reasoning
The error is still the same. Let me think about what could be wrong. The dropout is disabled (dropout=0.0 by default), so that shouldn't cause issues. Let me look at the reference more carefully.
In the reference:
```python
attn = torch.einsum("bnhid,bnhjd->bnhij", q, k) / math.sqrt(d)
```
This computes attention scores of shape (B, N, h, S, S) - for each query position, compute attention over all key positions.
Wait, I need to understand the einsum notation:
- "bnhid,bnhjd->bnhij"
- q: (B, N, h, S, d) indexed as b,n,h,i,d
- k: (B, N, h, S, d) indexed as b,n,h,j,d
- result: (B, N, h, S, S) indexed as b,n,h,i,j
So attn[b,n,h,i,j] = sum_d(q[b,n,h,i,d] * k[b,n,h,j,d]) / sqrt(d)
Then softmax is applied over the last dimension (j dimension):
```python
attn = F.softmax(attn, dim=-1)
```
This means for each (b, n, h, i), we softmax over j.
Then:
```python
out = torch.einsum("bnhij,bnhjd->bnhid", attn, v)
```
- attn: (B, N, h, S, S) indexed as b,n,h,i,j
- v: (B, N, h, S, d) indexed as b,n,h,j,d
- result: (B, N, h, S, d) indexed as b,n,h,i,d
So out[b,n,h,i,d] = sum_j(attn[b,n,h,i,j] * v[b,n,h,j,d])
In my kernel, I have:
- Each thread handles one query position (s)
- For that query position, I compute attention scores over all key positions
- Then apply softmax over the key positions
- Then compute weighted sum of V
This seems correct. Let me check the index calculations more carefully.
The input tensors:
- q, k, v, g are all (B, N, h, S, d)
- mask is (B, N, S)
In my kernel, I compute:
```cpp
int base_idx = ((b * N + n) * h + head) * S * d;
```
For tensor of shape (B, N, h, S, d), the element at position (b, n, h, s, dd) is at:
idx = (((b * N + n) * h + head) * S + s) * d + dd
So base_idx should be ((b * N + n) * h + head) * S * d, which is the starting index for each (b, n, head) combination. Then for position s and dimension dd, it's base_idx + s * d + dd.
This looks correct.
Now let me check the mask:
- mask is (B, N, S)
- mask_idx = (b * N + n) * S + s
Wait, in the original code:
```python
col_mask = msa_mask.permute(0, 2, 1) # (B, N, S)
mask = col_mask.view(B, N, 1, 1, S).expand_as(attn)
```
So the mask is broadcasted across the i dimension (query position) and head dimension.
The mask is of shape (B, N, 1, 1, S), so for each (b, n), the mask for key position j is the same regardless of query position i or head h.
In my kernel, I have:
```cpp
int mask_idx = (b * N + n) * S;
```
But this is the starting index for (b, n). I should be accessing mask[mask_idx + kv_s] where kv_s is the key position, which I do.
So the mask logic seems correct too.
Wait, I think the issue might be with how I'm handling the softmax. Let me check if I'm handling the case where all positions are masked correctly.
Actually, looking at the dropout - I'm not applying dropout in my kernel, but the reference applies it:
```python
attn = self.dropout(attn)
```
If dropout=0.0, then dropout should be identity. Let me verify this is the case...
Actually wait, the get_init_inputs function returns [msa_dim, num_heads] without dropout, so dropout defaults to 0.0. And dropout with p=0 should be identity.
Hmm, let me look more carefully at the numerical issues. The differences are around 0.06 on average and 0.34 maximum. This suggests a systematic issue rather than a small numerical precision issue.
Let me think... could there be an issue with the scale factor?
scale = 1.0 / math.sqrt(d) = 1.0 / sqrt(16) = 0.25
That should be fine.
Let me try a different approach - let me print out intermediate values from both implementations to see where they diverge.
Actually, I think the issue might be with my handling of the masked softmax. In the reference:
```python
mask = col_mask.view(B, N, 1, 1, S).expand_as(attn)
attn = attn.masked_fill(mask == 0, float("-inf"))
```
This sets masked positions to -inf BEFORE softmax.
Then:
```python
attn = F.softmax(attn, dim=-1)
```
Softmax over the last dimension (j dimension = key positions).
When all positions are masked, softmax of [-inf, -inf, ...] would give [nan, nan, ...] or [0, 0, ...] depending on implementation. PyTorch's softmax handles this by returning [nan] in that case, but let me check...
Actually wait, in the reference code, the softmax is applied after masking:
```python
attn = attn.masked_fill(mask == 0, float("-inf"))
attn = F.softmax(attn, dim=-1)
```
So the mask is applied to the attention scores before softmax. In my kernel, I do the same thing. Let me verify my logic again.
