Module `neuralops.tests.test_qcqa_attention_comprehensive`

Functions

def test_qcqa_basic()

Expand source code

def test_qcqa_basic():
    cfg = MockConfig()
    attn = QCQAAttention(config=cfg, num_kv_heads=2)
    x = torch.randn(2, 8, 64)
    out, _ = attn(x)
    assert out.shape == (2, 8, 64)

def test_qcqa_distilbert_weight_copy()

Expand source code

def test_qcqa_distilbert_weight_copy():
    cfg = MockConfig()
    old_attn = DummyDistilBertAttention(64)
    attn = QCQAAttention(config=cfg, num_kv_heads=2, pretrained_attn=old_attn)
    x = torch.randn(2, 8, 64)
    out, _ = attn(x)
    assert out.shape == (2, 8, 64)

def test_qcqa_gpt2_weight_copy()

Expand source code

def test_qcqa_gpt2_weight_copy():
    cfg = MockConfig(is_decoder=True, model_type="gpt2")
    old_attn = DummyGPT2Attention(64)
    attn = QCQAAttention(config=cfg, num_kv_heads=2, pretrained_attn=old_attn)
    assert attn.is_decoder
    x = torch.randn(2, 8, 64)
    out, _ = attn(x)
    assert out.shape == (2, 8, 64)

def test_qcqa_inject_safe()

Expand source code

def test_qcqa_inject_safe():
    class DummyLayer(nn.Module):
        def __init__(self):
            super().__init__()
            self.attention = DummyDistilBertAttention(64)

    layer = DummyLayer()
    res = inject_qcqa(layer=layer, model_config=MockConfig(), old_attn=layer.attention, num_kv_heads=2)
    assert res is not None
    x = torch.randn(2, 8, 64)
    out, _ = layer.attention(x)
    assert out.shape == (2, 8, 64)

def test_qcqa_qwen_gqa_and_rope()

Expand source code

def test_qcqa_qwen_gqa_and_rope():
    cfg = MockConfig(hidden_size=64, num_attention_heads=4, num_key_value_heads=2, head_dim=16, is_decoder=True, model_type="qwen2")
    old_attn = DummyQwenAttention(dim=64, num_kv_heads=2, head_dim=16)
    attn = QCQAAttention(config=cfg, num_kv_heads=2, pretrained_attn=old_attn)
    assert attn.kv_repeat == 2
    x = torch.randn(2, 8, 64)
    cos = torch.randn(2, 8, 16)
    sin = torch.randn(2, 8, 16)
    out, _ = attn(x, position_embeddings=(cos, sin))
    assert out.shape == (2, 8, 64)

def test_qcqa_roberta_weight_copy()

Expand source code

def test_qcqa_roberta_weight_copy():
    cfg = MockConfig()
    old_attn = DummyRobertaAttention(64)
    attn = QCQAAttention(config=cfg, num_kv_heads=2, pretrained_attn=old_attn)
    assert attn.is_nested_encoder
    x = torch.randn(2, 8, 64)
    out, _ = attn(x)
    assert out.shape == (2, 8, 64)

def test_qcqa_trainable_lora_weights()

Expand source code

def test_qcqa_trainable_lora_weights():
    cfg = MockConfig(lora_rank=8)
    attn = QCQAAttention(config=cfg, num_kv_heads=2)
    assert attn.q_proj.weight.requires_grad is False
    assert attn.q_proj.lora_A.requires_grad is True
    assert attn.q_proj.lora_B.requires_grad is True

Classes

class DummyGPT2Attention (dim=64)

Expand source code

class DummyGPT2Attention(nn.Module):
    def __init__(self, dim=64):
        super().__init__()
        self.c_attn = nn.Linear(dim, 3 * dim)
        self.c_proj = nn.Linear(dim, dim)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

torch.nn.modules.module.Module

class DummyQwenAttention (dim=64, num_kv_heads=2, head_dim=16)

Expand source code

class DummyQwenAttention(nn.Module):
    def __init__(self, dim=64, num_kv_heads=2, head_dim=16):
        super().__init__()
        self.q_proj = nn.Linear(dim, dim)
        self.k_proj = nn.Linear(dim, num_kv_heads * head_dim)
        self.v_proj = nn.Linear(dim, num_kv_heads * head_dim)
        self.o_proj = nn.Linear(dim, dim)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

torch.nn.modules.module.Module

class DummyRobertaAttention (dim=64)

Expand source code

class DummyRobertaAttention(nn.Module):
    def __init__(self, dim=64):
        super().__init__()
        self.self = DummyRobertaSelfAttention(dim)
        self.output = nn.Module()
        self.output.dense = nn.Linear(dim, dim)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

torch.nn.modules.module.Module

class DummyRobertaSelfAttention (dim=64)

Expand source code

class DummyRobertaSelfAttention(nn.Module):
    def __init__(self, dim=64):
        super().__init__()
        self.query = nn.Linear(dim, dim)
        self.key = nn.Linear(dim, dim)
        self.value = nn.Linear(dim, dim)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

torch.nn.modules.module.Module