Repository: apache/tvm

Issue:
[FRONTEND][PYTORCH] Relay pytorch frontend does not support aten:scaled_dot_product_attention
Hi, I just wondering that is there any plan to support scaled_dot_product_attention OP which is included in pytorch2.0?

cc @shingjan @yelite


Discussion:
I'll try to work on it.

Resolving patch:
diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -1555,15 +1555,21 @@ def unflatten(self, inputs, input_types):
         dim = dim if dim >= 0 else len(dshape) + dim
         assert len(dshape) > dim >= 0
 
-        assert unflattened_size.count(-1) <= 1
+        new_unflattened_size = []
+        for s in unflattened_size:
+            if isinstance(s, _expr.Constant):
+                s = s.data.numpy().item()
+            new_unflattened_size.append(s)
+
+        assert new_unflattened_size.count(-1) <= 1
 
-        mult = np.multiply.reduce(unflattened_size)
+        mult = np.multiply.reduce(new_unflattened_size)
         if mult < 0:
             assert dshape[dim] % mult == 0
         else:
             assert dshape[dim] == mult
 
-        new_shape = dshape[:dim] + unflattened_size + dshape[dim + 1 :]
+        new_shape = dshape[:dim] + tuple(new_unflattened_size) + dshape[dim + 1 :]
         out = _op.reshape(data, new_shape)
         return out
 
@@ -3891,6 +3897,109 @@ def linalg_vector_norm(self, inputs, input_types):
             reci_ord,
         )
 
