||
self.assertEqual(Fp8BlockComparable._infer_block_size(e_q, e_s), [128, 128])
|
||||
equal, _, _, num_exceed = _compare_quant_pair(e_q, e_s, a_q, a_s)
|
||||
self.assertFalse(equal)
|
||||
self.assertEqual(num_exceed, 0)
|
||||
|
||||
def test_3d_expert_tensor(self):
|
||||
q3 = self.e_q.reshape(2, 128, 256).contiguous()
|
||||
s3 = self.e_s.reshape(2, 1, 2)
|
||||
equal, *_ = _compare_quant_pair(q3, s3, q3.clone(), s3.clone())
|
||||
self.assertTrue(equal)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# select_comparable_weight
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestSelectComparableWeight(CustomTestCase):
|
||||
|
||||
def test_returns_none_when_not_a_quant_method(self):
|
||||
self.assertIsNone(select_comparable_weight(None))
|
||||
|
||||
def test_returns_none_for_raw_safe_method(self):
|
||||
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
|
||||
|
||||
# unquantized / int4 / mxfp8 all route to raw (None).
|
||||
fake = UnquantizedLinearMethod.__new__(UnquantizedLinearMethod)
|
||||
self.assertIsNone(select_comparable_weight(fake))
|
||||
|
||||
def test_raises_on_nvfp4(self):
|
||||
from sglang.srt.layers.quantization.modelopt_quant import (
|
||||
ModelOptFp4LinearMethod,
|
||||
)
|
||||
|
||||
# nvfp4 has no ComparableWeight yet -> must raise, not silently raw-compare.
|
||||
fake = ModelOptFp4LinearMethod.__new__(ModelOptFp4LinearMethod)
|
||||
with self.assertRaises(NotImplementedError):
|
||||
select_comparable_weight(fake)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user