WIP: DP4AMatMul fix matmul for subgoup size 64 GPUs (#23637)

### Description
This change moves away from using subgroup ops for quantization. This is
because on AMD GPUs subgroup size is 64 and that is not handled in our
quantization function, resulting in garbage output. Implementing
subgroup size 64 quantization requires changing the workgroup size and
then implementing support for subgroup size 128 becomes a challenge.

With the new implementation perf on intel ALD remains about the same
4.36s for 1000K prefill.


Tests for this change are present here 

https://github.com/microsoft/onnxruntime/blob/e66650350b85cb5e3a408f6576fe6a7f4f4ddebc/onnxruntime/test/contrib_ops/matmul_4bits_test.cc

However, to trigger the current issue they must be run on a GPU with
subgroup size 64.

Author: sushraja-msft

onnxruntime/contrib_ops/webgpu/quantization/matmul_nbits.cc

@@ -535,42 +535,23 @@ Status DP4AMatMulQuantizeProgram::GenerateShaderCode(ShaderHelper& shader) const
   shader.AddOutput("output", ShaderUsage::UseUniform);
   shader.AddOutput("scales", ShaderUsage::UseUniform);
 
-  shader.AdditionalImplementation() << R"ADDNL_FN(
-    var<workgroup> max_values : array<input_a_element_t, 4>;
- )ADDNL_FN";
-
   shader.MainFunctionBody() << R"MAIN_FN(
-  var local_a = input_a[global_idx];
-  var max_val = subgroupMax(abs(local_a));
-  var max_temp = max(max_val.xy, max_val.zw);
-  var scale = max(max_temp[0], max_temp[1]);
-  if (local_idx % sg_size == 0) {
-    max_values[local_idx / sg_size] = scale;
-  }
-  workgroupBarrier();
-
-  if (sg_size == 8)
+  var local_a : array<vec4<input_a_element_t>, 32>;
+  var max_value:vec4<input_a_element_t> = vec4<input_a_element_t>(0);
+  for (var idx:u32=0;idx<32;idx+=1)
   {
-    scale = max(max_values[0], max_values[1]);
-    scale = max(scale, max_values[2]);
-    scale = max(scale, max_values[3]);
+    local_a[idx] = input_a[workgroup_id.x*32 + idx];
+    max_value = max(max_value, abs(local_a[idx]));
   }
-  else if (sg_size == 16)
-  {
-    scale = max(max_values[0], max_values[1]);
-  }
-  else
-  {
-    scale = max_values[0];
-  }
-
-  var norm_a = local_a/scale;
-  output[global_idx] = pack4x8snorm(vec4<f32>(norm_a));
-  if (local_idx == 0)
+  var scale = max(max_value.x, max_value.y);
+  scale = max(scale, max_value.z);
+  scale = max(scale, max_value.w);
+  for (var idx:u32=0;idx<32;idx+=1)
   {
-    // 127 is the max value of signed int8 [-127,127] used by pack4x8snorm for 1.0f.
-    scales[workgroup_idx] = scale/127;
+    output[workgroup_id.x*32+idx] = pack4x8snorm(vec4<f32>(local_a[idx]/scale));
   }
+  // 127 is the max value of signed int8 [-127,127] used by pack4x8snorm for 1.0f.
+  scales[workgroup_id.x] = scale/127;
 )MAIN_FN";
   return Status::OK();
 }
@@ -838,7 +819,7 @@ Status MatMulNBits::ComputeInternal(onnxruntime::webgpu::ComputeContext& context
 
     constexpr uint32_t kBlockSizeA = 128;
     DP4AMatMulQuantizeProgram quantize_program;
-    quantize_program.SetWorkgroupSize(32);
+    quantize_program.SetWorkgroupSize(1);
     quantize_program.SetDispatchGroupSize(M * K / kBlockSizeA, 1, 1);
     TensorShape a_quant_shape{1, M, K / kU32Components};
     Tensor a_quant = context.CreateGPUTensor(DataTypeImpl::GetType<uint32_t>(), a_quant_shape);