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import math
from functools import partial
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from flash_attn.ops.fused_dense import FusedDense, FusedMLP
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("return_residual", [False, True])
@pytest.mark.parametrize("has_bias", [True, False])
@pytest.mark.parametrize("out_features", [1024, 4096])
@pytest.mark.parametrize("in_features", [1024, 4096])
def test_fused_linear_bias(in_features, out_features, has_bias, return_residual, dtype):
device = "cuda"
rtol, atol = (3e-3, 1e-2) if dtype == torch.bfloat16 else (3e-3, 1e-3)
# set seed
torch.random.manual_seed(0)
batch_size = 8
seqlen = 512
x_pt = torch.randn(
batch_size, seqlen, in_features, device=device, dtype=dtype, requires_grad=True
)
x = x_pt.detach().clone().requires_grad_()
model_pt = torch.nn.Linear(in_features, out_features, bias=has_bias, device=device, dtype=dtype)
model = FusedDense(
in_features,
out_features,
bias=has_bias,
return_residual=return_residual,
device=device,
dtype=dtype,
)
with torch.no_grad():
model.weight.copy_(model_pt.weight)
if has_bias:
model.bias.copy_(model_pt.bias)
out_pt = model_pt(x_pt)
if not return_residual:
out = model(x)
else:
out, x_copy = model(x)
x_copy = (
x_copy[..., :out_features]
if out_features < in_features
else F.pad(x_copy, (0, out_features - in_features))
)
x_pt_copy = (
x_pt[..., :out_features]
if out_features < in_features
else F.pad(x_pt, (0, out_features - in_features))
)
# Just add some random function of the residual
out_pt = out_pt + F.gelu(x_pt_copy)
out = out + F.gelu(x_copy)
# with torch.no_grad():
# out_fl = F.linear(x_pt.float(), model.weight.float(), model.bias.float()).half()
assert torch.allclose(out, out_pt, rtol=rtol, atol=atol)
# If we don't divide by batch_size, the gradient gets a bit too large.
g = torch.randn_like(out) / 32
out_pt.backward(g)
out.backward(g)
assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=atol)
# The error for d_weight and d_bias is quite a bit higher
assert torch.allclose(model.weight.grad, model_pt.weight.grad, rtol=rtol, atol=atol * 10)
if has_bias:
assert torch.allclose(model.bias.grad, model_pt.bias.grad, rtol=rtol, atol=atol * 5)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize("heuristic", ["auto", -1])
# @pytest.mark.parametrize('heuristic', ['auto'])
@pytest.mark.parametrize("checkpoint_lvl", [0, 1, 2])
# @pytest.mark.parametrize('checkpoint_lvl', [1])
@pytest.mark.parametrize("return_residual", [False, True])
# @pytest.mark.parametrize('return_residual', [False])
@pytest.mark.parametrize("has_bias2", [True, False])
@pytest.mark.parametrize("has_bias1", [True, False])
# @pytest.mark.parametrize('has_bias2', [True])
# @pytest.mark.parametrize('has_bias1', [True])
@pytest.mark.parametrize("activation", ["gelu_approx", "relu"])
# @pytest.mark.parametrize('activation', ['relu'])
@pytest.mark.parametrize("out_features", [1024, 4096])
@pytest.mark.parametrize("in_features", [1024, 4096])
# @pytest.mark.parametrize('out_features', [4096])
# @pytest.mark.parametrize('in_features', [1024])
def test_fused_mlp(
in_features,
out_features,
activation,
has_bias1,
has_bias2,
return_residual,
checkpoint_lvl,
heuristic,
dtype,
):
device = "cuda"
rtol, atol = (3e-3, 3e-2) if dtype == torch.bfloat16 else (3e-3, 1e-3)
# set seed
torch.random.manual_seed(0)
batch_size = 8
seqlen = 512
x_pt = torch.randn(
batch_size, seqlen, in_features, device=device, dtype=dtype, requires_grad=True
)
x = x_pt.detach().clone().requires_grad_()
model_pt_fc1 = torch.nn.Linear(
in_features, out_features, bias=has_bias1, device=device, dtype=dtype
)
model_pt_fc2 = torch.nn.Linear(
out_features, in_features, bias=has_bias2, device=device, dtype=dtype
)
model = FusedMLP(
in_features,
out_features,
in_features,
activation=activation,
bias1=has_bias1,
bias2=has_bias2,
return_residual=return_residual,
checkpoint_lvl=checkpoint_lvl,
heuristic=heuristic,
device=device,
dtype=dtype,
)
with torch.no_grad():
model.fc1.weight.copy_(model_pt_fc1.weight)
if has_bias1:
model.fc1.bias.copy_(model_pt_fc1.bias)
model.fc2.weight.copy_(model_pt_fc2.weight)
if has_bias2:
model.fc2.bias.copy_(model_pt_fc2.bias)
activation_fn = (
partial(F.gelu, approximate="tanh")
if activation == "gelu_approx"
else partial(F.relu, inplace=True)
)
out_pt = model_pt_fc2(activation_fn(model_pt_fc1(x_pt)))
if not return_residual:
out = model(x)
else:
out, x_copy = model(x)
# Just add some random function of the residual
out_pt = out_pt + F.gelu(x_pt)
out = out + F.gelu(x_copy)
assert torch.allclose(out, out_pt, rtol=rtol, atol=atol)
# If we don't divide by batch_size, the gradient gets a bit too large.
g = torch.randn_like(out) / 32
out_pt.backward(g)
out.backward(g)
# The error for relu is higher still
if activation == "relu":
atol = 1e-1 if dtype == torch.bfloat16 else 5e-2
assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=atol)
# The error for d_weight and d_bias is quite a bit higher
assert torch.allclose(
model.fc1.weight.grad, model_pt_fc1.weight.grad, rtol=rtol, atol=atol * 10
)
if has_bias1:
assert torch.allclose(model.fc1.bias.grad, model_pt_fc1.bias.grad, rtol=rtol, atol=atol * 5)
assert torch.allclose(
model.fc2.weight.grad, model_pt_fc2.weight.grad, rtol=rtol, atol=atol * 10
)
if has_bias2:
assert torch.allclose(model.fc2.bias.grad, model_pt_fc2.bias.grad, rtol=rtol, atol=atol * 5)
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