ViperEkura
9d3ae76683
- extract shared helpers for dataset writers, scheduler construction, thread interleaving, hf roundtrips, and moe configs - remove about 20 cases whose only assertions were format checks, restated declarations, fake-taxonomy duplicates, or test-local scaffolding - strengthen weak cases into exact reference comparisons, positional mask checks, and deterministic outcomes - replace two schedule factory smoke tests with cosine/sgdr formula assertions - delete root-level CLI tests whose merge-priority facts are covered by tests/config/test_cli.py - suite shrinks from 857 to 826 items; ruff format, import order, and pytest all green
120 lines
4.6 KiB
Python
120 lines
4.6 KiB
Python
import logging
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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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from astrai.extension import is_available, swiglu
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from astrai.model.components.mlp import MLP
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SWIGLU_AVAILABLE = (
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torch.cuda.is_available()
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and is_available("bf16_swiglu")
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and torch.cuda.get_device_capability() >= (8, 0)
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)
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skip_no_swiglu = pytest.mark.skipif(
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not SWIGLU_AVAILABLE,
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reason="BF16 SwiGLU requires a built kernel and compute capability 8.0+",
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)
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def reference_swiglu(x, up_weight, gate_weight):
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return F.linear(x, up_weight) * F.silu(F.linear(x, gate_weight))
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def test_cpu_and_training_calls_fall_back_with_gradients(monkeypatch):
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monkeypatch.setenv("ASTRAI_SWIGLU", "1")
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x = torch.randn(2, 8, requires_grad=True)
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up_weight = torch.randn(4, 8, requires_grad=True)
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gate_weight = torch.randn(4, 8, requires_grad=True)
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actual = swiglu(x, up_weight, gate_weight)
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expected = reference_swiglu(x, up_weight, gate_weight)
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torch.testing.assert_close(actual, expected)
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actual.sum().backward()
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assert x.grad is not None
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assert up_weight.grad is not None
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assert gate_weight.grad is not None
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def test_invalid_mode_warns_and_uses_auto(monkeypatch, caplog):
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monkeypatch.setenv("ASTRAI_SWIGLU", "invalid-test-mode")
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x = torch.randn(2, 8)
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up_weight = torch.randn(4, 8)
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gate_weight = torch.randn(4, 8)
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with caplog.at_level(logging.WARNING):
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actual = swiglu(x, up_weight, gate_weight)
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assert "using auto" in caplog.text
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torch.testing.assert_close(actual, reference_swiglu(x, up_weight, gate_weight))
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def test_mlp_routes_through_swiglu_backend(monkeypatch):
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sentinel = torch.randn(2, 4)
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def fake_swiglu(x, up_weight, gate_weight):
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assert x.shape == (2, 3)
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assert up_weight.shape == gate_weight.shape == (4, 3)
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return sentinel
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monkeypatch.setattr("astrai.model.components.mlp.swiglu", fake_swiglu)
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layer = MLP(3, 4)
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output = layer(torch.randn(2, 3))
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assert output["hidden_states"].shape == (2, 3)
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@skip_no_swiglu
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def test_mode_zero_disables_fused_kernel(monkeypatch):
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monkeypatch.setenv("ASTRAI_SWIGLU", "0")
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x = torch.randn(4, 1536, device="cuda", dtype=torch.bfloat16) * 0.1
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up_weight = torch.randn(6912, 1536, device="cuda", dtype=torch.bfloat16) * 0.02
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gate_weight = torch.randn_like(up_weight) * 0.02
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with torch.no_grad():
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actual = swiglu(x, up_weight, gate_weight)
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expected = reference_swiglu(x, up_weight, gate_weight)
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torch.testing.assert_close(actual, expected)
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@skip_no_swiglu
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def test_mode_one_forces_supported_shape(monkeypatch):
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monkeypatch.setenv("ASTRAI_SWIGLU", "1")
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x = torch.randn(4, 1536, device="cuda", dtype=torch.bfloat16) * 0.1
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up_weight = torch.randn(6912, 1536, device="cuda", dtype=torch.bfloat16) * 0.02
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gate_weight = torch.randn_like(up_weight) * 0.02
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with torch.no_grad():
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actual = swiglu(x, up_weight, gate_weight)
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expected = reference_swiglu(x, up_weight, gate_weight)
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torch.testing.assert_close(actual, expected, rtol=0.03, atol=0.01)
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@skip_no_swiglu
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def test_auto_uses_unfused_chain_until_shape_is_qualified(monkeypatch):
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# The fusion table is empty, so auto keeps the unfused linear-backend
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# chain. The linear backend may still dispatch its own GEMV for M=4,
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# hence the relaxed tolerance versus the pure-torch reference.
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monkeypatch.setenv("ASTRAI_SWIGLU", "auto")
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x = torch.randn(4, 1536, device="cuda", dtype=torch.bfloat16)
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up_weight = torch.randn(6912, 1536, device="cuda", dtype=torch.bfloat16)
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gate_weight = torch.randn_like(up_weight)
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with torch.no_grad():
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actual = swiglu(x, up_weight, gate_weight)
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expected = reference_swiglu(x, up_weight, gate_weight)
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torch.testing.assert_close(actual, expected, rtol=0.03, atol=0.1)
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@skip_no_swiglu
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def test_mode_one_falls_back_for_misaligned_storage(monkeypatch):
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"""Contiguous-but-offset views must route to the unfused torch chain
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even with ASTRAI_SWIGLU=1 instead of reaching the uint4-only kernel
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(regression: the fused primitive faulted with a misaligned-address
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CUDA error for such inputs)."""
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monkeypatch.setenv("ASTRAI_SWIGLU", "1")
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k = 1536
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x_base = torch.randn(2 * k + 8, device="cuda", dtype=torch.bfloat16) * 0.1
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x = x_base[1 : 1 + 2 * k].view(2, k)
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assert x.is_contiguous() and (x.data_ptr() & 15) != 0
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up_weight = torch.randn(64, k, device="cuda", dtype=torch.bfloat16) * 0.02
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gate_weight = torch.randn_like(up_weight)
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with torch.no_grad():
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actual = swiglu(x, up_weight, gate_weight)
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expected = reference_swiglu(x, up_weight, gate_weight)
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torch.testing.assert_close(actual, expected, rtol=0.03, atol=0.01)
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