ViperEkura
057c0d33df
- split quantize into quantize.cuh, templated on input type (bf16/fp16/fp32) - rename pybind entry quantize_bf16 to quantize; validate the fmt enum - fix fp8x2 packing: one 32-bit word packs two pairs (halves were dropped) - drop the dead OutFp8 template param; GEMM output is always bf16 - fp8_state.reset() restores recipe/format defaults too (test state leak) - rewrite tests for the two-primitive API with fp32-domain amax references
434 lines
17 KiB
Python
434 lines
17 KiB
Python
"""FP8 primitives: kernel-level (CUDA) and policy-level (CPU-verifiable) tests.
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The kernel-level tests exercise the two stateless primitives (``quantize`` for
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bf16/fp16/fp32 -> FP8, ``mm_fp8`` for the pre-quantized GEMM with transposed
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operands); the policy-level tests (recipes, autocast context, per-tensor meta,
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CPU fallbacks of the custom ops) run without a GPU.
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"""
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import threading
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import pytest
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import torch
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import torch.nn.functional as F
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import astrai.extension.fp8 as f8mod
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from astrai.extension.fp8 import (
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DelayedScaling,
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DynamicScaling,
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FP8Format,
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FP8TensorMeta,
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_ScaleRing,
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fp8_autocast,
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fp8_linear_enable,
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fp8_linear_enabled,
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fp8_state,
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)
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from astrai.extension.ops.fp8 import mm_fp8, quantize
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from tests.conftest import skip_no_fp8
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def _scale(tensor):
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return (tensor.abs().amax().float() / 448.0).clamp_min(1e-12)
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def _quantize(tensor, scale, fmt="e4m3"):
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"""Reference quantize: multiply by the reciprocal (the kernel's exact
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arithmetic — a plain divide flips fp8 boundary cases by one ulp)."""
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dtype = torch.float8_e5m2 if fmt == "e5m2" else torch.float8_e4m3fn
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return (tensor.float() * scale.reciprocal()).to(dtype).float()
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# --------------------------------------------------------------------------
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# Kernel-level (CUDA)
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# --------------------------------------------------------------------------
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@skip_no_fp8
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@pytest.mark.parametrize(
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("m", "n", "k"),
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[(16, 8, 32), (17, 9, 33), (31, 15, 64), (32, 48, 96)],
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)
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def test_fp8_mm_matches_explicit_quantization(m, n, k):
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torch.manual_seed(m + n + k)
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a = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
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b = torch.randn(k, n, device="cuda", dtype=torch.bfloat16)
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scale_a = _scale(a)
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scale_b = _scale(b)
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a8, _ = quantize(a, scale_a.reciprocal(), "e4m3")
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b8, _ = quantize(b, scale_b.reciprocal(), "e4m3")
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out = mm_fp8(a8, b8, scale_a * scale_b)
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expected = (_quantize(a, scale_a) @ _quantize(b, scale_b) * scale_a * scale_b).to(
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torch.bfloat16
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)
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assert out.dtype == torch.bfloat16
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assert out.shape == (m, n)
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torch.testing.assert_close(out, expected, atol=0.125, rtol=0.01)
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@skip_no_fp8
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@pytest.mark.parametrize("in_dtype", [torch.bfloat16, torch.float16, torch.float32])
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@pytest.mark.parametrize("fmt", ["e4m3", "e5m2"])
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def test_quantize_input_dtypes(in_dtype, fmt):
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"""quantize accepts bf16/fp16/fp32 inputs; bytes and amax match the
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explicit (value * multiplier) reference."""
