ViperEkura
5e76fbd1bf
- Finalize scale rings inside the quantize kernels: a last-block epilogue (threadfence + counter elect) folds amax into hist, reduces the window and publishes the next scale on device, zero extra launches; _ScaleRing packs [hist | scale | counter] into one CUDA buffer. - Split FP8QuantizeParams out of FP8Params so each operator owns its fields; linear_forward/backward_fp8 take optional ring arguments. - Drop the inference weight-quantization cache; the optimizer bumps the weight version every step, so a cache would miss anyway. - Zero amax scratch via empty + cudaMemsetAsync instead of torch::zeros, cutting a ~50us fill_ dispatch per quantize. - Stage crosswise-B operands K-major with cp.async (contract >= 8192) and PRMT-transpose per k_seg region in smem, interleaved with the MMAs; the sync LDG + byte-scatter path it replaces was long-scoreboard bound (ncu 4.6 vs 0.4 stalls/issue). - Load crosswise-A direct with an in-register PRMT transpose; its operands are typically L2-resident and the staging round trip measured as a net loss. - Enable grouped rasterization for the congruous NT forward (shared B stripe keeps the weight operand hot in L2) and make the smem budget layout-aware (Fp8GemmSmem) while holding two CTAs per SM. - Annotate ops/fp8.py return types; drop weight-cache and decorator tests, hoist their imports to module level. e2e 12L/dim1024/B4xT512 fused AdamW: fp8 137.8ms/step vs bf16 210.3ms, 1.53x. Kernel vs cuBLASLt _scaled_mm: fwd 1.03-1.09x, dX 1.33-1.47x, dW 1.30-1.39x (from 1.10/1.42-1.49/1.52-1.56x), before the pre-transposed copies cuBLASLt needs for dX/dW. fp8 train step vs bf16: 1.34x at 2048 tokens (was 1.25x), 1.08x at 512.
489 lines
18 KiB
Python
489 lines
18 KiB
Python
"""FP8 primitives: kernel-level (CUDA) and policy-level (CPU-verifiable) tests.
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The kernel-level tests exercise the pure FP8 path (quantize_bf16 + mm_fp8 for
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the forward GEMM, quantize + pre-quantized GEMMs for the backward); the
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policy-level tests (recipes, autocast context, per-tensor meta, CPU fallbacks
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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 (
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linear_backward_fp8,
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linear_forward_fp8,
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mm_fp8,
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quantize_bf16,
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)
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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):
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return (tensor.float() / scale).to(torch.float8_e4m3fn).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_bf16(a, scale_a, "e4m3")
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b8, _ = quantize_bf16(b, scale_b, "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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def test_quantize_bf16_returns_amax():
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"""quantize_bf16 returns (x8, amax); amax tracks the *raw* values and the
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caller never clears it (zero-initialized inside the kernel entry)."""
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torch.manual_seed(3)
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x = torch.randn(64, 128, device="cuda", dtype=torch.bfloat16)
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scale = torch.tensor([0.5], device="cuda")
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x8, amax = quantize_bf16(x, scale, "e4m3")
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assert x8.dtype == torch.float8_e4m3fn
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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(torch.float8_e4m3fn)
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assert torch.equal(x8, ref)
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@skip_no_fp8
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def test_quantize_bf16_e5m2_format():
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x = torch.randn(32, 64, device="cuda", dtype=torch.bfloat16)
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x8, amax = quantize_bf16(x, torch.tensor([0.1], 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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def test_quantize_ring_in_kernel_finalize():
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"""The quantize kernel finalizes the delayed-scaling ring in-kernel: the
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measured amax lands in hist[idx], the window reduces to the next step's
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scale on device, and the counter re-arms for the next launch."""
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torch.manual_seed(21)
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dev = torch.device("cuda")
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ring = _ScaleRing(dev, DelayedScaling(history_len=4, margin=0))
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x0 = torch.randn(256, 256, device=dev, dtype=torch.bfloat16)
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w = torch.randn(256, 256, device=dev, dtype=torch.bfloat16)
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sw = torch.tensor([1.0], device=dev)
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ring.seed(x0, "e4m3")
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hist0 = ring.hist.clone()
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# Step over three fresh tensors: each launch folds its amax into
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# hist[idx] and publishes max(hist)/448 as the next scale.
