#5383: [Falcon7b] Add perf and output verification settings to demo a…

…nd add demo to CI

Signed-off-by: Salar Hosseini <[email protected]>

models/demos/falcon7b/demo/demo.py

@@ -148,7 +148,19 @@ def run_falcon_demo_kv(
     num_layers=32,
     perf_mode=False,  # Option to measure perf using max seq length (with invalid outputs)
     greedy_sampling=False,  # Option to use greedy decoding instead of top-k/p
+    expected_perf_prefill_decode=None,  # Expected perf (t/s) for prefill and decode in perf mode
+    expected_greedy_output_path=None,  # Path for expected outputs for greedy decoding
+    save_generated_text_path=None,  # If provided, save generated text to this path (e.g. set to expected_greedy_output_path to update expected output)
 ):
+    assert not (expected_perf_prefill_decode and expected_greedy_output_path), "Cannot verify both perf and output!"
+    assert not (perf_mode and save_generated_text_path), "Cannot save generated text in perf mode!"
+    if expected_greedy_output_path is not None:
+        assert (
+            not perf_mode and greedy_sampling
+        ), "Output verification only supported for greedy sampling in default mode!"
+    elif expected_perf_prefill_decode is not None:
+        assert perf_mode, "Performance verification is only supported for perf mode!"
+
     disable_persistent_kernel_cache()
     disable_compilation_reports()
 
@@ -402,7 +414,8 @@ def run_falcon_demo_kv(
     logger.info("Finished inference prefill stage!")
     num_users_generated_prefill = num_users if not perf_mode else (N - N_warmup) * num_devices
 
-    generated_ids = torch.concat((prefill_ids[..., :num_input_tokens], output_ids), dim=1)
+    if not perf_mode:
+        generated_ids = torch.concat((prefill_ids[..., :num_input_tokens], output_ids), dim=1)
 
     ### Inference run decode ###
     logger.info("Running inference decode stage...")
@@ -422,9 +435,12 @@ def run_falcon_demo_kv(
         N = max_seq_len - num_input_tokens
         N_warmup = 0
     else:
-        N = 15
-        N_warmup = 5
-    for output_token_index in range(N):
+        N = 30
+        N_warmup = 10
+    print_per_generated_token = (
+        expected_greedy_output_path is None
+    )  # print per generated token if not verifying outputs
+    for output_token_index in range(N) if print_per_generated_token else tqdm(range(N), desc="Generating tokens"):
         time_decode_inference_start = time.time()
         (
             tt_decode_input_ids,
@@ -473,22 +489,27 @@ def run_falcon_demo_kv(
             generated_ids = torch.concat((generated_ids, decode_ids[:num_users]), dim=1)
             kv_cache_len += 1
 
-            # TODO: Remove if we don't want to print per generated token
-            os.system("clear")
-            print_output_prompts(generated_ids, tokenizer, batch_size)
+            if print_per_generated_token:
+                os.system("clear")
+                print_output_prompts(generated_ids, tokenizer, batch_size)
 
     logger.info("Finished inference decode stage!")
     num_tokens_generated_decode = global_batch * (output_token_index - N_warmup + 1)
     logger.info(f"Total number of tokens generated in decode: {num_tokens_generated_decode}")
 
     if not perf_mode:
         print_output_prompts(generated_ids, tokenizer, batch_size)
+        generated_text = tokenizer.batch_decode(generated_ids.tolist())
+
+        if save_generated_text_path is not None:
+            with open(save_generated_text_path, "w") as f:
+                json.dump(generated_text, f)
+    else:
+        generated_text = None
 
     for device in devices:
         device.disable_and_clear_program_cache()
 