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torch.manual_seed(3)
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x = torch.randn(64, 128, device="cuda", dtype=torch.float32) * 0.5
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x = x.to(in_dtype)
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scale = torch.tensor([0.5], device="cuda")
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x8, amax = quantize(x, scale, fmt)
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out_dtype = torch.float8_e5m2 if fmt == "e5m2" else torch.float8_e4m3fn
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assert x8.dtype == out_dtype
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assert x8.shape == x.shape
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assert amax.shape == (1,)
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torch.testing.assert_close(amax, x.abs().amax().float().reshape(1))
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ref = (x.float() * 0.5).to(out_dtype)
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assert torch.equal(x8, ref)
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@skip_no_fp8
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def test_quantize_e5m2_format():
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x = torch.randn(32, 64, device="cuda", dtype=torch.bfloat16)
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x8, amax = quantize(x, torch.tensor([10.0], device="cuda"), "e5m2")
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assert x8.dtype == torch.float8_e5m2
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torch.testing.assert_close(amax, x.abs().amax().float().reshape(1))
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@skip_no_fp8
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@pytest.mark.parametrize("trans_a", [False, True])
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@pytest.mark.parametrize("trans_b", [False, True])
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def test_mm_fp8_transposed_operands(trans_a, trans_b):
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"""mm_fp8 handles all four operand layouts via trans_a/trans_b."""
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torch.manual_seed(17)
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m, n, k = 19, 13, 37
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a = torch.randn(m, k, device="cuda", dtype=torch.bfloat16) # A [M][K]
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b = torch.randn(n, k, device="cuda", dtype=torch.bfloat16) # B^T [N][K]
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sa, sb = _scale(a), _scale(b)
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a8, _ = quantize(a, sa.reciprocal(), "e4m3")
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b8, _ = quantize(b, sb.reciprocal(), "e4m3")
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a_op = a8.t().contiguous() if trans_a else a8
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b_op = b8 if trans_b else b8.t().contiguous()
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out = mm_fp8(a_op, b_op, sa * sb, trans_a=trans_a, trans_b=trans_b)
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assert out.shape == (m, n)
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expected = (_quantize(a, sa) @ _quantize(b, sb).t() * sa * sb).to(torch.bfloat16)
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torch.testing.assert_close(out, expected, atol=0.125, rtol=0.01)
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@skip_no_fp8
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def test_delayed_scaling_forward_uses_snapshot_scale():
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"""The delayed scale for step N is computed from amax(steps < N); the
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forward must snapshot the scale before the ring update, so a changing
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amax across steps does not leak the next-step scale into the output."""
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torch.manual_seed(11)
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dev = torch.device("cuda")
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state = f8mod.fp8_state()
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state.reset()
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state.default_recipe = DelayedScaling(history_len=1, margin=0)
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state.default_format = FP8Format.E4M3
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try:
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m, n, k = 32, 16, 64
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x1 = torch.randn(m, k, device=dev, dtype=torch.bfloat16) * 0.5
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# Smaller amax than x1: the delayed scale (amax(x1)/448) still covers
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# x2 without fp8 saturation, while the next-step scale would differ.
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x2 = torch.randn(m, k, device=dev, dtype=torch.bfloat16) * 0.35
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w = torch.randn(n, k, device=dev, dtype=torch.bfloat16) * 0.5
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bias = torch.zeros(n, device=dev, dtype=torch.bfloat16)
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f8mod.fp8_linear_forward(x1, w, bias) # step 1: seeds the rings
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out2, _, _ = f8mod.fp8_linear_forward(x2, w, bias) # amax changes
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torch.cuda.synchronize()
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# The delayed scale for step 2 is amax(x1)/448 (history_len=1); the
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# GEMM must use that same scale for dequant as the quantize used.
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sx = _scale(x1)
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sw = _scale(w)
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qx = _quantize(x2, sx)
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qw = _quantize(w, sw)
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expected = (qx @ qw.t() * sx * sw + bias).to(torch.bfloat16)
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torch.testing.assert_close(out2, expected, atol=0.125, rtol=0.01)
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finally:
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state.reset()
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@skip_no_fp8
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def test_fp8_linear_forward_and_backward():
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"""The composed strategy path: forward quantize+GEMM+bias, backward
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dX/dW GEMMs on transposed operands (E5M2 in hybrid)."""