Actually, I think I see an issue! Looking at the reference:
```python
mask = col_mask.view(B, N, 1, 1, S).expand_as(attn)
```
This creates a mask of shape (B, N, 1, 1, S) and expands to (B, N, h, S, S).
The mask values are for the KEY dimension (last S), not the QUERY dimension.
So for a given (b, n, h, i, j), the mask value is col_mask[b, n, j].
In my kernel, I have:
```cpp
int mask_idx = (b * N + n) * S;
// ...
if (mask[mask_idx + kv_s] == 0.0f) {
scores[kv_s] = -INFINITY;
}
```
Where kv_s is the key position j. So mask[mask_idx + kv_s] = col_mask[b, n, kv_s] = col_mask[b, n, j]. This looks correct!
Hmm, let me look at the threshold I'm using:
```cpp
if (scores[kv_s] > -1e6f) {
```
This might not be correct. -INFINITY is typically represented as a very large negative number, and -1e6 might not catch all cases. Let me use a better check.
Actually, wait. There's another issue. Looking at the einsum again:
"bnhid,bnhjd->bnhij"
This means the attention is computed as q[i] dot k[j] for all i, j. So for each query position i, we compute attention to all key positions j.
My kernel is doing exactly this:
- For each query position s (which is i in the einsum)
- Compute scores[s] = q[s] dot k[kv_s] for all kv_s (which is j in the einsum)
- Apply softmax over kv_s dimension
- Compute output as weighted sum of V
This seems correct...
Let me try a simpler approach - just implement the kernel to match PyTorch exactly, without any optimizations, and see if that works.
Actually, I wonder if the issue is with how I'm indexing into the tensors. Let me be more careful.
For a tensor of shape (B, N, h, S, d), the strides would be:
- stride for B: N * h * S * d
- stride for N: h * S * d
- stride for h: S * d
- stride for S: d
- stride for d: 1
So for element (b, n, head, s, dd):
idx = b * (N * h * S * d) + n * (h * S * d) + head * (S * d) + s * d + dd
= ((b * N + n) * h + head) * S * d + s * d + dd
= base_idx + s * d + dd
where base_idx = ((b * N + n) * h + head) * S * d
This is exactly what I have.
Let me try a different debugging approach. Let me modify the kernel to just return the attention scores (before softmax) to see if they match.
Actually, let me try simplifying. The problem might be in the threshold checking. Let me change it to use a proper check for infinity.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nfrom torch.utils.cpp_extension import load_inline\n\n# Define CUDA source for fused attention operations\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Fused kernel for attention computation\n// Grid: (B * N * h) blocks\n// Block: each block handles one attention head for one (b, n) pair\n__global__ void fused_attention_kernel(\n const float* __restrict__ q, // (B, N, h, S, d)\n const float* __restrict__ k, // (B, N, h, S, d)\n const float* __restrict__ v, // (B, N, h, S, d)\n const float* __restrict__ g, // (B, N, h, S, d)\n const float* __restrict__ mask, // (B, N, S) - 0 or 1\n float* __restrict__ out, // (B, N, h, S, d)\n float scale,\n int B, int N, int h, int S, int d\n) {\n int b = blockIdx.x / (N * h);\n int remainder = blockIdx.x % (N * h);\n int n = remainder / h;\n int head = remainder % h;\n \n int s = threadIdx.x; // Each thread handles one query position\n \n extern __shared__ char shared_mem[];\n float* k_cache = reinterpret_cast<float*>(shared_mem); // S * d\n float* v_cache = k_cache + S * d; // S * d\n \n int base_idx = ((b * N + n) * h + head) * S * d;\n \n // Load K and V into shared memory\n for (int i = threadIdx.x; i < S * d; i += blockDim.x) {\n k_cache[i] = k[base_idx + i];\n v_cache[i] = v[base_idx + i];\n }\n __syncthreads();\n \n // Each thread computes attention for one query position\n if (s < S) {\n // Load query vector for this position\n float q_vec[16]; // Assuming d <= 16\n for (int dd = 0; dd < d; dd++) {\n q_vec[dd] = q[base_idx + s * d + dd];\n }\n \n // Compute