Add TTFT benchmarks + update sparsity benchmarks (#1140)

This PR adds in TTFT token benchmarks to torchAO, and also updates the benchmarking script to handle sparsity a bit nicer + use the 2:4 sparse checkpoints that are available. Additionally also adds in padding support for int8 dynamic quant + 2:4 sparsity, which we were missing before.
pytorch · Dec 5, 2024 · f04aec7 · f04aec7
1 parent 65b885f
commit f04aec7
Show file tree

Hide file tree

Showing 5 changed files with 136 additions and 16 deletions.
diff --git a/scripts/prepare.sh b/scripts/prepare.sh
@@ -2,7 +2,11 @@ python scripts/download.py --repo_id meta-llama/Llama-2-7b-chat-hf
 python scripts/download.py --repo_id meta-llama/Meta-Llama-3-8B
 python scripts/download.py --repo_id meta-llama/Meta-Llama-3.1-8B
 python scripts/download.py --repo_id meta-llama/Llama-3.2-3B
+python scripts/download.py --repo_id nm-testing/SparseLlama-3-8B-pruned_50.2of4
 python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/meta-llama/Llama-2-7b-chat-hf
 python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/meta-llama/Meta-Llama-3-8B
 python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/meta-llama/Meta-Llama-3.1-8B
 python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/meta-llama/Llama-3.2-3B
+# neuralmagic doesn't come with tokenizer, so we need to copy it over
+mkdir -p checkpoints/nm-testing/SparseLlama-3-8B-pruned_50.2of4/original && cp checkpoints/meta-llama/Meta-Llama-3-8B/original/tokenizer.model checkpoints/nm-testing/SparseLlama-3-8B-pruned_50.2of4/original/tokenizer.model
+python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/nm-testing/SparseLlama-3-8B-pruned_50.2of4
diff --git a/test/prototype/test_sparse_api.py b/test/prototype/test_sparse_api.py
@@ -50,6 +50,9 @@ def test_sparse(self):
         sparsify_(model, semi_sparse_weight())
         sparse_result = model(input)
 
+        if compile:
+            model = torch.compile(model)
+
         torch.testing.assert_close(dense_result, sparse_result, rtol=1e-3, atol=1e-3)
 
 

diff --git a/torchao/_models/llama/benchmarks.sh b/torchao/_models/llama/benchmarks.sh
@@ -52,7 +52,7 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --wr
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization autoquant --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization fp6 --write_result benchmark_results.txt
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization sparse-marlin --precision float16 --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization sparse-marlin --sparsity semi-structured --precision float16 --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-4-64 --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-2-8 --write_result benchmark_results.txt
 
@@ -62,7 +62,7 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --wr
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization autoquant --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization fp6 --write_result benchmark_results.txt --precision float16
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization sparse-marlin --precision float16 --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization sparse-marlin --sparsity semi-structured --precision float16 --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-4-64 --write_result benchmark_results.txt
 # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-2-8 --write_result benchmark_results.txt
 
@@ -79,3 +79,20 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --co
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization autoquant --write_result benchmark_results.txt --batch_size 1
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization autoquant --write_result benchmark_results.txt --batch_size 32
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization autoquant --write_result benchmark_results.txt --batch_size 128
+
+# TTFT benchmarks
+export MODEL_REPO=meta-llama/Meta-Llama-3.1-8B
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization int8dq --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization int8wo --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization int8dq --sparsity semi-structured --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization float8dq --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization float8wo --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization int4wo-64 --write_result benchmark_results.txt --prefill_size 8000
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization sparse-marlin --write_result benchmark_results.txt --prefill_size 8000 --precision float16 --sparsity semi-structured
+
+# 2:4 sparse model
+export MODEL_REPO=nm-testing/SparseLlama-3-8B-pruned_50.2of4
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --sparsity semi-structured --precision float16 --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization sparse-marlin --sparsity semi-structured --precision float16 --write_result benchmark_results.txt
diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py
@@ -17,6 +17,29 @@
 from torchao.quantization.quant_primitives import MappingType
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
 
+torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = False
+
+class HostEvent:
+    def __init__(self):
+        self.event_time = None
+
+    def record(self):
+        self.event_time = time.perf_counter()
+
+    def elapsed_time(self, other_event):
+        if self.event_time is None:
+            raise ValueError("Event not recorded!")
+        # return ms to match cuda event
+        return abs(other_event.event_time - self.event_time) * 1000
+
+def device_timer(device):
+    if "cuda" in device:
+        return torch.cuda.Event(enable_timing=True)
+    elif ("cpu" in device) or ("mps" in device):
+        return HostEvent()
+    else:
+        print(f"device={device} is not yet suppported")
+
 def device_sync(device):
     if "cuda" in device:
         torch.cuda.synchronize(device)
@@ -98,6 +121,10 @@ def generate(
     kv_cache_quantization: bool = False,
     cache_size: Optional[int] = None,
     linear_causal_mask: bool=False,
+    prefill_start_event: Optional[torch.cuda.Event]=None,
+    prefill_end_event: Optional[torch.cuda.Event]=None,
+    decode_start_event: Optional[torch.cuda.Event]=None,
+    decode_end_event: Optional[torch.cuda.Event]=None,
     **sampling_kwargs
 ) -> torch.Tensor:
     """
@@ -128,12 +155,21 @@ def generate(
         model.setup_caches(max_batch_size=batch_size, max_seq_length=cache_size, kv_cache_quantization=kv_cache_quantization, linear_causal_mask=linear_causal_mask, prompt_length=T)
 
     # execute prefill
+    if prefill_start_event is not None:
+        prefill_start_event.record()
     next_token = prefill(model, prompt.view(batch_size, -1), input_pos, **sampling_kwargs).clone()
     seq[:, T] = next_token.squeeze()
+    if prefill_end_event is not None:
+        prefill_end_event.record()
+
     # execute token generation
+    if decode_start_event is not None:
+        decode_start_event.record()
     input_pos = torch.tensor([T], device=device, dtype=torch.int)
     generated_tokens, _ = decode_n_tokens(model, next_token.view(batch_size, -1), input_pos, new_tokens-1, callback=callback, **sampling_kwargs)
     seq = torch.cat((seq[:, :T+1], *generated_tokens), dim=-1)
+    if decode_end_event is not None:
+        decode_end_event.record()
 
     return seq
 
@@ -157,6 +193,7 @@ def _load_model(checkpoint_path, device, precision):
 B_INST, E_INST = "[INST]", "[/INST]"
 
 def main(
+    prefill_size: Optional[int] = None,
     prompt: str = "Hello, my name is",
     interactive: bool = False,
     num_samples: int = 5,
@@ -166,6 +203,7 @@ def main(
     temperature: float = 0.8,
     checkpoint_path: Path = Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf/model.pth"),
     quantization: Optional[str] = None,
+    sparsity: Optional[str] = None,
     kv_cache_quantization: bool = False,
     cache_size: Optional[int] = None,
     linear_causal_mask: bool=False,
@@ -181,6 +219,10 @@ def main(
     """Generates text samples based on a pre-trained Transformer model and tokenizer.
     """
 
+    if prefill_size is not None and prefill_size > 0:
+        # create prompt of prefill size 
+        prompt = "prompt " * (int(prefill_size)-3)
+
     torchao.quantization.utils.recommended_inductor_config_setter()
 
     assert checkpoint_path.is_file(), checkpoint_path
@@ -205,6 +247,14 @@ def main(
 
     torch.manual_seed(1234)
 
+    def ffn_only(mod, fqn):
+        return isinstance(mod, torch.nn.Linear) and "feed_forward" in fqn 
+
+    def not_ffn_only(mod, fqn):
+        return isinstance(mod, torch.nn.Linear) and not ffn_only(mod, fqn)
+
+    def ffn_or_attn_only(mod, fqn):
+        return isinstance(mod, torch.nn.Linear) and ("feed_forward" in fqn or "attention" in fqn)   
 
