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bb31f8b707
supports_fp8_compute() and supports_nvfp4_compute() used the global is_nvidia() check which ignores the device argument, then defaulted to cuda:0 when device was None. In heterogeneous multi-GPU setups (e.g. RTX 5070 + RTX 3090 Ti) this causes the wrong GPU's compute capability to be checked, incorrectly disabling fp8 on capable devices. Replace the global is_nvidia() gate with per-device checks: - Default device=None to get_torch_device() explicitly - Early-return False for CPU/MPS devices - Use is_device_cuda(device) + torch.version.cuda instead of the global is_nvidia() Fixes #4589, relates to #4577, #12405 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
110 lines
4.7 KiB
Python
110 lines
4.7 KiB
Python
import pytest
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from unittest.mock import patch, MagicMock
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import torch
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import comfy.model_management as mm
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class FakeDeviceProps:
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"""Minimal stand-in for torch.cuda.get_device_properties return value."""
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def __init__(self, major, minor, name="FakeGPU"):
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self.major = major
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self.minor = minor
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self.name = name
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class TestSupportsFp8Compute:
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"""Tests for per-device fp8 compute capability detection."""
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def test_cpu_device_returns_false(self):
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assert mm.supports_fp8_compute(torch.device("cpu")) is False
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@pytest.mark.skipif(not hasattr(torch.backends, "mps"), reason="MPS backend not available")
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def test_mps_device_returns_false(self):
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assert mm.supports_fp8_compute(torch.device("mps")) is False
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@patch("comfy.model_management.SUPPORT_FP8_OPS", True)
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def test_cli_override_returns_true(self):
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assert mm.supports_fp8_compute(torch.device("cpu")) is True
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@patch("comfy.model_management.get_torch_device", return_value=torch.device("cpu"))
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def test_none_device_defaults_to_get_torch_device(self, mock_get):
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result = mm.supports_fp8_compute(None)
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mock_get.assert_called_once()
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assert result is False
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_each_cuda_device_checked_independently(self):
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"""On a multi-GPU system, each device should be queried for its own capabilities."""
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count = torch.cuda.device_count()
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if count < 2:
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pytest.skip("Need 2+ CUDA devices for multi-GPU test")
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results = {}
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for i in range(count):
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dev = torch.device(f"cuda:{i}")
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results[i] = mm.supports_fp8_compute(dev)
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props = torch.cuda.get_device_properties(dev)
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# Verify the result is consistent with the device's compute capability
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if props.major >= 9:
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assert results[i] is True, f"cuda:{i} ({props.name}) has SM {props.major}.{props.minor}, should support fp8"
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elif props.major < 8 or props.minor < 9:
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assert results[i] is False, f"cuda:{i} ({props.name}) has SM {props.major}.{props.minor}, should not support fp8"
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@patch("torch.version.cuda", None)
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@patch("comfy.model_management.SUPPORT_FP8_OPS", False)
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def test_rocm_build_returns_false(self):
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"""On ROCm, devices appear as cuda:N via HIP but torch.version.cuda is None."""
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dev = MagicMock()
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dev.type = "cuda"
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assert mm.supports_fp8_compute(dev) is False
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@patch("torch.version.cuda", "12.4")
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@patch("comfy.model_management.SUPPORT_FP8_OPS", False)
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@patch("torch.cuda.get_device_properties")
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def test_sm89_supports_fp8(self, mock_props):
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"""Ada Lovelace (SM 8.9, e.g. RTX 4080) should support fp8."""
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mock_props.return_value = FakeDeviceProps(major=8, minor=9)
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dev = torch.device("cuda:0")
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assert mm.supports_fp8_compute(dev) is True
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@patch("torch.version.cuda", "12.4")
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@patch("comfy.model_management.SUPPORT_FP8_OPS", False)
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@patch("torch.cuda.get_device_properties")
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def test_sm86_does_not_support_fp8(self, mock_props):
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"""Ampere (SM 8.6, e.g. RTX 3090) should not support fp8."""
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mock_props.return_value = FakeDeviceProps(major=8, minor=6)
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dev = torch.device("cuda:0")
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assert mm.supports_fp8_compute(dev) is False
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@patch("torch.version.cuda", "12.4")
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@patch("comfy.model_management.SUPPORT_FP8_OPS", False)
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@patch("torch.cuda.get_device_properties")
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def test_sm90_supports_fp8(self, mock_props):
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"""Hopper (SM 9.0) and above should support fp8."""
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mock_props.return_value = FakeDeviceProps(major=9, minor=0)
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dev = torch.device("cuda:0")
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assert mm.supports_fp8_compute(dev) is True
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class TestSupportsNvfp4Compute:
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"""Tests for per-device nvfp4 compute capability detection."""
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def test_cpu_device_returns_false(self):
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assert mm.supports_nvfp4_compute(torch.device("cpu")) is False
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@patch("torch.version.cuda", "12.4")
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@patch("torch.cuda.get_device_properties")
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def test_sm100_supports_nvfp4(self, mock_props):
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"""Blackwell (SM 10.0) should support nvfp4."""
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mock_props.return_value = FakeDeviceProps(major=10, minor=0)
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dev = torch.device("cuda:0")
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assert mm.supports_nvfp4_compute(dev) is True
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@patch("torch.version.cuda", "12.4")
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@patch("torch.cuda.get_device_properties")
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def test_sm89_does_not_support_nvfp4(self, mock_props):
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"""Ada Lovelace (SM 8.9) should not support nvfp4."""
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mock_props.return_value = FakeDeviceProps(major=8, minor=9)
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dev = torch.device("cuda:0")
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assert mm.supports_nvfp4_compute(dev) is False
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