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groupsize consistency #417

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22 changes: 11 additions & 11 deletions benchmarks/benchmark_hqq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ def bench_custom_kernel(
W_q,
scales,
zeros,
group_size,
groupsize,
transposed=False,
kernel_type="max_autotune",
fp8_fast_accum=False,
Expand All @@ -45,7 +45,7 @@ def fn():
scales.T,
zeros.T,
transposed=transposed,
group_size=group_size,
groupsize=groupsize,
fp8_fast_accum=fp8_fast_accum,
kernel_type=kernel_type,
)
Expand All @@ -65,11 +65,11 @@ def reference_fn():


def run_benchmark(
shape, group_size, dtype, axis=1, transposed=False, quant_dtype=torch.uint8
shape, groupsize, dtype, axis=1, transposed=False, quant_dtype=torch.uint8
):
qcfg = {
**BASE_QUANT_CONFIG,
**dict(group_size=group_size, axis=axis),
**dict(groupsize=groupsize, axis=axis),
}
M, N, K = shape

Expand Down Expand Up @@ -103,7 +103,7 @@ def run_benchmark(
scales = scales.reshape(N, -1)
zeros = zeros.reshape(N, -1)
tt_time = bench_custom_kernel(
x, W_q, scales, zeros, group_size, transposed=transposed
x, W_q, scales, zeros, groupsize, transposed=transposed
)

should_run_tinygemm = dtype == torch.bfloat16 and not transposed
Expand All @@ -114,7 +114,7 @@ def run_benchmark(
)
int4_time = bench_hqq(x, hqq_int4mm, transposed=transposed, tinygemm=True)

print(f"{shape=}, {group_size=}, {dtype=}, {transposed=}:")
print(f"{shape=}, {groupsize=}, {dtype=}, {transposed=}:")

print(
f"Ref: {ref_time:.4f}ms",
Expand Down Expand Up @@ -146,7 +146,7 @@ def run_benchmark(
"M",
"N",
"K",
"group_size",
"groupsize",
"dtype",
"transposed",
"ref",
Expand All @@ -159,16 +159,16 @@ def run_benchmark(
print(torch.cuda.get_device_properties(0))

for shape in SHAPES:
for group_size in GROUP_SIZES:
for groupsize in GROUP_SIZES:
for dtype in DTYPES:
for transposed in TRANSPOSED:
timings = run_benchmark(
shape, group_size, dtype, transposed=transposed
shape, groupsize, dtype, transposed=transposed
)
data.append((*shape, group_size, dtype, transposed, *timings))
data.append((*shape, groupsize, dtype, transposed, *timings))

output = StringIO()
df = pd.DataFrame(data, columns=HEADERS)
df.to_csv(output, index=False)
print(output.getvalue())
# df.to_csv("benchmark_hqq_tinygemm.csv", index=False)
# df.to_csv("benchmark_hqq_tinygemm.csv", index=False)
2 changes: 1 addition & 1 deletion benchmarks/dora/bench_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,7 +111,7 @@ def setup_dora_base_layers(layer_type, in_features, out_features, dtype):
# HQQ
quant_config = BaseQuantizeConfig(
nbits=4,
group_size=64,
groupsize=64,
quant_zero=False,
quant_scale=False,
offload_meta=True,
Expand Down
2 changes: 1 addition & 1 deletion test/dora/test_dora_layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,7 @@ def test_dora_layer(
elif model_type == "HQQDoRALinear":
quant_config = BaseQuantizeConfig(
nbits=4,
group_size=64,
groupsize=64,
quant_zero=False,
quant_scale=False,
offload_meta=True,
Expand Down
2 changes: 1 addition & 1 deletion test/dtypes/test_affine_quantized.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ class TestAffineQuantized(TestCase):
def test_tensor_core_layout_transpose(self):
t = torch.rand(128, 256, dtype=torch.bfloat16, device="cuda")
shape = t.shape
apply_int4_weight_only_quant = int4_weight_only(group_size=32)
apply_int4_weight_only_quant = int4_weight_only(groupsize=32)
aqt = apply_int4_weight_only_quant(t)
aqt_shape = aqt.shape
self.assertEqual(aqt_shape, shape)
Expand Down
30 changes: 15 additions & 15 deletions test/hqq/test_triton_mm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,16 +72,16 @@ def _arg_to_id(arg):


@pytest.mark.parametrize(
"shape, group_size, axis, dtype, transposed, kernel_type",
"shape, groupsize, axis, dtype, transposed, kernel_type",
TEST_CONFIGS,
ids=_arg_to_id,
)
def test_mixed_mm(
shape, group_size, axis, dtype, transposed, kernel_type, quant_dtype=torch.uint8
shape, groupsize, axis, dtype, transposed, kernel_type, quant_dtype=torch.uint8
):
qcfg = {
**BASE_QUANT_CONFIG,
**dict(group_size=group_size, axis=axis),
**dict(groupsize=groupsize, axis=axis),
}
M, N, K = shape

