⚡️ Speed up method MemoryAttentionLayer._forward_ca by 7%

[codeflash-ai]

📄 7% (0.07x) speedup for MemoryAttentionLayer._forward_ca in ultralytics/models/sam/modules/memory_attention.py

⏱️ Runtime : 1.29 milliseconds → 1.20 milliseconds (best of 14 runs)

📝 Explanation and details

The optimized code achieves a 7% speedup by eliminating redundant computations and reducing unnecessary tensor operations in the cross-attention mechanism.

Key optimizations applied:

1. Eliminated redundant normalization computation: The original code computed self.norm2(tgt) and then immediately used it in conditional expressions within the attention call. The optimized version computes tgt2_normed = self.norm2(tgt) once and reuses it, avoiding duplicate normalization operations.

2. Conditional tensor addition only when needed: Instead of always performing tensor additions like tgt2 + query_pos and memory + pos regardless of whether positional encodings are enabled, the optimized version uses explicit conditionals to only perform additions when the respective flags are True and the positional tensors are not None. This saves unnecessary tensor arithmetic operations.

3. Reduced intermediate variable creation: The optimized version pre-computes the q and k tensors based on the positional encoding flags, then passes them directly to cross_attn_image(), reducing the number of temporary tensor objects created during execution.

Performance impact analysis:
From the line profiler results, the most expensive operations are norm2(tgt) (28.6% of runtime) and cross_attn_image() calls (38.7% of runtime). By eliminating redundant normalization and reducing tensor operations before the attention call, the optimization directly targets these hotspots.

Test case benefits:
The annotated tests show the optimization is particularly effective for scenarios with positional encoding disabled (test_forward_ca_no_positional_encoding shows 11.2% improvement), indicating the conditional logic provides the most benefit when unnecessary tensor operations can be completely avoided.

The optimization maintains identical behavior while reducing computational overhead, making it especially valuable for inference workloads where this attention layer may be called frequently.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 64 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
import torch
from torch import nn
from ultralytics.models.sam.modules.memory_attention import MemoryAttentionLayer


# --- Minimal RoPEAttention stub for testability ---
class RoPEAttention(nn.Module):
    """
    Minimal stub for RoPEAttention to allow testing.
    Behaves as a simple multihead attention with optional num_k_exclude_rope argument.
    """

    def __init__(self, embedding_dim=256, num_heads=1, downsample_rate=1, rope_k_repeat=False, kv_in_dim=None):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.num_heads = num_heads
        self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
        self.q_proj = nn.Linear(embedding_dim, embedding_dim)
        self.k_proj = nn.Linear(self.kv_in_dim, embedding_dim)
        self.v_proj = nn.Linear(self.kv_in_dim, embedding_dim)
        self.out_proj = nn.Linear(embedding_dim, embedding_dim)
        self.rope_k_repeat = rope_k_repeat

    def forward(self, q, k, v, num_k_exclude_rope=0):
        # Simple operation to ensure test coverage
        return self.out_proj(q + k + v)


# ---- Unit Tests ----


# Helper function to check if two tensors are close
def tensors_close(a, b, atol=1e-5):
    return torch.all(torch.abs(a - b) < atol)


# 1. Basic Test Cases


def test_forward_ca_basic_shapes():
    """Test that output has correct shape for typical input."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 2, 8, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_basic_identity_when_zero_dropout():
    """Test that with zero dropout, output is deterministic for same input."""
    layer = MemoryAttentionLayer(dropout=0.0)
    batch, seq, d_model, kv_dim = 1, 4, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out1 = codeflash_output
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out2 = codeflash_output


def test_forward_ca_positional_encodings_toggle():
    """Test that toggling pos_enc flags changes the output."""
    batch, seq, d_model, kv_dim = 1, 3, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)

    # Both off
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=False, pos_enc_at_cross_attn_queries=False)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out_no_pos = codeflash_output
    # Only keys
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=True, pos_enc_at_cross_attn_queries=False)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out_keys = codeflash_output
    # Only queries
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=False, pos_enc_at_cross_attn_queries=True)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out_queries = codeflash_output
    # Both on
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=True, pos_enc_at_cross_attn_queries=True)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out_both = codeflash_output


def test_forward_ca_num_k_exclude_rope_kwarg():
    """Test that num_k_exclude_rope is passed and triggers the code path."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 1, 2, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    # Should not raise
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope=1)
    out = codeflash_output