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torch.manual_seed(7)
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m, n, k = 19, 13, 37
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x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
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weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
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bias = torch.randn(n, device="cuda", dtype=torch.bfloat16)
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state = f8mod.fp8_state()
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state.reset()
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state.default_recipe = DynamicScaling()
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try:
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out, _, _ = f8mod.fp8_linear_forward(x, weight, bias)
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sx, sw = _scale(x), _scale(weight)
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qx = _quantize(x, sx)
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qw = _quantize(weight, sw)
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expected_out = (qx @ qw.t() * sx * sw + bias).to(torch.bfloat16)
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torch.testing.assert_close(out, expected_out, atol=0.125, rtol=0.01)
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# backward through the aten::linear integration (hybrid E5M2). The
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# incoming gradient is 2*out of the *fp8* forward (bf16-rounded), not
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# 2*exact — derive the reference from the actual output.
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xr = x.detach().clone().requires_grad_()
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wr = weight.detach().clone().requires_grad_()
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br = bias.detach().clone().requires_grad_()
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with fp8_autocast(enabled=True):
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loss = F.linear(xr, wr, br).float().pow(2).sum()
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loss.backward()
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g = (2 * out.float()).to(torch.bfloat16).float() # actual grad wrt out
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# the dynamic path measures current-step amax in the bwd fmt (E5M2);
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# amax must be taken in fp32 — a bf16-rounded scale flips E5M2
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# boundary rounding (2-bit mantissa) and the reference drifts.
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e5 = 57344.0
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sg = (g.abs().amax() / e5).clamp_min(1e-12)
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sw5 = (weight.abs().amax().float() / e5).clamp_min(1e-12)
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sx5 = (x.abs().amax().float() / e5).clamp_min(1e-12)
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expected_grad_x = (
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_quantize(g, sg, "e5m2") @ _quantize(weight, sw5, "e5m2") * sg * sw5
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).to(torch.bfloat16)
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expected_grad_w = (
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_quantize(g, sg, "e5m2").t() @ _quantize(x, sx5, "e5m2") * sg * sx5
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).to(torch.bfloat16)
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torch.testing.assert_close(xr.grad, expected_grad_x, atol=0.5, rtol=0.05)
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torch.testing.assert_close(wr.grad, expected_grad_w, atol=0.5, rtol=0.05)
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torch.testing.assert_close(
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br.grad, g.sum(0).to(torch.bfloat16), atol=0.5, rtol=0.05
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)
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finally:
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state.reset()
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@skip_no_fp8
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def test_fp8_linear_backward_outside_autocast():
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"""aten::linear records an fp8 autograd node inside fp8_autocast; the
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backward runs fp8 kernels even after the context exits (loss.backward()
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placement is free), instead of falling back to bf16 mm."""
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torch.manual_seed(5)
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x = torch.randn(64, 128, device="cuda", dtype=torch.bfloat16, requires_grad=True)
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weight = torch.randn(
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96, 128, device="cuda", dtype=torch.bfloat16, requires_grad=True
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)
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bias = torch.randn(96, device="cuda", dtype=torch.bfloat16, requires_grad=True)
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xr, wr, br = (t.detach().clone().requires_grad_() for t in (x, weight, bias))
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calls = {"fwd": 0}
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orig = f8mod.fp8_linear_forward
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def spy(*args, **kwargs):
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calls["fwd"] += 1
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return orig(*args, **kwargs)
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f8mod.fp8_linear_forward = spy
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try:
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with fp8_autocast(enabled=True):
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out = F.linear(x, weight, bias)
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assert type(out.grad_fn).__name__ == "_LinearFp8Backward"
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out.float().pow(2).sum().backward() # outside the autocast region
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finally:
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f8mod.fp8_linear_forward = orig
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f8mod.fp8_state().reset()
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assert calls["fwd"] == 1 # fp8 kernels, not the bf16 fallback
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ref = F.linear(xr, wr, br)
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ref.float().pow(2).sum().backward()
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# E5M2 backward quantization noise: compare directions/norms (the
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# torchao/TE style) rather than elementwise against the bf16 reference.
