Falcon: batched generation (#26137)

src/transformers/models/falcon/modeling_falcon.py

@@ -67,6 +67,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
+# TODO (joao): Is this the same implementation as in Llama? If so, let's make them the same and add the copy facilities
 class FalconRotaryEmbedding(nn.Module):
     """Implementation of RotaryEmbedding from GPT-NeoX.
     This implementation is designed to operate on queries and keys that are compatible with `[batch_size,
@@ -99,19 +100,40 @@ class FalconRotaryEmbedding(nn.Module):
         self.cos_cached = self.cos_cached.type(dtype)
         self.sin_cached = self.sin_cached.type(dtype)
 
-    def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor:
+    def cos_sin(
+        self, seq_len: int, past_key_values_length: int, position_ids: torch.Tensor, device="cpu", dtype=torch.bfloat16
+    ) -> torch.Tensor:
         total_length = seq_len + past_key_values_length
         if total_length > self.seq_len_cached:
             self._set_cos_sin_cache(total_length, device, dtype)
-        return (
-            self.cos_cached[:, past_key_values_length : seq_len + past_key_values_length],
-            self.sin_cached[:, past_key_values_length : seq_len + past_key_values_length],
-        )
+        # Gather cos, sin at the designated position ids
+        cos = self.cos_cached.squeeze(0)[position_ids]  # [bs, seq_len, dim]
+        sin = self.sin_cached.squeeze(0)[position_ids]  # [bs, seq_len, dim]
+        return cos, sin
+
+    def forward(self, query, key, past_key_values_length, position_ids):
+        _, seq_len, _ = query.shape
+        cos, sin = self.cos_sin(seq_len, past_key_values_length, position_ids, query.device, query.dtype)
+        # Query and key's shapes are [bs * num_heads, seq_len, dim], might need manual expansion. Ifs and elses used to
+        # avoid unnecessary repeat_interleave operations.
+        query_expansion_factor = int(query.shape[0] / cos.shape[0])
+        if query_expansion_factor > 1:
+            query_cos = torch.repeat_interleave(cos, query_expansion_factor, dim=0)
+            query_sin = torch.repeat_interleave(sin, query_expansion_factor, dim=0)
+        else:
+            query_cos, query_sin = cos, sin
+
+        key_expansion_factor = int(key.shape[0] / cos.shape[0])
+        if key_expansion_factor > 1:
+            if key_expansion_factor != query_expansion_factor:
+                key_cos = torch.repeat_interleave(cos, key_expansion_factor, dim=0)
+                key_sin = torch.repeat_interleave(sin, key_expansion_factor, dim=0)
+            else:
+                key_cos, key_sin = query_cos, query_sin
+        else:
+            key_cos, key_sin = cos, sin
 
-    def forward(self, query, key, past_key_values_length=0):
-        batch, seq_len, head_dim = query.shape
-        cos, sin = self.cos_sin(seq_len, past_key_values_length, query.device, query.dtype)
-        return (query * cos) + (rotate_half(query) * sin), (key * cos) + (rotate_half(key) * sin)
+        return (query * query_cos) + (rotate_half(query) * query_sin), (key * key_cos) + (rotate_half(key) * key_sin)
 
 
 class FalconLinearScalingRotaryEmbedding(FalconRotaryEmbedding):
@@ -270,7 +292,7 @@ class FalconAttention(nn.Module):
                 f" {self.num_heads})."
             )
 
-        self.maybe_rotary = self._init_rope() if config.rotary else lambda q, k, t: (q, k)
+        self.maybe_rotary = self._init_rope() if config.rotary else lambda q, k, t, p: (q, k)
 
         # Layer-wise attention scaling
         self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
@@ -378,6 +400,7 @@ class FalconAttention(nn.Module):
         hidden_states: torch.Tensor,
         alibi: Optional[torch.Tensor],
         attention_mask: torch.Tensor,
+        position_ids: Optional[torch.LongTensor] = None,
         layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         head_mask: Optional[torch.Tensor] = None,
         use_cache: bool = False,
@@ -399,7 +422,7 @@ class FalconAttention(nn.Module):
         value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim)
 
         past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
-        query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
+        query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length, position_ids)
 
         if layer_past is not None:
             past_key, past_value = layer_past
@@ -415,7 +438,8 @@ class FalconAttention(nn.Module):
         else:
             present = None
 
-        attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
+        float_min = torch.finfo(query_layer.dtype).min
+        attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float_min).to(query_layer.dtype)
 
         query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
         key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
@@ -536,6 +560,7 @@ class FalconDecoderLayer(nn.Module):
         hidden_states: torch.Tensor,
         alibi: Optional[torch.Tensor],
         attention_mask: torch.Tensor,
+        position_ids: Optional[torch.LongTensor] = None,
         layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         head_mask: Optional[torch.Tensor] = None,
         use_cache: bool = False,
@@ -554,6 +579,7 @@ class FalconDecoderLayer(nn.Module):
             attention_layernorm_out,
             layer_past=layer_past,
             attention_mask=attention_mask,
+            position_ids=position_ids,
             alibi=alibi,
             head_mask=head_mask,
             use_cache=use_cache,
@@ -632,6 +658,11 @@ FALCON_INPUTS_DOCSTRING = r"""
             - 0 for tokens that are **masked**.
 