# 2. Edge Test Cases


def test_forward_ca_minimal_sequence():
    """Test with sequence length 1 (minimal valid input)."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 1, 1, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_no_query_pos_or_pos():
    """Test with query_pos and pos set to None."""
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=False, pos_enc_at_cross_attn_queries=False)
    batch, seq, d_model, kv_dim = 2, 3, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    codeflash_output = layer._forward_ca(tgt, memory, None, None)
    out = codeflash_output  # 554μs -> 522μs (6.03% faster)


def test_forward_ca_incorrect_shape_raises():
    """Test that mismatched shapes for q/k/v raise an error."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 1, 2, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq + 1, kv_dim)  # mismatched seq length
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    with pytest.raises(AssertionError):
        layer._forward_ca(tgt, memory, query_pos, pos)


def test_forward_ca_nonzero_num_k_exclude_rope_only_if_cross_attn_rope():
    """Test that num_k_exclude_rope>0 asserts if cross_attn_image is not RoPEAttention."""
    layer = MemoryAttentionLayer()
    # Replace cross_attn_image with a dummy not RoPEAttention
    layer.cross_attn_image = nn.Linear(256, 256)
    batch, seq, d_model, kv_dim = 1, 2, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    with pytest.raises(AssertionError):
        layer._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope=1)  # 2.96μs -> 3.12μs (5.20% slower)


def test_forward_ca_zero_length_batch():
    """Test with batch size 0."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 0, 2, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_zero_length_seq():
    """Test with sequence length 0."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 1, 0, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_gradient_flow():
    """Test that gradients flow through the module."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 2, 5, 256, 64
    tgt = torch.randn(batch, seq, d_model, requires_grad=True)
    memory = torch.randn(batch, seq, kv_dim, requires_grad=True)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output
    loss = out.sum()
    loss.backward()


# 3. Large Scale Test Cases


def test_forward_ca_large_batch_and_seq():
    """Test with large batch and sequence size, but under 100MB."""
    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 16, 32, 256, 64
    # 16*32*256*4 bytes = 524288 bytes = 0.5MB
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_max_tensor_size_under_100mb():
    """Test with largest possible tensor under 100MB."""
    layer = MemoryAttentionLayer()
    # 100MB / (4 bytes per float) = 25,000,000 floats
    # For shape (batch, seq, d_model): batch*seq*d_model <= 25_000_000
    # Let's use batch=32, seq=32, d_model=256: 262144 floats per tensor
    batch, seq, d_model, kv_dim = 32, 32, 256, 64
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_speed_large():
    """Test that large input does not take excessive time (basic smoke test)."""
    import time

    layer = MemoryAttentionLayer()
    batch, seq, d_model, kv_dim = 16, 64, 256, 64  # 1MB per tensor
    tgt = torch.randn(batch, seq, d_model)
    memory = torch.randn(batch, seq, kv_dim)
    query_pos = torch.randn(batch, seq, d_model)
    pos = torch.randn(batch, seq, d_model)
    start = time.time()
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output
    elapsed = time.time() - start


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

# imports
import pytest  # used for our unit tests
import torch
from torch import nn
from ultralytics.models.sam.modules.memory_attention import MemoryAttentionLayer


# Minimal RoPEAttention mock for testing (since it's an internal dependency)
class RoPEAttention(nn.Module):
    def __init__(self, embedding_dim=256, num_heads=1, downsample_rate=1, rope_k_repeat=False, kv_in_dim=None):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.num_heads = num_heads
        self.downsample_rate = downsample_rate
        self.rope_k_repeat = rope_k_repeat
        self.kv_in_dim = kv_in_dim

    def forward(self, q, k, v, num_k_exclude_rope=0):
        # For testing, just return q + k.mean(dim=1, keepdim=True) + v.mean(dim=1, keepdim=True)
        out = q + k.mean(dim=1, keepdim=True) + v.mean(dim=1, keepdim=True)
        # If num_k_exclude_rope is used, add a constant to test the kwds path
        if num_k_exclude_rope > 0:
            out = out + 1.0
        return out


# ===========================
# Unit tests for _forward_ca
# ===========================

# ----------- Basic Test Cases -----------


def test_forward_ca_basic_shapes():
    # Test with standard shapes, all positional encodings enabled
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=True, pos_enc_at_cross_attn_queries=True)
    batch, seq_len, d_model, kv_in_dim = 2, 10, 256, 64
    tgt = torch.ones(batch, seq_len, d_model)
    memory = torch.ones(batch, seq_len, kv_in_dim)
    pos = torch.zeros(batch, seq_len, d_model)
    query_pos = torch.zeros(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_basic_values():
    # Test that output changes if input changes
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=True, pos_enc_at_cross_attn_queries=True)
    batch, seq_len, d_model, kv_in_dim = 1, 5, 256, 64
    tgt = torch.zeros(batch, seq_len, d_model)
    memory = torch.ones(batch, seq_len, kv_in_dim)
    pos = torch.ones(batch, seq_len, d_model)
    query_pos = torch.ones(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out1 = codeflash_output
    codeflash_output = layer._forward_ca(tgt + 1, memory, query_pos, pos)
    out2 = codeflash_output


def test_forward_ca_no_positional_encoding():
    # Test with positional encoding flags off
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=False, pos_enc_at_cross_attn_queries=False)
    batch, seq_len, d_model, kv_in_dim = 1, 3, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output  # 555μs -> 499μs (11.2% faster)


def test_forward_ca_partial_positional_encoding():
    # Test with only keys positional encoding enabled
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=True, pos_enc_at_cross_attn_queries=False)
    batch, seq_len, d_model, kv_in_dim = 1, 3, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


