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# Copyright 2024 Databricks
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
import torch
from megablocks import ops
_BINNED_SCATTER_TESTS = (
(4, 2, 2, 1),
(4, 2, 2, 2),
(4, 2, 2, 4),
(1024, 1536, 4, 1),
(1024, 1536, 4, 2),
(1024, 1536, 4, 4),
(1024, 1536, 64, 1),
(1024, 1536, 64, 2),
(1024, 1536, 64, 4),
(1024, 1536, 128, 1),
(1024, 1536, 128, 2),
(1024, 1536, 128, 4),
(16384, 768, 4, 1),
(16384, 768, 4, 2),
(16384, 768, 4, 4),
(16384, 768, 64, 1),
(16384, 768, 64, 2),
(16384, 768, 64, 4),
(16384, 768, 128, 1),
(16384, 768, 128, 2),
(16384, 768, 128, 4),
)
@pytest.mark.gpu
@pytest.mark.parametrize(('sl', 'hs', 'ne', 'top_k'), _BINNED_SCATTER_TESTS)
def testBinnedScatter(sl: int, hs: int, ne: int, top_k: int):
# NOTE: Capacity factor == 1.
ec = (sl * top_k) // ne
# Create the data and indices.
x = torch.randn((sl, hs)).cuda().half()
# Randomly assign tokens to experts.
top_expert = torch.randint(0, ne, (sl * top_k,)).cuda().int()
_, indices = ops.sort(top_expert)
bins = ops.inclusive_cumsum(ops.histogram(top_expert, ne), 0)
# Sample weights for the scatter reduce.
weights = torch.rand((sl * top_k,)).cuda().half()
x = ops.binned_gather(x, indices, bins, ec, top_k)
def binned_scatter(
x: torch.Tensor,
indices: torch.Tensor,
weights: torch.Tensor,
bins: torch.Tensor,
top_k: int,
):
x = x.cpu().numpy()
indices = indices.cpu().numpy()
weights = weights.cpu().numpy()
bins = bins.cpu().numpy()
start = 0
out = np.zeros((sl, hs))
for i in range(ne):
end = bins[i]
for j in range(min(ec, end - start)):
index = indices[start + j]
scale = weights[index]
index //= top_k
out[index, :] += scale * x[i, j, :]
start = end
return torch.from_numpy(out).cuda().half()
out = ops.binned_scatter(x, indices, weights, bins, top_k)
expected_out = binned_scatter(x, indices, weights, bins, top_k)
# NOTE: We need to check approximate equality because the
# scatter reduce uses atomics.
assert np.testing.assert_allclose(
out.cpu(),
expected_out.cpu(),
rtol=5e-3,
) is None
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