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https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
[BugFix] Add support for weight shape constraints and group size selection in Machete (#4911)
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Sunny-bot1
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import shutil
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import unittest
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import numpy as np
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import paddle
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import paddle.device.cuda.graphs as graphs
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from fastdeploy.config import (
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CacheConfig,
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FDConfig,
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GraphOptimizationConfig,
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LoadConfig,
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ModelConfig,
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ParallelConfig,
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)
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from fastdeploy.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
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from fastdeploy.model_executor.layers.quantization.weight_only import (
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WINT4Config,
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WINT8Config,
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)
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from fastdeploy.scheduler import SchedulerConfig
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paddle.set_default_dtype("bfloat16")
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paddle.seed(1024)
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class QuantizedLinearWrapper(paddle.nn.Layer):
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def __init__(
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self,
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model_config: ModelConfig,
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tp_size: int = 1,
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prefix: str = "layer0",
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quant_type: str = "wint4",
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):
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super().__init__()
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self.model_config = model_config
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self.tp_size = tp_size
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self.prefix = prefix
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self.fd_config = FDConfig(
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model_config=self.model_config,
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parallel_config=ParallelConfig({"tensor_parallel_size": self.tp_size}),
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quant_config=WINT8Config({}) if quant_type == "wint8" else WINT4Config({}),
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load_config=LoadConfig({}),
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graph_opt_config=GraphOptimizationConfig({}),
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scheduler_config=SchedulerConfig({}),
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cache_config=CacheConfig({}),
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)
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self.fd_config.parallel_config.tp_group = None
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self.qkv_proj = QKVParallelLinear(
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self.fd_config,
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prefix=f"{prefix}.qkv_proj",
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with_bias=False,
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)
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self.o_proj = RowParallelLinear(
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self.fd_config,
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prefix=f"{prefix}.o_proj",
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input_size=self.fd_config.model_config.head_dim * self.fd_config.model_config.num_attention_heads,
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output_size=self.fd_config.model_config.hidden_size,
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)
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qkv_proj_weight_shape = [
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self.qkv_proj.input_size,
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self.qkv_proj.output_size,
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]
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o_proj_weight_shape = [
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self.o_proj.input_size,
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self.o_proj.output_size,
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]
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state_dict = {}
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state_dict[f"{prefix}.qkv_proj.weight"] = paddle.randn(qkv_proj_weight_shape, paddle.bfloat16)
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state_dict[f"{prefix}.o_proj.weight"] = paddle.randn(o_proj_weight_shape, paddle.bfloat16)
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self.qkv_proj.load_state_dict(state_dict)
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self.o_proj.load_state_dict(state_dict)
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self.input_size = self.o_proj.input_size
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self.output_size = self.qkv_proj.output_size
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def forward(self, x):
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x = self.o_proj(x)
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x = self.qkv_proj(x)
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return x
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class TestQuantizedLinear(unittest.TestCase):
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def setUp(self) -> None:
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self.model_name_or_path = None
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self.model_config = self.build_model_config()
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def build_model_config(self) -> ModelConfig:
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model_path = os.getenv("TEST_MODEL_PATH")
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if model_path:
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model_cofig_path = model_path
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else:
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model_cofig_path = self.build_config_json()
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return ModelConfig(
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{
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"model": model_cofig_path,
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"max_model_len": 2048,
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}
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)
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def build_config_json(self) -> str:
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config_dict = {
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"architectures": ["Ernie4_5_MoeForCausalLM"],
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"hidden_size": 8192,
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"num_attention_heads": 64,
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"num_key_value_heads": 8,
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"num_hidden_layers": 54,
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"dtype": "bfloat16",
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}
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tmp_dir = "./tmp_wint"
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os.makedirs(tmp_dir, exist_ok=True)
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with open(f"./{tmp_dir}/config.json", "w") as f:
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json.dump(config_dict, f)
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self.model_name_or_path = os.path.join(os.getcwd(), tmp_dir)
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return self.model_name_or_path
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def run_quantized_linear(self, type="qkv_proj", quant_type="wint4"):
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quantized_linear = QuantizedLinearWrapper(self.model_config, quant_type=quant_type)
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if type == "qkv_proj":
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input_size = quantized_linear.qkv_proj.input_size
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weight_size = quantized_linear.qkv_proj.output_size * quantized_linear.qkv_proj.input_size
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mm = quantized_linear.qkv_proj
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print(f"Input Size: {input_size}, Output Size: {quantized_linear.qkv_proj.output_size}")
