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[unitest]clean code (#5094)

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周周周
2025-11-18 17:21:35 +08:00
committed by GitHub
parent d11235333e
commit 6584ee90e8
3 changed files with 74 additions and 70 deletions
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85
tests/layers/test_attention_layer.py
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@@ -50,7 +50,9 @@ from fastdeploy.model_executor.layers.rotary_embedding import get_rope
from fastdeploy.model_executor.models.ernie4_5_moe import Ernie4_5_Attention
from fastdeploy.model_executor.ops.gpu import get_padding_offset
os.environ.setdefault("DG_NVCC_OVERRIDE_CPP_STANDARD", "17")
if "nvidia graphics device" in paddle.device.cuda.get_device_name().lower():
# (ZKK): CI machine.
os.environ.setdefault("DG_NVCC_OVERRIDE_CPP_STANDARD", "17")
class TestAttentionPerformance(unittest.TestCase):
@@ -119,10 +121,10 @@ class TestAttentionPerformance(unittest.TestCase):
"dtype": "bfloat16",
"hidden_size": 4096,
"max_position_embeddings": 131072,
"max_model_len": 2 * (9000 + 128),
"max_model_len": 5500,
"num_attention_heads": 32,
"num_key_value_heads": 4,
"num_hidden_layers": 39,
"num_hidden_layers": 5,
}
model_dir = tempfile.mkdtemp(prefix="tmp_model_config_")
config_path = os.path.join(model_dir, "config.json")
@@ -293,7 +295,6 @@ class TestAttentionPerformance(unittest.TestCase):
# Test parameters
test_steps = 100
prefill_batch_size = 1
decode_batch_size = 100 # This can be configured as needed
prefill_seq_len = 4096
use_dynamic_quant = True
act_tensor_dtype = paddle.bfloat16
@@ -317,8 +318,7 @@ class TestAttentionPerformance(unittest.TestCase):
paddle.device.synchronize()
import paddle.profiler as profiler
# import paddle.profiler as profiler
# p = profiler.Profiler(
# targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
# on_trace_ready=profiler.export_chrome_tracing("./profile_log"),
@@ -341,56 +341,57 @@ class TestAttentionPerformance(unittest.TestCase):
# p.stop()
decode_hidden_states = paddle.randn(
[decode_batch_size, self.fd_config.model_config.hidden_size], dtype=act_tensor_dtype
)
# p = profiler.Profiler(
# targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
# on_trace_ready=profiler.export_chrome_tracing("./profile_log"),
# )
forward_meta = self.create_forward_meta(
batch_size=decode_batch_size,
seq_len=5000,
mode=ForwardMode.DECODE,
fd_config=self.fd_config,
attn_backend=self.attn_backend,
use_dynamic_quant=use_dynamic_quant,
)
# p.start()
# p.step()
self.attn_backend.init_attention_metadata(forward_meta)
for decode_batch_size in [10, 20, 40, 60, 80, 100, 128]:
decode_hidden_states = paddle.randn(
[decode_batch_size, self.fd_config.model_config.hidden_size], dtype=act_tensor_dtype
)
p = profiler.Profiler(
targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
on_trace_ready=profiler.export_chrome_tracing("./profile_log"),
)
forward_meta = self.create_forward_meta(
batch_size=decode_batch_size,
seq_len=5000,
mode=ForwardMode.DECODE,
fd_config=self.fd_config,
attn_backend=self.attn_backend,
use_dynamic_quant=use_dynamic_quant,
)
p.start()
p.step()
self.attn_backend.init_attention_metadata(forward_meta)
paddle.device.synchronize()
paddle.device.synchronize()
# 必须要先预热一次！因为预处理被放到了第一层再做了！
self.attn_forward(forward_meta, decode_hidden_states)
# 必须要先预热一次！因为预处理被放到了第一层再做了！
self.attn_forward(forward_meta, decode_hidden_states)
attn_cuda_graphs = graphs.CUDAGraph()
attn_cuda_graphs.capture_begin()
attn_cuda_graphs = graphs.CUDAGraph()
attn_cuda_graphs.capture_begin()
self.attn_forward(forward_meta, decode_hidden_states)
self.attn_forward(forward_meta, decode_hidden_states)
attn_cuda_graphs.capture_end()
attn_cuda_graphs.capture_end()
start_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(test_steps)]
end_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(test_steps)]
for i in range(test_steps):
start_events[i].record()
start_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(test_steps)]
end_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(test_steps)]
for i in range(test_steps):
start_events[i].record()
attn_cuda_graphs.replay()
# self.attn_forward(forward_meta, decode_hidden_states)
attn_cuda_graphs.replay()
# self.attn_forward(forward_meta, decode_hidden_states)
end_events[i].record()
paddle.device.synchronize()
end_events[i].record()
paddle.device.synchronize()
times = np.array([round(s.elapsed_time(e), 1) for s, e in zip(start_events, end_events)])[1:]
print(times[-5:])
times = np.array([round(s.elapsed_time(e), 1) for s, e in zip(start_events, end_events)])[1:]
print(times[-5:])
p.stop()
# p.stop()
if __name__ == "__main__":