     if quantization:
         from torchao.quantization import (
@@ -228,9 +278,14 @@ def main(
             apply_spinquant(model)
         if "int8wo" in quantization:
             quantize_(model, int8_weight_only())
-        elif "int8dq" in quantization:
-            quantize_(model, int8_dynamic_activation_int8_weight())
-        elif "int4wo" in quantization:
+        if "int8dq" in quantization:
+            if sparsity and "semi" in sparsity:
+                from torchao.dtypes import SemiSparseLayout
+                quantize_(model, int8_dynamic_activation_int8_weight(layout=SemiSparseLayout()), filter_fn=ffn_only)
+                quantize_(model, int8_dynamic_activation_int8_weight(), filter_fn=not_ffn_only)
+            else:
+                quantize_(model, int8_dynamic_activation_int8_weight())
+        if "int4wo" in quantization:
             if "hqq" in quantization:
                 use_hqq=True
             else:
@@ -250,9 +305,9 @@ def main(
                         layout=MarlinQQQLayout(),
                     ),
                 )
-            else:
+            elif "semi" in sparsity:
                 from torchao.dtypes import MarlinSparseLayout
-                quantize_(model, int4_weight_only(layout=MarlinSparseLayout()))
+                quantize_(model, int4_weight_only(layout=MarlinSparseLayout()), filter_fn=ffn_or_attn_only)
         if "fp6" in quantization:
             quantize_(model, fpx_weight_only(3, 2))
         elif "embed-int8wo" in quantization:
@@ -426,6 +481,13 @@ def main(
             if not TORCH_VERSION_AT_LEAST_2_5:
                 unwrap_tensor_subclass(model)
 
+    # standalone sparsity
+    elif sparsity:
+        from torchao.sparsity import semi_sparse_weight, sparsify_
+        if "semi" in sparsity:
+            #TODO there is a bug here, need to fix
+            sparsify_(model.to(device), semi_sparse_weight(), filter_fn=ffn_only)
+
     model_size = get_model_size_in_bytes(model, ignore_embeddings=True) / 1e9
 
     if save:
@@ -451,6 +513,9 @@ def main(
 
     aggregate_metrics = {
         'tokens_per_sec': [],
+        'time': [],
+        'decode_tokens_per_sec': [],
+        'prefill_time': [],
     }
     start = -1 if compile else 0
 
@@ -485,6 +550,8 @@ def callback(x):
         else:
             callback = lambda x : x
         t0 = time.perf_counter()
+        prefill_start_event, prefill_end_event = device_timer(device), device_timer(device)
+        decode_start_event, decode_end_event = device_timer(device), device_timer(device)
         import contextlib
         if (i != num_samples - 1 or not profile):
             prof = contextlib.nullcontext()
@@ -504,6 +571,10 @@ def callback(x):
                 kv_cache_quantization=kv_cache_quantization,
                 cache_size=cache_size,
                 linear_causal_mask=linear_causal_mask,
+                prefill_start_event=prefill_start_event,
+                prefill_end_event=prefill_end_event,
+                decode_start_event=decode_start_event,
+                decode_end_event=decode_end_event,
             )
         if i == -1:
             print(f"Compilation time: {time.perf_counter() - t0:.2f} seconds")
@@ -513,7 +584,7 @@ def callback(x):
         device_sync(device=device) # MKG
         t = time.perf_counter() - t0
 
-        if not interactive:
+        if not interactive and prefill_size is None:
                 tok_list = y[0].tolist()
                 # truncate text after end of string token
                 tokens = tok_list if not tokenizer.eos_id() in tok_list else tok_list[:tok_list.index(tokenizer.eos_id())]
@@ -523,7 +594,14 @@ def callback(x):
         tokens_generated = (y.size(-1) - 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")
+        aggregate_metrics['time'].append(t)
+        decode_time = decode_start_event.elapsed_time(decode_end_event) / 1000
+        decode_tokens_sec = tokens_generated / decode_time
+        aggregate_metrics['decode_tokens_per_sec'].append(decode_tokens_sec)
+        prefill_time = prefill_start_event.elapsed_time(prefill_end_event) / 1000
+        aggregate_metrics['prefill_time'].append(prefill_time)
+        print(f"Sample {i+1} | overall time {t:.04f} s {tokens_sec:.02f} tokens/sec",
+              f"| prefill time {prefill_time:.04f} s decode {decode_tokens_sec:.02f} tokens/sec")
         print(f"Bandwidth achieved: {model_size * tokens_sec:.02f} GB/s")
 
         if memory_profile and i==0:
@@ -544,8 +622,15 @@ def callback(x):
             break
     print("==========")
 
