[ROCm] Implement RNN support

Author: Ruturaj4

jax/experimental/rnn.py

@@ -176,6 +176,50 @@ def init_lstm_weight(rng: PRNGKeyArray, input_size: int, hidden_size: int,
       rng, shape=(param_count,), dtype=jnp.float32, minval=-k, maxval=k)
 
 
+def swap_lstm_gates(weights, input_size, hidden_size, num_layers, bidirectional):
+  """
+    Swaps the weights for the input and output gates in an LSTM model's parameters.
+
+    This function is specifically designed for compatibility with MIOpen, where the gate ordering
+    differs from CuDNN. In CuDNN, the gates are ordered as:
+    - 0: Forget gate (f)
+    - 1: Input gate (i)
+    - 2: New memory gate (g)
+    - 3: Output gate (o)
+
+    However, in MIOpen, the ordering of the new memory (g) and output (o) gates is swapped:
+    - 0: Forget gate (f)
+    - 1: Input gate (i)
+    - 2: Output gate (o)
+    - 3: New memory gate (g)
+
+    This function rearranges the weights and biases for the gates to ensure that the model 
+    operates correctly with MIOpen by swapping the third (new memory) and fourth (output) gates.
+  """
+  weights = jnp.asarray(weights)
+  flat_shapes = _get_params_shapes_in_lstm(input_size, hidden_size, num_layers, bidirectional)
+  num_directions = 2 if bidirectional else 1
+
+  w_offsets = 0
+  for l in range(num_layers):
+    for direction in range(num_directions):
+      # Iterate through all weight and bias gate names to swap gates in both weights and biases.
+      for gate_name in ["W_ih", "W_hh", "b_ih", "b_hh"]:
+        shape = flat_shapes.pop(0)
+        num_elems = math.prod(shape)
+        matrix = weights[w_offsets:w_offsets + num_elems].reshape(shape)
+
+        # Swap between the input and output gates (third and fourth gates).
+        gates = jnp.split(matrix, 4, axis=0)
+        swapped_matrix = jnp.concatenate([gates[0], gates[1], gates[3], gates[2]], axis=0)
+
+        # Update the weights with swapped matrix.
+        weights = weights.at[w_offsets:w_offsets + num_elems].set(swapped_matrix.flatten())
+        w_offsets += num_elems
+
+  return weights
+
+
 def unpack_lstm_weights(
     weights: Array, input_size: int, hidden_size: int, num_layers: int,
     bidirectional: bool
@@ -437,7 +481,8 @@ def _gpu_lowering_strip_tf32(fn, *args, cudnn_allow_tf32, **kw):
 rnn_fwd_p.def_impl(partial(xla.apply_primitive, rnn_fwd_p))
 rnn_fwd_p.def_abstract_eval(rnn_abstract_eval)
 if gpu_rnn:
-  mlir.register_lowering(rnn_fwd_p, gpu_rnn.cudnn_rnn_lowering, platform='cuda')
+  mlir.register_lowering(rnn_fwd_p, gpu_rnn.cudnn_rnn_fwd_lowering, platform='cuda')
+  mlir.register_lowering(rnn_fwd_p, gpu_rnn.miopen_rnn_fwd_lowering, platform='rocm')
 
 
 def lstm_bwd(input_size: int, hidden_size: int, num_layers: int, dropout: float,
@@ -481,5 +526,7 @@ def rnn_bwd_abstract_eval(dy_aval, dhn_aval, dcn_aval, x_aval, h0_aval, c0_aval,
 if gpu_rnn:
   mlir.register_lowering(
       rnn_bwd_p, gpu_rnn.cudnn_rnn_bwd_lowering, platform='cuda')
+  mlir.register_lowering(
+      rnn_bwd_p, gpu_rnn.miopen_rnn_bwd_lowering, platform='rocm')
 
 lstm.defvjp(lstm_fwd, lstm_bwd)