attention scores\n float scores[32]; // S <= 32\n \n for (int kv_s = 0; kv_s < S; kv_s++) {\n float dot = 0.0f;\n for (int dd = 0; dd < d; dd++) {\n dot += q_vec[dd] * k_cache[kv_s * d + dd];\n }\n scores[kv_s] = dot * scale;\n }\n \n // Apply mask first\n int mask_idx = (b * N + n) * S;\n for (int kv_s = 0; kv_s < S; kv_s++) {\n if (mask[mask_idx + kv_s] == 0.0f) {\n scores[kv_s] = -CUDART_INF_F;\n }\n }\n \n // Compute max for numerical stability (after masking)\n float max_score = -CUDART_INF_F;\n for (int kv_s = 0; kv_s < S; kv_s++) {\n if (scores[kv_s] > max_score) {\n max_score = scores[kv_s];\n }\n }\n \n // Compute softmax\n float sum_exp = 0.0f;\n for (int kv_s = 0; kv_s < S; kv_s++) {\n float score = scores[kv_s];\n if (!isinf(score)) {\n scores[kv_s] = expf(score - max_score);\n sum_exp += scores[kv_s];\n }\n }\n \n // Normalize\n float inv_sum = sum_exp > 0.0f ? 1.0f / sum_exp : 0.0f;\n for (int kv_s = 0; kv_s < S; kv_s++) {\n if (!isinf(scores[kv_s])) {\n scores[kv_s] *= inv_sum;\n } else {\n scores[kv_s] = 0.0f;\n }\n }\n \n // Compute weighted sum of V\n float out_vec[16];\n for (int dd = 0; dd < d; dd++) {\n out_vec[dd] = 0.0f;\n }\n \n for (int kv_s = 0; kv_s < S; kv_s++) {\n for (int dd = 0; dd < d; dd++) {\n out_vec[dd] += scores[kv_s] * v_cache[kv_s * d + dd];\n }\n }\n \n // Apply gate and write output\n for (int dd = 0; dd < d; dd++) {\n out[base_idx + s * d + dd] = g[base_idx + s * d + dd] * out_vec[dd];\n }\n }\n}\n\ntorch::Tensor fused_attention_cuda(\n torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor g,\n torch::Tensor mask, float scale\n) {\n int B = q.size(0);\n int N = q.size(1);\n int h = q.size(2);\n int S = q.size(3);\n int d = q.size(4);\n \n auto out = torch::empty_like(q);\n \n int blocks = B * N * h;\n int threads = S;\n int shared_mem = S * d * 2 * sizeof(float);\n \n fused_attention_kernel<<<blocks, threads, shared_mem>>>(\n q.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(),\n g.data_ptr<float>(), mask.data_ptr<float>(), out.data_ptr<float>(),\n scale, B, N, h, S, d\n );\n \n return out;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_attention_cuda(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor g, torch::Tensor mask, float scale);\n\"\"\"\n\nfused_ops = load_inline(\n name='fused_attention',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_attention_cuda'],\n extra_cuda_cflags=['-O3', '-use_fast_math'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self, msa_dim, num_heads, dropout=0.0):\n super().__init__()\n self.num_heads = num_heads\n self.head_dim = msa_dim // num_heads\n assert msa_dim % num_heads == 0\n\n self.layer_norm = nn.LayerNorm(msa_dim)\n self.query = nn.Linear(msa_dim, msa_dim, bias=False)\n self.key = nn.Linear(msa_dim, msa_dim, bias=False)\n self.value = nn.Linear(msa_dim, msa_dim, bias=False)\n self.gate = nn.Linear(msa_dim, msa_dim)\n self.out_proj = nn.Linear(msa_dim, msa_dim)\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, msa_repr: torch.Tensor, msa_mask: torch.Tensor) -> torch.Tensor:\n B, S, N, D = msa_repr.shape\n h = self.num_heads\n d = self.head_dim\n\n # Transpose so column (residue position) is the batch dim for attention\n x = msa_repr.permute(0, 2, 1, 3) # (B, N, S, D)\n x = self.layer_norm(x)\n\n q = self.query(x).view(B, N, S, h, d).permute(0, 1, 3, 2, 4)\n k = self.key(x).view(B, N, S, h, d).permute(0, 1, 3, 2, 4)\n v = self.value(x).view(B, N, S, h, d).permute(0, 1, 3, 2, 4)\n g = torch.sigmoid(self.gate(x)).view(B, N, S, h, d).permute(0, 1, 3, 2, 4)\n\n # Use custom fused kernel for attention + gate\n scale = 1.0 / math.sqrt(d)\n col_mask = msa_mask.permute(0, 2, 1) # (B, N, S)\n \n out = fused_ops.fused_attention_cuda(q, k, v, g, col_mask, scale)\n out = out.permute(0, 1, 3, 2, 4).reshape(B, N, S, D)\n out = self.out_proj(out)\n return out.permute(0, 2, 1, 3) # back to (B, S, N, D)"
}