Expand Down Expand Up @@ -117,7 +117,7 @@ def test_mixed_mm(
scales.T,
zeros.T,
transposed=True,
group_size=group_size,
groupsize=groupsize,
fp8_fast_accum=False,
kernel_type=kernel_type,
)
Expand All @@ -132,7 +132,7 @@ def test_mixed_mm(
scales.T,
zeros.T,
transposed=False,
group_size=group_size,
groupsize=groupsize,
fp8_fast_accum=False,
kernel_type=kernel_type,
)
Expand All @@ -147,7 +147,7 @@ def test_mixed_mm(
# Only for debugging kernel without dependency on HQQ and with no autotuning
def _test_mixed_mm(
shape,
group_size,
groupsize,
BLOCK_M,
BLOCK_N,
BLOCK_K,
Expand All @@ -159,7 +159,7 @@ def _test_mixed_mm(
):
qcfg = {
**BASE_QUANT_CONFIG,
**dict(group_size=group_size, axis=axis),
**dict(groupsize=groupsize, axis=axis),
}
M, N, K = shape

Expand All @@ -169,9 +169,9 @@ def _test_mixed_mm(
quant_config.update({"weight_quant_params": qcfg})
W_q = torch.randint(0, int(2**4), size=(N, K), dtype=quant_dtype, device="cuda")

scales = torch.arange((N * K) // group_size, dtype=dtype, device="cuda")[:, None]
scales = torch.arange((N * K) // groupsize, dtype=dtype, device="cuda")[:, None]
zeros = torch.zeros_like(scales)
W_dq = ((W_q.reshape(-1, group_size) - zeros) * scales).reshape(N, K)
W_dq = ((W_q.reshape(-1, groupsize) - zeros) * scales).reshape(N, K)
scales = scales.reshape(N, -1)
zeros = zeros.reshape(N, -1)

Expand All @@ -187,7 +187,7 @@ def _test_mixed_mm(
scales.T,
zeros.T,
transposed=True,
group_size=group_size,
groupsize=groupsize,
fp8_fast_accum=False,
kernel_type=kernel_type,
BLOCK_M=BLOCK_M,
Expand All @@ -205,14 +205,14 @@ def _test_mixed_mm(
scales.T,
zeros.T,
transposed=False,
group_size=group_size,
groupsize=groupsize,
fp8_fast_accum=False,
kernel_type=kernel_type,
BLOCK_M=BLOCK_M,
BLOCK_N=BLOCK_N,
BLOCK_K=BLOCK_K,
)
msg = f"shape={shape}, group_size={group_size}, axis={axis}, dtype={dtype}, transposed={transposed}, kernel_type={kernel_type}, quant_dtype={quant_dtype}"
msg = f"shape={shape}, groupsize={groupsize}, axis={axis}, dtype={dtype}, transposed={transposed}, kernel_type={kernel_type}, quant_dtype={quant_dtype}"

check(
hqq_out,
Expand All @@ -229,18 +229,18 @@ def _test_mixed_mm(
BLOCK_M, BLOCK_N, BLOCK_K = shape
BLOCK_K = K // 2
BLOCK_N = N // 2
group_size = BLOCK_K
groupsize = BLOCK_K
_test_mixed_mm(
shape,
group_size=group_size,
groupsize=groupsize,
BLOCK_M=BLOCK_M,
BLOCK_N=BLOCK_N,
BLOCK_K=BLOCK_K,
transposed=False,
)
_test_mixed_mm(
shape,
group_size=group_size,
groupsize=groupsize,
BLOCK_M=BLOCK_M,
BLOCK_N=BLOCK_N,
BLOCK_K=BLOCK_K,
Expand Down
24 changes: 12 additions & 12 deletions test/hqq/test_triton_qkv_fused.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,42 +75,42 @@ def fuse_qkv(W_qs, scales, zeros):
"""
Args:
W_qs (list[torch.Tensor]): len 3 list of tensors with shapes Nq x K, Nk x K, Nv x K where Nk == Nv
scales (list[torch.Tensor]): each is N x (K // group_size), with same N requirements per W_qs
scales (list[torch.Tensor]): each is N x (K // groupsize), with same N requirements per W_qs
zeros (list[torch.Tensor]): same as scales