25
tests/layers/test_ffn.py
View File

@@ -39,6 +39,9 @@ from fastdeploy.scheduler import SchedulerConfig
from fastdeploy.worker.worker_process import init_distributed_environment
paddle.set_default_dtype("bfloat16")
if "nvidia graphics device" in paddle.device.cuda.get_device_name().lower():
# (ZKK): CI machine.
os.environ.setdefault("DG_NVCC_OVERRIDE_CPP_STANDARD", "17")
class FFNWrapper(paddle.nn.Layer):
@@ -46,7 +49,7 @@ class FFNWrapper(paddle.nn.Layer):
super().__init__()
self.model_config = model_config
self.intermediate_size = 3584
self.intermediate_size = self.model_config.intermediate_size
self.hidden_size = self.model_config.hidden_size
self.prefix = "hahahha"
self.fd_config = FDConfig(
@@ -94,10 +97,9 @@ class FFNWrapper(paddle.nn.Layer):
class TestFusedMoE(unittest.TestCase):
def setUp(self) -> None:
self.architectures = ["Ernie4_5_MoeForCausalLM"]
self.hidden_size = 7168
self.moe_intermediate_size = 1
self.moe_num_experts = 1
self.moe_k = 1
self.hidden_size = 4096
self.intermediate_size = 2048
self.num_layers = 1
self.hidden_act = "silu"
self.num_attention_heads = 64
self.model_config = self.build_model_config()
@@ -115,9 +117,7 @@ class TestFusedMoE(unittest.TestCase):
config_dict = {
"architectures": self.architectures,
"hidden_size": self.hidden_size,
"moe_intermediate_size": self.moe_intermediate_size,
"moe_num_experts": self.moe_num_experts,
"moe_k": self.moe_k,
"intermediate_size": self.intermediate_size,
"hidden_act": self.hidden_act,
"num_attention_heads": self.num_attention_heads,
"dtype": "bfloat16",
@@ -135,20 +135,17 @@ class TestFusedMoE(unittest.TestCase):
ffn = FFNWrapper(self.model_config)
# (ZKK): disable this test,
# CI machine does not support deepgemm blockwise_fp8, compilation error.
return
moe_cuda_graphs = [None] * 100
cache_hidden_states = [None] * 100
for idx, num_tokens in enumerate([10, 20, 40, 60, 80, 100, 128, 160, 192, 256, 512, 1024, 2048, 4096]):
test_token_nums = [10, 20, 40, 60, 80, 100, 128, 160, 192, 256, 4096, 4096 * 4]
for idx, num_tokens in enumerate(test_token_nums):
cache_hidden_states[idx] = paddle.rand((num_tokens, self.model_config.hidden_size), dtype=paddle.bfloat16)
moe_cuda_graphs[idx] = graphs.CUDAGraph()
moe_cuda_graphs[idx].capture_begin()
num_layers = 80
num_layers = self.num_layers
for _ in range(num_layers):
out = ffn.ffn(cache_hidden_states[idx])

34
tests/layers/test_fusedmoe.py
View File

@@ -535,12 +535,12 @@ class FuseMoEWrapper(paddle.nn.Layer):
class TestFusedMoE(unittest.TestCase):
def setUp(self) -> None:
self.architectures = ["Ernie4_5_MoeForCausalLM"]
self.hidden_size = 7168
self.moe_intermediate_size = 3584
self.moe_num_experts = 64
self.hidden_size = 4096
self.moe_intermediate_size = 2048
self.moe_num_experts = 160
self.moe_k = 8
self.hidden_act = "silu"
self.num_attention_heads = 64
self.num_layers = 2
self.num_attention_heads = -1
self.model_config = self.build_model_config()
def build_model_config(self) -> ModelConfig:
@@ -559,7 +559,6 @@ class TestFusedMoE(unittest.TestCase):
"moe_intermediate_size": self.moe_intermediate_size,
"moe_num_experts": self.moe_num_experts,
"moe_k": self.moe_k,
"hidden_act": self.hidden_act,
"num_attention_heads": self.num_attention_heads,
"dtype": "bfloat16",
}
@@ -590,16 +589,18 @@ class TestFusedMoE(unittest.TestCase):
# 这行代码必须保留，否则影响均匀性！
paddle.seed(ep_rank + 100)
num_layers = 80
real_weight_layers = 20
num_layers = self.num_layers
real_weight_layers = num_layers // 2
fused_moe = [None] * real_weight_layers
for i in range(real_weight_layers):
fused_moe[i] = FuseMoEWrapper(self.model_config, tp_size, tp_rank, ep_size, ep_rank, nnodes=nnodes)
moe_cuda_graphs = [None] * 100
cache_hidden_states = [None] * 100
test_token_nums = [10, 20, 40, 60, 80, 100, 128, 160, 192, 256]
# test_token_nums = [1024 * i for i in [1,2,4,8,16,32]]
is_decoder = fused_moe[0].fd_config.model_config.moe_phase.phase == "decode"
test_token_nums = [4096 * i for i in [1, 2, 4, 8]]
if is_decoder:
test_token_nums = [10, 20, 40, 60, 80, 100, 128, 160, 192, 256]
for idx, num_tokens in enumerate(test_token_nums):
cache_hidden_states[idx] = paddle.rand((num_tokens, self.model_config.hidden_size), dtype=paddle.bfloat16)
@@ -610,12 +611,14 @@ class TestFusedMoE(unittest.TestCase):
return out
moe_cuda_graphs[idx] = graphs.CUDAGraph()
moe_cuda_graphs[idx].capture_begin()
if is_decoder:
moe_cuda_graphs[idx] = graphs.CUDAGraph()
moe_cuda_graphs[idx].capture_begin()
fake_model_run()
moe_cuda_graphs[idx].capture_end()
if is_decoder:
moe_cuda_graphs[idx].capture_end()
num_tests = 20
start_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(num_tests)]
@@ -623,7 +626,10 @@ class TestFusedMoE(unittest.TestCase):
for i in range(num_tests):
start_events[i].record()
moe_cuda_graphs[idx].replay()
if is_decoder:
moe_cuda_graphs[idx].replay()
else:
fake_model_run()
end_events[i].record()
paddle.device.cuda.synchronize()