             [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
         head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
             Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
 
@@ -836,6 +867,7 @@ class FalconModel(FalconPreTrainedModel):
         input_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
         head_mask: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
@@ -892,6 +924,14 @@ class FalconModel(FalconPreTrainedModel):
             alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
         else:
             alibi = None
+            if position_ids is None:
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+                position_ids = torch.arange(
+                    past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+                )
+                position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+            else:
+                position_ids = position_ids.view(-1, seq_length).long()
 
         causal_mask = self._prepare_attn_mask(
             attention_mask,
@@ -922,6 +962,7 @@ class FalconModel(FalconPreTrainedModel):
                     hidden_states,
                     alibi,
                     causal_mask,
+                    position_ids,
                     head_mask[i],
                 )
             else:
@@ -929,6 +970,7 @@ class FalconModel(FalconPreTrainedModel):
                     hidden_states,
                     layer_past=layer_past,
                     attention_mask=causal_mask,
+                    position_ids=position_ids,
                     head_mask=head_mask[i],
                     use_cache=use_cache,
                     output_attentions=output_attentions,
@@ -988,13 +1030,23 @@ class FalconForCausalLM(FalconPreTrainedModel):
         input_ids: torch.LongTensor,
         past_key_values: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> dict:
         if past_key_values is not None:
             input_ids = input_ids[:, -1:]
 
+        # Note: versions of Falcon with alibi do not use position_ids. It is used with RoPE.
+        if not self.transformer.use_alibi and attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
         return {
             "input_ids": input_ids,
+            "position_ids": position_ids,
             "past_key_values": past_key_values,
             "use_cache": kwargs.get("use_cache"),
             "attention_mask": attention_mask,
@@ -1011,6 +1063,7 @@ class FalconForCausalLM(FalconPreTrainedModel):
         input_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
         head_mask: Optional[torch.Tensor] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
         labels: Optional[torch.Tensor] = None,
@@ -1032,6 +1085,7 @@ class FalconForCausalLM(FalconPreTrainedModel):
             input_ids,
             past_key_values=past_key_values,
             attention_mask=attention_mask,
+            position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
             use_cache=use_cache,

tests/models/falcon/test_modeling_falcon.py

@@ -19,8 +19,16 @@ import unittest
 
 from parameterized import parameterized
 
-from transformers import AutoConfig, AutoModel, AutoTokenizer, FalconConfig, is_torch_available, set_seed
-from transformers.testing_utils import CaptureLogger, require_torch, slow, tooslow, torch_device
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    FalconConfig,
+    is_torch_available,
+    set_seed,
+)
+from transformers.testing_utils import CaptureLogger, require_bitsandbytes, require_torch, slow, tooslow, torch_device
 from transformers.utils import logging as transformers_logging
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -502,6 +510,35 @@ class FalconLanguageGenerationTest(unittest.TestCase):
                 outputs_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=True)
                 self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)
 
+    @require_bitsandbytes
+    @slow
+    def test_batched_generation(self):
+        tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b", padding_side="left")
+        tokenizer.pad_token = tokenizer.eos_token
+        model = AutoModelForCausalLM.from_pretrained(
+            "tiiuae/falcon-7b",
+            device_map="auto",
+            load_in_4bit=True,
+        )
+
+        test_text = "A sequence: 1, 2"  # should generate the rest of the sequence
+
+        unpadded_inputs = tokenizer([test_text], return_tensors="pt").to("cuda:0")
+        unpadded_inputs.pop("token_type_ids")
+        unpadded_gen_out = model.generate(**unpadded_inputs, max_new_tokens=20)
+        unpadded_gen_text = tokenizer.batch_decode(unpadded_gen_out, skip_special_tokens=True)
+
+        dummy_text = "This is a longer text " * 2  # forces left-padding on `test_text`
+        padded_inputs = tokenizer([test_text, dummy_text], return_tensors="pt", padding=True).to("cuda:0")
+        padded_inputs.pop("token_type_ids")
+        padded_gen_out = model.generate(**padded_inputs, max_new_tokens=20)
+        padded_gen_text = tokenizer.batch_decode(padded_gen_out, skip_special_tokens=True)
+
+        expected_output = "A sequence: 1, 2, 3, 4, 5, 6, 7, 8, "
+        self.assertLess(unpadded_inputs.input_ids.shape[-1], padded_inputs.input_ids.shape[-1])  # left-padding exists
+        self.assertEqual(unpadded_gen_text[0], expected_output)
+        self.assertEqual(padded_gen_text[0], expected_output)
+
 
 # TODO Lysandre: Remove this in version v4.34
 class FalconOverrideTest(unittest.TestCase):