Model: Remove backwards compatability hacks

Now that exllamav2 is required to be the latest, don't add attribute
checks unless the feature is not in the release build.

Signed-off-by: kingbri <bdashore3@proton.me>

backends/exllamav2/model.py

@@ -139,23 +139,10 @@ class ExllamaV2Container:
         )
 
         # Enable CFG if present
-        use_cfg = unwrap(kwargs.get("use_cfg"), False)
-        if hasattr(ExLlamaV2Sampler.Settings, "cfg_scale"):
-            self.use_cfg = use_cfg
-        elif use_cfg:
-            logger.warning(
-                "CFG is not supported by the currently installed ExLlamaV2 version."
-            )
+        self.use_cfg = unwrap(kwargs.get("use_cfg"), False)
 
         # Enable fasttensors loading if present
-        use_fasttensors = unwrap(kwargs.get("fasttensors"), False)
-        if hasattr(ExLlamaV2Config, "fasttensors"):
-            self.config.fasttensors = use_fasttensors
-        elif use_fasttensors:
-            logger.warning(
-                "fasttensors is not supported by "
-                "the currently installed ExllamaV2 version."
-            )
+        self.config.fasttensors = unwrap(kwargs.get("fasttensors"), False)
 
         # Turn off flash attention if CFG is on
         # Workaround until batched FA2 is fixed in exllamav2 upstream
@@ -189,13 +176,7 @@ class ExllamaV2Container:
         # Set num of experts per token if provided
         num_experts_override = kwargs.get("num_experts_per_token")
         if num_experts_override:
-            if hasattr(self.config, "num_experts_per_token"):
-                self.config.num_experts_per_token = num_experts_override
-            else:
-                logger.warning(
-                    "MoE experts per token override is not "
-                    "supported by the current ExLlamaV2 version."
-                )
+            self.config.num_experts_per_token = kwargs.get("num_experts_per_token")
 
         chunk_size = min(
             unwrap(kwargs.get("chunk_size"), 2048), self.config.max_seq_len
@@ -410,6 +391,10 @@ class ExllamaV2Container:
             self.draft_cache,
         )
 
+        # Always return logprobs and logits
+        self.generator.return_probabilities = True
+        self.generator.return_logits = True
+
         logger.info("Model successfully loaded.")
 
     def unload(self, loras_only: bool = False):
@@ -470,21 +455,7 @@ class ExllamaV2Container:
     def check_unsupported_settings(self, **kwargs):
         """Check and warn the user if a sampler is unsupported. Meant for dev wheels!"""
 
-        if kwargs.get("max_temp") > 0.0 and not hasattr(
-            ExLlamaV2Sampler.Settings, "max_temp"
-        ):
-            logger.warning(
-                "DynaTemp parameters are not supported by the currently "
-                "installed ExLlamaV2 version."
-            )
-
-        if (unwrap(kwargs.get("smoothing_factor"), 0.0)) > 0.0 and not hasattr(
-            ExLlamaV2Sampler.Settings, "smoothing_factor"
-        ):
-            logger.warning(
-                "Smoothing factor is not supported by the currently "
-                "installed ExLlamaV2 version."
-            )
+        pass
 
     def generate(self, prompt: str, **kwargs):
         """Generate a response to a prompt"""
@@ -566,20 +537,27 @@ class ExllamaV2Container:
         gen_settings.mirostat = unwrap(kwargs.get("mirostat"), False)
 
         # DynaTemp settings
-        if hasattr(gen_settings, "max_temp"):
-            max_temp = unwrap(kwargs.get("max_temp"), 0.0)
-            min_temp = unwrap(kwargs.get("min_temp"), 0.0)
-
-            if max_temp < min_temp or (
-                0 not in {min_temp, max_temp} and max_temp == min_temp
-            ):
-                logger.warning(
-                    "Max temp is less than or equal to min temp, skipping DynaTemp."
-                )
+        max_temp = unwrap(kwargs.get("max_temp"), 0.0)
+        min_temp = unwrap(kwargs.get("min_temp"), 0.0)
 
+        if max_temp > min_temp:
             gen_settings.max_temp = max_temp
             gen_settings.min_temp = min_temp
-            gen_settings.temp_exponent = kwargs.get("temp_exponent")
+            gen_settings.temp_exponent = unwrap(kwargs.get("temp_exponent"), 1.0)
+        else:
+            # Force to default values
+            gen_settings.max_temp = 0.0
+            gen_settings.min_temp = 0.0
+            gen_settings.temp_exponent = 1.0
+
+        # Warn if max/min temp values are > 0
+        # and if they're less than or equal to each other
+        if max_temp < min_temp or (
+            0 not in {min_temp, max_temp} and max_temp == min_temp
+        ):
+            logger.warning(
+                "Max temp is less than or equal to min temp, skipping DynaTemp."
+            )
 
         # Default tau and eta fallbacks don't matter if mirostat is off
         gen_settings.mirostat_tau = unwrap(kwargs.get("mirostat_tau"), 1.5)
@@ -760,7 +738,7 @@ class ExllamaV2Container:
                 gen_settings.token_repetition_range = generated_tokens
 
             # Generate
-            chunk, eos, tokens, _, *extra_parts = self.generator.stream()
+            chunk, eos, tokens, _, _ = self.generator.stream()
 
             if token_healing:
                 # Extract healed token