Returns:
qkv (torch.Tensor): (N_qkv x K) where N_qkv = Nq + Nk + Nv
scales (torch.Tensor): (N_qkv x (K // group_size))
zeros (torch.Tensor): (N_qkv x (K // group_size))
scales (torch.Tensor): (N_qkv x (K // groupsize))
zeros (torch.Tensor): (N_qkv x (K // groupsize))
"""
qkv = torch.cat(W_qs, dim=0) # Fuse along N
fused_scales = torch.cat([s for s in scales], dim=0)
fused_zeros = torch.cat([z for z in zeros], dim=0)
return qkv, fused_scales, fused_zeros


def ref_proj(x, packed_w, scale, zero, group_size, kernel_type, transposed=False):
def ref_proj(x, packed_w, scale, zero, groupsize, kernel_type, transposed=False):
return triton_mixed_mm(
x,
packed_w,
scale.T,
zero.T,
transposed=transposed,
group_size=group_size,
groupsize=group_size,
fp8_fast_accum=False,
kernel_type=kernel_type,
)


@pytest.mark.parametrize(
"q_shape, kv_shape, group_size, axis, dtype, transposed, kernel_type",
"q_shape, kv_shape, groupsize, axis, dtype, transposed, kernel_type",
TEST_CONFIGS,
ids=_arg_to_id,
)
def test_mixed_mm(
q_shape,
kv_shape,
group_size,
groupsize,
axis,
dtype,
transposed,
Expand All @@ -136,7 +136,7 @@ def test_mixed_mm(

qcfg = {
**BASE_QUANT_CONFIG,
**dict(group_size=group_size, axis=axis),
**dict(groupsize=group_size, axis=axis),
}

quant_config = BaseQuantizeConfig(
Expand Down Expand Up @@ -172,7 +172,7 @@ def test_mixed_mm(
xs = [torch.randn(seqlen, n, dtype=dtype, device=device) for n in Ns]
x_fused = torch.cat(xs, dim=1)
q_ref, k_ref, v_ref = [
ref_proj(x, p, s, z, group_size, kernel_type, transposed=True)
ref_proj(x, p, s, z, groupsize, kernel_type, transposed=True)
for x, p, s, z in zip(xs, packed_ws, scales, zeros)
]
tt_fused = triton_mixed_mm(
Expand All @@ -181,7 +181,7 @@ def test_mixed_mm(
scales_fused.T,
zeros_fused.T,
transposed=True,
group_size=group_size,
groupsize=group_size,
fp8_fast_accum=False,
kernel_type=kernel_type,
)
Expand All @@ -191,7 +191,7 @@ def test_mixed_mm(
x = torch.randn(seqlen, K, dtype=dtype, device=device)

q_ref, k_ref, v_ref = [
ref_proj(x, p, s, z, group_size, kernel_type)
ref_proj(x, p, s, z, groupsize, kernel_type)
for p, s, z in zip(packed_ws, scales, zeros)
]

Expand All @@ -201,7 +201,7 @@ def test_mixed_mm(
scales_fused.T,
zeros_fused.T,
transposed=False,
group_size=group_size,
groupsize=group_size,
fp8_fast_accum=False,
kernel_type=kernel_type,
)
Expand Down
2 changes: 1 addition & 1 deletion test/integration/test_integration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -833,7 +833,7 @@ def test_int4_weight_only_quant_subclass_api_grouped(self, device, dtype):
def api(mod):
if TORCH_VERSION_AFTER_2_4:
kwargs_copy = kwargs.copy()
kwargs_copy["group_size"] = groupsize
kwargs_copy["groupsize"] = groupsize
del kwargs_copy["groupsize"]
quantize(mod, int4_weight_only(**kwargs_copy))
unwrap_tensor_subclass(mod)
Expand Down
4 changes: 2 additions & 2 deletions test/quantization/test_galore_quant.py
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Expand Up @@ -42,7 +42,7 @@ def test_galore_quantize_blockwise(dim1, dim2, dtype, signed, blocksize):
bnb_norm = (g.reshape(-1, blocksize) / qstate.absmax[:, None]).reshape(g.shape)

tt_q, tt_norm, tt_absmax = triton_quantize_blockwise(
g, qmap, group_size=blocksize, return_normalized=True
g, qmap, groupsize=blocksize, return_normalized=True
)
tt_check = torch.allclose(ref_bnb, tt_q)

Expand Down Expand Up @@ -87,5 +87,5 @@ def test_galore_dequant_blockwise(dim1, dim2, dtype, signed, blocksize):
q, qstate = F.quantize_blockwise(g, code=qmap, blocksize=blocksize)

dq_ref = F.dequantize_blockwise(q, qstate)
dq = triton_dequant_blockwise(q, qmap, qstate.absmax, group_size=blocksize)
dq = triton_dequant_blockwise(q, qmap, qstate.absmax, groupsize=blocksize)
assert torch.allclose(dq, dq_ref)
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