Proper flash-attn parameters. (#244)

* Proper flash-attn parameters.

* Set the flash attention parameters.

* Add more validations.

* Setup the o_ flash attn parameters.

* More flash-attn support.

* Set more flash attn parameters.

candle-flash-attn/src/lib.rs

@@ -6,7 +6,14 @@ use candle::cuda_backend::WrapErr;
 use candle::{CpuStorage, Error, Layout, Result, Shape};
 use half::f16;
 
-pub struct FlashHdim32Sm80;
+pub struct FlashHdim32Sm80 {
+    pub softmax_scale: f32,
+    pub causal: bool,
+}
+
+fn round_multiple(x: usize, m: usize) -> usize {
+    (x + m - 1) / m * m
+}
 
 impl candle::CustomOp3 for FlashHdim32Sm80 {
     fn name(&self) -> &'static str {
@@ -28,28 +35,108 @@ impl candle::CustomOp3 for FlashHdim32Sm80 {
     fn cuda_fwd(
         &self,
         q: &candle::CudaStorage,
-        _q_l: &Layout,
+        q_l: &Layout,
         k: &candle::CudaStorage,
-        _k_l: &Layout,
+        k_l: &Layout,
         v: &candle::CudaStorage,
-        _v_l: &Layout,
+        v_l: &Layout,
     ) -> Result<(candle::CudaStorage, Shape)> {
+        // https://github.com/Dao-AILab/flash-attention/blob/b252072409e69c25f2b9d473cc534e49b24decd2/csrc/flash_attn/flash_api.cpp#L187
         let dev = q.device();
-        let out_shape = Shape::from(&[1]);
+        let out_shape = q_l.shape().clone();
+        let out_l = Layout::contiguous(&out_shape);
+
         let q = q.as_cuda_slice::<f16>()?;
         let k = k.as_cuda_slice::<f16>()?;
         let v = v.as_cuda_slice::<f16>()?;
+
+        let q_stride = q_l.stride();
+        let k_stride = k_l.stride();
+        let v_stride = v_l.stride();
+        let o_stride = out_l.stride();
+
+        let q_rank = q_stride.len();
+        let k_rank = k_stride.len();
+        let v_rank = v_stride.len();
+        let o_rank = o_stride.len();
+
+        if q_rank != 4 || k_rank != 4 || v_rank != 4 {
+            candle::bail!(
+                "flash-attn expects input tensors of rank 4 (q: {q_rank}, k: {k_rank}, v: {v_rank}"
+            )
+        }
+        if q_stride[q_rank - 1] != 1 {
+            candle::bail!("the last dim of q must be contiguous {q_stride:?}")
+        }
+        if k_stride[k_rank - 1] != 1 {
+            candle::bail!("the last dim of k must be contiguous {k_stride:?}")
+        }
+        if v_stride[v_rank - 1] != 1 {
+            candle::bail!("the last dim of v must be contiguous {v_stride:?}")
+        }
+
+        let (b_sz, seqlen_q, num_heads, head_size_og) = q_l.shape().dims4()?;
+        let (_b_sz, seqlen_k, num_heads_k, _head_size_og) = k_l.shape().dims4()?;
+        let expected_kv = (b_sz, seqlen_k, num_heads_k, head_size_og);
+        if expected_kv != k_l.shape().dims4()? {
+            candle::bail!("shape mismatch q {:?} and k {:?}", q_l.shape(), k_l.shape())
+        }
+        if expected_kv != v_l.shape().dims4()? {
+            candle::bail!("shape mismatch q {:?} and v {:?}", q_l.shape(), v_l.shape())
+        }
+        if head_size_og > 256 {
+            candle::bail!("only supports head dimension at most 256 (got {head_size_og})")
+        }
+        if num_heads % num_heads_k != 0 {
+            candle::bail!("number of k/v heads {num_heads_k} must divide number of heads in query {num_heads}")
+        }
+
+        let head_size = round_multiple(head_size_og, 8);
+        let head_size_rounded = round_multiple(head_size, 32);
+        let seqlen_q_rounded = round_multiple(seqlen_q, 128);
+        let seqlen_k_rounded = round_multiple(seqlen_k, 128);
+
         let elem_count = out_shape.elem_count();
         let dst = unsafe { dev.alloc::<f16>(elem_count) }.w()?;
+        let softmax_lse = dev.alloc_zeros::<f32>(b_sz * num_heads * seqlen_q).w()?;
+
+        let causal = if self.causal { 1 } else { 0 };
 
         unsafe {
             let q_ptr = *q.device_ptr() as *const core::ffi::c_void;
             let k_ptr = *k.device_ptr() as *const core::ffi::c_void;
             let v_ptr = *v.device_ptr() as *const core::ffi::c_void;
             let dst_ptr = *dst.device_ptr() as *const core::ffi::c_void;
+            let softmax_lse_ptr = *softmax_lse.device_ptr() as *const core::ffi::c_void;
             ffi::run_mha(
-                q_ptr, k_ptr, v_ptr, dst_ptr, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1,
-                1, 1, 1,
+                q_ptr,
+                k_ptr,
+                v_ptr,
+                dst_ptr,
+                softmax_lse_ptr,
+                /* q_batch_stride */ q_stride[0] as u32,
+                /* k_batch_stride */ k_stride[0] as u32,
+                /* v_batch_stride */ v_stride[0] as u32,
+                /* o_batch_stride */ o_stride[0] as u32,
+                /* q_row_stride */ q_stride[q_rank - 3] as u32,
+                /* k_row_stride */ k_stride[k_rank - 3] as u32,
+                /* v_row_stride  */ v_stride[v_rank - 3] as u32,
+                /* o_row_stride  */ o_stride[o_rank - 3] as u32,
+                /* q_head_stride */ q_stride[q_rank - 2] as u32,
+                /* k_head_stride */ k_stride[k_rank - 2] as u32,
+                /* v_head_stride */ v_stride[v_rank - 2] as u32,
+                /* o_head_stride */ o_stride[o_rank - 2] as u32,
+                /* b */ b_sz as u32,
+                /* h */ num_heads as u32,
+                /* h_k */ num_heads_k as u32,
+                /* d */ head_size as u32,
+                /* d_rounded */ head_size_rounded as u32,
+                /* softmax_scale*/ self.softmax_scale,
+                /* seqlen_q */ seqlen_q as u32,
+                /* seqlen_k */ seqlen_k as u32,
+                /* seqlen_q_rounded */ seqlen_q_rounded as u32,
+                /* seqlen_k_rounded */ seqlen_k_rounded as u32,
+                /* is_causal */ causal,
             )
         }