[Llama] Use rocm ukernel when available + use num_layer for pkv.

python/turbine_models/custom_models/stateless_llama.py

@@ -71,9 +71,9 @@
 """
 
 
-def slice_up_to_step(global_pkv, seq_step, heads, hidden_dim):
+def slice_up_to_step(global_pkv, seq_step, heads, hidden_dim, num_layers):
     all_pkv_tensors = []
-    for i in range(heads * 2):
+    for i in range(num_layers * 2):
         # Numpy semantic: sliced = global_pkv[i, 0, 0:seq_step, 0:heads, 0:hidden_dim]
         # Generates tensor<1 x 1 x seq_step x heads x hidden_dim>
         sliced = IREE.tensor_slice(
@@ -121,12 +121,13 @@ def export_transformer_model(
         token=hf_auth_token,
     )
     # TODO: generate these values instead of magic numbers
+    NUM_LAYERS = mod.config.num_hidden_layers
     HEADS = mod.config.num_attention_heads
     HIDDEN_DIM = int(mod.config.hidden_size / HEADS)
     BATCH_SIZE = 1
     MAX_STEP_SEQ = mod.config.max_position_embeddings - 1
     global_pkv = torch.zeros(
-        size=(HEADS * 2, BATCH_SIZE, MAX_STEP_SEQ, HEADS, HIDDEN_DIM),
+        size=(NUM_LAYERS * 2, BATCH_SIZE, MAX_STEP_SEQ, HEADS, HIDDEN_DIM),
         dtype=dtype,
     )
 
@@ -161,7 +162,7 @@ def run_initialize(self, x=AbstractTensor(BATCH_SIZE, None, dtype=torch.int64)):
             self.global_seq_step = IREE.tensor_dim(
                 state[0], 1
             )  # ? dimension of arbitrarily 0th kv tensor
-            for i in range(HEADS * 2):
+            for i in range(NUM_LAYERS * 2):
                 slice_of_state = IREE.tensor_reshape(
                     state[i], 1, 1, self.global_seq_step, HEADS, HIDDEN_DIM
                 )
@@ -172,7 +173,7 @@ def run_initialize(self, x=AbstractTensor(BATCH_SIZE, None, dtype=torch.int64)):
 
         def run_forward(self, x=AbstractTensor(1, 1, dtype=torch.int64)):
             state_arg = slice_up_to_step(
-                self.global_state, self.global_seq_step, HEADS, HIDDEN_DIM
+                self.global_state, self.global_seq_step, HEADS, HIDDEN_DIM, NUM_LAYERS
             )
             forw_const = (
                 [state_arg[0].dynamic_dim(1) < MAX_STEP_SEQ]
@@ -183,7 +184,7 @@ def run_forward(self, x=AbstractTensor(1, 1, dtype=torch.int64)):
                 + [x.dynamic_dim(1) < MAX_STEP_SEQ for x in state_arg[1:]]
             )
             token, *state_update = self.forward(x, *state_arg, constraints=forw_const)
-            for i in range(HEADS * 2):
+            for i in range(NUM_LAYERS * 2):
                 update = IREE.tensor_reshape(
                     state_update[i], 1, 1, 1, HEADS, HIDDEN_DIM
                 )
@@ -226,7 +227,7 @@ def run_cached_initialize(
             self, x=AbstractTensor(BATCH_SIZE, None, dtype=torch.int64)
         ):
             state_arg = slice_up_to_step(
-                self.global_state, self.global_seq_step, HEADS, HIDDEN_DIM
+                self.global_state, self.global_seq_step, HEADS, HIDDEN_DIM, NUM_LAYERS
             )
             forw_const = (
                 [x.dynamic_dim(1) < MAX_STEP_SEQ]
@@ -243,7 +244,7 @@ def run_cached_initialize(
             len_of_new_tokens = IREE.tensor_dim(
                 state[0], 1
             )  # ? dimension of arbitrarily 0th kv tensor
-            for i in range(HEADS * 2):
+            for i in range(NUM_LAYERS * 2):
                 slice_of_state = IREE.tensor_reshape(
                     state[i], 1, 1, len_of_new_tokens, HEADS, HIDDEN_DIM
                 )
@@ -278,7 +279,7 @@ def evict_kvcache_space(self):
             sink_size = 4
             window_size = 252
             most_recent_window = self.global_seq_step + (-window_size)
-            for i in range(HEADS * 2):
+            for i in range(NUM_LAYERS * 2):
                 update_window_state = IREE.tensor_slice(
                     self.global_state,
                     i,
@@ -339,12 +340,14 @@ def evict_kvcache_space(self):
                 [
                     "--iree-rocm-target-chip=" + target_triple,
                     "--iree-rocm-link-bc=true",
-                    "--iree-rocm-bc-dir=/opt/rocm/amdgcn/bitcode",
                     "--iree-vm-bytecode-module-strip-source-map=true",
                     "--iree-opt-strip-assertions=true",
                     "--iree-vm-target-truncate-unsupported-floats",
                 ]
             )
+            ukernel_supported_arch = {"gfx90a", "gfx940", "gfx1030", "gfx1100"}
+            if target_triple in ukernel_supported_arch:
+                flags.extend(["--iree-rocm-enable-ukernels=argmax"])
         elif device == "cuda":
             flags.extend(
                 [