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Optimize Linear
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@mejai1206
mejai1206 authored Sep 18, 2023
1 parent 801b646 commit c60beee
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Showing 6 changed files with 279 additions and 72 deletions.
3 changes: 3 additions & 0 deletions tests/test_geglu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,9 @@ def train(func):

@pytest.mark.parametrize("num_batches, m_size, n_size, k_size", [(1, 16, 16, 16)])
def test_geglu(num_batches, m_size, n_size, k_size, device, dtype):
if dtype is torch.bfloat16:
pytest.skip("Triton has a bug.")

factory_kwargs = {"device": device, "dtype": dtype}
input = torch.randn(num_batches, m_size, k_size, **factory_kwargs)
x_size = n_size // 2
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3 changes: 3 additions & 0 deletions tests/test_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,9 @@ def train(func):

@pytest.mark.parametrize("m_size, n_size, k_size", [(32, 32, 32)])
def test_linear(m_size, n_size, k_size, device, dtype):
if dtype is torch.bfloat16:
pytest.skip("Triton has a bug.")

factory_kwargs = {"device": device, "dtype": dtype}
input = torch.randn(m_size, k_size, **factory_kwargs, requires_grad=True)
weight = torch.randn(n_size, k_size, **factory_kwargs, requires_grad=True)
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14 changes: 7 additions & 7 deletions trident/kernel/attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,15 +120,15 @@ def forward(
score = tl.where(condition, score, float("-inf"))

key = tl.load(key_block_ptr)
score += tl.dot(query, key, use_accelerator, dtype)
score += tl.dot(query, key, allow_tf32=use_accelerator, out_dtype=dtype)
peak = tl.maximum(max, tl.max(score, 1))
alpha = tl.math.exp2(max - peak)
beta = tl.math.exp2(score - peak[:, None])
sum = sum * alpha + tl.sum(beta, 1)
max = peak
output *= alpha[:, None].to(dtype)
value = tl.load(value_block_ptr)
gamma = tl.dot(beta.to(dtype), value, use_accelerator, dtype)
gamma = tl.dot(beta.to(dtype), value, allow_tf32=use_accelerator, out_dtype=dtype)
output += gamma.to(dtype)
key_block_ptr = tl.advance(key_block_ptr, (0, y_block_size))
value_block_ptr = tl.advance(value_block_ptr, (y_block_size, 0))
Expand Down Expand Up @@ -254,7 +254,7 @@ def backward(
else:
score = tl.zeros((y_block_size, y_block_size), dtype)

score += tl.dot(query, tl.trans(key), use_accelerator, dtype)
score += tl.dot(query, tl.trans(key), allow_tf32=use_accelerator, out_dtype=dtype)
score *= score_scale
log2sum = tl.load(log2sum_block_ptr)
alpha = tl.math.exp2(score - log2sum[:, None]).to(dtype)
Expand All @@ -266,14 +266,14 @@ def backward(
grad_dropout = tl.where(output > 0.0, dropout_scale, 0.0).to(dtype)
grad_output *= grad_dropout

grad_value += tl.dot(tl.trans(alpha), grad_output, use_accelerator, dtype)
grad_value += tl.dot(tl.trans(alpha), grad_output, allow_tf32=use_accelerator, out_dtype=dtype)
delta = tl.load(delta_block_ptr)
grad_alpha = tl.zeros((y_block_size, y_block_size), dtype) - delta[:, None]
grad_alpha += tl.dot(grad_output, tl.trans(value), use_accelerator, dtype)
grad_alpha += tl.dot(grad_output, tl.trans(value), allow_tf32=use_accelerator, out_dtype=dtype)
grad_softmax = (alpha * grad_alpha * softmax_scale).to(dtype)
grad_key += tl.dot(tl.trans(grad_softmax), query, use_accelerator, dtype)
grad_key += tl.dot(tl.trans(grad_softmax), query, allow_tf32=use_accelerator, out_dtype=dtype)
grad_query = tl.load(grad_query_block_ptr)
grad_query += tl.dot(grad_softmax, key, use_accelerator, dtype)
grad_query += tl.dot(grad_softmax, key, allow_tf32=use_accelerator, out_dtype=dtype)
tl.store(grad_query_block_ptr, grad_query)

