wehub-resource-sync
227 lines
7.5 KiB
Python
227 lines
7.5 KiB
Python
# Copyright (c) 2024 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 numpy as np
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import paddle
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from paddle import nn, static
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from paddle.nn import TransformerEncoder, TransformerEncoderLayer
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def get_r50_program():
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paddle.enable_static()
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from paddle.vision.models import wide_resnet50_2
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with paddle.pir_utils.IrGuard():
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infer_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with static.program_guard(infer_program, startup_program):
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scope = paddle.static.global_scope()
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input_data = paddle.static.data(
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shape=[-1, 3, 224, 224], dtype='float32', name='input'
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)
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model = wide_resnet50_2()
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model.eval()
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output = model(input_data)
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place = paddle.CUDAPlace(0)
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exe = static.Executor(place)
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exe.run(startup_program)
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params = infer_program.global_block().all_parameters()
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param_dict = {}
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for v in params:
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name = v.get_defining_op().attrs()["parameter_name"]
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param_dict.update({name: np.array(scope.var(name).get_tensor())})
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return infer_program, scope, param_dict
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def get_r50_refit_program(save_path):
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paddle.enable_static()
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from paddle.vision.models import wide_resnet50_2
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infer_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(infer_program, startup_program):
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scope = paddle.static.global_scope()
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input_data = paddle.static.data(
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shape=[-1, 3, 224, 224], dtype='float32', name='input'
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)
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model = wide_resnet50_2()
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model.eval()
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output = model(input_data)
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place = paddle.CUDAPlace(0)
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exe = paddle.static.Executor(place)
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exe.run(startup_program)
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_ = exe.run(
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infer_program,
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feed={'input': np.random.randn(1, 3, 224, 224).astype(np.float32)},
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fetch_list=[output],
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)
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paddle.static.save_inference_model(
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path_prefix=save_path,
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feed_vars=[input_data],
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fetch_vars=[output],
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executor=exe,
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program=infer_program,
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)
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params = infer_program.global_block().all_parameters()
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param_dict = {}
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for v in params:
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name = v.get_defining_op().attrs()["parameter_name"]
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param_dict.update({name: np.array(scope.var(name).get_tensor())})
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return infer_program, scope, param_dict
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def get_dummy_program():
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paddle.enable_static()
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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default_startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, default_startup_program):
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scope = paddle.static.global_scope()
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input = paddle.static.data(
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shape=[-1, 64], dtype='float32', name='input'
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)
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weight_numpy = np.random.rand(64, 64).astype('float32')
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weight = paddle.create_parameter(
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name="w",
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shape=[64, 64],
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dtype='float32',
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attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Assign(weight_numpy)
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),
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)
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bias_numpy = np.random.rand(64).astype('float32')
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bias = paddle.create_parameter(
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name="b",
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shape=[64],
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dtype='float32',
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attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Assign(bias_numpy)
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),
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)
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x = paddle.matmul(input, weight)
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x_1 = paddle.add(x, bias)
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x_1 = paddle.unsqueeze(x_1, axis=0)
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x_1 = paddle.squeeze(x_1, axis=0)
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y = paddle.nn.functional.relu(x_1)
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y_gelu_1 = paddle.nn.functional.gelu(y)
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y_gelu_2 = paddle.nn.functional.gelu(x_1)
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# Concatenate the outputs of the two GELU operations
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concat_out = paddle.concat([y_gelu_1, y_gelu_2], axis=-1)
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output = paddle.unsqueeze(concat_out, axis=0)
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exe = paddle.static.Executor(paddle.CUDAPlace(0))
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exe.run(default_startup_program)
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params = main_program.global_block().all_parameters()
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param_dict = {}
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# save parameters
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for v in params:
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name = v.get_defining_op().attrs()["parameter_name"]
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param_dict.update({name: np.array(scope.var(name).get_tensor())})
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return main_program, scope, param_dict
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class BertModel(nn.Layer):
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def __init__(
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self,
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vocab_size,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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):
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super().__init__()
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self.embeddings = nn.Embedding(vocab_size, hidden_size)
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encoder_layer = TransformerEncoderLayer(
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hidden_size, num_attention_heads, hidden_size * 4
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)
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self.encoder = TransformerEncoder(encoder_layer, num_hidden_layers)
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def forward(self, input_ids):
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embeddings = self.embeddings(input_ids)
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encoded_output = self.encoder(embeddings)
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return encoded_output
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def get_bert_program():
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paddle.enable_static()
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vocab_size = 30522 # BERT base vocab size
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hidden_size = 768
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num_hidden_layers = 2
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num_attention_heads = 12
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seq_length = 128
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with paddle.pir_utils.IrGuard():
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main_program = static.default_main_program()
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startup_program = static.default_startup_program()
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with static.program_guard(main_program, startup_program):
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scope = paddle.static.global_scope()
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input_ids = static.data(
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name='input_ids', shape=[-1, -1], dtype='int64'
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)
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bert_model = BertModel(
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vocab_size, hidden_size, num_hidden_layers, num_attention_heads
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)
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bert_model.eval()
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logits = bert_model(input_ids)
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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pir_program = main_program
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with (
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paddle.pir_utils.IrGuard(),
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paddle.static.program_guard(pir_program, startup_program),
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):
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x = np.ones([1, seq_length]).astype('int64')
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executor = paddle.static.Executor(place)
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executor.run(startup_program)
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fetches = executor.run(
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pir_program,
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feed={"input_ids": x},
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fetch_list=pir_program.list_vars()[-3],
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)
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params = main_program.global_block().all_parameters()
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param_dict = {}
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# save parameters
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for v in params:
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name = v.get_defining_op().attrs()["parameter_name"]
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param_dict.update({name: np.array(scope.var(name).get_tensor())})
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return pir_program, scope, param_dict
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class SimpleGatherNet(nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(149600, 1)
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def forward(self, map_vector_features, polyline_mask):
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map_vector_features = map_vector_features[polyline_mask]
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return map_vector_features
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