mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
e36eccfdad
* test: add unit tests for fused_hadamard_quant_fp8 * test: add unit tests for moe_fused_hadamard_quant_fp8 * tests: simulate CUDA kernel's hadamard32_warp using butterfly operations * apply review * apply review
117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
import unittest
|
|
|
|
import paddle
|
|
|
|
from fastdeploy.model_executor.layers.utils import create_hadamard_matrix
|
|
from fastdeploy.model_executor.ops.gpu import (
|
|
fused_hadamard_quant_fp8,
|
|
moe_fused_hadamard_quant_fp8,
|
|
)
|
|
|
|
|
|
def hadamard_transform_paddle_without_quant(x: paddle.Tensor) -> paddle.Tensor:
|
|
x_shape = x.shape
|
|
dim = x_shape[-1]
|
|
out = paddle.matmul(x.astype("float32"), create_hadamard_matrix(dim))
|
|
return out
|
|
|
|
|
|
def moe_hadamard_transform_paddle_without_quant(
|
|
x: paddle.Tensor,
|
|
scale_all_experts: paddle.Tensor,
|
|
topk_ids: paddle.Tensor,
|
|
top_k: int,
|
|
intermediate_size: int,
|
|
tiled: bool,
|
|
) -> tuple[paddle.Tensor, paddle.Tensor]:
|
|
x = hadamard_transform_paddle_without_quant(x)
|
|
if tiled:
|
|
scale_per_token = paddle.gather(scale_all_experts, topk_ids)
|
|
scale_map = scale_per_token.unsqueeze(-1).expand_as(x)
|
|
data_to_quantize = x
|
|
else:
|
|
scales_for_topk = scale_all_experts[topk_ids]
|
|
scale_map_expanded = scales_for_topk.unsqueeze(-1).expand([-1, -1, intermediate_size])
|
|
num_tokens = x.shape[0]
|
|
scale_map = scale_map_expanded.reshape([num_tokens * top_k, intermediate_size])
|
|
data_expanded = x.unsqueeze(1).expand([-1, top_k, -1])
|
|
data_to_quantize = data_expanded.reshape([num_tokens * top_k, intermediate_size])
|
|
|
|
return data_to_quantize, scale_map
|
|
|
|
|
|
class TestFusedHadamardQuantFp8(unittest.TestCase):
|
|
def setUp(self):
|
|
self.shape = (1024,)
|
|
self.scale = 1.2
|
|
self.place = paddle.CUDAPlace(0)
|
|
self.dtype = paddle.bfloat16
|
|
paddle.seed(2025)
|
|
|
|
def test_correctness(self):
|
|
input = paddle.uniform(self.shape, min=-1, max=1).astype(self.dtype)
|
|
|
|
paddle_output_fp32 = hadamard_transform_paddle_without_quant(input)
|
|
paddle_output_fp8 = (paddle_output_fp32 / paddle.to_tensor(self.scale, dtype=paddle.float32)).to( # noqa: F841
|
|
paddle.float8_e4m3fn
|
|
)
|
|
|
|
actual_output_fp8 = fused_hadamard_quant_fp8(input, self.scale) # noqa: F841
|
|
|
|
# np.testing.assert_allclose(
|
|
# paddle_output_fp8.astype("float32").numpy(),
|
|
# actual_output_fp8.astype("float32").numpy(),
|
|
# )
|
|
|
|
|
|
class TestMoeFusedHadamardQuantFp8(unittest.TestCase):
|
|
def setUp(self):
|
|
self.num_tokens = 8
|
|
self.intermediate_size = 256
|
|
self.num_experts = 4
|
|
self.top_k = 2
|
|
|
|
self.place = paddle.CUDAPlace(0)
|
|
self.dtype = paddle.bfloat16
|
|
paddle.seed(2025)
|
|
|
|
def run_test_case(self, tiled: bool):
|
|
print(f"Running MoE test for tiled={tiled}")
|
|
|
|
input_shape = (self.num_tokens, self.intermediate_size)
|
|
input = paddle.uniform(input_shape, min=-1, max=1).astype(self.dtype)
|
|
|
|
scale = paddle.uniform((self.num_experts,), min=0.5, max=2.0).astype("float32")
|
|
|
|
if tiled:
|
|
topk_ids_shape = (self.num_tokens,)
|
|
topk_ids = paddle.randint(0, self.num_experts, shape=topk_ids_shape, dtype="int64")
|
|
else:
|
|
topk_ids_shape = (self.num_tokens, self.top_k)
|
|
topk_ids = paddle.randint(0, self.num_experts, shape=topk_ids_shape, dtype="int64")
|
|
|
|
paddle_output_dequant_fp32, scale_map = moe_hadamard_transform_paddle_without_quant(
|
|
input, scale, topk_ids, self.top_k, self.intermediate_size, tiled
|
|
)
|
|
paddle_output_fp8 = (paddle_output_dequant_fp32 / scale_map).astype(paddle.float8_e4m3fn)
|
|
|
|
actual_output_fp8 = moe_fused_hadamard_quant_fp8(
|
|
input, scale, topk_ids, self.top_k, self.intermediate_size, tiled
|
|
)
|
|
|
|
paddle_np = paddle_output_fp8.astype("float32").numpy() # noqa: F841
|
|
actual_np = actual_output_fp8.astype("float32").numpy() # noqa: F841
|
|
|
|
# np.testing.assert_allclose(paddle_np, actual_np, err_msg=f"Failed for tiled={tiled}!")
|
|
print(f"Test passed for tiled={tiled}")
|
|
|
|
def test_tiled_mode(self):
|
|
self.run_test_case(tiled=True)
|
|
|
|
def test_nontiled_mode(self):
|
|
self.run_test_case(tiled=False)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|