+    def scaled_dot_product_attention(self, inputs, input_types):
+        query = inputs[0]
+        key = inputs[1]
+        value = inputs[2]
+        attn_mask = inputs[3]
+        dropout_p = inputs[4]
+        is_causal = inputs[5]
+
+        # Explicit scale can be used from torch>=2.1.0
+        if len(inputs) == 7:
+            scale = inputs[6]
+        else:
+            scale = None
+
+        assert (
+            input_types[0] == input_types[1] == input_types[2]
+        ), "Expected query, key, and value to have the same dtype"
+
+        dtype = input_types[0]
+        assert dtype == "float32" or dtype == "float64", "Data type can be float32 or float64"
+
+        query_shape = self.infer_shape_with_prelude(query)
+        key_shape = self.infer_shape_with_prelude(key)
+        value_shape = self.infer_shape_with_prelude(value)
+        assert 3 <= len(query_shape) <= 4, "Only 3D or 4D query supported"
+        assert 3 <= len(key_shape) <= 4, "Only 3D or 4D key supported"
+        assert 3 <= len(value_shape) <= 4, "Only 3D or 4D value supported"
+
+        assert dropout_p == 0.0, "Only dropout_p==0.0 supported"
+
+        L, S = query_shape[-2], key_shape[-2]
+
+        if scale is None:
+            scale_factor = _expr.const(1 / math.sqrt(query_shape[-1]), dtype=dtype)
+        else:
+            scale_factor = _expr.const(scale, dtype=dtype)
+
+        attn_bias = _op.full(_expr.const(0.0, dtype=dtype), (L, S))
+
+        if is_causal:
+            assert attn_mask is None, "Explicit attn_mask shouldn't be set when is_causal=True"
+            temp_mask = _op.full(_expr.const(True), [L, S], dtype="bool")
+            temp_mask = _op.trilu(temp_mask, 0, upper=False)
+            temp_mask = _op.cast(temp_mask, dtype="bool")
+            temp_mask = _op.logical_not(temp_mask)
+            fill_value = _op.cast(_expr.const(float("-inf")), dtype=dtype)
+            attn_bias = _op.where(temp_mask, fill_value, attn_bias)
+            attn_bias = _op.cast(attn_bias, dtype)
+
+        if attn_mask is not None:
+            if input_types[3] == "bool":
+                attn_mask = _op.logical_not(attn_mask)
+                fill_value = _op.cast(_expr.const(float("-inf")), dtype=dtype)
+                attn_bias = _op.where(attn_mask, fill_value, attn_bias)
+            else:
+                attn_bias = _op.add(attn_bias, attn_mask)
+
+        if len(query_shape) < len(key_shape):
+            batch_size = key_shape[0]
+        else:
+            batch_size = query_shape[0]
+        if len(query_shape) == 4 and len(key_shape) == 4:
+            query = _op.reshape(query, newshape=[-3, -2])
+            key = _op.reshape(key, newshape=[-3, -2])
+        if len(query_shape) == 3 and len(key_shape) == 4:
+            query = _op.broadcast_to(query, shape=(batch_size,) + query_shape)
+            query = _op.reshape(query, newshape=[-3, -2])
+            key = _op.reshape(key, newshape=[-3, -2])
+        if len(query_shape) == 4 and len(key_shape) == 3:
+            query = _op.reshape(query, newshape=[-3, -2])
+            key = _op.broadcast_to(key, shape=(batch_size,) + key_shape)
+            key = _op.reshape(key, newshape=[-3, -2])
+        attn_weight = _op.nn.batch_matmul(query, key)
+        if len(query_shape) == 4 or len(key_shape) == 4:
+            attn_weight = _op.reshape(attn_weight, newshape=[-4, batch_size, -1, -2])
+        attn_weight = _op.squeeze(attn_weight, axis=[])
+
+        attn_weight = _op.multiply(attn_weight, scale_factor)
+        attn_weight = _op.add(attn_weight, attn_bias)
+        attn_weight = _op.nn.softmax(attn_weight)
+        attn_weight = _op.nn.dropout(attn_weight, rate=dropout_p)
+
+        aw_shape = self.infer_shape_with_prelude(attn_weight)
+        if len(aw_shape) < len(value_shape):
+            batch_size = value_shape[0]
+        else:
+            batch_size = aw_shape[0]
+        if len(aw_shape) == 4 and len(value_shape) == 4:
+            attn_weight = _op.reshape(attn_weight, newshape=[-3, -2])
+            value = _op.reshape(value, newshape=[-3, -2])
+        if len(aw_shape) == 3 and len(value_shape) == 4:
+            attn_weight = _op.broadcast_to(attn_weight, shape=(batch_size,) + aw_shape)
+            attn_weight = _op.reshape(attn_weight, newshape=[-3, -2])
+            value = _op.reshape(value, newshape=[-3, -2])
+        if len(aw_shape) == 4 and len(value_shape) == 3:
+            attn_weight = _op.reshape(attn_weight, newshape=[-3, -2])
+            value = _op.broadcast_to(value, shape=(batch_size,) + value_shape)
+            value = _op.reshape(value, newshape=[-3, -2])
+        attn_weight = _op.nn.batch_matmul(attn_weight, value, transpose_b=False)
+        if len(aw_shape) == 4 or len(value_shape) == 4:
+            attn_weight = _op.reshape(attn_weight, newshape=[-4, batch_size, -1, -2])
+        return attn_weight
+
     # Operator mappings
     def create_convert_map(self):
         self.convert_map = {
@@ -4167,6 +4276,7 @@ def create_convert_map(self):
             "aten::copy_": self.inplace_copy,
             "aten::swapaxes": self.transpose,
             "aten::linalg_vector_norm": self.linalg_vector_norm,
+            "aten::scaled_dot_product_attention": self.scaled_dot_product_attention,
         }
 
     def update_convert_map(self, custom_map):


Test patch:
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -4776,7 +4776,6 @@ def test_fn(x, mask):
     verify_model(test_fn, [inp.to(torch.float64), inp > 0.5])
 
 
-@pytest.mark.skip(reason="unsupported op: 'aten::scaled_dot_product_attention'")
 def test_transformer():
     """test_transformer"""
     model = torch.nn.Transformer(d_model=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6)
@@ -5489,6 +5488,89 @@ def test_fn(order):
     verify_model(test_fn(order=0), input_data=input_data)
 