grad_query_block_ptr = tl.advance(grad_query_block_ptr, (y_block_size, 0))
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71 changes: 53 additions & 18 deletions trident/kernel/geglu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,28 +18,48 @@
from trident import language, util


def num_warps_and_stages_for_geglu(size):
if size >= 2**15:
num_warps = 8
num_stages = 3
elif size >= 2**14:
num_warps = 4
num_stages = 4
else:
num_warps = 2
num_stages = 5
return num_warps, num_stages


def geglu_configs():
configs = []
for m_block_size in [32, 64]:
for k_block_size in [32, 64, 128, 256]:
for x_block_size in [32, 64]:
for num_stages in [2, 3]:
config = triton.Config(
{
"m_block_size": m_block_size,
"k_block_size": k_block_size,
"x_block_size": x_block_size,
},
2 if k_block_size <= 64 else 4,
num_stages,
)
configs.append(config)
for k_block_size in [32, 64]:
for m_block_size in [16, 64, 128]:
for x_block_size in [32, 64, 128]:
num_warps, num_stages = num_warps_and_stages_for_geglu(m_block_size * x_block_size)
config = triton.Config(
{
"m_block_size": m_block_size,
"k_block_size": k_block_size,
"x_block_size": x_block_size,
},
num_warps=num_warps,
num_stages=num_stages,
)
configs.append(config)
return configs


class GEGLU:
@staticmethod
@util.autotune(configs=geglu_configs(), key=["m_size", "k_size", "x_size"])
@util.autotune(geglu_configs(), ["m_size", "k_size", "x_size"])
@triton.heuristics(
{
"require_m_boundary_check": lambda args: args["m_size"] % args["m_block_size"] == 0,
"require_k_boundary_check": lambda args: args["k_size"] % args["k_block_size"] == 0,
"require_x_boundary_check": lambda args: args["x_size"] % args["x_block_size"] == 0,
}
)
@triton.jit
def forward(
output_ptr: tl.tensor,
Expand All @@ -61,6 +81,9 @@ def forward(
m_block_size: tl.constexpr,
k_block_size: tl.constexpr,
x_block_size: tl.constexpr,
require_m_boundary_check: tl.constexpr,
require_k_boundary_check: tl.constexpr,
require_x_boundary_check: tl.constexpr,
):
pid = tl.program_id(0)
num_m_blocks = tl.cdiv(m_size, m_block_size)
Expand Down Expand Up @@ -115,6 +138,9 @@ def forward(
m_block_size,
x_block_size,
k_block_size,
require_m_boundary_check,
require_x_boundary_check,
require_k_boundary_check,
dtype,
)
gate = language.Linear.forward(
Expand All @@ -134,12 +160,21 @@ def forward(
m_block_size,
x_block_size,
k_block_size,
require_m_boundary_check,
require_x_boundary_check,
require_k_boundary_check,
dtype,
)
output = state * language.math.GELU.forward(gate)
tl.store(output_block_ptr, output.to(dtype), boundary_check=(0, 1))
tl.store(state_block_ptr, state.to(dtype), boundary_check=(0, 1))
tl.store(gate_block_ptr, gate.to(dtype), boundary_check=(0, 1))

if require_m_boundary_check & require_x_boundary_check:
tl.store(output_block_ptr, output.to(dtype))
tl.store(state_block_ptr, state.to(dtype))
tl.store(gate_block_ptr, gate.to(dtype))
else:
tl.store(output_block_ptr, output.to(dtype), boundary_check=(0, 1))
tl.store(state_block_ptr, state.to(dtype), boundary_check=(0, 1))
tl.store(gate_block_ptr, gate.to(dtype), boundary_check=(0, 1))

@staticmethod
@triton.jit
Expand Down
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