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elif type == "o_proj":
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input_size = quantized_linear.o_proj.input_size
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weight_size = quantized_linear.o_proj.output_size * quantized_linear.o_proj.input_size
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mm = quantized_linear.o_proj
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print(f"Input Size: {input_size}, Output Size: {quantized_linear.o_proj.output_size}")
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else:
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input_size = quantized_linear.input_size
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weight_size = (
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quantized_linear.qkv_proj.output_size * quantized_linear.qkv_proj.input_size
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+ quantized_linear.o_proj.output_size * quantized_linear.o_proj.input_size
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)
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mm = quantized_linear
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print(f"========Method: {type}, Quant Type: {quant_type}=========")
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print(
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"{:<15} {:<40} {:<15} {:<15} {:<15}".format(
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"Batch Size", "Last 5 Times (us)", "Last Time (us)", "TFlops", "TB/s"
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)
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)
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num_layers = self.model_config.num_hidden_layers
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real_weight_layers = self.model_config.num_hidden_layers
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linear = [None] * real_weight_layers
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for i in range(real_weight_layers):
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linear[i] = mm
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linear_cuda_graphs = [None] * 2000
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input = [None] * 2000
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# for idx, bsz in enumerate([1024 * i for i in [1,2,4,8,16,32,64]]):
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for idx, bsz in enumerate([1, 8, 16, 32, 128, 1024]):
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input[idx] = paddle.rand((bsz, input_size), dtype=paddle.bfloat16)
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def fake_model_run():
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for j in range(num_layers):
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out = linear[j % real_weight_layers](input[idx])
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return out
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fake_model_run()
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linear_cuda_graphs[idx] = graphs.CUDAGraph()
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linear_cuda_graphs[idx].capture_begin()
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fake_model_run()
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linear_cuda_graphs[idx].capture_end()
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num_tests = 20
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start_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(num_tests)]
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end_events = [paddle.device.cuda.Event(enable_timing=True) for _ in range(num_tests)]
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for i in range(num_tests):
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start_events[i].record()
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linear_cuda_graphs[idx].replay()
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end_events[i].record()
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paddle.device.synchronize()
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times = np.array([round(s.elapsed_time(e), 2) for s, e in zip(start_events, end_events)])[1:]
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times = times * 1e3 / num_layers
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times = np.array([round(time, 2) for time in times])
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last_5_times = times[-5:]
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last_time = times[-1] # us
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flops = 2 * bsz * weight_size
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memory = weight_size
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tfloaps = round(flops / (1e12) / (last_time * 1e-6), 1)
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tbps = round(memory / (1e12) / (last_time * 1e-6), 1)
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print("{:<15} {:<40} {:<15} {:<15} {:<15}".format(bsz, str(last_5_times), last_time, tfloaps, tbps))
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def test_quantized_linear(self):
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for type in ["qkv_proj", "o_proj", "out_proj+qkv_proj"]:
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for quant_type in ["wint4", "wint8"]:
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for use_machete in ["0", "1"]:
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os.environ["FD_USE_MACHETE"] = use_machete
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self.run_quantized_linear(type, quant_type)
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self.run_quantized_linear(type, "block_wise_fp8")
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def tearDown(self) -> None:
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if self.model_name_or_path:
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print("Remove tmp model config file")
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shutil.rmtree(self.model_name_or_path)
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class TestQuantizedLinearGroupSize64(TestQuantizedLinear):
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def setUp(self) -> None:
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self.model_name_or_path = None
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self.model_config = self.build_model_config()
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def build_model_config(self) -> ModelConfig:
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model_path = os.getenv("TEST_MODEL_PATH")
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if model_path:
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model_cofig_path = model_path
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else:
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model_cofig_path = self.build_config_json()
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return ModelConfig(
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{
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"model": model_cofig_path,
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"max_model_len": 2048,
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}
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)
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def build_config_json(self) -> str:
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config_dict = {
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"architectures": ["Ernie4_5_MoeForCausalLM"],
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"hidden_size": 2880,
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"head_dim": 64,
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"num_attention_heads": 64,
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"num_key_value_heads": 8,
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"num_hidden_layers": 24,
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"dtype": "bfloat16",
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}
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tmp_dir = "./tmp_wint"
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os.makedirs(tmp_dir, exist_ok=True)
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with open(f"./{tmp_dir}/config.json", "w") as f:
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json.dump(config_dict, f)
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self.model_name_or_path = os.path.join(os.getcwd(), tmp_dir)
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return self.model_name_or_path
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def test_quantized_linear(self):
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for type in ["qkv_proj", "o_proj"]:
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for quant_type in ["wint4", "wint8"]:
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for use_machete in ["0", "1"]:
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os.environ["FD_USE_MACHETE"] = use_machete
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self.run_quantized_linear(type, quant_type)
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if __name__ == "__main__":
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unittest.main()
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