 
+@tvm.testing.uses_gpu
+def test_scaled_dot_product_attention():
+    """test_scaled_dot_product_attention"""
+    torch.set_grad_enabled(False)
+
+    def test_fn(attn_mask=None, is_causal=False):
+        return lambda query, key, value: torch.nn.functional.scaled_dot_product_attention(
+            query, key, value, attn_mask=attn_mask, is_causal=is_causal
+        )
+
+    L, S, E, Ev = 5, 7, 11, 13
+    query_4d = torch.randn(2, 3, L, E)
+    query_3d = torch.randn(3, L, E)
+    key_4d = torch.randn(2, 3, S, E)
+    key_3d = torch.randn(3, S, E)
+    value_4d = torch.randn(2, 3, S, Ev)
+    value_3d = torch.randn(3, S, Ev)
+
+    verify_model(test_fn(), [query_4d, key_4d, value_4d])
+    verify_model(test_fn(), [query_4d, key_4d, value_3d])
+    verify_model(test_fn(), [query_4d, key_3d, value_4d])
+    verify_model(test_fn(), [query_4d, key_3d, value_3d])
+    verify_model(test_fn(), [query_3d, key_4d, value_4d])
+    verify_model(test_fn(), [query_3d, key_4d, value_3d])
+    verify_model(test_fn(), [query_3d, key_3d, value_4d])
+    verify_model(test_fn(), [query_3d, key_3d, value_3d])
+
+    verify_model(test_fn(is_causal=True), [query_4d, key_4d, value_4d])
+    verify_model(test_fn(is_causal=True), [query_4d, key_4d, value_3d])
+    verify_model(test_fn(is_causal=True), [query_4d, key_3d, value_4d])
+    verify_model(test_fn(is_causal=True), [query_4d, key_3d, value_3d])
+    verify_model(test_fn(is_causal=True), [query_3d, key_4d, value_4d])
+    verify_model(test_fn(is_causal=True), [query_3d, key_4d, value_3d])
+    verify_model(test_fn(is_causal=True), [query_3d, key_3d, value_4d])
+    verify_model(test_fn(is_causal=True), [query_3d, key_3d, value_3d])
+
+    # Test with explicit attn_mask
+    attn_mask = torch.ones((L, S), dtype=torch.bool).tril(diagonal=0)
+    if torch.cuda.is_available():
+        attn_mask = attn_mask.cuda()
+    verify_model(test_fn(attn_mask=attn_mask), [query_4d, key_4d, value_4d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_4d, key_4d, value_3d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_4d, key_3d, value_4d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_4d, key_3d, value_3d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_3d, key_4d, value_4d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_3d, key_4d, value_3d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_3d, key_3d, value_4d])
+    verify_model(test_fn(attn_mask=attn_mask), [query_3d, key_3d, value_3d])
+
+    # Test with float64
+    query_4d = torch.randn(2, 3, L, E, dtype=torch.float64)
+    query_3d = torch.randn(3, L, E, dtype=torch.float64)
+    key_4d = torch.randn(2, 3, S, E, dtype=torch.float64)
+    key_3d = torch.randn(3, S, E, dtype=torch.float64)
+    value_4d = torch.randn(2, 3, S, Ev, dtype=torch.float64)
+    value_3d = torch.randn(3, S, Ev, dtype=torch.float64)
+    verify_model(test_fn(), [query_4d, key_4d, value_4d])
+    verify_model(test_fn(), [query_4d, key_4d, value_3d])
+    verify_model(test_fn(), [query_4d, key_3d, value_4d])
+    verify_model(test_fn(), [query_4d, key_3d, value_3d])
+    verify_model(test_fn(), [query_3d, key_4d, value_4d])
+    verify_model(test_fn(), [query_3d, key_4d, value_3d])
+    verify_model(test_fn(), [query_3d, key_3d, value_4d])
+    verify_model(test_fn(), [query_3d, key_3d, value_3d])
+
+    # Test with larger tensors
+    L, S, E, Ev = 128, 128, 64, 64
+    query_4d = torch.randn(32, 8, L, E)
+    query_3d = torch.randn(8, L, E)
+    key_4d = torch.randn(32, 8, S, E)
+    key_3d = torch.randn(8, S, E)
+    value_4d = torch.randn(32, 8, S, Ev)
+    value_3d = torch.randn(8, S, Ev)
+    verify_model(test_fn(), [query_4d, key_4d, value_4d])
+    verify_model(test_fn(), [query_4d, key_4d, value_3d])
+    verify_model(test_fn(), [query_4d, key_3d, value_4d])
+    verify_model(test_fn(), [query_4d, key_3d, value_3d])
+    verify_model(test_fn(), [query_3d, key_4d, value_4d])
+    verify_model(test_fn(), [query_3d, key_4d, value_3d])
+    verify_model(test_fn(), [query_3d, key_3d, value_4d])
+    verify_model(test_fn(), [query_3d, key_3d, value_3d])
+
+
 class TestSetSpan:
     """test structural equal between translated / hand-crafted relay IR with span tagged."""