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# Copyright (C) 2023, Tri Dao.
import math
import torch
import pytest
from einops import rearrange
from causal_conv1d.causal_conv1d_interface import causal_conv1d_fn, causal_conv1d_ref
from causal_conv1d.causal_conv1d_interface import causal_conv1d_update, causal_conv1d_update_ref
@pytest.mark.parametrize("channel_last", [False, True])
# @pytest.mark.parametrize('channel_last', [True])
@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
# @pytest.mark.parametrize('itype', [torch.float16])
@pytest.mark.parametrize("silu_activation", [False, True])
# @pytest.mark.parametrize('silu_activation', [True])
@pytest.mark.parametrize("has_bias", [False, True])
# @pytest.mark.parametrize('has_bias', [True])
@pytest.mark.parametrize("width", [2, 3, 4])
# @pytest.mark.parametrize('width', [2])
@pytest.mark.parametrize(
"seqlen", [8, 16, 32, 64, 128, 151, 256, 372, 512, 784, 1024, 1134, 2048, 4096]
)
# @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 512, 784, 1024, 2048, 4096])
# @pytest.mark.parametrize('seqlen', [128])
def test_causal_conv1d(seqlen, width, has_bias, silu_activation, itype, channel_last):
device = "cuda"
rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (3e-3, 5e-3)
if itype == torch.bfloat16:
rtol, atol = 1e-2, 5e-2
rtolw, atolw = (1e-3, 1e-3)
# set seed
torch.random.manual_seed(0)
batch_size = 2
# batch_size = 1
dim = 4096 + 32 # Try dim not divisible by 64
# dim = 64
if not channel_last:
x = torch.randn(batch_size, 4096 + dim + 64, seqlen, device=device, dtype=itype)[:, 4096:4096 + dim, :].requires_grad_()
else:
x = rearrange(
torch.randn(batch_size, seqlen, 4096 + dim + 64, device=device, dtype=itype)[:, :, 4096:4096 + dim], "b s d -> b d s"
).requires_grad_()
weight = torch.randn(dim, width, device=device, dtype=torch.float32, requires_grad=True)
if has_bias:
bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
else:
bias = None
x_ref = x.detach().clone().requires_grad_()
weight_ref = weight.detach().clone().requires_grad_()
bias_ref = bias.detach().clone().requires_grad_() if bias is not None else None
activation = None if not silu_activation else "silu"
out = causal_conv1d_fn(x, weight, bias, activation=activation)
out_ref = causal_conv1d_ref(x_ref, weight_ref, bias_ref, activation=activation)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
g = torch.randn_like(out)
out_ref.backward(g)
out.backward(g)
print(f"dx max diff: {(x.grad - x_ref.grad).abs().max().item()}")
print(f"dweight max diff: {(weight.grad - weight_ref.grad).abs().max().item()}")
if has_bias:
print(f"dbias max diff: {(bias.grad - bias_ref.grad).abs().max().item()}")
assert torch.allclose(x.grad, x_ref.grad.to(dtype=itype), rtol=rtol, atol=atol)
assert torch.allclose(weight.grad, weight_ref.grad, rtol=rtolw, atol=atolw)
if has_bias:
assert torch.allclose(bias.grad, bias_ref.grad, rtol=rtolw, atol=atolw)
@pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
# @pytest.mark.parametrize('itype', [torch.float16])
@pytest.mark.parametrize("silu_activation", [False, True])
# @pytest.mark.parametrize('silu_activation', [False])
@pytest.mark.parametrize("has_bias", [False, True])
# @pytest.mark.parametrize('has_bias', [True])
@pytest.mark.parametrize("width", [2, 3, 4])
# @pytest.mark.parametrize('width', [2])
@pytest.mark.parametrize("dim", [2048, 2048 + 16, 4096])
# @pytest.mark.parametrize("dim", [2048])
def test_causal_conv1d_update(dim, width, has_bias, silu_activation, itype):
device = "cuda"
rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (3e-3, 5e-3)
if itype == torch.bfloat16:
rtol, atol = 1e-2, 5e-2
rtolw, atolw = (1e-3, 1e-3)
# set seed
torch.random.manual_seed(0)
batch_size = 2
# batch_size = 1
# dim = 64
x = torch.randn(batch_size, dim, device=device, dtype=itype)
conv_state = torch.randn(batch_size, dim, width, device=device, dtype=itype)
weight = torch.randn(dim, width, device=device, dtype=torch.float32, requires_grad=True)
if has_bias:
bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
else:
bias = None
conv_state_ref = conv_state.detach().clone()
activation = None if not silu_activation else "silu"
out = causal_conv1d_update(x, conv_state, weight, bias, activation=activation)
out_ref = causal_conv1d_update_ref(x, conv_state_ref, weight, bias, activation=activation)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
assert torch.equal(conv_state, conv_state_ref)
assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
# @pytest.mark.parametrize("channel_last", [False, True])
@pytest.mark.parametrize('channel_last', [True])
# @pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
@pytest.mark.parametrize('itype', [torch.bfloat16])
# @pytest.mark.parametrize("silu_activation", [False, True])
@pytest.mark.parametrize('silu_activation', [True])
# @pytest.mark.parametrize("has_bias", [False, True])
@pytest.mark.parametrize('has_bias', [True])
# @pytest.mark.parametrize("width", [2, 3, 4])
@pytest.mark.parametrize('width', [4])
@pytest.mark.parametrize(
# "seqlen", [8, 16, 32, 64, 128, 151, 256, 372, 512, 784, 1024, 1134, 2048, 4096]
"seqlen", [2048]
)
# @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 512, 784, 1024, 2048, 4096])
# @pytest.mark.parametrize('seqlen', [128])
def test_causal_conv1d_race_condition(seqlen, width, has_bias, silu_activation, itype, channel_last):
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 2
# batch_size = 1
dim = 4096 + 32 # Try dim not divisible by 64
# dim = 64
if not channel_last:
x = torch.randn(batch_size, 4096 + dim + 64, seqlen, device=device, dtype=itype)[:, 4096:4096 + dim, :].requires_grad_()
else:
x = rearrange(
torch.randn(batch_size, seqlen, 4096 + dim + 64, device=device, dtype=itype)[:, :, 4096:4096 + dim], "b s d -> b d s"
).requires_grad_()
weight = torch.randn(dim, width, device=device, dtype=torch.float32, requires_grad=True)
if has_bias:
bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
else:
bias = None
activation = None if not silu_activation else "silu"
out0 = causal_conv1d_fn(x, weight, bias, activation=activation)
g = torch.randn_like(out0)
dx0, dw0, db0 = torch.autograd.grad(out0, (x, weight, bias), g)
dw_atol = 1e-4
db_atol = 1e-4
for i in range(10000):
out = causal_conv1d_fn(x, weight, bias, activation=activation)
dx, dw, db = torch.autograd.grad(out, (x, weight, bias), g)
dw_equal = torch.allclose(dw, dw0, atol=dw_atol)
# if not dw_equal:
# breakpoint()
if has_bias:
db_equal = torch.allclose(db, db0, atol=db_atol)
# if not db_equal:
# breakpoint()
assert torch.equal(out, out0)
assert torch.equal(dx, dx0)
assert dw_equal
if has_bias:
assert dw_equal
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