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idx = 0
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for _ in range(3):
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x = torch.randn(256, 256, device=dev, dtype=torch.bfloat16) * (2.0 + 4.0 * _)
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_ = linear_forward_fp8(
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x,
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w,
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None,
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ring.scale,
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sw,
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"e4m3",
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None,
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ring.state,
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idx,
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0,
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)
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torch.cuda.synchronize()
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expected_hist = hist0.clone()
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expected_hist[idx] = x.abs().amax().float()
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torch.testing.assert_close(ring.hist, expected_hist)
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expected_scale = (expected_hist.max() / 448.0).reshape(1)
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torch.testing.assert_close(ring.scale, expected_scale, rtol=1e-6, atol=1e-12)
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# counter re-armed to int32 zero
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assert ring.state[-1].view(torch.int32).item() == 0
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hist0 = expected_hist.clone()
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idx = (idx + 1) % 4
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@skip_no_fp8
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def test_fp8_linear_forward_and_backward():
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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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grad = torch.randn(m, n, device="cuda", dtype=torch.bfloat16)
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bias = torch.randn(n, device="cuda", dtype=torch.bfloat16)
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scale_x, scale_w, scale_g = _scale(x), _scale(weight), _scale(grad)
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out, x8, w8, amax_x, amax_w = linear_forward_fp8(x, weight, bias, scale_x, scale_w)
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grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
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grad, x, weight, [1, 1, 1], scale_g, scale_w, scale_x, "e4m3"
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)
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qx = _quantize(x, scale_x)
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qw = _quantize(weight, scale_w)
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qg = _quantize(grad, scale_g)
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expected_out = (qx @ qw.t() * scale_x * scale_w + bias).to(torch.bfloat16)
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expected_grad_x = (qg @ qw * scale_g * scale_w).to(torch.bfloat16)
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expected_grad_w = (qg.t() @ qx * scale_g * scale_x).to(torch.bfloat16)
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torch.testing.assert_close(out, expected_out, atol=0.125, rtol=0.01)
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torch.testing.assert_close(grad_x, expected_grad_x, atol=0.125, rtol=0.01)
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torch.testing.assert_close(grad_w, expected_grad_w, atol=0.125, rtol=0.01)
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torch.testing.assert_close(grad_b, grad.sum(0).to(torch.bfloat16))
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torch.testing.assert_close(amax_x, x.abs().amax().float().reshape(1))
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torch.testing.assert_close(amax_w, weight.abs().amax().float().reshape(1))
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torch.testing.assert_close(amax_g, grad.abs().amax().float().reshape(1))
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@skip_no_fp8
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def test_linear_backward_e5m2_gradients():
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"""Hybrid backward: gradient GEMMs run in E5M2 (larger dynamic range)."""
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torch.manual_seed(5)
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m, n, k = 32, 16, 64
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x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16) * 3.0
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weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
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grad = torch.randn(m, n, device="cuda", dtype=torch.bfloat16) * 10.0
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sg = _scale(grad) * 0.5
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sw = _scale(weight)
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sx = _scale(x)
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grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
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grad, x, weight, [1, 1, 1], sg, sw, sx, "e5m2"
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)
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def q5(t, s):
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return (t.float() / s).to(torch.float8_e5m2).float()
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qg = q5(grad, sg)
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qw = q5(weight, sw)
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qx = q5(x, sx)
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expected_grad_x = (qg @ qw * sg * sw).to(torch.bfloat16)
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expected_grad_w = (qg.t() @ qx * sg * sx).to(torch.bfloat16)
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torch.testing.assert_close(grad_x, expected_grad_x, atol=0.5, rtol=0.05)
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torch.testing.assert_close(grad_w, expected_grad_w, atol=0.5, rtol=0.05)
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torch.testing.assert_close(amax_g, grad.abs().amax().float().reshape(1))
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@skip_no_fp8
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def test_fp8_linear_static_fp8_weight_and_bias():
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"""Static fp8 inference: pre-quantized w8/b8 + their scales take the GEMM
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directly (no weight quantize, amax_w = 0); the bias is fused in the
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epilogue (bf16 and fp8 bias share the fused path)."""
