Add support for batched generation and synthetic long prompt

generate.py

@@ -7,7 +7,7 @@
 import sys
 import time
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Union
 
 import torch
 import torch._dynamo.config
@@ -24,7 +24,9 @@ def device_sync(device):
 
 torch._inductor.config.coordinate_descent_tuning = True
 torch._inductor.config.triton.unique_kernel_names = True
-torch._inductor.config.fx_graph_cache = True # Experimental feature to reduce compilation times, will be on by default in future
+# Experimental features to reduce compilation times, will be on by default in future
+torch._inductor.config.fx_graph_cache = True 
+torch._functorch.config.enable_autograd_cache = True
 
 default_device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
@@ -50,7 +52,7 @@ def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = Non
     return probs
 
 def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
-    probs = logits_to_probs(logits[0, -1], temperature, top_k)
+    probs = logits_to_probs(logits[:, -1], temperature, top_k)
     idx_next = multinomial_sample_one_no_sync(probs)
     return idx_next, probs
 
@@ -76,7 +78,7 @@ def decode_n_tokens(model: Transformer, cur_token: torch.Tensor, input_pos: torc
             new_tokens.append(next_token.clone())
             callback(new_tokens[-1])
             new_probs.append(next_prob.clone())
-            cur_token = next_token.view(1, -1)
+            cur_token = next_token.clone()
 
     return new_tokens, new_probs
 
@@ -139,6 +141,7 @@ def generate(
     model: Transformer,
     prompt: torch.Tensor,
     max_new_tokens: int,
+    batch_size: int,
     *,
     interactive: bool,
     draft_model: Transformer,
@@ -152,7 +155,7 @@ def generate(
 
     is_speculative = draft_model is not None
     # create an empty tensor of the expected final shape and fill in the current tokens
-    T = prompt.size(0)
+    T = prompt.size(-1)
     T_new = T + max_new_tokens
     if interactive:
         max_seq_length = 350
@@ -162,20 +165,22 @@ def generate(
     device, dtype = prompt.device, prompt.dtype
     max_seq_length = max_seq_length + speculate_k + 1 if is_speculative else max_seq_length
     with torch.device(device):
-        model.setup_caches(max_batch_size=1, max_seq_length=max_seq_length)
+        model.setup_caches(max_batch_size=batch_size, max_seq_length=max_seq_length)
         if is_speculative and draft_model is not model:
-            draft_model.setup_caches(max_batch_size=1, max_seq_length=max_seq_length)
+            draft_model.setup_caches(max_batch_size=batch_size, max_seq_length=max_seq_length)
 
     # create an empty tensor of the expected final shape and fill in the current tokens
-    empty = torch.empty(T_new, dtype=dtype, device=device)
-    empty[:T] = prompt
+    empty = torch.empty(batch_size, T_new, dtype=dtype, device=device)
+    # We are just making the same prompt for every batch
+    prompt = prompt.view(1, -1).repeat(batch_size, 1)
+    empty[:, :T] = prompt
     seq = empty
     input_pos = torch.arange(0, T, device=device)
 
-    next_token = prefill(model, prompt.view(1, -1), input_pos, **sampling_kwargs).clone()
+    next_token = prefill(model, prompt.view(batch_size, -1), input_pos, **sampling_kwargs).clone()
     if is_speculative:
-        prefill(draft_model, prompt.view(1, -1), input_pos, **sampling_kwargs)
-    seq[T] = next_token
+        prefill(draft_model, prompt.view(batch_size, -1), input_pos, **sampling_kwargs)
+    seq[:, T] = next_token.squeeze()
 
     input_pos = torch.tensor([T], device=device, dtype=torch.int)
     accept_counts = [0] * (speculate_k + 1)
@@ -197,8 +202,8 @@ def generate(
             input_pos = input_pos + num_added
             next_token = next_tokens[-1]
     else:
-        generated_tokens, _ = decode_n_tokens(model, next_token.view(1, -1), input_pos, max_new_tokens - 1, callback=callback, **sampling_kwargs)
-        seq[T + 1:] = torch.cat(generated_tokens)
+        generated_tokens, _ = decode_n_tokens(model, next_token.view(batch_size, -1), input_pos, max_new_tokens - 1, callback=callback, **sampling_kwargs)
+        seq[:, T + 1:] = torch.cat(generated_tokens, dim=-1)
 
     generate_stats = {
         'accept_counts': accept_counts
@@ -245,6 +250,7 @@ def _load_model(checkpoint_path, device, precision, use_tp):
 
 def _get_model_size(model):
     model_size = 0
+    params = 0
     for name, child in model.named_children():
         if not isinstance(child, torch.nn.Embedding):
             model_size += sum(
@@ -253,15 +259,22 @@ def _get_model_size(model):
                     for p in itertools.chain(child.parameters(), child.buffers())
                 ]
             )
-    return model_size
+            params += sum(
+                [
+                    p.numel()
+                    for p in itertools.chain(child.parameters(), child.buffers())
+                ]
+            )
+    return model_size, params
 
