Changes in modelling_RW.py to be able to handle past_key_values for faster model generations

The current code has missed out passing past_key_values in every forward pass for fast generation of tokens. This results in lot of recompute. This "modelling_RW.py" I am uploading deals with this in the way pytorch huggingface transformers package generation/utils.py wants. All the changes are basically around including past_key_values everywhere. I think this will apply on all falcon models These are the changes specifically

Class RotaryEmbedding forward method
Include past_seq_length in forward pass and apply rotary embedding according to the position of the query token ---- if else condition added

_make_causal_mask function
to give masking according to the way F.scaled dot product attention behaves. F.scaled_dot_product attention treats the attention_mask matrix as receiving attentions. For example if attention_mask is
[[True, False], [True, True]]. It would mean the first token is "receiving" attentions from first token and not second token. This is unlike what we generally end up thinking which is first token is giving attention to itself and not to the second one. Due to reason the past_key_values attentions are all True in make_causal mask function. Also I have reversed the inequality above that due to the same reason.

Class Attention forward method
a) past_key_value length is passed in rotary function ---- if,else loop added
b) concatenation of past key and current key is done after permuting the past key shape to match the current key shape
c) to keep key_layer shape consistent with the output expectation which is (batch_size, head_dim, seq_length), another permutation done before creating "present" to return in the output

Class RWModel prepare_attn_mask method
Have removed src_length > 1 criteria for making causal mask

RW causal LM prepare inputs for generation
Read pastkey values from the input coming from huggingface generate method and dont call convert_to_rw_cache method

modelling_RW.py

@@ -87,10 +87,18 @@ class RotaryEmbedding(torch.nn.Module):
 
         return self.cos_cached, self.sin_cached
 
-    def forward(self, q, k):
-        batch, seq_len, head_dim = q.shape
+    def forward(self, q, k, past_seq_length=None):
+        if past_seq_length == None : 
+            batch, seq_len, head_dim = q.shape
+        else : 
+            # print("past_seq_length", past_seq_length)
+            batch, input_seq_len, head_dim = q.shape 
+            seq_len = past_seq_length + input_seq_len
         cos, sin = self.cos_sin(seq_len, q.device, q.dtype)
-        return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
+        if past_seq_length != None : 
+            return (q * cos[:, past_seq_length:, :]) + (rotate_half(q) * sin[:, past_seq_length:, :]), (k * cos[:, past_seq_length:, :]) + (rotate_half(k) * sin[:, past_seq_length:, :])
+        else : 
+            return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
 
 
 def _make_causal_mask(
@@ -100,10 +108,10 @@ def _make_causal_mask(
     mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
     # ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
     seq_ids = torch.arange(target_length, device=device)
-    mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
+    mask[:, past_key_values_length:] = seq_ids[:, None] >= seq_ids[None, :]
 
     if past_key_values_length > 0:
-        mask[:, :past_key_values_length] = False
+        mask[:, :past_key_values_length] = True
 
     expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
     return expanded_mask
@@ -264,20 +272,27 @@ class Attention(nn.Module):
         )
         value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
 
-        query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
+        if layer_past is not None : 
+            past_key, past_value = layer_past
+            past_kv_length = past_key.shape[2]
+            query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
+        else : 
+            query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
 
         if layer_past is not None:
             past_key, past_value = layer_past
             # concatenate along seq_length dimension:
             #  - key: [batch_size * self.num_heads, head_dim, kv_length]
             #  - value: [batch_size * self.num_heads, kv_length, head_dim]
+            past_key = past_key.permute(0, 2, 1)
             key_layer = torch.cat((past_key, key_layer), dim=1)
             value_layer = torch.cat((past_value, value_layer), dim=1)
 
         _, kv_length, _ = key_layer.shape
 
         if use_cache is True:
-            present = (key_layer, value_layer)
+            key_layer_permute = key_layer.permute(0, 2, 1)
+            present = (key_layer_permute, value_layer)
         else:
             present = None
 
@@ -286,9 +301,14 @@ class Attention(nn.Module):
             key_layer_ = key_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
             value_layer_ = value_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
 
-            attn_output = F.scaled_dot_product_attention(
-                query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
-            )
+            if attention_mask is not None : 
+                attn_output = F.scaled_dot_product_attention(
+                    query_layer_, key_layer_, value_layer_, attention_mask, 0.0, is_causal=False
+                )
+            else :
+                attn_output = F.scaled_dot_product_attention(
+                    query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
+                ) 
 
             x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
             x = x.permute(0, 2, 1, 3)
@@ -528,10 +548,10 @@ class RWModel(RWPreTrainedModel):
         device = attention_mask.device
         _, src_length = input_shape
 
-        if src_length > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape, device=device, past_key_values_length=past_key_values_length
-            )
+        # if src_length > 1:
+        combined_attention_mask = _make_causal_mask(
+            input_shape, device=device, past_key_values_length=past_key_values_length
+        )
 
         # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
         expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
@@ -710,16 +730,19 @@ class RWForCausalLM(RWPreTrainedModel):
         **kwargs,
     ) -> dict:
         # only last token for input_ids if past is not None
-        if past:
+        if kwargs.get("past_key_values", None) : 
             input_ids = input_ids[:, -1].unsqueeze(-1)
-
+            past_key_values = kwargs["past_key_values"]
             # the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
-            if past[0][0].shape[0] == input_ids.shape[0]:
-                past = self._convert_to_rw_cache(past)
+            # if kwargs["past_key_values"][0][0].shape[0] == input_ids.shape[0]:
+            #     past_key_values = self._convert_to_rw_cache(kwargs["past_key_values"])
+                # past_key_values = kwargs["past_key_values"]
+        else : 
+            past_key_values = None
 
         return {
             "input_ids": input_ids,
-            "past_key_values": past,
+            "past_key_values": past_key_values,
             "use_cache": kwargs.get("use_cache"),
             "attention_mask": attention_mask,
         }