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def _direction(a, b):
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cos = torch.nn.functional.cosine_similarity(
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a.float().flatten(), b.float().flatten(), dim=0
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)
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return cos > 0.99 and 0.9 < a.float().norm() / b.float().norm() < 1.1
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assert _direction(x.grad, xr.grad)
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assert _direction(weight.grad, wr.grad)
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assert _direction(bias.grad, br.grad)
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@skip_no_fp8
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def test_mm_fp8_matches_scaled_mm():
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torch.manual_seed(11)
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m, n, k = 512, 4096, 4096
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a = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
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b = torch.randn(k, n, device="cuda", dtype=torch.bfloat16)
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sa = _scale(a)
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sb = _scale(b)
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a8, _ = quantize(a, sa.reciprocal(), "e4m3")
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b8, _ = quantize(b, sb.reciprocal(), "e4m3")
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out = mm_fp8(a8, b8, sa * sb)
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assert out.dtype == torch.bfloat16
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assert out.shape == (m, n)
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ref = (a8.float().double() @ b8.float().double() * sa * sb).to(torch.bfloat16)
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torch.testing.assert_close(out, ref, atol=6.0, rtol=0.05)
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try:
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torch._scaled_mm(a8, b8, sa, sb, out_dtype=torch.bfloat16)
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except (RuntimeError, NotImplementedError):
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return
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torch.testing.assert_close(
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out,
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torch._scaled_mm(a8, b8, sa, sb, out_dtype=torch.bfloat16),
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atol=2.0,
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rtol=0.01,
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)
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# --------------------------------------------------------------------------
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# Policy-level (CPU-verifiable)
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# --------------------------------------------------------------------------
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def test_recipe_scale_from_history():
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"""Delayed: max over the window + margin; dynamic: current amax."""
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hist = torch.tensor([1.0, 2.0, 0.5])
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d = DelayedScaling(history_len=3, margin=0)
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assert torch.allclose(d.scale_from_history(hist, "e4m3"), torch.tensor(2.0 / 448.0))
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d_m = DelayedScaling(history_len=3, margin=2)
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assert torch.allclose(
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d_m.scale_from_history(hist, "e4m3"), torch.tensor(2.0 / 448.0 / 4.0)
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)
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dyn = DynamicScaling()
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amax = torch.tensor([0.25])
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assert torch.allclose(
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dyn.scale_from_history(amax, "e4m3"), torch.tensor(0.25 / 448.0)
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)
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assert torch.allclose(
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dyn.scale_from_history(amax, "e5m2"), torch.tensor(0.25 / 57344.0)
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)
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def test_fp8_format_enum():
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assert FP8Format.HYBRID.fwd() == "e4m3"
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assert FP8Format.HYBRID.bwd() == "e5m2"
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assert FP8Format.E4M3.fwd() == FP8Format.E4M3.bwd() == "e4m3"
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assert FP8Format.E5M2.fwd() == FP8Format.E5M2.bwd() == "e5m2"
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def test_fp8_autocast_context():
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"""fp8_autocast sets and restores recipe + format on the global state."""
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state = fp8_state()
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prev = (state.enabled, state.recipe, state.fp8_format)
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try:
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with fp8_autocast(enabled=True, fp8_format="hybrid", update_interval=8):
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assert state.enabled
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assert isinstance(state.recipe, DelayedScaling)
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assert state.recipe.history_len == 8
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assert state.fp8_format is FP8Format.HYBRID
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with fp8_autocast(enabled=True, recipe=DynamicScaling(), fp8_format="e4m3"):
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assert isinstance(state.recipe, DynamicScaling)
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assert state.fp8_format is FP8Format.E4M3
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assert state.fp8_format is FP8Format.HYBRID # restored on exit
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assert not state.enabled
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finally:
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state.enabled, state.recipe, state.fp8_format = prev
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def test_fp8_tensor_meta_delayed_update():
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"""Meta seeds from data; hist/scale are packed views of one state buffer."""
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meta = FP8TensorMeta(torch.device("cpu"), DelayedScaling(history_len=4, margin=0))
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w = torch.randn(8, 8)
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meta.w.seed(w, "e4m3")
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assert meta.w.initialized
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torch.testing.assert_close(meta.w.scale, (w.abs().amax() / 448.0).reshape(1))
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# [hist | scale] packing: views alias the single state buffer.