 B_INST, E_INST = "[INST]", "[/INST]"
 
 def main(
-    prompt: str = "Hello, my name is",
+    prompt: Union[int, str] = "Hello, my name is",
     interactive: bool = False,
     num_samples: int = 5,
     max_new_tokens: int = 100,
+    batch_size: int = 1,
     top_k: int = 200,
     temperature: float = 0.8,
     checkpoint_path: Path = Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf/model.pth"),
@@ -307,11 +320,15 @@ def main(
 
     tokenizer = get_tokenizer(tokenizer_path, checkpoint_path)
 
-    encoded = encode_tokens(tokenizer, prompt, bos=True, device=device)
-    prompt_length = encoded.size(0)
+    if isinstance(prompt, str):
+        encoded = encode_tokens(tokenizer, prompt, bos=True, device=device)
+    else:
+        # generate a fully synthetic prompt
+        encoded = torch.randint(0, 1024, (prompt,), device=device, dtype=torch.int64)
+    prompt_length = encoded.size(-1)
 
     torch.manual_seed(1234)
-    model_size = _get_model_size(model)
+    model_size, params = _get_model_size(model)
     if compile:
         if is_speculative and use_tp: # and ("cuda" in device):
             torch._inductor.config.triton.cudagraph_trees = False # Bug with cudagraph trees in this case
@@ -371,6 +388,7 @@ def callback(x):
                 model,
                 encoded,
                 max_new_tokens,
+                batch_size=batch_size,
                 draft_model=draft_model,
                 speculate_k=speculate_k,
                 interactive=interactive,
@@ -391,21 +409,30 @@ def callback(x):
         t = time.perf_counter() - t0
 
         if not interactive:
-            print(tokenizer.decode(y.tolist()))
+            # Just displaying the first generation
+            if batch_size > 1:
+                print("Only displaying the first generation of the batch")
+            print(tokenizer.decode(y[0].tolist()))
         else:
             print()
-        tokens_generated = y.size(0) - prompt_length
-        tokens_sec = tokens_generated / t
-        aggregate_metrics['tokens_per_sec'].append(tokens_sec)
-        print(f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_sec:.02f} tokens/sec")
-        print(f"Bandwidth achieved: {model_size * tokens_sec / 1e9:.02f} GB/s")
+        tokens_generated = y.size(-1) - prompt_length
+        generated_tokens_sec = tokens_generated / t
+        aggregate_metrics['tokens_per_sec'].append(generated_tokens_sec)
+        print(f"Time for inference {i + 1}: {t:.02f} sec total, {generated_tokens_sec:.02f} tokens/sec")
+        print(f"Bandwidth achieved: {model_size * generated_tokens_sec / 1e9:.02f} GB/s")
+        total_tokens_sec = y.numel() / t
+        print(f"FLOPS achieved: {params * total_tokens_sec * 2 / 1e12:.02f} TF/s")
+        print()
     print("==========")
     if is_speculative:
         counts_aggregated = [sum(i) for i in zip(*aggregate_metrics['accept_counts'])]
         acceptance_probs = [i/sum(counts_aggregated) for i in counts_aggregated]
         print(f"Acceptance probs: {acceptance_probs}")
         print(f"Mean Accepted: {sum([idx * i for idx, i in enumerate(counts_aggregated)])/sum(counts_aggregated)}")
 
+    print(f"Batch Size: {batch_size}")
+    print(f"Prompt Length: {prompt_length}")
+    print(f"Generated tokens: {max_new_tokens}")
     print(f"Average tokens/sec: {torch.mean(torch.tensor(aggregate_metrics['tokens_per_sec'])).item():.2f}")
     print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB")
 
@@ -414,10 +441,17 @@ def callback(x):
     import argparse
     parser = argparse.ArgumentParser(description='Your CLI description.')
 
-    parser.add_argument('--prompt', type=str, default="Hello, my name is", help='Input prompt.')
+    def int_or_str(x):
+        try:
+            return int(x)
+        except:
+            return x
+
+    parser.add_argument('--prompt', type=int_or_str, default="Hello, my name is", help="Input prompt. If it's an integer, will instead generate a synthetic prompt.")
     parser.add_argument('--interactive', action='store_true', help='Whether to launch in interactive mode')
     parser.add_argument('--num_samples', type=int, default=5, help='Number of samples.')
     parser.add_argument('--max_new_tokens', type=int, default=200, help='Maximum number of new tokens.')
+    parser.add_argument('--batch_size', type=int, default=1, help='Batch size to benchmark with')
     parser.add_argument('--top_k', type=int, default=200, help='Top-k for sampling.')
     parser.add_argument('--temperature', type=float, default=0.8, help='Temperature for sampling.')
     parser.add_argument('--checkpoint_path', type=Path, default=Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf/model.pth"), help='Model checkpoint path.')
@@ -430,7 +464,7 @@ def callback(x):
 
     args = parser.parse_args()
     main(
-        args.prompt, args.interactive, args.num_samples, args.max_new_tokens, args.top_k,
+        args.prompt, args.interactive, args.num_samples, args.max_new_tokens, args.batch_size, args.top_k,
         args.temperature, args.checkpoint_path, args.compile, args.compile_prefill, args.profile, args.draft_checkpoint_path,
         args.speculate_k, args.device
     )