Process separate q/k/v weights in MHA converter

Summary:
ATT. The converter was not ready for the `not self._qkv_same_embed_dim` case [here](https://github.com/pytorch/pytorch/blob/80ed3e9ccdaab20814b4156611a19043aaaaef03/torch/nn/modules/activation.py#L1074) with separate q/k/v weights. Here we cover this case.

Intenral:

This causes a failure in the AIT lowering of the IGCTR MC model. See the post: https://fb.workplace.com/groups/gpuinference/permalink/2872581106223872/ .

Differential Revision: D61155566

fx2ait/fx2ait/converters/ait_module_converters.py

@@ -63,6 +63,7 @@ def multi_head_attention_module(
     )
 
     # Bind constant tensor for MHA module
+    q_w, k_w, v_w = None, None, None
     qkv_weight, qkv_bias = None, None
     for k, v in submod.named_parameters():
         ait_data = _TorchConstantTensorData(v.data.contiguous().cuda().half())
@@ -81,6 +82,27 @@ def multi_head_attention_module(
                     name=make_str_ait_friendly(f"{target}.{k}"),
                 )
                 qkv_bias._bind_data(ait_data)
+        elif k == "q_proj_weight":
+            q_w = Tensor(
+                shape=v.shape,
+                dtype="float16",
+                name=make_str_ait_friendly(f"{target}.{k}"),
+            )
+            q_w._bind_data(ait_data)
+        elif k == "k_proj_weight":
+            k_w = Tensor(
+                shape=v.shape,
+                dtype="float16",
+                name=make_str_ait_friendly(f"{target}.{k}"),
+            )
+            k_w._bind_data(ait_data)
+        elif k == "v_proj_weight":
+            v_w = Tensor(
+                shape=v.shape,
+                dtype="float16",
+                name=make_str_ait_friendly(f"{target}.{k}"),
+            )
+            v_w._bind_data(ait_data)
         elif "out_proj" in k:
             if "weight" in k:
                 tensor = attn.proj.weight.tensor()
@@ -90,7 +112,15 @@ def multi_head_attention_module(
             tensor._bind_data(ait_data)
 
     # Swap out qkv tensor used by nn.CrossAttention.
-    q_w, k_w, v_w = chunk()(qkv_weight, 3)
+    if qkv_weight is not None:
+        assert q_w is None
+        assert k_w is None
+        assert v_w is None
+        q_w, k_w, v_w = chunk()(qkv_weight, 3)
+    else:
+        assert q_w is not None
+        assert k_w is not None
+        assert v_w is not None
     q_b, k_b, v_b = chunk()(qkv_bias, 3)
 
     attn.proj_q.weight._tensor = q_w

fx2ait/fx2ait/test/converters/converters_module/test_ait_multihead_attention.py

@@ -18,7 +18,7 @@
 
 
 class TestMultiHeadAttentionConverter(AITTestCase):
-    def test_multihead_attention_cross_attenytion(self):
+    def test_multihead_attention_cross_attention(self):
         class TestModule(torch.nn.Module):
             def __init__(self, dim, nheads):
                 super().__init__()
@@ -77,3 +77,37 @@ def forward(self, x):
             expected_ops={torch.nn.MultiheadAttention},
             leaf_module=torch.nn.MultiheadAttention,
         )
+
+    def test_multihead_attention_different_kv_dims(self):
+        class TestModule(torch.nn.Module):
+            def __init__(self, qdim, kdim, vdim, nheads):
+                super().__init__()
+                self.attn = torch.nn.MultiheadAttention(
+                    embed_dim=qdim,
+                    num_heads=nheads,
+                    batch_first=True,
+                    kdim=kdim,
+                    vdim=vdim,
+                )
+
+            def forward(self, q, k, v):
+                return self.attn(query=q, key=k, value=v)
+
+        batch_size = 2
+        seqlen = 4
+        qdim = 512
+        kdim = 128
+        vdim = 128
+        num_heads = 8
+
+        q = torch.ones(batch_size, seqlen, qdim).cuda().half()
+        k = torch.ones(batch_size, seqlen, kdim).cuda().half()
+        v = torch.ones(batch_size, seqlen, vdim).cuda().half()
+        model = TestModule(qdim, kdim, vdim, num_heads).eval().half().cuda()
+
+        self.run_test(
+            model,
+            [q, k, v],
+            expected_ops={torch.nn.MultiheadAttention},
+            leaf_module=torch.nn.MultiheadAttention,
+        )