From de354f7ded52bfa857089769225cdf1ee694bfd6 Mon Sep 17 00:00:00 2001
From: Tim Dettmers <tim.dettmers@gmail.com>
Date: Tue, 16 Aug 2022 12:00:54 -0700
Subject: Added fused bias to matmullt.
---
tests/test_autograd.py | 49 ++++++++++++++++++++++++++++++++++++++-----------
1 file changed, 38 insertions(+), 11 deletions(-)
(limited to 'tests')
diff --git a/tests/test_autograd.py b/tests/test_autograd.py
index f1a15f5..0cd17c9 100644
--- a/tests/test_autograd.py
+++ b/tests/test_autograd.py
@@ -1,4 +1,4 @@
-from itertools import product
+from itertools import product, permutations
import pytest
import torch
@@ -241,11 +241,20 @@ decomp = [0.0, 6.0]
funcs = [(torch.matmul, bnb.matmul)]
str_funcs = ["matmul"]
req_grad = [(False, False), (True, False), (True, True), (False, True)]
-req_grad_str = ["FF", "TF", "TT", "FT"]
+req_grad = list(product([True, False], repeat=3))
+req_grad_str = []
+for c in req_grad:
+ strval = ''
+ for v in c:
+ if v == True: strval += 'T'
+ else: strval += 'F'
+ req_grad_str.append(strval)
+
transpose = [(False, True), (False, False)]
str_transpose = ["NT", "NN"]
dtype = [torch.float16]
has_fp16_weights = [True, False]
+has_bias = [True, False]
values = list(
product(
dim1,
@@ -258,6 +267,7 @@ values = list(
transpose,
decomp,
has_fp16_weights,
+ has_bias
)
)
str_values = list(
@@ -272,18 +282,14 @@ str_values = list(
str_transpose,
decomp,
has_fp16_weights,
+ has_bias
)
)
-names = [
- "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}".format(
- *vals
- )
- for vals in str_values
-]
+names = ["dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}_has_bias_{10}".format(*vals) for vals in str_values]
@pytest.mark.parametrize(
- "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights",
+ "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights, has_bias",
values,
ids=names,
)
@@ -298,10 +304,14 @@ def test_matmullt(
transpose,
decomp,
has_fp16_weights,
+ has_bias
):
dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
outlier_dim = torch.randint(0, dimA[1], size=(dimA[1] // 8,), device="cuda")
+ if has_bias == False:
+ req_grad = list(req_grad)
+ req_grad[2] = False
for i in range(k):
@@ -322,6 +332,11 @@ def test_matmullt(
requires_grad=req_grad[1],
dtype=dtype,
)
+ bias = None
+ bias2 = None
+ if has_bias:
+ bias = torch.randn(dim4, device='cuda', dtype=dtype, requires_grad=req_grad[2])
+ bias2 = bias.clone()
torch.nn.init.xavier_uniform_(B)
B2 = B.clone()
@@ -342,10 +357,13 @@ def test_matmullt(
if not transpose[0] and transpose[1]:
out_torch = funcs[0](A, B.t())
- out_bnb = funcs[1](A, B2, state=state)
+ out_bnb = funcs[1](A, B2, state=state, bias=bias2)
elif not transpose[0] and not transpose[1]:
out_torch = funcs[0](A, B)
- out_bnb = funcs[1](A, B2.t(), state=state)
+ out_bnb = funcs[1](A, B2.t(), state=state, bias=bias2)
+
+ if has_bias:
+ out_torch += bias
n = out_bnb.numel()
err = torch.abs(out_bnb - out_torch).mean().item()
@@ -367,6 +385,9 @@ def test_matmullt(
gradB1 = B.grad
A.grad = None
B.grad = None
+ if has_bias:
+ gradBias1 = bias.grad
+ bias.grad = None
loss_torch = torch.nn.functional.mse_loss(
out_torch, target
@@ -376,6 +397,9 @@ def test_matmullt(
gradB2 = B.grad
A.grad = None
B.grad = None
+ if has_bias:
+ gradBias2 = bias.grad
+ bias.grad = None
if req_grad[0]:
torch.testing.assert_allclose(
@@ -397,3 +421,6 @@ def test_matmullt(
torch.testing.assert_allclose(
gradB1, gradB2, atol=0.18, rtol=0.3
)
+
+ if req_grad[2]:
+ torch.testing.assert_allclose(gradBias1, gradBias2)
--
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