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torch.manual_seed(9)
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m, n, k = 67, 45, 129
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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) * 0.5
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bias = torch.randn(n, device="cuda", dtype=torch.bfloat16) * 0.5
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sx, sw, sb = _scale(x), _scale(weight), _scale(bias)
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w8, _ = quantize_bf16(weight, sw, "e4m3")
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b8, _ = quantize_bf16(bias, sb, "e4m3")
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out, x8, w8_back, amax_x, amax_w = linear_forward_fp8(x, w8, b8, sx, sw, "e4m3", sb)
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qx = _quantize(x, sx)
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qw = _quantize(weight, sw)
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qb = _quantize(bias, sb)
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expected = (qx @ qw.t() * sx * sw + qb * sb).to(torch.bfloat16)
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torch.testing.assert_close(out, expected, atol=0.125, rtol=0.01)
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torch.testing.assert_close(amax_x, x.abs().amax().float().reshape(1))
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assert amax_w.item() == 0.0 # nothing measured on the static path
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assert w8_back is w8 # pre-quantized w handed straight back
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# bf16 bias stays bf16 on the same fused-epilogue path
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out_bf16bias, *_ = linear_forward_fp8(x, w8, bias, sx, sw, "e4m3")
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expected_b = (qx @ qw.t() * sx * sw + bias.float()).to(torch.bfloat16)
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torch.testing.assert_close(out_bf16bias, expected_b, atol=0.125, rtol=0.01)
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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 = {"bwd": 0}
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orig = f8mod.linear_backward_fp8
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def spy(*args, **kwargs):
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calls["bwd"] += 1
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return orig(*args, **kwargs)
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f8mod.linear_backward_fp8 = 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.linear_backward_fp8 = orig
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f8mod.fp8_state().reset()
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assert calls["bwd"] == 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 = torch.tensor([2.5], device="cuda")
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sb = torch.tensor([1.5], device="cuda")
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a8, _ = quantize_bf16(a, sa, "e4m3")
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b8, _ = quantize_bf16(b, sb, "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() * 2.5 * 1.5).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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@skip_no_fp8
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def test_mm_fp8_fp8_output():
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"""mm_fp8 with out_dtype='e4m3' produces an FP8 output (layer-to-layer)."""
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torch.manual_seed(12)
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m, n, k = 256, 128, 64
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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 = torch.tensor([2.0], device="cuda")
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sb = torch.tensor([1.0], device="cuda")
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os_ = torch.tensor([0.5], device="cuda")
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a8, _ = quantize_bf16(a, sa, "e4m3")
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b8, _ = quantize_bf16(b, sb, "e4m3")
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out8 = mm_fp8(a8, b8, sa, sb, out_dtype="e4m3", out_scale=os_)
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assert out8.dtype == torch.float8_e4m3fn
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assert out8.shape == (m, n)
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ref = (a8.float().double() @ b8.float().double() * 2.0 * 1.0 * 0.5).to(
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torch.bfloat16
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)
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torch.testing.assert_close(
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out8.float().to(torch.bfloat16), ref, atol=6.0, rtol=0.05
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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
|
|
assert state.fp8_format is FP8Format.HYBRID
|
|
with fp8_autocast(enabled=True, recipe=DynamicScaling(), fp8_format="e4m3"):
|
|
assert isinstance(state.recipe, DynamicScaling)
|
|
assert state.fp8_format is FP8Format.E4M3
|
|
assert state.fp8_format is FP8Format.HYBRID # restored on exit
|
|
assert not state.enabled
|
|
finally:
|
|
state.enabled, state.recipe, state.fp8_format = prev
|
|
|
|
|
|
def test_fp8_tensor_meta_delayed_update():
|
|
"""Meta seeds from data; hist/scale are packed views of one state buffer."""
|
|
meta = FP8TensorMeta(torch.device("cpu"), DelayedScaling(history_len=4, margin=0))
|
|
w = torch.randn(8, 8)
|
|
meta.w.seed(w, "e4m3")
|
|
assert meta.w.initialized
|
|
torch.testing.assert_close(meta.w.scale, (w.abs().amax() / 448.0).reshape(1))
|
|
# [hist | scale | counter] packing: views alias the single state buffer.
|
|
assert meta.w.state.numel() == 4 + 2
|
|
assert meta.w.hist.data_ptr() == meta.w.state.data_ptr()
|
|
assert meta.w.scale.data_ptr() == meta.w.state[4:].data_ptr()
|
|
# counter slot stays int32-zero (float bits) between launches
|
|
assert meta.w.state[-1].view(torch.int32).item() == 0
|
|
meta.w.advance()
|
|
assert meta.w.idx == 1
|
|
|
|
|
|
def test_quantize_bf16_cpu_fallback():
|
|
"""CPU fallback of the quantize primitive (scale semantics + amax)."""