+    #ignore first sample for warmup
     tokpersec = torch.mean(torch.tensor(aggregate_metrics['tokens_per_sec'])).item()
+    ttft = torch.mean(torch.tensor(aggregate_metrics['prefill_time'])).item()
+    decode_tokpersec = torch.mean(torch.tensor(aggregate_metrics['decode_tokens_per_sec'])).item()
     bandwidth = model_size * tokpersec
+    mem = torch.cuda.max_memory_reserved() /1e9
+    print(f"Average overall tokens/sec: {tokpersec:.2f}")
+    print(f"Average decode tokens/sec: {decode_tokens_sec:.04f} s")
+    print(f"Average TTFT: {ttft:.04f} s")
     if device == "cuda": 
         mem = torch.cuda.max_memory_reserved() /1e9
     elif device == "xpu":
@@ -557,15 +642,17 @@ def callback(x):
     print(f"Peak Memory Usage: {mem:.02f} GB")
     print(f"Model Size: {model_size:.02f} GB")
     if write_result:
-        result_txt = f"\n{datetime.today().strftime('%Y%m%d%H%M%S')}, tok/s={tokpersec:6.2f}, mem/s={bandwidth:7.2f} GB/s, peak_mem={mem:5.2f} GB, model_size={model_size:5.2f} GB "
-        result_txt += f"quant: {quantization}, mod: {checkpoint_path.parent.name}, kv_quant: {kv_cache_quantization}, compile: {compile}, compile_prefill: {compile_prefill}, dtype: {precision}, device: {device} "
+        result_txt = f"\n{datetime.today().strftime('%Y%m%d%H%M%S')}, tok/s={tokpersec:6.2f}, tok/s_decode={decode_tokpersec:6.2f}, ttft={ttft:5.4f}, mem/s={bandwidth:7.2f} GB/s, peak_mem={mem:5.2f} GB, model_size={model_size:5.2f} GB "
+        result_txt += f"quant: {quantization}, sparse: {sparsity}, mod: {checkpoint_path.parent.name}, kv_quant: {kv_cache_quantization}, compile: {compile}, compile_prefill: {compile_prefill}, dtype: {precision}, device: {device} "
         result_txt += f"repro: python generate.py "
         result_txt += f"--quantization {quantization} " if quantization else ""
+        result_txt += f"--sparsity {sparsity} " if sparsity else ""
         result_txt += f"--checkpoint_path {checkpoint_path} "
         result_txt += f"--device {device} "
         result_txt += f"--precision {precision} "
         result_txt += f"--compile " if compile else ""
         result_txt += f"--compile_prefill " if compile_prefill else ""
+        result_txt += f"--prefill_size {prefill_size}" if prefill_size else ""
         result_txt += f"--profile {profile} " if profile else ""
         result_txt += f"--profile {memory_profile} " if memory_profile else ""
         result_txt += f"--interactive " if interactive else ""
@@ -587,7 +674,7 @@ def callback(x):
 if __name__ == '__main__':
     import argparse
     parser = argparse.ArgumentParser(description='Your CLI description.')
-
+    parser.add_argument('--prefill_size', type=int, default=0, help='Whether to run in ttft mode')
     parser.add_argument('--prompt', type=str, default="Hello, my name is", help='Input 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.')
@@ -603,6 +690,11 @@ def callback(x):
             +'embed-int8wo, marlin_qqq'
         )
     )
+    parser.add_argument('-s', '--sparsity', type=str, 
+        help=(
+            'Which sparsity techniques to apply: semi-structured'
+        )
+    )
     parser.add_argument('--kv_cache_quantization', action='store_true', help='Whether to quantize the KV cache')
     parser.add_argument('--cache_size', type=int, default=None, help='Force size of cache to be a certain number of tokens, if not set, will use max_new_tokens+prompt_size')
     parser.add_argument('--linear_causal_mask', action='store_true', help='Whether to use the memory efficient, but slightly less fast, linear causal mask (important for long context lengths)')
@@ -617,6 +709,6 @@ def callback(x):
 
     args = parser.parse_args()
     main(
-        args.prompt, args.interactive, args.num_samples, args.max_new_tokens, args.batch_size, args.top_k,
-        args.temperature, args.checkpoint_path, args.quantization, args.kv_cache_quantization, args.cache_size, args.linear_causal_mask, args.save, args.compile, args.compile_prefill, args.profile, args.memory_profile, args.device, args.precision, args.write_result
+        args.prefill_size, args.prompt, args.interactive, args.num_samples, args.max_new_tokens, args.batch_size, args.top_k,
+        args.temperature, args.checkpoint_path, args.quantization, args.sparsity, args.kv_cache_quantization, args.cache_size, args.linear_causal_mask, args.save, args.compile, args.compile_prefill, args.profile, args.memory_profile, args.device, args.precision, args.write_result
     )