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assert meta.w.state.numel() == 4 + 2
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assert meta.w.hist.data_ptr() == meta.w.state.data_ptr()
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assert meta.w.scale.data_ptr() == meta.w.state[4:].data_ptr()
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meta.w.advance()
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assert meta.w.idx == 1
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# update folds a fresh amax into the window and publishes the next scale
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amax = torch.tensor([8.0])
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meta.w.update(amax, "e4m3")
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torch.testing.assert_close(meta.w.scale, torch.tensor([8.0 / 448.0]))
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def test_quantize_cpu_fallback():
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"""CPU fallback of the quantize primitive (scale semantics + amax)."""
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x = torch.randn(16, 32, dtype=torch.bfloat16)
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scale = torch.tensor([0.5]) # quantize multiplier
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x8, amax = quantize(x, scale, "e4m3")
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assert x8.dtype == torch.float8_e4m3fn
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ref = (x.float() * 0.5).to(torch.float8_e4m3fn)
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assert torch.equal(x8, ref)
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torch.testing.assert_close(amax, x.abs().amax().float().reshape(1))
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def test_mm_fp8_cpu_fallback():
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a8 = torch.tensor([[1.0, 2.0]], dtype=torch.float8_e4m3fn)
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b8 = torch.tensor([[3.0], [4.0]], dtype=torch.float8_e4m3fn)
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scale = torch.tensor([1.0])
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out = mm_fp8(a8, b8, scale)
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ref = (a8.float() @ b8.float() * 1.0).to(torch.bfloat16)
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torch.testing.assert_close(out, ref)
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# --------------------------------------------------------------------------
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# torch-autocast parity: context semantics (nesting, thread locality, switch)
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# --------------------------------------------------------------------------
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def _linear():
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"""Shared helper: a small bf16 linear operand set on CUDA (grad-tracking
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so aten::linear records an autograd node)."""
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torch.manual_seed(31)
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x = torch.randn(16, 128, device="cuda", dtype=torch.bfloat16, requires_grad=True)
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w = torch.randn(64, 128, device="cuda", dtype=torch.bfloat16, requires_grad=True)
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return x, w
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@skip_no_fp8
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def test_nested_disabled_region_redispatches_bf16():
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"""A nested fp8_autocast(enabled=False) region temporarily restores the
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bf16 aten::linear path (torch's nested-disable semantics), and fp8
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resumes when it exits."""
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x, w = _linear()
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with fp8_autocast(enabled=True):
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F.linear(x, w)
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with fp8_autocast(enabled=False):
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out_bf16 = F.linear(x, w)
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assert type(out_bf16.grad_fn).__name__ != "_LinearFp8Backward"
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assert out_bf16.dtype == torch.bfloat16
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out_again = F.linear(x, w)
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assert type(out_again.grad_fn).__name__ == "_LinearFp8Backward"
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@skip_no_fp8
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def test_global_switch_routes_without_region():
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"""fp8_linear_enable(True) routes aten::linear to fp8 outside any region
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(the persistent default); disabling restores bf16."""
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x, w = _linear()
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state = fp8_state()
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try:
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fp8_linear_enable(True)
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out = F.linear(x, w)
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assert type(out.grad_fn).__name__ == "_LinearFp8Backward"
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fp8_linear_enable(False)
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out = F.linear(x, w)
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assert type(out.grad_fn).__name__ != "_LinearFp8Backward"
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|
finally:
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|
state.reset()
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|
|
|
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def test_autocast_state_is_thread_local():
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|
"""torch parity: the active config is thread-local — another thread does
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|
not see an open region (CPU-only check of the flag, no kernels)."""
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|
seen = {}
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with fp8_autocast(enabled=True):
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assert fp8_linear_enabled()
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t = threading.Thread(target=lambda: seen.update(enabled=fp8_linear_enabled()))
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|
t.start()
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t.join()
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assert seen["enabled"] is False
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|
assert not fp8_linear_enabled()
|