|
|
x = torch.randn(16, 32, dtype=torch.bfloat16)
|
|
scale = torch.tensor([0.5])
|
|
x8, amax = quantize_bf16(x, scale, "e4m3")
|
|
assert x8.dtype == torch.float8_e4m3fn
|
|
ref = (x.float() / 0.5).to(torch.float8_e4m3fn)
|
|
assert torch.equal(x8, ref)
|
|
torch.testing.assert_close(amax, x.abs().amax().float().reshape(1))
|
|
|
|
|
|
def test_mm_fp8_cpu_fallback():
|
|
a8 = torch.tensor([[1.0, 2.0]], dtype=torch.float8_e4m3fn)
|
|
b8 = torch.tensor([[3.0], [4.0]], dtype=torch.float8_e4m3fn)
|
|
sa = torch.tensor([2.0])
|
|
sb = torch.tensor([0.5])
|
|
out = mm_fp8(a8, b8, sa, sb)
|
|
ref = (a8.float() @ b8.float() * 2.0 * 0.5).to(torch.bfloat16)
|
|
torch.testing.assert_close(out, ref)
|
|
|
|
|
|
def test_mm_fp8_fp8_output_cpu():
|
|
"""CPU fallback with an FP8 output (out_dtype='e4m3' + out_scale)."""
|
|
a8 = torch.tensor([[1.0, 2.0]], dtype=torch.float8_e4m3fn)
|
|
b8 = torch.tensor([[3.0], [4.0]], dtype=torch.float8_e4m3fn)
|
|
sa = torch.tensor([2.0])
|
|
sb = torch.tensor([0.5])
|
|
os_ = torch.tensor([0.25])
|
|
out8 = mm_fp8(a8, b8, sa, sb, out_dtype="e4m3", out_scale=os_)
|
|
assert out8.dtype == torch.float8_e4m3fn
|
|
ref = (a8.float() @ b8.float() * 2.0 * 0.5 * 0.25).to(torch.float8_e4m3fn)
|
|
assert torch.equal(out8, ref)
|
|
|
|
|
|
# --------------------------------------------------------------------------
|
|
# torch-autocast parity: context semantics (nesting, thread locality, switch)
|
|
# --------------------------------------------------------------------------
|
|
|
|
|
|
def _linear():
|
|
"""Shared helper: a small bf16 linear operand set on CUDA (grad-tracking
|
|
so aten::linear records an autograd node)."""
|
|
torch.manual_seed(31)
|
|
x = torch.randn(16, 128, device="cuda", dtype=torch.bfloat16, requires_grad=True)
|
|
w = torch.randn(64, 128, device="cuda", dtype=torch.bfloat16, requires_grad=True)
|
|
return x, w
|
|
|
|
|
|
@skip_no_fp8
|
|
def test_nested_disabled_region_redispatches_bf16():
|
|
"""A nested fp8_autocast(enabled=False) region temporarily restores the
|
|
bf16 aten::linear path (torch's nested-disable semantics), and fp8
|
|
resumes when it exits."""
|
|
x, w = _linear()
|
|
with fp8_autocast(enabled=True):
|
|
out_fp8 = F.linear(x, w)
|
|
with fp8_autocast(enabled=False):
|
|
out_bf16 = F.linear(x, w)
|
|
assert type(out_bf16.grad_fn).__name__ != "_LinearFp8Backward"
|
|
assert out_bf16.dtype == torch.bfloat16
|
|
out_again = F.linear(x, w)
|
|
assert type(out_again.grad_fn).__name__ == "_LinearFp8Backward"
|
|
|
|
|
|
@skip_no_fp8
|
|
def test_global_switch_routes_without_region():
|
|
"""fp8_linear_enable(True) routes aten::linear to fp8 outside any region
|
|
(the persistent default); disabling restores bf16."""
|
|
x, w = _linear()
|
|
state = fp8_state()
|
|
try:
|
|
fp8_linear_enable(True)
|
|
out = F.linear(x, w)
|
|
assert type(out.grad_fn).__name__ == "_LinearFp8Backward"
|
|
fp8_linear_enable(False)
|
|
out = F.linear(x, w)
|
|
assert type(out.grad_fn).__name__ != "_LinearFp8Backward"
|
|
finally:
|
|
state.reset()
|
|
|
|
|
|
def test_autocast_state_is_thread_local():
|
|
"""torch parity: the active config is thread-local — another thread does
|
|
not see an open region (CPU-only check of the flag, no kernels)."""
|
|
seen = {}
|
|
with fp8_autocast(enabled=True):
|
|
assert fp8_linear_enabled()
|
|
t = threading.Thread(target=lambda: seen.update(enabled=fp8_linear_enabled()))
|
|
t.start()
|
|
t.join()
|
|
assert seen["enabled"] is False
|
|
assert not fp8_linear_enabled()
|