Approx gelu

unsloth/kernels/geglu.py

@@ -19,7 +19,7 @@ from .utils import calculate_settings
 
 
 @triton.jit
-def _forward_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
+def _exact_forward_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
     block_idx = tl.program_id(0)
     offsets = block_idx*BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
     mask = offsets < n_elements
@@ -43,13 +43,13 @@ def geglu_forward_kernel(gate, up):
     n_elements = gate.numel()
     out = torch.empty((batch, seq_len, hd), dtype = gate.dtype, device = "cuda")
     grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-    _forward_kernel[grid](gate, up, out, n_elements, BLOCK_SIZE = 1024,)
+    _exact_forward_kernel[grid](gate, up, out, n_elements, BLOCK_SIZE = 1024,)
     return out
 pass
 
 
 @triton.jit
-def _backward_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
+def _exact_backward_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
     """
     f = 1/2 * e * (1 + erf(1/sqrt(2) * e))
     h = f * up
@@ -99,6 +99,42 @@ def geglu_backward_kernel(DW, e, g):
     batch_seq_len, hd = e.shape
     n_elements = e.numel()
     grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-    _backward_kernel[grid](DW, e, g, n_elements, BLOCK_SIZE = 1024,)
+    _exact_backward_kernel[grid](DW, e, g, n_elements, BLOCK_SIZE = 1024,)
     return DW, e, g
 pass
+
+
+@triton.jit
+def _approx_forward_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
+    block_idx = tl.program_id(0)
+    offsets = block_idx*BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+
+    # f = 1/2 * e * (1 + tanh( sqrt(2/pi) * (x + 0.044715 * x^3 ) ))
+    # f = 1/2 * e * (1 + tanh( sqrt(2/pi) * x * (1 + 0.044715 * x^2 ) ))
+    # h = f * up
+    s = 0.7978845608028654 # math.sqrt(2 / math.pi)
+    
+    e_row = tl.load(e + offsets, mask = mask, other = 0).to(tl.float32)
+    g_row = tl.load(g + offsets, mask = mask, other = 0)#.to(tl.float32)
+
+    f_row = 0.5 * e_row * (
+        tl.math.tanh(s * e_row * (1.0 + 0.044715 * e_row * e_row)) \
+        + 1.0
+    )
+    f_row = f_row.to(g_row.dtype) # Exact copy from HF
+    h_row = f_row * g_row
+
+    # Store h
+    tl.store(h + offsets, h_row, mask = mask)
+pass
+
+
+def geglu_approx_forward_kernel(gate, up):
+    batch, seq_len, hd = gate.shape
+    n_elements = gate.numel()
+    out = torch.empty((batch, seq_len, hd), dtype = gate.dtype, device = "cuda")
+    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
+    _approx_forward_kernel[grid](gate, up, out, n_elements, BLOCK_SIZE = 1024,)
+    return out
+pass

unsloth/models/gemma.py

@@ -48,7 +48,7 @@ def fast_geglu_inference(self, X):
 
     gate = fast_linear_forward(self.gate_proj, X, out = temp[0])
     up   = fast_linear_forward(self.  up_proj, X, out = temp[1])
-    gate = torch.nn.functional.gelu(gate)
+    gate = torch.nn.functional.gelu(gate, approximate = "tanh")
     gate *= up
 
     # X = self.down_proj(gate)