wehub-resource-sync
153 lines
5.5 KiB
Python
153 lines
5.5 KiB
Python
# 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 unittest
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import numpy as np
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import paddle
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from paddlenlp.utils.optimizer import AdamWMini
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class SimpleTransformerPaddle(paddle.nn.Layer):
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def __init__(self, dim=2048, n_heads=32, vocab_size=100, dtype="float32"):
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super().__init__()
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self.dim = dim
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self.n_heads = n_heads
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self.head_dim = dim // n_heads
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# Embedding layer
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self.embd = paddle.nn.Embedding(vocab_size, dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02))
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# Query/Key/Value projections
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self.wq = paddle.nn.Linear(dim, dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02))
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self.wk = paddle.nn.Linear(dim, dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02))
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self.wv = paddle.nn.Linear(dim, dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02))
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# Attention projection
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self.wo = paddle.nn.Linear(dim, dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02))
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# LayerNorm layers
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self.ln1 = paddle.nn.LayerNorm(dim)
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self.ln2 = paddle.nn.LayerNorm(dim)
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# MLP layers
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self.mlp = paddle.nn.Sequential(
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paddle.nn.Linear(dim, 4 * dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02)),
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paddle.nn.ReLU(),
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paddle.nn.Linear(4 * dim, dim, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02)),
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)
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# Output layer
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self.lm_head = paddle.nn.Linear(dim, vocab_size, weight_attr=paddle.nn.initializer.Normal(mean=0.0, std=0.02))
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# Bias parameters
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self.bias = paddle.create_parameter(
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[dim], dtype=dtype, default_initializer=paddle.nn.initializer.Constant(value=0.0)
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)
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def forward(self, input_ids):
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batch_size = input_ids.shape[0]
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seq_len = input_ids.shape[1]
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# Embedding
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hidden_states = self.embd(input_ids) # [batch_size, seq_len, dim]
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# Query/Key/Value projections and reshape for multi-head attention
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query = self.wq(hidden_states) # [batch_size, seq_len, dim]
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key = self.wk(hidden_states) # [batch_size, seq_len, dim]
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value = self.wv(hidden_states) # [batch_size, seq_len, dim]
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# Reshape to [batch_size, seq_len, n_heads, head_dim]
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query = query.reshape([batch_size, seq_len, self.n_heads, self.head_dim])
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key = key.reshape([batch_size, seq_len, self.n_heads, self.head_dim])
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value = value.reshape([batch_size, seq_len, self.n_heads, self.head_dim])
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# Transpose to [batch_size, n_heads, seq_len, head_dim]
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query = query.transpose([0, 2, 1, 3])
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key = key.transpose([0, 2, 1, 3])
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value = value.transpose([0, 2, 1, 3])
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# Scaled dot-product attention
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scale = self.head_dim**-0.5
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attn_weights = paddle.matmul(query * scale, key.transpose([0, 1, 3, 2]))
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attn_weights = paddle.nn.functional.softmax(attn_weights, axis=-1)
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# Apply attention to values
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attn_output = paddle.matmul(attn_weights, value) # [batch_size, n_heads, seq_len, head_dim]
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# Reshape back to [batch_size, seq_len, dim]
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attn_output = attn_output.transpose([0, 2, 1, 3])
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attn_output = attn_output.reshape([batch_size, seq_len, self.dim])
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# Attention output projection with residual connection and layer norm
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attn_output = self.wo(attn_output)
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hidden_states = self.ln1(hidden_states + attn_output)
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# Feed forward with residual connection and layer norm
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feed_forward = self.mlp(hidden_states)
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hidden_states = self.ln2(hidden_states + feed_forward)
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# Output
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output = self.lm_head(hidden_states + self.bias)
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return output
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def generate_data(batch_size=32, seq_len=64, vocab_size=100):
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x = np.random.randint(0, vocab_size, size=(batch_size, seq_len))
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y = np.random.randint(0, vocab_size, size=(batch_size, seq_len))
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return x, y
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class TestAdamWMini(unittest.TestCase):
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def setUp(self):
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# Set random seeds for reproducibility
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SEED = 1
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np.random.seed(SEED)
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paddle.seed(SEED)
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DTYPE = "float32"
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paddle.set_default_dtype(DTYPE)
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def test_adamw_mini(self):
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lr = 1e-3
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beta1 = 0.9
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beta2 = 0.999
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epsilon = 1e-8
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weight_decay = 0.01
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dim = 2048
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n_heads = 32
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model = SimpleTransformerPaddle()
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optimizer = AdamWMini(
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model.named_parameters(),
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learning_rate=lr,
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weight_decay=weight_decay,
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beta1=beta1,
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beta2=beta2,
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epsilon=epsilon,
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dim=dim,
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n_heads=n_heads,
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)
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for _ in range(2):
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x_np, _ = generate_data()
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x = paddle.to_tensor(x_np, dtype="int64")
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output = model(x)
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loss = paddle.mean(output)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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