-    generated_text = tokenizer.batch_decode(generated_ids.tolist())
-
     measurements = {
         "preprocessing": profiler.get("tokenizing_inputs"),
         "loading_weights": profiler.get("loading_weights"),
@@ -500,8 +521,11 @@ def run_falcon_demo_kv(
         "inference_prefill": time_prefill_inference,
         "inference_decode": time_decode_inference,
         "inference_total": time_prefill_inference + time_decode_inference,
-        "inference_throughput_prefill": num_users_generated_prefill / time_prefill_inference,
-        "inference_throughput_decode": num_tokens_generated_decode / time_decode_inference,
+        "inference_user_throughput_prefill": num_users_generated_prefill / time_prefill_inference,  # users/s
+        "inference_token_throughput_prefill": num_users_generated_prefill
+        / time_prefill_inference
+        * prefill_ids.shape[1],  # tokens/s
+        "inference_token_throughput_decode": num_tokens_generated_decode / time_decode_inference,  # tokens/s
     }
 
     logger.info(f"pre processing: {round(measurements['preprocessing'], 5)} s")
@@ -516,13 +540,57 @@ def run_falcon_demo_kv(
     logger.info(f"prefill inference time: {round(measurements['inference_prefill'], 5)} s")
     logger.info(f"decode inference time: {round(measurements['inference_decode'], 5)} s")
     logger.info(f"total inference time: {round(measurements['inference_total'], 5)} s")
-    logger.info(f"inference throughput prefill: {round(measurements['inference_throughput_prefill'], 5)} users/s")
+    logger.info(f"inference throughput prefill: {round(measurements['inference_user_throughput_prefill'], 5)} users/s")
     logger.info(
-        f"inference throughput prefill | seq_len={prefill_ids.shape[1]} : {round(measurements['inference_throughput_prefill']*prefill_ids.shape[1], 5)} tok/s"
+        f"inference throughput prefill | seq_len={prefill_ids.shape[1]} : {round(measurements['inference_token_throughput_prefill'], 5)} tok/s"
     )
-    logger.info(f"inference throughput decode: {round(measurements['inference_throughput_decode'], 5)} tok/s")
+    logger.info(f"inference throughput decode: {round(measurements['inference_token_throughput_decode'], 5)} tok/s")
     logger.info(
-        f"inference throughput decode (per user): {round(measurements['inference_throughput_decode']/global_batch, 5)} tok/s/user"
+        f"inference throughput decode (per user): {round(measurements['inference_token_throughput_decode']/global_batch, 5)} tok/s/user"
+    )
+
+    # Verify output or perf if expected values are provided
+    perf_prefill_decode = [
+        measurements["inference_token_throughput_prefill"],
+        measurements["inference_token_throughput_decode"],
+    ]
+    verify_output_or_perf(
+        generated_text, perf_prefill_decode, expected_greedy_output_path, expected_perf_prefill_decode
     )
 
     return generated_text, measurements
+
+
+def verify_output_or_perf(
+    generated_text, perf_prefill_decode, expected_greedy_output_path, expected_perf_prefill_decode
+):
+    assert expected_perf_prefill_decode is None or expected_greedy_output_path is None
+    if expected_perf_prefill_decode is not None:
+        does_pass = True
+        if perf_prefill_decode[0] < expected_perf_prefill_decode[0]:
+            does_pass = False
+            logger.warning(
+                f"Prefill perf {perf_prefill_decode[0]} is lower than expected {expected_perf_prefill_decode[0]}"
+            )
+        if perf_prefill_decode[1] < expected_perf_prefill_decode[1]:
+            does_pass = False
+            logger.warning(
+                f"Decode perf {perf_prefill_decode[1]} is lower than expected {expected_perf_prefill_decode[1]}"
+            )
+        if does_pass:
+            logger.info("Perf Check Passed!")
+        else:
+            logger.warning("Perf Check Failed!")
+            assert (
+                does_pass
+            ), f"Prefill or decode perf is lower than {expected_perf_prefill_decode}. See earlier warnings for more details."
+    elif expected_greedy_output_path is not None:
+        with open(expected_greedy_output_path, "r") as f:
+            expected_output = json.load(f)
+        does_pass = generated_text == expected_output
+        if does_pass:
+            logger.info("Output Check Passed!")
+        else:
+            assert (
+                does_pass
+            ), f"Generated text does not match expected output! \n\n Generated text:\n {generated_text} \n\n Expected output:\n {expected_output}"