[FRONTEND] Support passing dtype as constexpr for tma load (#4821)

Fixing an compile error like below when passing dtype through kernel arg
for `tl._experimental_descriptor_load`:

 AttributeError: 'constexpr' object has no attribute 'to_ir'

python/test/unit/hopper/test_experimental_tma.py

@@ -57,7 +57,7 @@ def kernel(Z, desc, SIZE: tl.constexpr, BYVAL_TMA: tl.constexpr):
 @triton.jit
 def matmul_kernel_tma(a_desc_ptr, b_desc_ptr, c_desc_ptr,  #
                       M, N, K, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
-                      BYVAL_TMA: tl.constexpr):
+                      BYVAL_TMA: tl.constexpr, dtype: tl.constexpr):
     if not BYVAL_TMA:
         tl.extra.cuda.experimental_tensormap_fenceproxy_acquire(a_desc_ptr)
         tl.extra.cuda.experimental_tensormap_fenceproxy_acquire(b_desc_ptr)
@@ -72,11 +72,11 @@ def matmul_kernel_tma(a_desc_ptr, b_desc_ptr, c_desc_ptr,  #
     offs_k = 0
     accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
     for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
-        a = tl._experimental_descriptor_load(a_desc_ptr, [offs_am, offs_k], [BLOCK_SIZE_M, BLOCK_SIZE_K], tl.float16)
-        b = tl._experimental_descriptor_load(b_desc_ptr, [offs_k, offs_bn], [BLOCK_SIZE_K, BLOCK_SIZE_N], tl.float16)
+        a = tl._experimental_descriptor_load(a_desc_ptr, [offs_am, offs_k], [BLOCK_SIZE_M, BLOCK_SIZE_K], dtype)
+        b = tl._experimental_descriptor_load(b_desc_ptr, [offs_k, offs_bn], [BLOCK_SIZE_K, BLOCK_SIZE_N], dtype)
         accumulator = tl.dot(a, b, acc=accumulator)
         offs_k += BLOCK_SIZE_K
-    accumulator = accumulator.to(tl.float16)
+    accumulator = accumulator.to(dtype)
     tl._experimental_descriptor_store(c_desc_ptr, accumulator, [offs_am, offs_bn])
 
 
@@ -101,7 +101,7 @@ def test_experimental_tma_matmul(num_stages, BLOCK_M, BLOCK_N, BLOCK_K, byval_tm
         desc_c = create_tma_desc_gmem_ptr(C.data_ptr(), [M, N], [BLOCK_M, BLOCK_N], C.element_size())
     kernel = matmul_kernel_tma[(triton.cdiv(M, BLOCK_M) * triton.cdiv(N, BLOCK_N), 1,
                                 1)](desc_a, desc_b, desc_c, M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, BYVAL_TMA=byval_tma,
-                                    num_warps=8, num_stages=num_stages)
+                                    num_warps=8, num_stages=num_stages, dtype=tl.float16)
     ref_out = torch.matmul(A.to(torch.float32), B.to(torch.float32)).to(torch.float16)
     torch.testing.assert_close(ref_out, C, rtol=1e-3, atol=1e-3)
     if BLOCK_M >= 64 and BLOCK_N >= 64:

python/triton/language/core.py

@@ -1613,7 +1613,7 @@ def _experimental_descriptor_load(desc_pointer, offsets, shape, dtype, _builder=
 
     This loads a tensor of data based on the descriptor and offsets.
     """
-    type = block_type(dtype, shape)
+    type = block_type(_constexpr_to_value(dtype), shape)
     return semantic.descriptor_load(desc_pointer, offsets, "", "", type, _builder)