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c19219b
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zhoutianzi666 Dec 11, 2025
b8508d7
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zhoutianzi666 Dec 11, 2025
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61 changes: 30 additions & 31 deletions tests/layers/test_attention_layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,9 @@ def setUp(self):
paddle.set_device("gpu")
paddle.set_default_dtype("bfloat16")

# os.environ["FD_ATTENTION_BACKEND"] = "FLASH_ATTN"
# os.environ["FLAGS_flash_attn_version"] = "3"

self.model_dir = self.create_model_config_json()
self.fd_config = self.create_fd_config_from_model_path(self.model_dir, tensor_parallel_size=1)
self.fd_config.parallel_config.tp_group = [0]
Expand Down Expand Up @@ -125,7 +128,7 @@ def create_model_config_json(self) -> str:
"hidden_size": 8192,
"num_attention_heads": 64,
"num_key_value_heads": 8,
"num_hidden_layers": 2,
"num_hidden_layers": 54,
}
model_dir = tempfile.mkdtemp(prefix="tmp_model_config_")
config_path = os.path.join(model_dir, "config.json")
Expand Down Expand Up @@ -302,27 +305,21 @@ def test_decode_performance_with_prefill(self):
# Test parameters
test_steps = 100

# prefill_batch_size = 1
# prefill_seq_len = 4096

# prefill_hidden_states = paddle.randn(
# [prefill_batch_size * prefill_seq_len, self.fd_config.model_config.hidden_size],
# dtype=act_tensor_dtype,
# )
prefill_batch_size = 1
prefill_seq_len = 4096

# forward_meta = self.create_forward_meta(
# batch_size=prefill_batch_size,
# seq_len=prefill_seq_len,
# mode=ForwardMode.EXTEND,
# fd_config=self.fd_config,
# attn_backend=self.attn_backend,
# cache_quant_type_str=self.cache_quant_type_str,
# )

# self.attn_backend.init_attention_metadata(forward_meta)
# self.attn_forward(forward_meta, prefill_hidden_states)
forward_meta, prefill_hidden_states = self.create_forward_meta(
batch_size=prefill_batch_size,
seq_len=prefill_seq_len,
mode=ForwardMode.EXTEND,
fd_config=self.fd_config,
attn_backend=self.attn_backend,
cache_quant_type_str=self.cache_quant_type_str,
)

# paddle.device.synchronize()
self.attn_backend.init_attention_metadata(forward_meta)
self.attn_forward(forward_meta, prefill_hidden_states)
paddle.device.synchronize()

# import paddle.profiler as profiler
# p = profiler.Profiler(
Expand All @@ -332,22 +329,24 @@ def test_decode_performance_with_prefill(self):
# p.start()
# p.step()

# 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()

# self.attn_forward(forward_meta, prefill_hidden_states)
self.attn_forward(forward_meta, prefill_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:])
# return
times = np.array([round(s.elapsed_time(e), 1) for s, e in zip(start_events, end_events)])[1:]
print(times[-5:])
del forward_meta

# p.stop()

# ----------------------decoder ---------------------#

# p = profiler.Profiler(
# targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
# on_trace_ready=profiler.export_chrome_tracing("./profile_log"),
Expand All @@ -359,7 +358,7 @@ def test_decode_performance_with_prefill(self):
for decode_batch_size in [32, 16, 8, 4, 2]:
forward_meta, hidden_states = self.create_forward_meta(
batch_size=decode_batch_size,
seq_len=36 * 1024,
seq_len=8 * 1024,
mode=ForwardMode.DECODE,
fd_config=self.fd_config,
attn_backend=self.attn_backend,
Expand Down
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