fix: on-the-fly int4 quantize parameter (#1920)

Mirror to https://github.com/meta-llama/llama-models/pull/324 with some
clean up

```
with-proxy pip install -e .
export INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct
export INFERENCE_CHECKPOINT_DIR=../checkpoints/Llama-4-Scout-17B-16E-Instruct
export QUANTIZATION_TYPE=int4_mixed
with-proxy llama stack build --run --template meta-reference-gpu
```

# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]

[//]: # (## Documentation)

llama_stack/models/llama/llama4/quantization/loader.py

@@ -91,7 +91,7 @@ def convert_to_quantized_model(
             log_status(f"Rank {rank}: Quantizing int4 weights from bf16")
 
             def apply_quantization(_, weight):
-                return quantize_int4(weight, fp8_activation_scale_ub, output_device=torch.device("cuda"))
+                return quantize_int4(weight, output_device=torch.device("cuda"))
 
     else:
         fp8_scales_path = os.path.join(checkpoint_dir, f"fp8_scales_{rank}.pt")

llama_stack/models/llama/quantize_impls.py

@@ -65,7 +65,7 @@ class Int4Weights(
     Int4ScaledWeights,
     collections.namedtuple(
         "Int4Weights",
-        ["weight", "scale", "zero_point", "shape", "activation_scale_ub"],
+        ["weight", "scale", "zero_point", "shape"],
     ),
 ):
     pass
@@ -184,20 +184,13 @@ def quantize_fp8(
 @torch.inference_mode()
 def quantize_int4(
     w: Tensor,
-    fp8_activation_scale_ub: float,
     output_device: Optional[torch.device] = None,
 ) -> Int4Weights:
     """Quantize [n, k/2] weight tensor.
 
     Args:
         w (Tensor): [n, k/2] input high precision tensor to quantize.
-        fp8_activation_scale_ub (float): Upper bound for activation max.
     """
-    activation_scale_ub = torch.tensor(
-        [fp8_activation_scale_ub],
-        dtype=torch.float,
-        device=output_device,
-    )
     if w.ndim >= 3:
         wq, scale, zero_point = zip(*[int4_row_quantize(i) for i in w], strict=False)
         wq = torch.stack([pack_int4(i) for i in wq], dim=0)
@@ -212,7 +205,6 @@ def quantize_int4(
         scale=scale.to(output_device),
         zero_point=zero_point.to(output_device),
         shape=wq.shape,
-        activation_scale_ub=activation_scale_ub,
     )
 
 
@@ -247,26 +239,18 @@ def load_int4(
     w: Tensor,
     scale: Tensor,
     zero_point: Tensor,
-    fp8_activation_scale_ub: float,
     output_device: Optional[torch.device] = None,
 ) -> Int4Weights:
     """Load INT4 [n, k/2] weight tensor.
 
     Args:
         w (Tensor): [n, k/2] input INT4.
-        fp8_activation_scale_ub (float): Upper bound for activation max.
     """
-    activation_scale_ub = torch.tensor(
-        [fp8_activation_scale_ub],
-        dtype=torch.float,
-        device=output_device,
-    )
     return Int4Weights(
         weight=w.to(torch.int8).to(device=output_device),
         scale=scale.to(device=output_device),
         zero_point=zero_point.to(device=output_device),
         shape=w.shape,
-        activation_scale_ub=activation_scale_ub,
     )