test: sample is not deterministic

Also modify the temperature in decode test to avoid granite early
stopping.

backends/neuron/tests/server/test_decode.py

@@ -11,7 +11,14 @@ def test_decode(neuron_model_config):
     for do_sample in [True, False]:
         mode = "sample" if do_sample else "greedy"
         print(f"{config_name}[{mode}]")
-        _test_decode(config_name, generator, do_sample)
+        generated_text = _test_decode(config_name, generator, do_sample)
+        if not do_sample:
+            expected_text = {
+                "llama": " The world was holding its breath as the world's top scientists and engineers gathered at the secret underground facility",
+                "qwen2": " I was sitting in my room, staring at the clock, when a knock at the door. I",
+                "granite": "\n\nThis opening line is from George Orwell's dystopian novel, \"1",
+            }[config_name]
+            assert generated_text == expected_text
         generator.clear()
 
 
@@ -21,7 +28,11 @@ def _test_decode(config_name, generator, do_sample):
     )
     max_new_tokens = 20
     request = create_request(
-        id=0, inputs=input_text, max_new_tokens=max_new_tokens, do_sample=do_sample
+        id=0,
+        inputs=input_text,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        temperature=0.9,
     )
     max_length = generator.model.neuron_config.sequence_length
     batch = Batch(id=0, requests=[request], size=1, max_tokens=max_length)
@@ -38,18 +49,4 @@ def _test_decode(config_name, generator, do_sample):
     output = generations[0].generated_text
     assert output.generated_tokens == max_new_tokens
     assert output.finish_reason == 0
-    if do_sample:
-        expected_text = {
-            "llama": " I sat alone in the café",
-            "qwen2": " The air was so still",
-            "granite": "1984, George Orwell",
-        }[config_name]
-        assert expected_text in output.text
-    else:
-        print(output.text)
-        expected_text = {
-            "llama": " The world was holding its breath as the world's top scientists and engineers gathered at the secret underground facility",
-            "qwen2": " I was sitting in my room, staring at the ceiling, when the door opened and in came a",
-            "granite": "\n\nThis opening line from George Orwell's dystopian novel \"198",
-        }[config_name]
-        assert output.text == expected_text
+    return output.text

backends/neuron/tests/server/test_prefill.py

@@ -44,23 +44,17 @@ def _test_prefill(config_name, generator, batch_size, do_sample):
     # because of static batching
     assert next_batch.max_tokens == batch_size * max_length
     assert len(generations) == batch_size
-    if do_sample:
-        expectations = {
-            "llama": [358, " I"],
-            "qwen2": [576, " The"],
-            "granite": [308, " ("],
-        }[config_name]
-    else:
-        expectations = {
-            "llama": [578, " The"],
-            "qwen2": [358, " I"],
-            "granite": [203, "\n"],
-        }[config_name]
-    for g in generations:
-        tokens = g.tokens
-        assert tokens.ids[0] == expectations[0]
-        assert tokens.texts[0] == expectations[1]
-
+    expectations = {
+        "llama": [578, " The"],
+        "qwen2": [358, " I"],
+        "granite": [203, "\n"],
+    }[config_name]
+    # Greedy mode should always generate the same output
+    if not do_sample:
+        for g in generations:
+            tokens = g.tokens
+            assert tokens.ids[0] == expectations[0]
+            assert tokens.texts[0] == expectations[1]
 
 def test_prefill_truncate(neuron_model_config):
     config_name = neuron_model_config["name"]
@@ -88,8 +82,8 @@ def test_prefill_truncate(neuron_model_config):
     # be different because of the truncation
     expectations = {
         "llama": [" He", "iens", "\x08", " He"],
-        "qwen2": [" He", " The", " He", " He"],
-        "granite": ["\n", "\n", " I", " He"],
+        "qwen2": [" He", "<|endoftext|>", " ", " The"],
+        "granite": ["\n", "\n", "\n", "\n"],
     }[config_name]
     for i, g in enumerate(generations):
         tokens = g.tokens