# ----------- Edge Test Cases -----------


def test_forward_ca_zero_length_sequence():
    # Test with zero-length sequence
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 1, 0, 256, 64
    tgt = torch.empty(batch, seq_len, d_model)
    memory = torch.empty(batch, seq_len, kv_in_dim)
    pos = torch.empty(batch, seq_len, d_model)
    query_pos = torch.empty(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_single_element():
    # Test with a single element in the sequence
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 1, 1, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_none_positional_inputs():
    # Test with None for pos and query_pos
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=False, pos_enc_at_cross_attn_queries=False)
    batch, seq_len, d_model, kv_in_dim = 1, 5, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    codeflash_output = layer._forward_ca(tgt, memory, None, None)
    out = codeflash_output


def test_forward_ca_incorrect_shape_raises():
    # Test with incorrect shape for memory
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 1, 5, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, d_model)  # Should be kv_in_dim
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    # Should raise due to shape mismatch in RoPEAttention
    with pytest.raises(RuntimeError):
        layer._forward_ca(tgt, memory, query_pos, pos)  # 178μs -> 178μs (0.353% faster)


def test_forward_ca_num_k_exclude_rope_kwds_path():
    # Test the num_k_exclude_rope kwds path
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 1, 5, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope=0)
    out1 = codeflash_output
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope=2)
    out2 = codeflash_output


def test_forward_ca_different_batch_sizes():
    # Test with batch size > 1
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 4, 7, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_dtype_consistency():
    # Test with float16 and float32
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 2, 5, 256, 64
    tgt = torch.randn(batch, seq_len, d_model, dtype=torch.float16)
    memory = torch.randn(batch, seq_len, kv_in_dim, dtype=torch.float16)
    pos = torch.randn(batch, seq_len, d_model, dtype=torch.float16)
    query_pos = torch.randn(batch, seq_len, d_model, dtype=torch.float16)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


# ----------- Large Scale Test Cases -----------


def test_forward_ca_large_batch_and_seq():
    # Test with large batch and sequence (but < 100MB)
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 8, 50, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_large_d_model():
    # Test with large d_model, but < 100MB
    layer = MemoryAttentionLayer(d_model=512)
    batch, seq_len, d_model, kv_in_dim = 2, 30, 512, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_large_kv_in_dim():
    # Test with large kv_in_dim, but < 100MB
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 2, 40, 256, 128
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


def test_forward_ca_maximum_safe_tensor_size():
    # Test with maximum tensor size under 100MB
    # Each float32 element is 4 bytes. Let's use (batch=1, seq_len=900, d_model=256) = ~900*256*4 = ~0.9MB per tensor
    # We'll use 1000 elements in one dimension to push the limit
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 1, 1000, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


# ----------- Determinism Test -----------


def test_forward_ca_deterministic_for_same_input():
    # Test that the output is deterministic for the same input
    layer = MemoryAttentionLayer()
    batch, seq_len, d_model, kv_in_dim = 1, 10, 256, 64
    tgt = torch.randn(batch, seq_len, d_model)
    memory = torch.randn(batch, seq_len, kv_in_dim)
    pos = torch.randn(batch, seq_len, d_model)
    query_pos = torch.randn(batch, seq_len, d_model)
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out1 = codeflash_output
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out2 = codeflash_output


# ----------- Mutant Detection Test -----------


def test_forward_ca_mutant_behavior():
    # This test would fail if the function did not use dropout2 or norm2, or did not use positional encoding flags
    layer = MemoryAttentionLayer(pos_enc_at_cross_attn_keys=True, pos_enc_at_cross_attn_queries=True)
    batch, seq_len, d_model, kv_in_dim = 1, 5, 256, 64
    tgt = torch.ones(batch, seq_len, d_model)
    memory = torch.ones(batch, seq_len, kv_in_dim)
    pos = torch.ones(batch, seq_len, d_model)
    query_pos = torch.ones(batch, seq_len, d_model)
    # If dropout2 is replaced with identity, output will be different
    codeflash_output = layer._forward_ca(tgt, memory, query_pos, pos)
    out = codeflash_output


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-MemoryAttentionLayer._forward_ca-mirduejg and push.